diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0148d341df5f5dce/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0148d341df5f5dce/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6fad355c8c29796a8c7fa9b23ace93c17c3e4425 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0148d341df5f5dce/run_manifest.json @@ -0,0 +1,69 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:09:45.223312+00:00", + "ended_at": "2026-05-19T16:09:52.840365+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_0148d341df5f5dce", + "problem_id": "v2p_m1_6002c32a7e48a151", + "dataset_id": "m1", + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "slice_level_consistency", + "intended_facet_id": "conditional_interaction_hotspots", + "variant_semantic_role": "filtered_stable_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=Survey_Date, measure_col=Efficiency_Rating.", + "bindings": { + "group_col": "Survey_Date", + "measure_col": "Efficiency_Rating", + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "notes": [ + "default_facets=conditional_interaction_hotspots", + "template_selection_mode=rule", + "problem_index_within_template=3", + "sql_variant_index=1/2", + "binding_index=134" + ], + "template_selection_mode": "rule", + "selected_template_rank": 12, + "problem_index_within_template": 3, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0148d341df5f5dce/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0148d341df5f5dce/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..dc02f87ef10f4a7312a3ef3c68aa49c30dad03c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0148d341df5f5dce/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:09:48.517653+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3291.84, "started_at": "2026-05-19T16:09:45.224985+00:00", "ended_at": "2026-05-19T16:09:48.516865+00:00", "prompt_metrics": {"chars": 16428, "bytes_utf8": 16428, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-1703-7952-8567-4ba8e9d6b182\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:09:52.840248+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3320.41, "started_at": "2026-05-19T16:09:49.518862+00:00", "ended_at": "2026-05-19T16:09:52.839297+00:00", "prompt_metrics": {"chars": 16428, "bytes_utf8": 16428, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-2801-7b02-933b-4e243a7e27f7\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ee6a0749e686c6b8c04515061327d3a334e769fb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16315, "bytes_utf8": 16315, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 349, "bytes_utf8": 349, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16694, "cached_input_tokens": 12032, "output_tokens": 324, "reasoning_output_tokens": 220}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..952ea1b6afa129683790a35766228f948ecda076 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_0412d1c7c316e5f5", + "api_calls": 0, + "input_tokens": 16694, + "cached_input_tokens": 12032, + "output_tokens": 324, + "total_tokens": 17018, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8424.61, + "sql_execution_elapsed_ms_total": 1.03, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_attempt_1.metadata.json new file mode 100644 index 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"estimated_tokens": null + }, + "usage": { + "input_tokens": 16694, + "cached_input_tokens": 12032, + "output_tokens": 324, + "reasoning_output_tokens": 220 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e13376e18b64e488ec67afa086c972b8764f5628 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=Has_Children.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Has_Children", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..045b0da9028447d070c74642a483607243bc2df0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-16ef-7732-9584-aa86cef2248e"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n \\\"Has_Children\\\",\\n COUNT(*) AS \\\"support\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Has_Children\\\"\\nORDER BY \\\"support\\\" ASC, \\\"Has_Children\\\"\\nLIMIT 12;\",\"notes\":\"Applied the provided Low-Support Group Count template with group_col=\\\"Has_Children\\\" and top_k=12 on single table \\\"m1\\\".\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16694,"cached_input_tokens":12032,"output_tokens":324,"reasoning_output_tokens":220}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a11d38cc5f2b6f6aa816fad659e6da55b344cbd8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n \"Has_Children\",\n COUNT(*) AS \"support\"\nFROM \"m1\"\nGROUP BY \"Has_Children\"\nORDER BY \"support\" ASC, \"Has_Children\"\nLIMIT 12;","notes":"Applied the provided Low-Support Group Count template with group_col=\"Has_Children\" and top_k=12 on single table \"m1\"."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..eb25acecb36ef3901ff7295c0e73e5ec7bcc54a7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16304, "bytes_utf8": 16304, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 351, "bytes_utf8": 351, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16677, "cached_input_tokens": 12032, "output_tokens": 291, "reasoning_output_tokens": 195}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..3d551ef3aa076cadc8305c1f3e32332b1a72fb38 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_06e4799f5ca8a8e8", + "api_calls": 0, + "input_tokens": 16677, + "cached_input_tokens": 12032, + "output_tokens": 291, + "total_tokens": 16968, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10573.79, + "sql_execution_elapsed_ms_total": 1.19, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e9f78dba085166df5e1bbf670826ff0c043a7a72 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:28:18.005140+00:00", + "ended_at": "2026-05-19T15:28:28.578967+00:00", + "elapsed_ms": 10573.79, + "prompt_metrics": { + "chars": 16304, + "bytes_utf8": 16304, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 713, + "bytes_utf8": 713, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 351, + "bytes_utf8": 351, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16677, + "cached_input_tokens": 12032, + "output_tokens": 291, + "reasoning_output_tokens": 195 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..3aa672f947f1ca929fd1a772a3677efa586905d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Gender, measure_col=Age.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "Gender", + "measure_col": "Age", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 41.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a1fb29cf50efb14368e367c0c4f3e264921978d0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40da-2349-7a01-8d79-f802273b05f9"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Gender\\\", SUM(CAST(\\\"Age\\\" AS NUMERIC)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Gender\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the required grouped numeric sum template with \\\"Gender\\\" as the grouping column and cast \\\"Age\\\" to NUMERIC because the SQLite schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16677,"cached_input_tokens":12032,"output_tokens":291,"reasoning_output_tokens":195}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b0dec5b2bcbfe233fba8586700e58504ed0dde91 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"Gender\", SUM(CAST(\"Age\" AS NUMERIC)) AS \"total_measure\"\nFROM \"m1\"\nGROUP BY \"Gender\"\nORDER BY \"total_measure\" DESC;","notes":"Used the required grouped numeric sum template with \"Gender\" as the grouping column and cast \"Age\" to NUMERIC because the SQLite schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..6c886fd4827b5e121456e1cdd3545ca6e4584f04 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:11:24.799231+00:00", + "ended_at": "2026-05-19T16:11:28.221052+00:00", + "elapsed_ms": 3421.8, + "returncode": 1, + "prompt_metrics": { + "chars": 16403, + "bytes_utf8": 16403, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ef8e1dc01970ec33c40c00621edf6381c9eb2db9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:11:29.223077+00:00", + "ended_at": "2026-05-19T16:11:34.248141+00:00", + "elapsed_ms": 5025.03, + "returncode": 1, + "prompt_metrics": { + "chars": 16403, + "bytes_utf8": 16403, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..cd1ccd153c46f35832b779e66498a80420ba267f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=Location_Type, measure_col=Age.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Location_Type", + "measure_col": "Age", + "top_k": 15, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..cd1ccd153c46f35832b779e66498a80420ba267f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=Location_Type, measure_col=Age.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Location_Type", + "measure_col": "Age", + "top_k": 15, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..501ea0ce1e27c5bc592e2efcd08fa95c11c47eb8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4101-9c25-7e40-ac07-eec69444e7ef"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a23a17e6b2691396865df664d246160f0225bdbf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4101-9c25-7e40-ac07-eec69444e7ef"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..1dd40c1206d6821df3bc62bcc301d1447629a5a4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4101-ad51-75a3-b323-0ad439537fd3"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..9e47dd6a0b6a465685a13ab02a298cec24039294 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4101-ad51-75a3-b323-0ad439537fd3"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..0d9e3edc77598aacc70a575fefc5289d393c113e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Location_Type, measure_col=WFH_Days_Per_Week. +Result preview: [{"Location_Type": "Urban", "group_value": 1944.0}, {"Location_Type": "Suburban", "group_value": 1911.0}, {"Location_Type": "Rural", "group_value": 431.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..a02d2d5c7446c18cef67b09c7dcaca492df33c93 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/generated_sql.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_m1_0978a623dc7f6b02 +-- problem_id: v2p_m1_088d53eb6133696a +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "Location_Type", SUM(CAST("WFH_Days_Per_Week" AS REAL)) AS group_value + FROM "m1" + GROUP BY "Location_Type" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."Location_Type", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.1 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c100b2f6347c8adc64e2a36c127e798191c0ac32 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"Location_Type\", SUM(CAST(\"WFH_Days_Per_Week\" AS REAL)) AS group_value\n FROM \"m1\"\n GROUP BY \"Location_Type\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"Location_Type\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Location_Type\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Location_Type\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Location_Type\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\", \"columns\": [\"Location_Type\", \"group_value\"], \"rows\": [{\"Location_Type\": \"Urban\", \"group_value\": 1944.0}, {\"Location_Type\": \"Suburban\", \"group_value\": 1911.0}, {\"Location_Type\": \"Rural\", \"group_value\": 431.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.57}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..13d00edc82ecbe6cc8521ccc12c47f2ed27006bf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:47:01.624418+00:00", + "ended_at": "2026-05-19T15:47:13.400476+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_0978a623dc7f6b02", + "problem_id": "v2p_m1_088d53eb6133696a", + "dataset_id": "m1", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Location_Type, measure_col=WFH_Days_Per_Week.", + "bindings": { + "group_col": "Location_Type", + "measure_col": "WFH_Days_Per_Week", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 4.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=1", + "sql_variant_index=1/2", + "binding_index=72" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 1, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_0978a623dc7f6b02.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_0978a623dc7f6b02", + "api_calls": 0, + "input_tokens": 16827, + "cached_input_tokens": 15744, + "output_tokens": 421, + "total_tokens": 17248, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11770.09, + "sql_execution_elapsed_ms_total": 1.57, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3b84e96e31ad40b4c0ca04f4f51b192e51519098 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:47:13.397085+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11770.09, "started_at": "2026-05-19T15:47:01.625725+00:00", "ended_at": "2026-05-19T15:47:13.395852+00:00", "prompt_metrics": {"chars": 16899, "bytes_utf8": 16899, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 632, "bytes_utf8": 632, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16827, "cached_input_tokens": 15744, "output_tokens": 421, "reasoning_output_tokens": 246}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Location_Type\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Location_Type\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Location_Type\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the planned template with group_col=\\\"Location_Type\\\" and measure_col=\\\"WFH_Days_Per_Week\\\". Cast \\\"WFH_Days_Per_Week\\\" from TEXT to REAL for SQLite aggregation.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e82f4b457e40cb1e849611f11428309950f98541 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_0978a623dc7f6b02", + "api_calls": 0, + "input_tokens": 16827, + "cached_input_tokens": 15744, + "output_tokens": 421, + "total_tokens": 17248, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11770.09, + "sql_execution_elapsed_ms_total": 1.57, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0d70406a5f7394bdfc4320bcb709e1e1fddf8190 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16902, "bytes_utf8": 16902, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 670, "bytes_utf8": 670, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16829, "cached_input_tokens": 15744, "output_tokens": 512, "reasoning_output_tokens": 334}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..2532a620a8ab8ead075eac69a24136bffc812ae5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_0b10a62218209cf5", + "api_calls": 0, + "input_tokens": 16829, + "cached_input_tokens": 15744, + "output_tokens": 512, + "total_tokens": 17341, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11213.87, + "sql_execution_elapsed_ms_total": 2.6, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7dbe27ab4cc9a772b279cb5177875fdfc1eb3fb3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:47:13.402858+00:00", + "ended_at": "2026-05-19T15:47:24.616774+00:00", + "elapsed_ms": 11213.87, + "prompt_metrics": { + "chars": 16902, + "bytes_utf8": 16902, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1037, + "bytes_utf8": 1037, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 670, + "bytes_utf8": 670, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16829, + "cached_input_tokens": 15744, + "output_tokens": 512, + "reasoning_output_tokens": 334 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b19d9add51bb9f80c7cf4941243b2cefd0788d91 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Location_Type, measure_col=WFH_Days_Per_Week.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "Location_Type", + "measure_col": "WFH_Days_Per_Week", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 4.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..33d4332af852bc92fdab492264de7fb041f84fbe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40eb-76ff-70c1-a1d9-1da41f6c4e84"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Location_Type\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Location_Type\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Location_Type\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the planned Relative-to-Total Extreme Threshold template with group_col=\\\"Location_Type\\\" and measure_col=\\\"WFH_Days_Per_Week\\\". The measure is cast from TEXT to REAL so SQLite can sum it correctly.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16829,"cached_input_tokens":15744,"output_tokens":512,"reasoning_output_tokens":334}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0655efebe771ab84c97c8c187bac7b294777acfa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"Location_Type\", SUM(CAST(\"WFH_Days_Per_Week\" AS REAL)) AS group_value\n FROM \"m1\"\n GROUP BY \"Location_Type\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"Location_Type\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Applied the planned Relative-to-Total Extreme Threshold template with group_col=\"Location_Type\" and measure_col=\"WFH_Days_Per_Week\". The measure is cast from TEXT to REAL so SQLite can sum it correctly."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c46bb86528bb05578f0eaff1df98044dc3969218 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16506, "bytes_utf8": 16506, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1945, "bytes_utf8": 1945, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16719, "cached_input_tokens": 15744, "output_tokens": 3248, "reasoning_output_tokens": 2584}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..afc4271d2d89dd6f9ef9c97940aa7e81f66c10ab --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_0d4d344ddc1436c1", + "api_calls": 0, + "input_tokens": 16719, + "cached_input_tokens": 15744, + "output_tokens": 3248, + "total_tokens": 19967, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 46168.19, + "sql_execution_elapsed_ms_total": 7.93, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fe366d3fce7513c6304d97a31c0306f59e031fef --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:52:52.913522+00:00", + "ended_at": "2026-05-19T15:53:39.081737+00:00", + "elapsed_ms": 46168.19, + "prompt_metrics": { + "chars": 16506, + "bytes_utf8": 16506, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2579, + "bytes_utf8": 2579, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1945, + "bytes_utf8": 1945, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16719, + "cached_input_tokens": 15744, + "output_tokens": 3248, + "reasoning_output_tokens": 2584 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..23c3cad6638489ebd97706dacb4941cb7e738926 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Gender, measure_col=Years_Experience.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Gender", + "measure_col": "Years_Experience", + "top_k": 10, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f1b36b800a0473b91a3c08a9115b55bf33755181 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f0-a4d2-7e82-b3c6-e22dd9fd3c25"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"Gender\\\",\\n CAST(\\\"Years_Experience\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Gender\\\"\\n ORDER BY CAST(\\\"Years_Experience\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"Gender\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Gender\\\" IS NOT NULL\\n AND \\\"Years_Experience\\\" IS NOT NULL\\n AND TRIM(\\\"Years_Experience\\\") <> ''\\n),\\n\\\"eligible\\\" AS (\\n SELECT *\\n FROM \\\"ordered\\\"\\n WHERE \\\"cnt\\\" >= 5\\n),\\n\\\"positions\\\" AS (\\n SELECT DISTINCT\\n \\\"Gender\\\",\\n (1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS \\\"pos\\\",\\n CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT) AS \\\"lower_rn\\\",\\n CASE\\n WHEN (1.0 + 0.9 * (\\\"cnt\\\" - 1)) > CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT)\\n THEN CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT) + 1\\n ELSE CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT)\\n END AS \\\"upper_rn\\\"\\n FROM \\\"eligible\\\"\\n),\\n\\\"bounds\\\" AS (\\n SELECT\\n p.\\\"Gender\\\",\\n p.\\\"pos\\\",\\n p.\\\"lower_rn\\\",\\n p.\\\"upper_rn\\\",\\n MAX(CASE WHEN e.\\\"rn\\\" = p.\\\"lower_rn\\\" THEN e.\\\"measure_value\\\" END) AS \\\"lower_val\\\",\\n MAX(CASE WHEN e.\\\"rn\\\" = p.\\\"upper_rn\\\" THEN e.\\\"measure_value\\\" END) AS \\\"upper_val\\\"\\n FROM \\\"positions\\\" AS p\\n JOIN \\\"eligible\\\" AS e\\n ON e.\\\"Gender\\\" = p.\\\"Gender\\\"\\n GROUP BY\\n p.\\\"Gender\\\",\\n p.\\\"pos\\\",\\n p.\\\"lower_rn\\\",\\n p.\\\"upper_rn\\\"\\n)\\nSELECT\\n \\\"Gender\\\",\\n CASE\\n WHEN \\\"lower_rn\\\" = \\\"upper_rn\\\" THEN \\\"lower_val\\\"\\n ELSE \\\"lower_val\\\" + (\\\"pos\\\" - \\\"lower_rn\\\") * (\\\"upper_val\\\" - \\\"lower_val\\\")\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"bounds\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"Emulates `PERCENTILE_CONT(0.9)` in SQLite for each `Gender` by linearly interpolating ordered `CAST(\\\"Years_Experience\\\" AS REAL)` values; excludes null/blank measures and keeps only groups with at least 5 rows.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16719,"cached_input_tokens":15744,"output_tokens":3248,"reasoning_output_tokens":2584}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b4c8b595f8579744c55254675e96a3ff9f7296b5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n SELECT\n \"Gender\",\n CAST(\"Years_Experience\" AS REAL) AS \"measure_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Gender\"\n ORDER BY CAST(\"Years_Experience\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"Gender\"\n ) AS \"cnt\"\n FROM \"m1\"\n WHERE \"Gender\" IS NOT NULL\n AND \"Years_Experience\" IS NOT NULL\n AND TRIM(\"Years_Experience\") <> ''\n),\n\"eligible\" AS (\n SELECT *\n FROM \"ordered\"\n WHERE \"cnt\" >= 5\n),\n\"positions\" AS (\n SELECT DISTINCT\n \"Gender\",\n (1.0 + 0.9 * (\"cnt\" - 1)) AS \"pos\",\n CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT) AS \"lower_rn\",\n CASE\n WHEN (1.0 + 0.9 * (\"cnt\" - 1)) > CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT)\n THEN CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT) + 1\n ELSE CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT)\n END AS \"upper_rn\"\n FROM \"eligible\"\n),\n\"bounds\" AS (\n SELECT\n p.\"Gender\",\n p.\"pos\",\n p.\"lower_rn\",\n p.\"upper_rn\",\n MAX(CASE WHEN e.\"rn\" = p.\"lower_rn\" THEN e.\"measure_value\" END) AS \"lower_val\",\n MAX(CASE WHEN e.\"rn\" = p.\"upper_rn\" THEN e.\"measure_value\" END) AS \"upper_val\"\n FROM \"positions\" AS p\n JOIN \"eligible\" AS e\n ON e.\"Gender\" = p.\"Gender\"\n GROUP BY\n p.\"Gender\",\n p.\"pos\",\n p.\"lower_rn\",\n p.\"upper_rn\"\n)\nSELECT\n \"Gender\",\n CASE\n WHEN \"lower_rn\" = \"upper_rn\" THEN \"lower_val\"\n ELSE \"lower_val\" + (\"pos\" - \"lower_rn\") * (\"upper_val\" - \"lower_val\")\n END AS \"percentile_measure\"\nFROM \"bounds\"\nORDER BY \"percentile_measure\" DESC;","notes":"Emulates `PERCENTILE_CONT(0.9)` in SQLite for each `Gender` by linearly interpolating ordered `CAST(\"Years_Experience\" AS REAL)` values; excludes null/blank measures and keeps only groups with at least 5 rows."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7b6b45e63c9a9d601bdbb698e499599632146f94 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16349, "bytes_utf8": 16349, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 381, "bytes_utf8": 381, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16683, "cached_input_tokens": 12032, "output_tokens": 275, "reasoning_output_tokens": 180}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..a81667a848601f66f58b6b927a5d3e1b5d28fae5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_0d6aed317412ef3a", + "api_calls": 0, + "input_tokens": 16683, + "cached_input_tokens": 12032, + "output_tokens": 275, + "total_tokens": 16958, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8145.35, + "sql_execution_elapsed_ms_total": 1.06, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5f0eaff5a8893d112fff4e0327471cc6ee4649eb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:28:44.475828+00:00", + "ended_at": "2026-05-19T15:28:52.621206+00:00", + "elapsed_ms": 8145.35, + "prompt_metrics": { + "chars": 16349, + "bytes_utf8": 16349, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 735, + "bytes_utf8": 735, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 381, + "bytes_utf8": 381, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16683, + "cached_input_tokens": 12032, + "output_tokens": 275, + "reasoning_output_tokens": 180 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a175f39177f351cb8fe650a9c5f523cce95f79fd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Education_Level, measure_col=Years_Experience.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "Education_Level", + "measure_col": "Years_Experience", + "top_k": 16, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 5.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f9ea15fc7a17d4e73ebc83d8ea0d5bb8b44a6b39 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40da-8ac0-7042-9586-b5dd91fc640a"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Education_Level\\\", SUM(CAST(\\\"Years_Experience\\\" AS REAL)) AS total_measure\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Education_Level\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Applied the planned grouped-sum template with \\\"Education_Level\\\" as the grouping column and cast \\\"Years_Experience\\\" from TEXT to REAL for SQLite numeric aggregation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16683,"cached_input_tokens":12032,"output_tokens":275,"reasoning_output_tokens":180}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..92c9312f810530f4915ec6e0700bd2f3ce469a90 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"Education_Level\", SUM(CAST(\"Years_Experience\" AS REAL)) AS total_measure\nFROM \"m1\"\nGROUP BY \"Education_Level\"\nORDER BY total_measure DESC;","notes":"Applied the planned grouped-sum template with \"Education_Level\" as the grouping column and cast \"Years_Experience\" from TEXT to REAL for SQLite numeric aggregation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..997a9eedaea0e72acc39485357fe0df5d7802a62 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 17134, "bytes_utf8": 17134, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 631, "bytes_utf8": 631, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16888, "cached_input_tokens": 12032, "output_tokens": 689, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..893bb001af99cd7479e7538970cae5dad00d4e67 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_0e74392c15c2f499", + "api_calls": 0, + "input_tokens": 16888, + "cached_input_tokens": 12032, + "output_tokens": 689, + "total_tokens": 17577, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14408.08, + "sql_execution_elapsed_ms_total": 1.13, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..cf9351c4f7967b5da2f31c23bee24df59f23f3ef --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:41:26.603832+00:00", + "ended_at": "2026-05-19T15:41:41.011942+00:00", + "elapsed_ms": 14408.08, + "prompt_metrics": { + "chars": 17134, + "bytes_utf8": 17134, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1016, + "bytes_utf8": 1016, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 631, + "bytes_utf8": 631, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16888, + "cached_input_tokens": 12032, + "output_tokens": 689, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c56799b7809adc4caea0624e0f5aabe6ffb4fdb8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=Job_Level, condition_col=Gender.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "Job_Level", + "condition_col": "Gender", + "condition_value": "Female", + "positive_value": "Female", + "negative_value": "Male", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f5fa462c109bd1be57c319526b5ef8c6386a44ac --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e6-2bc3-7bc1-b6e7-153613afe544"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"Job_Level\\\",\\n SUM(CASE WHEN \\\"Gender\\\" = 'Female' THEN 1 ELSE 0 END) AS \\\"numerator_count\\\",\\n SUM(CASE WHEN \\\"Gender\\\" = 'Male' THEN 1 ELSE 0 END) AS \\\"denominator_count\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Job_Level\\\"\\n)\\nSELECT \\\"Job_Level\\\",\\n CAST(\\\"numerator_count\\\" AS FLOAT) / NULLIF(\\\"denominator_count\\\", 0) AS \\\"condition_ratio\\\"\\nFROM grouped\\nORDER BY \\\"condition_ratio\\\" DESC;\",\"notes\":\"Computes the Female-to-Male count ratio for each Job_Level using the planned grouped conditional ratio template.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16888,"cached_input_tokens":12032,"output_tokens":689,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5ccaa068a04dd5a402c0b1bf4e31e7b21e0f06f1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"Job_Level\",\n SUM(CASE WHEN \"Gender\" = 'Female' THEN 1 ELSE 0 END) AS \"numerator_count\",\n SUM(CASE WHEN \"Gender\" = 'Male' THEN 1 ELSE 0 END) AS \"denominator_count\"\n FROM \"m1\"\n GROUP BY \"Job_Level\"\n)\nSELECT \"Job_Level\",\n CAST(\"numerator_count\" AS FLOAT) / NULLIF(\"denominator_count\", 0) AS \"condition_ratio\"\nFROM grouped\nORDER BY \"condition_ratio\" DESC;","notes":"Computes the Female-to-Male count ratio for each Job_Level using the planned grouped conditional ratio template."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0e74392c15c2f499/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..42f6337d8bf4c99f7589b80899f79a5190be4857 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16613, "bytes_utf8": 16613, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 438, "bytes_utf8": 438, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16751, "cached_input_tokens": 12032, "output_tokens": 474, "reasoning_output_tokens": 353}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..85316d5740333793b6af4cfa5d28e94205743f0e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_0fb3892cb073099d", + "api_calls": 0, + "input_tokens": 16751, + "cached_input_tokens": 12032, + "output_tokens": 474, + "total_tokens": 17225, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10330.09, + "sql_execution_elapsed_ms_total": 1.55, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1b8c77e4bca765d84fac5255fac693188432b38a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:04.685032+00:00", + "ended_at": "2026-05-19T16:03:15.015157+00:00", + "elapsed_ms": 10330.09, + "prompt_metrics": { + "chars": 16613, + "bytes_utf8": 16613, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 793, + "bytes_utf8": 793, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 438, + "bytes_utf8": 438, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16751, + "cached_input_tokens": 12032, + "output_tokens": 474, + "reasoning_output_tokens": 353 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4d90c435f33195cd8ebbd8cc40dedf0671dd2c08 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=Team_Collaboration_Frequency, condition_col=Job_Level.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Team_Collaboration_Frequency", + "condition_col": "Job_Level", + "condition_value": "Junior", + "positive_value": "Junior", + "negative_value": "Mid-Level", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 8.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f405340c72cde9dd7f0fc9856d277d8868d9dd3c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-fa87-74d3-9d1d-b4da44fda952"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"Team_Collaboration_Frequency\\\",\\n AVG(CASE WHEN \\\"Job_Level\\\" = 'Junior' THEN 1.0 ELSE 0.0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Team_Collaboration_Frequency\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Computes the rate of records with Job_Level = 'Junior' within each Team_Collaboration_Frequency group, following the Grouped Condition Rate template.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16751,"cached_input_tokens":12032,"output_tokens":474,"reasoning_output_tokens":353}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2c0b42bcb6b03fb137267ddf8617180fdc5f3bad --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"Team_Collaboration_Frequency\",\n AVG(CASE WHEN \"Job_Level\" = 'Junior' THEN 1.0 ELSE 0.0 END) AS \"condition_rate\"\nFROM \"m1\"\nGROUP BY \"Team_Collaboration_Frequency\"\nORDER BY \"condition_rate\" DESC;","notes":"Computes the rate of records with Job_Level = 'Junior' within each Team_Collaboration_Frequency group, following the Grouped Condition Rate template."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0fb3892cb073099d/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..8ca311de3784dbf0a886800c952566ef872f6b7a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16347, "bytes_utf8": 16347, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 425, "bytes_utf8": 425, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16693, "cached_input_tokens": 4480, "output_tokens": 423, "reasoning_output_tokens": 308}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..69ca27bddb9b54def5579a61286e3f24bc1abaf5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_11a6c0b0473fea94", + "api_calls": 0, + "input_tokens": 16693, + "cached_input_tokens": 4480, + "output_tokens": 423, + "total_tokens": 17116, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11225.11, + "sql_execution_elapsed_ms_total": 1.13, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..22099612d2f84afc47c54c1ed93a532fc16c953f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:28:52.626175+00:00", + "ended_at": "2026-05-19T15:29:03.851307+00:00", + "elapsed_ms": 11225.11, + "prompt_metrics": { + "chars": 16347, + "bytes_utf8": 16347, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 778, + "bytes_utf8": 778, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 425, + "bytes_utf8": 425, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16693, + "cached_input_tokens": 4480, + "output_tokens": 423, + "reasoning_output_tokens": 308 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5b4b553ed20b042ec6b0c099023ae8e6af0ba294 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Marital_Status, measure_col=WFH_Days_Per_Week.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "Marital_Status", + "measure_col": "WFH_Days_Per_Week", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 4.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4d14a62764fff0866f37a42f19fcd388a00066d3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40da-aa94-7e51-9af9-5a0697537c58"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Marital_Status\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS total_measure\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Marital_Status\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Applied the provided grouped numeric sum template with \\\"Marital_Status\\\" as the grouping column and \\\"WFH_Days_Per_Week\\\" as the measure. CAST to REAL is used because the schema stores this numeric field as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16693,"cached_input_tokens":4480,"output_tokens":423,"reasoning_output_tokens":308}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8e7b319f6747f20fa2bb6736262bc85134796395 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"Marital_Status\", SUM(CAST(\"WFH_Days_Per_Week\" AS REAL)) AS total_measure\nFROM \"m1\"\nGROUP BY \"Marital_Status\"\nORDER BY total_measure DESC;","notes":"Applied the provided grouped numeric sum template with \"Marital_Status\" as the grouping column and \"WFH_Days_Per_Week\" as the measure. CAST to REAL is used because the schema stores this numeric field as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_11a6c0b0473fea94/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..42ae8daf746cf44855892917c63d61ad108c75b3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_125f5b1eba679c5e", + "api_calls": 0, + "input_tokens": 16810, + "cached_input_tokens": 15744, + "output_tokens": 704, + "total_tokens": 17514, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14136.92, + "sql_execution_elapsed_ms_total": 9.11, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7c81ee5a51ddda3d5ad7328ee5818332fe605f62 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:36:28.411215+00:00", + "ended_at": "2026-05-19T15:36:42.548182+00:00", + "elapsed_ms": 14136.92, + "prompt_metrics": { + "chars": 16783, + "bytes_utf8": 16783, + "lines": 458, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1056, + "bytes_utf8": 1056, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 686, + "bytes_utf8": 686, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16810, + "cached_input_tokens": 15744, + "output_tokens": 704, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..481aa86edb785955ef01470f551998c4f8c1ca03 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,458 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Team_Collaboration_Frequency, measure_col=Years_Experience.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "Team_Collaboration_Frequency", + "measure_col": "Years_Experience", + "item_col": "Quality_Score", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..550466894713ceeac81561798c77e4ed56d8a4cb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e1-9efb-7903-849e-ae6a827b32c6"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"Team_Collaboration_Frequency\\\",\\n \\\"Quality_Score\\\",\\n SUM(CAST(\\\"Years_Experience\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Years_Experience\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Years_Experience\\\" AS REAL))) OVER (PARTITION BY \\\"Team_Collaboration_Frequency\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Team_Collaboration_Frequency\\\", \\\"Quality_Score\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the fixed template bindings exactly: group_col=Team_Collaboration_Frequency, item_col=Quality_Score, measure_col=Years_Experience. Cast Years_Experience to REAL because the SQLite schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16810,"cached_input_tokens":15744,"output_tokens":704,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_125f5b1eba679c5e/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f8623c454c7ec48bbe392cb1c6b96f2bd8b7e71e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:34.099127+00:00", + "ended_at": "2026-05-19T16:03:37.314400+00:00", + "elapsed_ms": 3215.23, + "returncode": 1, + "prompt_metrics": { + "chars": 16629, + "bytes_utf8": 16629, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8629f254665829de20094c0018ad405722fa707e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:38.317637+00:00", + "ended_at": "2026-05-19T16:03:41.418441+00:00", + "elapsed_ms": 3100.76, + "returncode": 1, + "prompt_metrics": { + "chars": 16629, + "bytes_utf8": 16629, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5775f833fcc964db69855cf1ff705b5afb8854e9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Stress_Level, condition_col=Company_Size.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Stress_Level", + "condition_col": "Company_Size", + "condition_value": "Medium (201-1000)", + "positive_value": "Large (1001-5000)", + "negative_value": "Medium (201-1000)", + "top_k": 18, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 41.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..5775f833fcc964db69855cf1ff705b5afb8854e9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Stress_Level, condition_col=Company_Size.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Stress_Level", + "condition_col": "Company_Size", + "condition_value": "Medium (201-1000)", + "positive_value": "Large (1001-5000)", + "negative_value": "Medium (201-1000)", + "top_k": 18, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 41.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4e454e95e0863f8f389c3bdea4ee5857d40aa6ea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-6d4f-7da2-9375-c09246732faf"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..edf715c400848b5a3e775c869624841ef87a8923 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-6d4f-7da2-9375-c09246732faf"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..26d419b17ca0d933c161af83e6f7b3a3c4f38d4c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-7de5-72c0-86f2-e88552d748da"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..5d5e5391cbee53c6c755a499c01e81a3464f323d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-7de5-72c0-86f2-e88552d748da"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14061aef2f48765b/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7a1005eab09f9b72ab00dc2006e79aa3d52673ff --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16525, "bytes_utf8": 16525, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1952, "bytes_utf8": 1952, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16725, "cached_input_tokens": 15744, "output_tokens": 1178, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7981d3badc2dcadec93b3bfef11bbc78e77715f4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_14e94e8b2ecb8e75", + "api_calls": 0, + "input_tokens": 16725, + "cached_input_tokens": 15744, + "output_tokens": 1178, + "total_tokens": 17903, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17613.48, + "sql_execution_elapsed_ms_total": 14.14, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9a2f8cb91ef32470c4f2afc5c1025a6b6afbf13b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:56:58.566806+00:00", + "ended_at": "2026-05-19T15:57:16.180321+00:00", + "elapsed_ms": 17613.48, + "prompt_metrics": { + "chars": 16525, + "bytes_utf8": 16525, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2566, + "bytes_utf8": 2566, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1952, + "bytes_utf8": 1952, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16725, + "cached_input_tokens": 15744, + "output_tokens": 1178, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..14c36897ee964fcceae2371793d9c4e14c2bfcca --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Has_Children, measure_col=Productivity_Score.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Has_Children", + "measure_col": "Productivity_Score", + "top_k": 18, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 94.8, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..643123584a9b32eb3d21b729a54dd6700be7590d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-644d-77e1-bc6e-99f96193efec"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"Has_Children\\\",\\n CAST(\\\"Productivity_Score\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Has_Children\\\"\\n ORDER BY CAST(\\\"Productivity_Score\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (PARTITION BY \\\"Has_Children\\\") AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Has_Children\\\" IS NOT NULL\\n AND \\\"Productivity_Score\\\" IS NOT NULL\\n),\\n\\\"positions\\\" AS (\\n SELECT\\n \\\"Has_Children\\\",\\n ((\\\"cnt\\\" - 1) * 0.9) + 1.0 AS \\\"pos\\\",\\n CAST(((\\\"cnt\\\" - 1) * 0.9) + 1.0 AS INTEGER) AS \\\"lo_rn\\\",\\n CASE\\n WHEN (((\\\"cnt\\\" - 1) * 0.9) + 1.0) = CAST(((\\\"cnt\\\" - 1) * 0.9) + 1.0 AS INTEGER)\\n THEN CAST(((\\\"cnt\\\" - 1) * 0.9) + 1.0 AS INTEGER)\\n ELSE CAST(((\\\"cnt\\\" - 1) * 0.9) + 1.0 AS INTEGER) + 1\\n END AS \\\"hi_rn\\\"\\n FROM \\\"ranked\\\"\\n GROUP BY \\\"Has_Children\\\", \\\"cnt\\\"\\n),\\n\\\"picked\\\" AS (\\n SELECT\\n p.\\\"Has_Children\\\",\\n p.\\\"pos\\\",\\n p.\\\"lo_rn\\\",\\n p.\\\"hi_rn\\\",\\n MAX(CASE WHEN r.\\\"rn\\\" = p.\\\"lo_rn\\\" THEN r.\\\"measure_value\\\" END) AS \\\"lo_val\\\",\\n MAX(CASE WHEN r.\\\"rn\\\" = p.\\\"hi_rn\\\" THEN r.\\\"measure_value\\\" END) AS \\\"hi_val\\\"\\n FROM \\\"positions\\\" p\\n JOIN \\\"ranked\\\" r\\n ON r.\\\"Has_Children\\\" = p.\\\"Has_Children\\\"\\n GROUP BY p.\\\"Has_Children\\\", p.\\\"pos\\\", p.\\\"lo_rn\\\", p.\\\"hi_rn\\\"\\n)\\nSELECT\\n \\\"Has_Children\\\",\\n CASE\\n WHEN \\\"lo_rn\\\" = \\\"hi_rn\\\" THEN \\\"lo_val\\\"\\n ELSE \\\"lo_val\\\" + ((\\\"pos\\\" - \\\"lo_rn\\\") * (\\\"hi_val\\\" - \\\"lo_val\\\"))\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"picked\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite-compatible rewrite of the grouped percentile template for group_col=Has_Children and measure_col=Productivity_Score. Since SQLite does not support PERCENTILE_CONT, this emulates the continuous 90th percentile using linear interpolation over ordered CAST(\\\"Productivity_Score\\\" AS REAL) values within each group.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16725,"cached_input_tokens":15744,"output_tokens":1178,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d4c1ec108f9654c8387416cce4f5f402f5bcdd06 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n SELECT\n \"Has_Children\",\n CAST(\"Productivity_Score\" AS REAL) AS \"measure_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Has_Children\"\n ORDER BY CAST(\"Productivity_Score\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (PARTITION BY \"Has_Children\") AS \"cnt\"\n FROM \"m1\"\n WHERE \"Has_Children\" IS NOT NULL\n AND \"Productivity_Score\" IS NOT NULL\n),\n\"positions\" AS (\n SELECT\n \"Has_Children\",\n ((\"cnt\" - 1) * 0.9) + 1.0 AS \"pos\",\n CAST(((\"cnt\" - 1) * 0.9) + 1.0 AS INTEGER) AS \"lo_rn\",\n CASE\n WHEN (((\"cnt\" - 1) * 0.9) + 1.0) = CAST(((\"cnt\" - 1) * 0.9) + 1.0 AS INTEGER)\n THEN CAST(((\"cnt\" - 1) * 0.9) + 1.0 AS INTEGER)\n ELSE CAST(((\"cnt\" - 1) * 0.9) + 1.0 AS INTEGER) + 1\n END AS \"hi_rn\"\n FROM \"ranked\"\n GROUP BY \"Has_Children\", \"cnt\"\n),\n\"picked\" AS (\n SELECT\n p.\"Has_Children\",\n p.\"pos\",\n p.\"lo_rn\",\n p.\"hi_rn\",\n MAX(CASE WHEN r.\"rn\" = p.\"lo_rn\" THEN r.\"measure_value\" END) AS \"lo_val\",\n MAX(CASE WHEN r.\"rn\" = p.\"hi_rn\" THEN r.\"measure_value\" END) AS \"hi_val\"\n FROM \"positions\" p\n JOIN \"ranked\" r\n ON r.\"Has_Children\" = p.\"Has_Children\"\n GROUP BY p.\"Has_Children\", p.\"pos\", p.\"lo_rn\", p.\"hi_rn\"\n)\nSELECT\n \"Has_Children\",\n CASE\n WHEN \"lo_rn\" = \"hi_rn\" THEN \"lo_val\"\n ELSE \"lo_val\" + ((\"pos\" - \"lo_rn\") * (\"hi_val\" - \"lo_val\"))\n END AS \"percentile_measure\"\nFROM \"picked\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite-compatible rewrite of the grouped percentile template for group_col=Has_Children and measure_col=Productivity_Score. Since SQLite does not support PERCENTILE_CONT, this emulates the continuous 90th percentile using linear interpolation over ordered CAST(\"Productivity_Score\" AS REAL) values within each group."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_14e94e8b2ecb8e75/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3aad51b15e50110716c533ad158aced6bae99cfc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16653, "bytes_utf8": 16653, "lines": 460, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 414, "bytes_utf8": 414, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16780, "cached_input_tokens": 15744, "output_tokens": 436, "reasoning_output_tokens": 316}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..1874b33e47122e62f9217c392ef00f2f5700911c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_15ba276ac92518aa", + "api_calls": 0, + "input_tokens": 16780, + "cached_input_tokens": 15744, + "output_tokens": 436, + "total_tokens": 17216, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 19827.27, + "sql_execution_elapsed_ms_total": 0.77, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8a5cb2f35cdc0d8a11581ce269d820308343b4cb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:44:13.632226+00:00", + "ended_at": "2026-05-19T15:44:33.459524+00:00", + "elapsed_ms": 19827.27, + "prompt_metrics": { + "chars": 16653, + "bytes_utf8": 16653, + "lines": 460, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1114, + "bytes_utf8": 1114, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 414, + "bytes_utf8": 414, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16780, + "cached_input_tokens": 15744, + "output_tokens": 436, + "reasoning_output_tokens": 316 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..3927a39b4bf2e5eee13bfd9a45d9c725c5ecdb52 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,460 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=Has_Children, group_col_2=Survey_Date.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "Has_Children", + "group_col_2": "Survey_Date", + "predicate_col": "Survey_Date", + "predicate_op": "=", + "predicate_value": "2024-05-04", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 41.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..77e337a2b534b66206ec92992419dfe95119e1f7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40e8-b848-7af2-8633-c6458939694b"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 17415. Please try again in 522ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"Has_Children\\\", \\\"Survey_Date\\\", COUNT(*) AS row_count\\nFROM \\\"m1\\\"\\nWHERE \\\"Survey_Date\\\" = '2024-05-04'\\nGROUP BY \\\"Has_Children\\\", \\\"Survey_Date\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Uses the provided filtered two-dimensional group count template with \\\"Has_Children\\\" and \\\"Survey_Date\\\", filtered to rows where \\\"Survey_Date\\\" = '2024-05-04'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16780,"cached_input_tokens":15744,"output_tokens":436,"reasoning_output_tokens":316}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..400cad2b387f99e283cbe509a86df28f0cac0caf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"Has_Children\", \"Survey_Date\", COUNT(*) AS row_count\nFROM \"m1\"\nWHERE \"Survey_Date\" = '2024-05-04'\nGROUP BY \"Has_Children\", \"Survey_Date\"\nORDER BY row_count DESC;","notes":"Uses the provided filtered two-dimensional group count template with \"Has_Children\" and \"Survey_Date\", filtered to rows where \"Survey_Date\" = '2024-05-04'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_15ba276ac92518aa/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..07d8de557d0e67f8f836601c4d024a704c0f0121 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16506, "bytes_utf8": 16506, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 623, "bytes_utf8": 623, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16740, "cached_input_tokens": 12032, "output_tokens": 521, "reasoning_output_tokens": 343}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..c1e8e7f5ef36a142e0ca536378031e990d6b724d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_187cf7cd3ad6f77b", + "api_calls": 0, + "input_tokens": 16740, + "cached_input_tokens": 12032, + "output_tokens": 521, + "total_tokens": 17261, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11647.9, + "sql_execution_elapsed_ms_total": 5.24, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..21a505336978bd59a8c4b6792cd6e1b98f011955 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:46:01.621168+00:00", + "ended_at": "2026-05-19T15:46:13.269117+00:00", + "elapsed_ms": 11647.9, + "prompt_metrics": { + "chars": 16506, + "bytes_utf8": 16506, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 999, + "bytes_utf8": 999, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 623, + "bytes_utf8": 623, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16740, + "cached_input_tokens": 12032, + "output_tokens": 521, + "reasoning_output_tokens": 343 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e2c04c1b95fe958b6445dbe72bba08b404078995 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Meetings_Per_Week.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "Meetings_Per_Week", + "top_k": 10, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 10.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..3b583508e518762085af4d8ce00b99b5ecf5d701 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ea-5e1c-7930-90dd-d89ecd274675"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n SELECT CAST(\\\"Meetings_Per_Week\\\" AS REAL) AS \\\"Meetings_Per_Week\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"Meetings_Per_Week\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Meetings_Per_Week\\\" IS NOT NULL\\n)\\nSELECT \\\"Meetings_Per_Week\\\"\\nFROM buckets\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"Meetings_Per_Week\\\" DESC;\",\"notes\":\"Applied the Quantile Tail Slice template with \\\"Meetings_Per_Week\\\" as the measure column and 10 tiles, returning the top decile. Cast to REAL because the SQLite schema stores this field as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16740,"cached_input_tokens":12032,"output_tokens":521,"reasoning_output_tokens":343}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..50492c02240d093bcd15e7e9a3098369dddb8ae4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH buckets AS (\n SELECT CAST(\"Meetings_Per_Week\" AS REAL) AS \"Meetings_Per_Week\",\n NTILE(10) OVER (ORDER BY CAST(\"Meetings_Per_Week\" AS REAL) DESC) AS \"tail_bucket\"\n FROM \"m1\"\n WHERE \"Meetings_Per_Week\" IS NOT NULL\n)\nSELECT \"Meetings_Per_Week\"\nFROM buckets\nWHERE \"tail_bucket\" = 1\nORDER BY \"Meetings_Per_Week\" DESC;","notes":"Applied the Quantile Tail Slice template with \"Meetings_Per_Week\" as the measure column and 10 tiles, returning the top decile. Cast to REAL because the SQLite schema stores this field as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_187cf7cd3ad6f77b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..2552e633751c17d17a1b50f8125540ffe2d49353 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Team_Collaboration_Frequency, measure_col=Commute_Time_Minutes. +Result preview: [{"Team_Collaboration_Frequency": "Daily", "group_value": 13287.0}, {"Team_Collaboration_Frequency": "Few times per week", "group_value": 11118.0}, {"Team_Collaboration_Frequency": "Weekly", "group_value": 8879.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..1fe973b34a07ac5b92f4d6f599d5fc7cbc70d31a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/generated_sql.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_m1_1a526838d64cf612 +-- problem_id: v2p_m1_a7619fed2f127898 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "Team_Collaboration_Frequency", SUM(CAST("Commute_Time_Minutes" AS REAL)) AS group_value + FROM "m1" + GROUP BY "Team_Collaboration_Frequency" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."Team_Collaboration_Frequency", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.1 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..610c03e02505248aa1bea76174d5f5d4a120e307 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"Team_Collaboration_Frequency\", SUM(CAST(\"Commute_Time_Minutes\" AS REAL)) AS group_value\n FROM \"m1\"\n GROUP BY \"Team_Collaboration_Frequency\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"Team_Collaboration_Frequency\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Team_Collaboration_Frequency\\\", SUM(CAST(\\\"Commute_Time_Minutes\\\" AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Team_Collaboration_Frequency\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Team_Collaboration_Frequency\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\", \"columns\": [\"Team_Collaboration_Frequency\", \"group_value\"], \"rows\": [{\"Team_Collaboration_Frequency\": \"Daily\", \"group_value\": 13287.0}, {\"Team_Collaboration_Frequency\": \"Few times per week\", \"group_value\": 11118.0}, {\"Team_Collaboration_Frequency\": \"Weekly\", \"group_value\": 8879.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.78}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0a627858c3598857a49e1933c18d87de60cfdd6e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:50:28.543545+00:00", + "ended_at": "2026-05-19T15:50:38.491738+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_1a526838d64cf612", + "problem_id": "v2p_m1_a7619fed2f127898", + "dataset_id": "m1", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Team_Collaboration_Frequency, measure_col=Commute_Time_Minutes.", + "bindings": { + "group_col": "Team_Collaboration_Frequency", + "measure_col": "Commute_Time_Minutes", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=9", + "sql_variant_index=1/2", + "binding_index=80" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 9, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_1a526838d64cf612.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_1a526838d64cf612", + "api_calls": 0, + "input_tokens": 16831, + "cached_input_tokens": 15744, + "output_tokens": 417, + "total_tokens": 17248, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9942.42, + "sql_execution_elapsed_ms_total": 1.78, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..011dea7d36ff1a379e9fc2cf6728ae716371c177 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:50:38.488344+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 9942.42, "started_at": "2026-05-19T15:50:28.545018+00:00", "ended_at": "2026-05-19T15:50:38.487466+00:00", "prompt_metrics": {"chars": 16936, "bytes_utf8": 16936, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 730, "bytes_utf8": 730, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16831, "cached_input_tokens": 15744, "output_tokens": 417, "reasoning_output_tokens": 228}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Team_Collaboration_Frequency\\\", SUM(CAST(\\\"Commute_Time_Minutes\\\" AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Team_Collaboration_Frequency\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Team_Collaboration_Frequency\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Used the provided template with group_col bound to \\\"Team_Collaboration_Frequency\\\" and measure_col bound to \\\"Commute_Time_Minutes\\\". CAST to REAL is included because the schema stores numeric values as TEXT in SQLite.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8efce4f81f41e87592bffffd8f0970f525642415 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_1a526838d64cf612", + "api_calls": 0, + "input_tokens": 16831, + "cached_input_tokens": 15744, + "output_tokens": 417, + "total_tokens": 17248, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9942.42, + "sql_execution_elapsed_ms_total": 1.78, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1a526838d64cf612/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f0777f4fa121035c0296e307478d216158aeb93c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16675, "bytes_utf8": 16675, "lines": 460, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 556, "bytes_utf8": 556, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16785, "cached_input_tokens": 12032, "output_tokens": 332, "reasoning_output_tokens": 185}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..29f4f8b04d1c54b032a15869db3bc469673ef5b7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_1afaf01168c1270c", + "api_calls": 0, + "input_tokens": 16785, + "cached_input_tokens": 12032, + "output_tokens": 332, + "total_tokens": 17117, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9054.56, + "sql_execution_elapsed_ms_total": 1.34, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ba221f935e353e8fd8ca0a7db769893b642c190a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:43:08.426151+00:00", + "ended_at": "2026-05-19T15:43:17.480734+00:00", + "elapsed_ms": 9054.56, + "prompt_metrics": { + "chars": 16675, + "bytes_utf8": 16675, + "lines": 460, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 923, + "bytes_utf8": 923, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 556, + "bytes_utf8": 556, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16785, + "cached_input_tokens": 12032, + "output_tokens": 332, + "reasoning_output_tokens": 185 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ebbc2da52932769ab87082aa9f840c7fbf448877 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,460 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=Has_Children, group_col_2=Internet_Speed_Category.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "Has_Children", + "group_col_2": "Internet_Speed_Category", + "predicate_col": "Meetings_Per_Week", + "predicate_op": ">=", + "predicate_value": 10.0, + "top_k": 11, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 10.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8a0c9a82e57d0989d3bb61c0405bbff151f39010 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e7-b99b-7bc2-b9f1-3fcfc227193b"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"Has_Children\\\", \\\"Internet_Speed_Category\\\", COUNT(*) AS row_count\\nFROM \\\"m1\\\"\\nWHERE CAST(\\\"Meetings_Per_Week\\\" AS REAL) >= 10.0\\nGROUP BY \\\"Has_Children\\\", \\\"Internet_Speed_Category\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Applied the provided filtered two-dimensional group count template with group_col=\\\"Has_Children\\\", group_col_2=\\\"Internet_Speed_Category\\\", and predicate \\\"Meetings_Per_Week\\\" >= 10.0. Used CAST(... AS REAL) because the schema stores numeric-looking fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16785,"cached_input_tokens":12032,"output_tokens":332,"reasoning_output_tokens":185}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a58eb7b725ccd2b4f1bd826ffc88288736318c61 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"Has_Children\", \"Internet_Speed_Category\", COUNT(*) AS row_count\nFROM \"m1\"\nWHERE CAST(\"Meetings_Per_Week\" AS REAL) >= 10.0\nGROUP BY \"Has_Children\", \"Internet_Speed_Category\"\nORDER BY row_count DESC;","notes":"Applied the provided filtered two-dimensional group count template with group_col=\"Has_Children\", group_col_2=\"Internet_Speed_Category\", and predicate \"Meetings_Per_Week\" >= 10.0. Used CAST(... AS REAL) because the schema stores numeric-looking fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1afaf01168c1270c/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5e679ff015a3dff872194f06803ce6611ff2f1a6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:57:16.203556+00:00", + "ended_at": "2026-05-19T15:57:19.522482+00:00", + "elapsed_ms": 3318.89, + "returncode": 1, + "prompt_metrics": { + "chars": 16529, + "bytes_utf8": 16529, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..15be266a48437d91f8f7323f52285b4e527747d2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:57:20.524757+00:00", + "ended_at": "2026-05-19T15:57:23.735645+00:00", + "elapsed_ms": 3210.85, + "returncode": 1, + "prompt_metrics": { + "chars": 16529, + "bytes_utf8": 16529, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9e369b9bae727c0835a23434a68899f3eec3c249 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Location_Type, measure_col=Task_Completion_Rate.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Location_Type", + "measure_col": "Task_Completion_Rate", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..9e369b9bae727c0835a23434a68899f3eec3c249 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Location_Type, measure_col=Task_Completion_Rate.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Location_Type", + "measure_col": "Task_Completion_Rate", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6fef68d7bae726cbfabd972f54c6694f4276c069 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-a942-73c3-8ceb-009e94501670"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c31fb8c72605e8497c266e5ac2eb951edfd5dddb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-a942-73c3-8ceb-009e94501670"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4d29a588be16f5891f6d784c4b6401855b5849a4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-ba2d-7080-b32b-f4dad018b681"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..357650f363b728445936678a79740109214b1f03 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-ba2d-7080-b32b-f4dad018b681"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c3d240370b25a45/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f79edf0fc4962053498247acccbb9b83b22f7475 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16884, "bytes_utf8": 16884, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 698, "bytes_utf8": 698, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16817, "cached_input_tokens": 15744, "output_tokens": 597, "reasoning_output_tokens": 402}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..aef2d06043ace4ebe49b5da2c9e0d0afed23320c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_1c81aceb60acc633", + "api_calls": 0, + "input_tokens": 16817, + "cached_input_tokens": 15744, + "output_tokens": 597, + "total_tokens": 17414, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14208.29, + "sql_execution_elapsed_ms_total": 2.75, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..27a7bd3c79e54f11e08cd71d831ba9d7bc15a4ab --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:48:45.558256+00:00", + "ended_at": "2026-05-19T15:48:59.766584+00:00", + "elapsed_ms": 14208.29, + "prompt_metrics": { + "chars": 16884, + "bytes_utf8": 16884, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1117, + "bytes_utf8": 1117, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 698, + "bytes_utf8": 698, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16817, + "cached_input_tokens": 15744, + "output_tokens": 597, + "reasoning_output_tokens": 402 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7647f1c10b205be08c89a5456e73a0978d0969ec --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Industry, measure_col=Quality_Score.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "Industry", + "measure_col": "Quality_Score", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.225, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0083880c668e96382e115f5f72a2497ebe68cfab --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ec-de70-7862-93aa-40ac6661224b"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"Industry\\\", SUM(CAST(\\\"Quality_Score\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Industry\\\"\\n), \\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"Industry\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Applied the planned relative-to-total threshold template with group_col=\\\"Industry\\\" and measure_col=\\\"Quality_Score\\\". CAST to REAL is used because \\\"Quality_Score\\\" is stored as TEXT in the SQLite schema.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16817,"cached_input_tokens":15744,"output_tokens":597,"reasoning_output_tokens":402}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b9dfb25f14e80a3e0a6d714c944783114466346 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT \"Industry\", SUM(CAST(\"Quality_Score\" AS REAL)) AS \"group_value\"\n FROM \"m1\"\n GROUP BY \"Industry\"\n), \"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT g.\"Industry\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;","notes":"Applied the planned relative-to-total threshold template with group_col=\"Industry\" and measure_col=\"Quality_Score\". CAST to REAL is used because \"Quality_Score\" is stored as TEXT in the SQLite schema."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..946414923b0dcf272f6b3a252359cf7dfc3ba1d3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Industry, measure_col=Quality_Score. +Result preview: [{"Industry": "Technology", "group_value": 39951.9}, {"Industry": "Finance", "group_value": 19014.6}, {"Industry": "Healthcare", "group_value": 15927.6}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..5ab6ecfb6d892afd613c5b96a0e368542780e964 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/generated_sql.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_m1_1c81aceb60acc633 +-- problem_id: v2p_m1_beb6fa3fd68c3b56 +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT "Industry", SUM(CAST("Quality_Score" AS REAL)) AS "group_value" + FROM "m1" + GROUP BY "Industry" +), "total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT g."Industry", g."group_value" +FROM "grouped" AS g +CROSS JOIN "total" AS t +WHERE g."group_value" > t."total_value" * 0.1 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..eb1039e76c94b8f70bd61faf46907dedfa6c366a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT \"Industry\", SUM(CAST(\"Quality_Score\" AS REAL)) AS \"group_value\"\n FROM \"m1\"\n GROUP BY \"Industry\"\n), \"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT g.\"Industry\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"Industry\\\", SUM(CAST(\\\"Quality_Score\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Industry\\\"\\n), \\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"Industry\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\", \"columns\": [\"Industry\", \"group_value\"], \"rows\": [{\"Industry\": \"Technology\", \"group_value\": 39951.9}, {\"Industry\": \"Finance\", \"group_value\": 19014.6}, {\"Industry\": \"Healthcare\", \"group_value\": 15927.6}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.75}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..d18a4795d25513068983d8f41d5f29ab8ae85f3f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:48:45.556941+00:00", + "ended_at": "2026-05-19T15:48:59.774058+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_1c81aceb60acc633", + "problem_id": "v2p_m1_beb6fa3fd68c3b56", + "dataset_id": "m1", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Industry, measure_col=Quality_Score.", + "bindings": { + "group_col": "Industry", + "measure_col": "Quality_Score", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.225, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=5", + "sql_variant_index=1/2", + "binding_index=76" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 5, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_1c81aceb60acc633.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_1c81aceb60acc633", + "api_calls": 0, + "input_tokens": 16817, + "cached_input_tokens": 15744, + "output_tokens": 597, + "total_tokens": 17414, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14208.29, + "sql_execution_elapsed_ms_total": 2.75, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6b45fa30eea0af16c41c6871ac2b327bc5e624ae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:48:59.768250+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 14208.29, "started_at": "2026-05-19T15:48:45.558256+00:00", "ended_at": "2026-05-19T15:48:59.766584+00:00", "prompt_metrics": {"chars": 16884, "bytes_utf8": 16884, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 698, "bytes_utf8": 698, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16817, "cached_input_tokens": 15744, "output_tokens": 597, "reasoning_output_tokens": 402}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"Industry\\\", SUM(CAST(\\\"Quality_Score\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Industry\\\"\\n), \\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"Industry\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Applied the planned relative-to-total threshold template with group_col=\\\"Industry\\\" and measure_col=\\\"Quality_Score\\\". CAST to REAL is used because \\\"Quality_Score\\\" is stored as TEXT in the SQLite schema.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..301ff6644951f0b66be04b0a983dc2920be3bc86 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_1c81aceb60acc633", + "api_calls": 0, + "input_tokens": 16817, + "cached_input_tokens": 15744, + "output_tokens": 597, + "total_tokens": 17414, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14208.29, + "sql_execution_elapsed_ms_total": 2.75, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1c81aceb60acc633/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1958bf81aaf386981f3d8d0b68732210bc6b8e48 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16509, "bytes_utf8": 16509, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 572, "bytes_utf8": 572, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16738, "cached_input_tokens": 15744, "output_tokens": 438, "reasoning_output_tokens": 286}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d33a1febd34aa33f87d60a16cc53ad79cf440d28 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_1cacaa98c22d42c3", + "api_calls": 0, + "input_tokens": 16738, + "cached_input_tokens": 15744, + "output_tokens": 438, + "total_tokens": 17176, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18210.59, + "sql_execution_elapsed_ms_total": 3.94, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..01c45a35743f2cdc32b1899d48ad9e22e479acef --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:44:33.464209+00:00", + "ended_at": "2026-05-19T15:44:51.674834+00:00", + "elapsed_ms": 18210.59, + "prompt_metrics": { + "chars": 16509, + "bytes_utf8": 16509, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1598, + "bytes_utf8": 1598, + "lines": 6, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 572, + "bytes_utf8": 572, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16738, + "cached_input_tokens": 15744, + "output_tokens": 438, + "reasoning_output_tokens": 286 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..20fc9ea69087d1ec2bc3de65a04df2462fb1b80f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Productivity_Score.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "Productivity_Score", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 98.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0469805db8756349f4013ecb15f56f31bc50061 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,6 @@ +{"type":"thread.started","thread_id":"019e40e9-05d1-70f1-8723-7026f36613b1"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 17373. Please try again in 521ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1993291, Requested 17373. Please try again in 319ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n SELECT\\n \\\"Productivity_Score\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"Productivity_Score\\\" AS REAL) DESC) AS tail_bucket\\n FROM \\\"m1\\\"\\n)\\nSELECT \\\"Productivity_Score\\\"\\nFROM buckets\\nWHERE tail_bucket = 1\\nORDER BY CAST(\\\"Productivity_Score\\\" AS REAL) DESC;\",\"notes\":\"Uses the planned Quantile Tail Slice template on \\\"Productivity_Score\\\". CAST to REAL is added because the schema stores this measure as TEXT, and numeric ordering is required for a correct top-decile tail slice.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16738,"cached_input_tokens":15744,"output_tokens":438,"reasoning_output_tokens":286}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d18b00b6fac13e33caf20cf29ee29519d5b204bc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH buckets AS (\n SELECT\n \"Productivity_Score\",\n NTILE(10) OVER (ORDER BY CAST(\"Productivity_Score\" AS REAL) DESC) AS tail_bucket\n FROM \"m1\"\n)\nSELECT \"Productivity_Score\"\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY CAST(\"Productivity_Score\" AS REAL) DESC;","notes":"Uses the planned Quantile Tail Slice template on \"Productivity_Score\". CAST to REAL is added because the schema stores this measure as TEXT, and numeric ordering is required for a correct top-decile tail slice."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1cacaa98c22d42c3/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..726cdbc5da791e8bb22b17a95f3fe84b054329d6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16917, "bytes_utf8": 16917, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 758, "bytes_utf8": 758, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16827, "cached_input_tokens": 15744, "output_tokens": 678, "reasoning_output_tokens": 476}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..68d28894a756081328bb440d4bc536a21d651106 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_1e9ca03df90fecd1", + "api_calls": 0, + "input_tokens": 16827, + "cached_input_tokens": 15744, + "output_tokens": 678, + "total_tokens": 17505, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13857.05, + "sql_execution_elapsed_ms_total": 1.29, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0cc0beee85f94fcf2286e6e08a5661a279e16a65 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:50:14.682454+00:00", + "ended_at": "2026-05-19T15:50:28.539542+00:00", + "elapsed_ms": 13857.05, + "prompt_metrics": { + "chars": 16917, + "bytes_utf8": 16917, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1157, + "bytes_utf8": 1157, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 758, + "bytes_utf8": 758, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16827, + "cached_input_tokens": 15744, + "output_tokens": 678, + "reasoning_output_tokens": 476 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1477383faa761b81f0d16d6b03ebb3212a424592 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Manager_Support_Level, measure_col=Meetings_Per_Week.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "Manager_Support_Level", + "measure_col": "Meetings_Per_Week", + "top_k": 19, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 9.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..02d4d9aa9c802da545271cd7b5778be142f802f2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ee-3ab5-7ca0-913a-2f7430c669de"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT\\n \\\"Manager_Support_Level\\\",\\n SUM(CAST(NULLIF(\\\"Meetings_Per_Week\\\", '') AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Manager_Support_Level\\\"\\n), total AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM grouped\\n)\\nSELECT\\n g.\\\"Manager_Support_Level\\\",\\n g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Used the planned Relative-to-Total Extreme Threshold template with group_col=\\\"Manager_Support_Level\\\" and measure_col=\\\"Meetings_Per_Week\\\". Cast the TEXT measure column to REAL for SQLite aggregation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16827,"cached_input_tokens":15744,"output_tokens":678,"reasoning_output_tokens":476}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..30600562d5567fab25fabca140147da68e1d2b19 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT\n \"Manager_Support_Level\",\n SUM(CAST(NULLIF(\"Meetings_Per_Week\", '') AS REAL)) AS \"group_value\"\n FROM \"m1\"\n GROUP BY \"Manager_Support_Level\"\n), total AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM grouped\n)\nSELECT\n g.\"Manager_Support_Level\",\n g.\"group_value\"\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;","notes":"Used the planned Relative-to-Total Extreme Threshold template with group_col=\"Manager_Support_Level\" and measure_col=\"Meetings_Per_Week\". Cast the TEXT measure column to REAL for SQLite aggregation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_1e9ca03df90fecd1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7a2c147942bbe1e2bab22157ad7f07d2b95e0b2f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:42.524943+00:00", + "ended_at": "2026-05-19T16:07:45.885707+00:00", + "elapsed_ms": 3360.74, + "returncode": 1, + "prompt_metrics": { + "chars": 16317, + "bytes_utf8": 16317, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2448affb5961ccaf9c47de29818bf8e4e8befa08 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:46.888014+00:00", + "ended_at": "2026-05-19T16:07:50.042530+00:00", + "elapsed_ms": 3154.48, + "returncode": 1, + "prompt_metrics": { + "chars": 16317, + "bytes_utf8": 16317, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a972b4bd52db7a487d708c1b6b64ef281c5cb52e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=Has_Children.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Has_Children", + "top_k": 17, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a972b4bd52db7a487d708c1b6b64ef281c5cb52e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=Has_Children.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Has_Children", + "top_k": 17, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..277a46ed8f6f91247048faa1bf81c10c648582a1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-37c1-7e91-b130-3940d22f688a"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9e0cafbc078c67e47e03a0371e156463a2b8c66e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-37c1-7e91-b130-3940d22f688a"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..c8ebdfa6c5cf1f3cca0bdb94e02ff820dbfa6185 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-48d7-72d0-915d-6ff916e44f50"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e8fe2c75c787b961bd75849ef6036cb2ff6329a0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-48d7-72d0-915d-6ff916e44f50"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2036cb9ddb570ac9/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..d0474a409383d3c1e608be4547282183ceb2c5ec --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=Has_Children, group_col_2=Work_Life_Balance. +Result preview: [{"Has_Children": "Yes", "Work_Life_Balance": "8", "row_count": 109}, {"Has_Children": "No", "Work_Life_Balance": "8", "row_count": 108}, {"Has_Children": "No", "Work_Life_Balance": "9", "row_count": 76}, {"Has_Children": "Yes", "Work_Life_Balance": "9", "row_count": 54}, {"Has_Children": "No", "Work_Life_Balance": "10", "row_count": 43}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..fdfaf41bc1dd16e8b0f39c48ea0d9e1f695c5350 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/generated_sql.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_m1_2309a5c59929db44 +-- problem_id: v2p_m1_028ae97eabc101d5 +-- realization_mode: agent +-- source_kind: agent +SELECT "Has_Children", "Work_Life_Balance", COUNT(*) AS row_count +FROM "m1" +WHERE CAST("Work_Life_Balance" AS REAL) >= 8.0 +GROUP BY "Has_Children", "Work_Life_Balance" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a85c4fffe714f85de67ce65a2269db8798c3371e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"Has_Children\", \"Work_Life_Balance\", COUNT(*) AS row_count\nFROM \"m1\"\nWHERE CAST(\"Work_Life_Balance\" AS REAL) >= 8.0\nGROUP BY \"Has_Children\", \"Work_Life_Balance\"\nORDER BY row_count DESC;", "result": "{\"query\": \"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"Has_Children\\\", \\\"Work_Life_Balance\\\", COUNT(*) AS row_count\\nFROM \\\"m1\\\"\\nWHERE CAST(\\\"Work_Life_Balance\\\" AS REAL) >= 8.0\\nGROUP BY \\\"Has_Children\\\", \\\"Work_Life_Balance\\\"\\nORDER BY row_count DESC;\", \"columns\": [\"Has_Children\", \"Work_Life_Balance\", \"row_count\"], \"rows\": [{\"Has_Children\": \"Yes\", \"Work_Life_Balance\": \"8\", \"row_count\": 109}, {\"Has_Children\": \"No\", \"Work_Life_Balance\": \"8\", \"row_count\": 108}, {\"Has_Children\": \"No\", \"Work_Life_Balance\": \"9\", \"row_count\": 76}, {\"Has_Children\": \"Yes\", \"Work_Life_Balance\": \"9\", \"row_count\": 54}, {\"Has_Children\": \"No\", \"Work_Life_Balance\": \"10\", \"row_count\": 43}, {\"Has_Children\": \"Yes\", \"Work_Life_Balance\": \"10\", \"row_count\": 33}], \"row_count_returned\": 6, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 0.89}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0913107604517ad5b6a60efe343a6a0b2475a2fb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/run_manifest.json @@ -0,0 +1,93 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:43:59.812192+00:00", + "ended_at": "2026-05-19T15:44:13.630212+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_2309a5c59929db44", + "problem_id": "v2p_m1_028ae97eabc101d5", + "dataset_id": "m1", + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "slice_level_consistency", + "intended_facet_id": "conditional_interaction_hotspots", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=Has_Children, group_col_2=Work_Life_Balance.", + "bindings": { + "group_col": "Has_Children", + "group_col_2": "Work_Life_Balance", + "predicate_col": "Work_Life_Balance", + "predicate_op": ">=", + "predicate_value": 8.0, + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 8.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "notes": [ + "default_facets=conditional_interaction_hotspots", + "template_selection_mode=rule", + "problem_index_within_template=8", + "sql_variant_index=1/1", + "binding_index=55" + ], + "template_selection_mode": "rule", + "selected_template_rank": 5, + "problem_index_within_template": 8, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_2309a5c59929db44.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_2309a5c59929db44", + "api_calls": 0, + "input_tokens": 16787, + "cached_input_tokens": 15744, + "output_tokens": 435, + "total_tokens": 17222, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13813.3, + "sql_execution_elapsed_ms_total": 0.89, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2c81b61d1b4bb5f3915bc4ecebdf94abea14e008 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:44:13.628028+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 13813.3, "started_at": "2026-05-19T15:43:59.813928+00:00", "ended_at": "2026-05-19T15:44:13.627256+00:00", "prompt_metrics": {"chars": 16661, "bytes_utf8": 16661, "lines": 460, "estimated_tokens": null}, "response_metrics": {"chars": 490, "bytes_utf8": 490, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16787, "cached_input_tokens": 15744, "output_tokens": 435, "reasoning_output_tokens": 296}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"Has_Children\\\", \\\"Work_Life_Balance\\\", COUNT(*) AS row_count\\nFROM \\\"m1\\\"\\nWHERE CAST(\\\"Work_Life_Balance\\\" AS REAL) >= 8.0\\nGROUP BY \\\"Has_Children\\\", \\\"Work_Life_Balance\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Used the provided filtered two-dimensional group count template with group_col=\\\"Has_Children\\\", group_col_2=\\\"Work_Life_Balance\\\", and a numeric cast on \\\"Work_Life_Balance\\\" because the schema stores it as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..2afac25326ebe0bf8de1bd18d11cf8f33c372f4b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_2309a5c59929db44", + "api_calls": 0, + "input_tokens": 16787, + "cached_input_tokens": 15744, + "output_tokens": 435, + "total_tokens": 17222, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13813.3, + "sql_execution_elapsed_ms_total": 0.89, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2309a5c59929db44/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..5f6ceeb4a8d5613471f2f63a3105e89db7dcea4f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Response_Quality, measure_col=Age. +Result preview: [{"Response_Quality": "Low", "percentile_measure": 48.0}, {"Response_Quality": "Medium", "percentile_measure": 47.0}, {"Response_Quality": "High", "percentile_measure": 46.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..2455da16e52242f4b2c846046e4e57f7fee11bb0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/generated_sql.sql @@ -0,0 +1,65 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_m1_249b31099d1e3aec +-- problem_id: v2p_m1_d623ff814f884430 +-- realization_mode: agent +-- source_kind: agent +WITH "ordered" AS ( + SELECT + "Response_Quality", + CAST("Age" AS REAL) AS "age_value", + ROW_NUMBER() OVER ( + PARTITION BY "Response_Quality" + ORDER BY CAST("Age" AS REAL) + ) AS "rn", + COUNT(*) OVER ( + PARTITION BY "Response_Quality" + ) AS "cnt" + FROM "m1" + WHERE "Age" IS NOT NULL +), +"positions" AS ( + SELECT DISTINCT + "Response_Quality", + ((("cnt" - 1) * 0.9) + 1.0) AS "pos" + FROM "ordered" +), +"bounds" AS ( + SELECT + "Response_Quality", + "pos", + CAST("pos" AS INTEGER) AS "lower_rn", + CAST("pos" AS INTEGER) + CASE + WHEN "pos" > CAST("pos" AS INTEGER) THEN 1 + ELSE 0 + END AS "upper_rn" + FROM "positions" +) +SELECT + b."Response_Quality", + CASE + WHEN b."lower_rn" = b."upper_rn" THEN + MAX(CASE WHEN o."rn" = b."lower_rn" THEN o."age_value" END) + ELSE + MAX(CASE WHEN o."rn" = b."lower_rn" THEN o."age_value" END) + + (b."pos" - b."lower_rn") * ( + MAX(CASE WHEN o."rn" = b."upper_rn" THEN o."age_value" END) - + MAX(CASE WHEN o."rn" = b."lower_rn" THEN o."age_value" END) + ) + END AS "percentile_measure" +FROM "bounds" AS b +JOIN "ordered" AS o + ON o."Response_Quality" = b."Response_Quality" +GROUP BY + b."Response_Quality", + b."pos", + b."lower_rn", + b."upper_rn" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..fd278eea633e482c73b9a2d4b99e0c68a33a7f64 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n SELECT\n \"Response_Quality\",\n CAST(\"Age\" AS REAL) AS \"age_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Response_Quality\"\n ORDER BY CAST(\"Age\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"Response_Quality\"\n ) AS \"cnt\"\n FROM \"m1\"\n WHERE \"Age\" IS NOT NULL\n),\n\"positions\" AS (\n SELECT DISTINCT\n \"Response_Quality\",\n (((\"cnt\" - 1) * 0.9) + 1.0) AS \"pos\"\n FROM \"ordered\"\n),\n\"bounds\" AS (\n SELECT\n \"Response_Quality\",\n \"pos\",\n CAST(\"pos\" AS INTEGER) AS \"lower_rn\",\n CAST(\"pos\" AS INTEGER) + CASE\n WHEN \"pos\" > CAST(\"pos\" AS INTEGER) THEN 1\n ELSE 0\n END AS \"upper_rn\"\n FROM \"positions\"\n)\nSELECT\n b.\"Response_Quality\",\n CASE\n WHEN b.\"lower_rn\" = b.\"upper_rn\" THEN\n MAX(CASE WHEN o.\"rn\" = b.\"lower_rn\" THEN o.\"age_value\" END)\n ELSE\n MAX(CASE WHEN o.\"rn\" = b.\"lower_rn\" THEN o.\"age_value\" END) +\n (b.\"pos\" - b.\"lower_rn\") * (\n MAX(CASE WHEN o.\"rn\" = b.\"upper_rn\" THEN o.\"age_value\" END) -\n MAX(CASE WHEN o.\"rn\" = b.\"lower_rn\" THEN o.\"age_value\" END)\n )\n END AS \"percentile_measure\"\nFROM \"bounds\" AS b\nJOIN \"ordered\" AS o\n ON o.\"Response_Quality\" = b.\"Response_Quality\"\nGROUP BY\n b.\"Response_Quality\",\n b.\"pos\",\n b.\"lower_rn\",\n b.\"upper_rn\"\nORDER BY \"percentile_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"Response_Quality\\\",\\n CAST(\\\"Age\\\" AS REAL) AS \\\"age_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Response_Quality\\\"\\n ORDER BY CAST(\\\"Age\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"Response_Quality\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Age\\\" IS NOT NULL\\n),\\n\\\"positions\\\" AS (\\n SELECT DISTINCT\\n \\\"Response_Quality\\\",\\n (((\\\"cnt\\\" - 1) * 0.9) + 1.0) AS \\\"pos\\\"\\n FROM \\\"ordered\\\"\\n),\\n\\\"bounds\\\" AS (\\n SELECT\\n \\\"Response_Quality\\\",\\n \\\"pos\\\",\\n CAST(\\\"pos\\\" AS INTEGER) AS \\\"lower_rn\\\",\\n CAST(\\\"pos\\\" AS INTEGER) + CASE\\n WHEN \\\"pos\\\" > CAST(\\\"pos\\\" AS INTEGER) THEN 1\\n ELSE 0\\n END AS \\\"upper_rn\\\"\\n FROM \\\"positions\\\"\\n)\\nSELECT\\n b.\\\"Response_Quality\\\",\\n CASE\\n WHEN b.\\\"lower_rn\\\" = b.\\\"upper_rn\\\" THEN\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lower_rn\\\" THEN o.\\\"age_value\\\" END)\\n ELSE\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lower_rn\\\" THEN o.\\\"age_value\\\" END) +\\n (b.\\\"pos\\\" - b.\\\"lower_rn\\\") * (\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"upper_rn\\\" THEN o.\\\"age_value\\\" END) -\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lower_rn\\\" THEN o.\\\"age_value\\\" END)\\n )\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"bounds\\\" AS b\\nJOIN \\\"ordered\\\" AS o\\n ON o.\\\"Response_Quality\\\" = b.\\\"Response_Quality\\\"\\nGROUP BY\\n b.\\\"Response_Quality\\\",\\n b.\\\"pos\\\",\\n b.\\\"lower_rn\\\",\\n b.\\\"upper_rn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\", \"columns\": [\"Response_Quality\", \"percentile_measure\"], \"rows\": [{\"Response_Quality\": \"Low\", \"percentile_measure\": 48.0}, {\"Response_Quality\": \"Medium\", \"percentile_measure\": 47.0}, {\"Response_Quality\": \"High\", \"percentile_measure\": 46.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 6.15}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7b1043f37d54fa78825a9e726823e801cb7e0f97 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:51:59.623786+00:00", + "ended_at": "2026-05-19T15:52:52.911518+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_249b31099d1e3aec", + "problem_id": "v2p_m1_d623ff814f884430", + "dataset_id": "m1", + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_concentration_consistency", + "intended_facet_id": "rare_target_concentration", + "variant_semantic_role": "ranked_signal_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Response_Quality, measure_col=Age.", + "bindings": { + "group_col": "Response_Quality", + "measure_col": "Age", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "notes": [ + "default_facets=rare_target_concentration", + "template_selection_mode=rule", + "problem_index_within_template=1", + "sql_variant_index=2/2", + "binding_index=84" + ], + "template_selection_mode": "rule", + "selected_template_rank": 8, + "problem_index_within_template": 1, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_249b31099d1e3aec.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_249b31099d1e3aec", + "api_calls": 0, + "input_tokens": 16719, + "cached_input_tokens": 15744, + "output_tokens": 3655, + "total_tokens": 20374, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 53276.48, + "sql_execution_elapsed_ms_total": 6.15, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..aa3e8a04fc74de66555f9099b4ffcf475bd64784 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:52:52.903388+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 53276.48, "started_at": "2026-05-19T15:51:59.625443+00:00", "ended_at": "2026-05-19T15:52:52.901954+00:00", "prompt_metrics": {"chars": 16503, "bytes_utf8": 16503, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 1717, "bytes_utf8": 1717, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16719, "cached_input_tokens": 15744, "output_tokens": 3655, "reasoning_output_tokens": 3106}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"Response_Quality\\\",\\n CAST(\\\"Age\\\" AS REAL) AS \\\"age_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Response_Quality\\\"\\n ORDER BY CAST(\\\"Age\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"Response_Quality\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Age\\\" IS NOT NULL\\n),\\n\\\"positions\\\" AS (\\n SELECT DISTINCT\\n \\\"Response_Quality\\\",\\n (((\\\"cnt\\\" - 1) * 0.9) + 1.0) AS \\\"pos\\\"\\n FROM \\\"ordered\\\"\\n),\\n\\\"bounds\\\" AS (\\n SELECT\\n \\\"Response_Quality\\\",\\n \\\"pos\\\",\\n CAST(\\\"pos\\\" AS INTEGER) AS \\\"lower_rn\\\",\\n CAST(\\\"pos\\\" AS INTEGER) + CASE\\n WHEN \\\"pos\\\" > CAST(\\\"pos\\\" AS INTEGER) THEN 1\\n ELSE 0\\n END AS \\\"upper_rn\\\"\\n FROM \\\"positions\\\"\\n)\\nSELECT\\n b.\\\"Response_Quality\\\",\\n CASE\\n WHEN b.\\\"lower_rn\\\" = b.\\\"upper_rn\\\" THEN\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lower_rn\\\" THEN o.\\\"age_value\\\" END)\\n ELSE\\n MAX(CA"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..b5fc405db1ee8116a429a1e10baa55b5544a7e6d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_249b31099d1e3aec", + "api_calls": 0, + "input_tokens": 16719, + "cached_input_tokens": 15744, + "output_tokens": 3655, + "total_tokens": 20374, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 53276.48, + "sql_execution_elapsed_ms_total": 6.15, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_249b31099d1e3aec/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6a84d1e501d398a78474947bbb9faa5f811b3d9e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 17161, "bytes_utf8": 17161, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 717, "bytes_utf8": 717, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16892, "cached_input_tokens": 12032, "output_tokens": 380, "reasoning_output_tokens": 199}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..435691c4a57ed41d7066215bcedc279bef040b1d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_25394aa0d0870dfa", + "api_calls": 0, + "input_tokens": 16892, + "cached_input_tokens": 12032, + "output_tokens": 380, + "total_tokens": 17272, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11383.14, + "sql_execution_elapsed_ms_total": 1.11, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d9c42e49ca50702926e51eabcf52ed1727aa66c1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:40:58.282199+00:00", + "ended_at": "2026-05-19T15:41:09.665358+00:00", + "elapsed_ms": 11383.14, + "prompt_metrics": { + "chars": 17161, + "bytes_utf8": 17161, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1078, + "bytes_utf8": 1078, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 717, + "bytes_utf8": 717, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16892, + "cached_input_tokens": 12032, + "output_tokens": 380, + "reasoning_output_tokens": 199 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c2a8b11006188a392a281a876ab50581c558741c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=Location_Type, condition_col=Response_Quality.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "Location_Type", + "condition_col": "Response_Quality", + "condition_value": "High", + "positive_value": "High", + "negative_value": "Medium", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..403cf8b6e5685d8f2258c9594670970881b9c3c6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e5-bd3e-76e2-bfd3-cbc59ff9f1c8"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"Location_Type\\\",\\n SUM(CASE WHEN \\\"Response_Quality\\\" = 'High' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"Response_Quality\\\" = 'Medium' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Location_Type\\\"\\n)\\nSELECT \\\"Location_Type\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Computes the ratio of High to Medium Response_Quality within each Location_Type, following the planned grouped ratio template. The ratio is NULL when a location type has no Medium responses.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16892,"cached_input_tokens":12032,"output_tokens":380,"reasoning_output_tokens":199}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..12a298a940e05190035a0205f10a5bddf0682026 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"Location_Type\",\n SUM(CASE WHEN \"Response_Quality\" = 'High' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"Response_Quality\" = 'Medium' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"m1\"\n GROUP BY \"Location_Type\"\n)\nSELECT \"Location_Type\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Computes the ratio of High to Medium Response_Quality within each Location_Type, following the planned grouped ratio template. The ratio is NULL when a location type has no Medium responses."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_25394aa0d0870dfa/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9afc468d26cfa69f36cce0be4a96235c15e47d04 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16510, "bytes_utf8": 16510, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 780, "bytes_utf8": 780, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16719, "cached_input_tokens": 12032, "output_tokens": 737, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..97e1f6f59f66083695e8d8ec602da8748a5b0815 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_26585a9c4ac09d48", + "api_calls": 0, + "input_tokens": 16719, + "cached_input_tokens": 12032, + "output_tokens": 737, + "total_tokens": 17456, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14853.76, + "sql_execution_elapsed_ms_total": 3.96, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3a22b9828eed3afe252ee9751549885f4039a31e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:53:39.094788+00:00", + "ended_at": "2026-05-19T15:53:53.948578+00:00", + "elapsed_ms": 14853.76, + "prompt_metrics": { + "chars": 16510, + "bytes_utf8": 16510, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1194, + "bytes_utf8": 1194, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 780, + "bytes_utf8": 780, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16719, + "cached_input_tokens": 12032, + "output_tokens": 737, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2937184de0fe5c69932cf39ca1133dbc8a7affb2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=Gender, measure_col=Years_Experience.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Gender", + "measure_col": "Years_Experience", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 5.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..bd4fe368cf748932a187b580d619e135ef698571 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-592b-7d21-b1d6-440649378868"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nSELECT\\n \\\"Gender\\\",\\n \\\"percentile_measure\\\"\\nFROM (\\n SELECT\\n \\\"Gender\\\",\\n CAST(\\\"Years_Experience\\\" AS REAL) AS \\\"percentile_measure\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Gender\\\"\\n ORDER BY CAST(\\\"Years_Experience\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"Gender\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Gender\\\" IS NOT NULL\\n AND \\\"Years_Experience\\\" IS NOT NULL\\n)\\nWHERE \\\"rn\\\" = ((95 * \\\"cnt\\\") + 99) / 100\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses the nearest-rank method to return the 95th percentile point per Gender, casting \\\"Years_Experience\\\" from TEXT to REAL for numeric ordering.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16719,"cached_input_tokens":12032,"output_tokens":737,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c961dc9291323cbee7377f93c58f0b5e4469bd93 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nSELECT\n \"Gender\",\n \"percentile_measure\"\nFROM (\n SELECT\n \"Gender\",\n CAST(\"Years_Experience\" AS REAL) AS \"percentile_measure\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Gender\"\n ORDER BY CAST(\"Years_Experience\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"Gender\"\n ) AS \"cnt\"\n FROM \"m1\"\n WHERE \"Gender\" IS NOT NULL\n AND \"Years_Experience\" IS NOT NULL\n)\nWHERE \"rn\" = ((95 * \"cnt\") + 99) / 100\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses the nearest-rank method to return the 95th percentile point per Gender, casting \"Years_Experience\" from TEXT to REAL for numeric ordering."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_26585a9c4ac09d48/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d9d32f3527ff6978b193c5e1fec47429ac2cca68 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16899, "bytes_utf8": 16899, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 674, "bytes_utf8": 674, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16819, "cached_input_tokens": 12032, "output_tokens": 482, "reasoning_output_tokens": 312}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8f48be44174e522febed290dace1cb63ea95422f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_289cec0f1b38e654", + "api_calls": 0, + "input_tokens": 16819, + "cached_input_tokens": 12032, + "output_tokens": 482, + "total_tokens": 17301, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11395.52, + "sql_execution_elapsed_ms_total": 1.35, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7c3c291339d8b6c84047135610f5e96159900a7e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:51:09.345685+00:00", + "ended_at": "2026-05-19T15:51:20.741237+00:00", + "elapsed_ms": 11395.52, + "prompt_metrics": { + "chars": 16899, + "bytes_utf8": 16899, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1041, + "bytes_utf8": 1041, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 674, + "bytes_utf8": 674, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16819, + "cached_input_tokens": 12032, + "output_tokens": 482, + "reasoning_output_tokens": 312 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8eb754e0f81c54b59fc0188668df612561355537 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Stress_Level, measure_col=Job_Satisfaction.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "Stress_Level", + "measure_col": "Job_Satisfaction", + "top_k": 16, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0252c6f1e79fc9b931fe13580be42ff77caf3594 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ef-1041-7801-a850-ed81e3e1ec9a"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Stress_Level\\\", SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Stress_Level\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Stress_Level\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Uses the required Relative-to-Total Extreme Threshold template with group_col=\\\"Stress_Level\\\" and measure_col=\\\"Job_Satisfaction\\\". CAST to REAL is applied because the schema stores numeric-looking values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16819,"cached_input_tokens":12032,"output_tokens":482,"reasoning_output_tokens":312}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1135e5f0e67cb36fd908b5be53141ea72406d8e0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"Stress_Level\", SUM(CAST(\"Job_Satisfaction\" AS REAL)) AS group_value\n FROM \"m1\"\n GROUP BY \"Stress_Level\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"Stress_Level\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Uses the required Relative-to-Total Extreme Threshold template with group_col=\"Stress_Level\" and measure_col=\"Job_Satisfaction\". CAST to REAL is applied because the schema stores numeric-looking values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_289cec0f1b38e654/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..01eac6f5195b3b9d78233b8793c58907761fa5aa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Industry, measure_col=Job_Satisfaction. +Result preview: [{"Industry": "Retail", "Years_Experience": "0", "total_measure": 2348.6, "share_within_group": 23.19879886998953}, {"Industry": "Media", "Years_Experience": "0", "total_measure": 1591.9, "share_within_group": 23.17074945781116}, {"Industry": "Retail", "Years_Experience": "1", "total_measure": 2289.7, "share_within_group": 22.61700152116794}, {"Industry": "Manufacturing", "Years_Experience": "0", "total_measure": 1715.6, "share_within_group": 19.41646483623441}, {"Industry": "Consulting", "Years_Experience": "0", "total_measure": 1582.7, "share_within_group": 18.746816701214094}] Results were truncated. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..0264ef524edac0e510bcc78eb9114efedf408e4b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_m1_2994e13b03622d40 +-- problem_id: v2p_m1_8fb281b270079630 +-- realization_mode: agent +-- source_kind: agent +SELECT + "Industry", + "Years_Experience", + SUM(CAST("Job_Satisfaction" AS REAL)) AS total_measure, + SUM(CAST("Job_Satisfaction" AS REAL)) * 100.0 / SUM(SUM(CAST("Job_Satisfaction" AS REAL))) OVER (PARTITION BY "Industry") AS share_within_group +FROM "m1" +GROUP BY "Industry", "Years_Experience" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..407b6ab77a84f66ad8e854d74bfeeb1dc0bd903b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"Industry\",\n \"Years_Experience\",\n SUM(CAST(\"Job_Satisfaction\" AS REAL)) AS total_measure,\n SUM(CAST(\"Job_Satisfaction\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"Job_Satisfaction\" AS REAL))) OVER (PARTITION BY \"Industry\") AS share_within_group\nFROM \"m1\"\nGROUP BY \"Industry\", \"Years_Experience\"\nORDER BY share_within_group DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"Industry\\\",\\n \\\"Years_Experience\\\",\\n SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL))) OVER (PARTITION BY \\\"Industry\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Industry\\\", \\\"Years_Experience\\\"\\nORDER BY share_within_group DESC;\", \"columns\": [\"Industry\", \"Years_Experience\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"Industry\": \"Retail\", \"Years_Experience\": \"0\", \"total_measure\": 2348.6, \"share_within_group\": 23.19879886998953}, {\"Industry\": \"Media\", \"Years_Experience\": \"0\", \"total_measure\": 1591.9, \"share_within_group\": 23.17074945781116}, {\"Industry\": \"Retail\", \"Years_Experience\": \"1\", \"total_measure\": 2289.7, \"share_within_group\": 22.61700152116794}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"0\", \"total_measure\": 1715.6, \"share_within_group\": 19.41646483623441}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"0\", \"total_measure\": 1582.7, \"share_within_group\": 18.746816701214094}, {\"Industry\": \"Non-profit\", \"Years_Experience\": \"1\", \"total_measure\": 674.7, \"share_within_group\": 18.10594675826535}, {\"Industry\": \"Government\", \"Years_Experience\": \"0\", \"total_measure\": 1263.9, \"share_within_group\": 18.096042609243458}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"0\", \"total_measure\": 3186.8, \"share_within_group\": 17.750002784925755}, {\"Industry\": \"Education\", \"Years_Experience\": \"0\", \"total_measure\": 2424.8, \"share_within_group\": 17.71789326006898}, {\"Industry\": \"Government\", \"Years_Experience\": \"1\", \"total_measure\": 1203.1, \"share_within_group\": 17.225531183780994}, {\"Industry\": \"Finance\", \"Years_Experience\": \"1\", \"total_measure\": 3259.6, \"share_within_group\": 15.297469037596032}, {\"Industry\": \"Technology\", \"Years_Experience\": \"1\", \"total_measure\": 6879.8, \"share_within_group\": 15.249169361577625}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"2\", \"total_measure\": 1254.0, \"share_within_group\": 14.853420195439739}, {\"Industry\": \"Finance\", \"Years_Experience\": \"0\", \"total_measure\": 3091.6, \"share_within_group\": 14.509036469699318}, {\"Industry\": \"Technology\", \"Years_Experience\": \"0\", \"total_measure\": 6241.2, \"share_within_group\": 13.833703860501508}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"3\", \"total_measure\": 1158.9, \"share_within_group\": 13.726976606455436}, {\"Industry\": \"Non-profit\", \"Years_Experience\": \"0\", \"total_measure\": 505.7, \"share_within_group\": 13.570738514383855}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"1\", \"total_measure\": 1143.0, \"share_within_group\": 13.538643766656795}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"2\", \"total_measure\": 1196.0, \"share_within_group\": 13.535842821249917}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"1\", \"total_measure\": 1180.3, \"share_within_group\": 13.35815659023518}, {\"Industry\": \"Education\", \"Years_Experience\": \"1\", \"total_measure\": 1828.1, \"share_within_group\": 13.357835973578068}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"4\", \"total_measure\": 1171.1, \"share_within_group\": 13.25403472237941}, {\"Industry\": \"Media\", \"Years_Experience\": \"3\", \"total_measure\": 880.7, \"share_within_group\": 12.818945315342852}, {\"Industry\": \"Media\", \"Years_Experience\": \"6\", \"total_measure\": 873.5, \"share_within_group\": 12.714146398264996}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"1\", \"total_measure\": 2196.3, \"share_within_group\": 12.233064866490661}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"2\", \"total_measure\": 2162.8, \"share_within_group\": 12.046474840980741}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"3\", \"total_measure\": 1036.4, \"share_within_group\": 11.729554765838976}, {\"Industry\": \"Media\", \"Years_Experience\": \"1\", \"total_measure\": 750.1, \"share_within_group\": 10.918009402791727}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"3\", \"total_measure\": 1937.2, \"share_within_group\": 10.789916340830354}, {\"Industry\": \"Technology\", \"Years_Experience\": \"3\", \"total_measure\": 4761.2, \"share_within_group\": 10.55326392690825}, {\"Industry\": \"Education\", \"Years_Experience\": \"5\", \"total_measure\": 1425.7, \"share_within_group\": 10.417519144209972}, {\"Industry\": \"Retail\", \"Years_Experience\": \"5\", \"total_measure\": 1046.5, \"share_within_group\": 10.337027598332643}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"4\", \"total_measure\": 858.2, \"share_within_group\": 10.165235416049748}, {\"Industry\": \"Technology\", \"Years_Experience\": \"2\", \"total_measure\": 4563.1, \"share_within_group\": 10.114172608769858}, {\"Industry\": \"Education\", \"Years_Experience\": \"2\", \"total_measure\": 1329.5, \"share_within_group\": 9.714590518501199}, {\"Industry\": \"Technology\", \"Years_Experience\": \"4\", \"total_measure\": 4337.3, \"share_within_group\": 9.613683867550021}, {\"Industry\": \"Government\", \"Years_Experience\": \"2\", \"total_measure\": 664.7, \"share_within_group\": 9.516923429356853}, {\"Industry\": \"Education\", \"Years_Experience\": \"3\", \"total_measure\": 1290.4, \"share_within_group\": 9.428888758987549}, {\"Industry\": \"Finance\", \"Years_Experience\": \"2\", \"total_measure\": 1880.1, \"share_within_group\": 8.823405183944134}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"6\", \"total_measure\": 1556.0, \"share_within_group\": 8.666688946072698}, {\"Industry\": \"Finance\", \"Years_Experience\": \"4\", \"total_measure\": 1830.6, \"share_within_group\": 8.591099159474567}, {\"Industry\": \"Non-profit\", \"Years_Experience\": \"2\", \"total_measure\": 314.4, \"share_within_group\": 8.437097466723914}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"5\", \"total_measure\": 699.5, \"share_within_group\": 8.285460467870891}, {\"Industry\": \"Non-profit\", \"Years_Experience\": \"7\", \"total_measure\": 300.0, \"share_within_group\": 8.050665521683126}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"5\", \"total_measure\": 700.0, \"share_within_group\": 7.922316032504131}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"5\", \"total_measure\": 1350.3, \"share_within_group\": 7.520970490926712}, {\"Industry\": \"Finance\", \"Years_Experience\": \"7\", \"total_measure\": 1585.1, \"share_within_group\": 7.438955139125498}, {\"Industry\": \"Non-profit\", \"Years_Experience\": \"3\", \"total_measure\": 273.6, \"share_within_group\": 7.342206955775011}, {\"Industry\": \"Media\", \"Years_Experience\": \"4\", \"total_measure\": 500.0, \"share_within_group\": 7.277702574851171}, {\"Industry\": \"Technology\", \"Years_Experience\": \"5\", \"total_measure\": 3250.7, \"share_within_group\": 7.205220332521351}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 4.83}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6b49e127aa752d70b87fc34eb8b62ed637c42491 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/run_manifest.json @@ -0,0 +1,91 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:34:30.895462+00:00", + "ended_at": "2026-05-19T15:34:47.274767+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_2994e13b03622d40", + "problem_id": "v2p_m1_8fb281b270079630", + "dataset_id": "m1", + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "dependency_strength_similarity", + "intended_facet_id": "pairwise_conditional_dependency", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Industry, measure_col=Job_Satisfaction.", + "bindings": { + "group_col": "Industry", + "measure_col": "Job_Satisfaction", + "item_col": "Years_Experience", + "top_k": 10, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "item_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "notes": [ + "default_facets=pairwise_conditional_dependency", + "template_selection_mode=rule", + "problem_index_within_template=2", + "sql_variant_index=1/2", + "binding_index=25" + ], + "template_selection_mode": "rule", + "selected_template_rank": 3, + "problem_index_within_template": 2, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_2994e13b03622d40.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_2994e13b03622d40", + "api_calls": 0, + "input_tokens": 16800, + "cached_input_tokens": 12032, + "output_tokens": 677, + "total_tokens": 17477, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16366.55, + "sql_execution_elapsed_ms_total": 4.83, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6fedaf475544628a365f8660ca75d41fa79e1ebd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:34:47.266623+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 16366.55, "started_at": "2026-05-19T15:34:30.898368+00:00", "ended_at": "2026-05-19T15:34:47.264963+00:00", "prompt_metrics": {"chars": 16748, "bytes_utf8": 16748, "lines": 458, "estimated_tokens": null}, "response_metrics": {"chars": 640, "bytes_utf8": 640, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16800, "cached_input_tokens": 12032, "output_tokens": 677, "reasoning_output_tokens": 504}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"Industry\\\",\\n \\\"Years_Experience\\\",\\n SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL))) OVER (PARTITION BY \\\"Industry\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Industry\\\", \\\"Years_Experience\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Uses the provided template with bound roles group_col=\\\"Industry\\\", item_col=\\\"Years_Experience\\\", and measure_col=\\\"Job_Satisfaction\\\". \\\"Job_Satisfaction\\\" is cast to REAL because the SQLite schema stores it as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7c628c7596e91b1343af212d2c11d9625a817ca3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_2994e13b03622d40", + "api_calls": 0, + "input_tokens": 16800, + "cached_input_tokens": 12032, + "output_tokens": 677, + "total_tokens": 17477, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16366.55, + "sql_execution_elapsed_ms_total": 4.83, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2994e13b03622d40/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2c286ffbbcad8c6c/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2c286ffbbcad8c6c/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..62249a33fbe879c938be75291176e98247372985 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2c286ffbbcad8c6c/run_manifest.json @@ -0,0 +1,67 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:06:45.598671+00:00", + "ended_at": "2026-05-19T16:06:52.695156+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_2c286ffbbcad8c6c", + "problem_id": "v2p_m1_de610a30909dae96", + "dataset_id": "m1", + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_set_consistency", + "intended_facet_id": "low_support_extremes", + "variant_semantic_role": "rare_extreme_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Work_Hours_Per_Week.", + "bindings": { + "measure_col": "Work_Hours_Per_Week", + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 46.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "notes": [ + "default_facets=low_support_extremes", + "template_selection_mode=rule", + "problem_index_within_template=8", + "sql_variant_index=1/1", + "binding_index=115" + ], + "template_selection_mode": "rule", + "selected_template_rank": 10, + "problem_index_within_template": 8, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2c286ffbbcad8c6c/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2c286ffbbcad8c6c/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bccce0007e227b990c8686d4dcc2147e7bd69d12 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2c286ffbbcad8c6c/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:06:48.459732+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2858.05, "started_at": "2026-05-19T16:06:45.600162+00:00", "ended_at": "2026-05-19T16:06:48.458244+00:00", "prompt_metrics": {"chars": 16276, "bytes_utf8": 16276, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fd-5984-76a2-a102-b31749b83c81\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:06:52.695038+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3231.95, "started_at": "2026-05-19T16:06:49.461745+00:00", "ended_at": "2026-05-19T16:06:52.693747+00:00", "prompt_metrics": {"chars": 16276, "bytes_utf8": 16276, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fd-687a-7781-b2ec-887a8eaeec9d\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1cc84e0fd7eac444adac63ca91b0a96d9605af39 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16319, "bytes_utf8": 16319, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 16319, "bytes_utf8": 16319, "lines": 454, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 343, "bytes_utf8": 343, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16691, "cached_input_tokens": 12032, "output_tokens": 348, "reasoning_output_tokens": 249}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..0460fe6bc00f5a46377960b7f6d0574034181d6f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_2cfd85e1479bcac8", + "api_calls": 0, + "input_tokens": 16691, + "cached_input_tokens": 12032, + "output_tokens": 348, + "total_tokens": 17039, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11617.0, + "sql_execution_elapsed_ms_total": 1.6, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..714b49f3d3833a5a9bd4c32aab160385db73e22d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:13.833526+00:00", + "ended_at": "2026-05-19T16:07:17.144169+00:00", + "elapsed_ms": 3310.61, + "returncode": 1, + "prompt_metrics": { + "chars": 16319, + "bytes_utf8": 16319, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2a883c4423cc385f6ad1320b54d3f5d54bf8b79d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:18.146741+00:00", + "ended_at": "2026-05-19T16:07:26.453172+00:00", + "elapsed_ms": 8306.39, + "prompt_metrics": { + "chars": 16319, + "bytes_utf8": 16319, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 700, + "bytes_utf8": 700, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 343, + "bytes_utf8": 343, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16691, + "cached_input_tokens": 12032, + "output_tokens": 348, + "reasoning_output_tokens": 249 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..3f1b51f3a8ad9710a879ed5cd74dbd13f1f6d35b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Marital_Status.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Marital_Status", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 10.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..3f1b51f3a8ad9710a879ed5cd74dbd13f1f6d35b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Marital_Status.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Marital_Status", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 10.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..833e5c3a4b487a71ce04cf230def380bbadf58d6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-c7ce-7610-a740-566447cb87fc"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2074ce314d7de7979e1ed71ee2e82fbb4c02a766 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-c7ce-7610-a740-566447cb87fc"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..fe75c4ab91ad8365eabed390883731ea94f6a7d6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-d89b-73e1-b062-6f9c1bc07270"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n \\\"Marital_Status\\\",\\n COUNT(*) AS \\\"support\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Marital_Status\\\"\\nORDER BY \\\"support\\\" ASC, \\\"Marital_Status\\\"\\nLIMIT 11;\",\"notes\":\"Uses the required Low-Support Group Count template with group_col bound to Marital_Status and top_k set to 11.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16691,"cached_input_tokens":12032,"output_tokens":348,"reasoning_output_tokens":249}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..42af72d2f48c93847cc8f747d8e4fff635648519 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n \"Marital_Status\",\n COUNT(*) AS \"support\"\nFROM \"m1\"\nGROUP BY \"Marital_Status\"\nORDER BY \"support\" ASC, \"Marital_Status\"\nLIMIT 11;","notes":"Uses the required Low-Support Group Count template with group_col bound to Marital_Status and top_k set to 11."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_2cfd85e1479bcac8/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bb8adaae4980c57c7cf203b264fad1f9158705c8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16911, "bytes_utf8": 16911, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 931, "bytes_utf8": 931, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16827, "cached_input_tokens": 15744, "output_tokens": 775, "reasoning_output_tokens": 514}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..02824704cb80588ea53ad32c468c9c9baacafed6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_33fc53a063f7684e", + "api_calls": 0, + "input_tokens": 16827, + "cached_input_tokens": 15744, + "output_tokens": 775, + "total_tokens": 17602, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16200.08, + "sql_execution_elapsed_ms_total": 3.35, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2173250077cf1bea2753d8a76bc8c62167f50e71 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:49:17.877114+00:00", + "ended_at": "2026-05-19T15:49:34.077228+00:00", + "elapsed_ms": 16200.08, + "prompt_metrics": { + "chars": 16911, + "bytes_utf8": 16911, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1361, + "bytes_utf8": 1361, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 931, + "bytes_utf8": 931, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16827, + "cached_input_tokens": 15744, + "output_tokens": 775, + "reasoning_output_tokens": 514 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2684dfdf31a8d08ef14013e9ab3ed9b9afc1415a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Home_Office_Quality, measure_col=Innovation_Score.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "Home_Office_Quality", + "measure_col": "Innovation_Score", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 84.2, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa68d7153f3604e7137a06b280f917a5c45f908d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ed-5cda-7fe0-9338-d162d698c42a"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"Home_Office_Quality\\\",\\n SUM(CAST(\\\"Innovation_Score\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Home_Office_Quality\\\" IS NOT NULL\\n AND TRIM(\\\"Home_Office_Quality\\\") <> ''\\n AND \\\"Innovation_Score\\\" IS NOT NULL\\n AND TRIM(\\\"Innovation_Score\\\") <> ''\\n GROUP BY \\\"Home_Office_Quality\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"Home_Office_Quality\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the planned relative-to-total threshold template with group_col=Home_Office_Quality and measure_col=Innovation_Score, casting the text measure to REAL and filtering blank/null inputs for stability.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16827,"cached_input_tokens":15744,"output_tokens":775,"reasoning_output_tokens":514}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..097ae4e1d108dfeae2e356b03dbb8c6964dda352 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT\n \"Home_Office_Quality\",\n SUM(CAST(\"Innovation_Score\" AS REAL)) AS \"group_value\"\n FROM \"m1\"\n WHERE \"Home_Office_Quality\" IS NOT NULL\n AND TRIM(\"Home_Office_Quality\") <> ''\n AND \"Innovation_Score\" IS NOT NULL\n AND TRIM(\"Innovation_Score\") <> ''\n GROUP BY \"Home_Office_Quality\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT g.\"Home_Office_Quality\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;","notes":"Uses the planned relative-to-total threshold template with group_col=Home_Office_Quality and measure_col=Innovation_Score, casting the text measure to REAL and filtering blank/null inputs for stability."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..ca50bf5b7910a2fba070bee5c3139d9f1148db7f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Home_Office_Quality, measure_col=Innovation_Score. +Result preview: [{"Home_Office_Quality": "Good", "group_value": 43517.1}, {"Home_Office_Quality": "Average", "group_value": 32614.2}, {"Home_Office_Quality": "Excellent", "group_value": 25505.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..c280a0c0b9a9f78e366b8a8a91a28cceb525ee72 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/generated_sql.sql @@ -0,0 +1,33 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_m1_33fc53a063f7684e +-- problem_id: v2p_m1_172019caf2ca4e53 +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT + "Home_Office_Quality", + SUM(CAST("Innovation_Score" AS REAL)) AS "group_value" + FROM "m1" + WHERE "Home_Office_Quality" IS NOT NULL + AND TRIM("Home_Office_Quality") <> '' + AND "Innovation_Score" IS NOT NULL + AND TRIM("Innovation_Score") <> '' + GROUP BY "Home_Office_Quality" +), +"total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT g."Home_Office_Quality", g."group_value" +FROM "grouped" AS g +CROSS JOIN "total" AS t +WHERE g."group_value" > t."total_value" * 0.1 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..87ba176066a78faf77826c63361003ea78d07525 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT\n \"Home_Office_Quality\",\n SUM(CAST(\"Innovation_Score\" AS REAL)) AS \"group_value\"\n FROM \"m1\"\n WHERE \"Home_Office_Quality\" IS NOT NULL\n AND TRIM(\"Home_Office_Quality\") <> ''\n AND \"Innovation_Score\" IS NOT NULL\n AND TRIM(\"Innovation_Score\") <> ''\n GROUP BY \"Home_Office_Quality\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT g.\"Home_Office_Quality\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"Home_Office_Quality\\\",\\n SUM(CAST(\\\"Innovation_Score\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Home_Office_Quality\\\" IS NOT NULL\\n AND TRIM(\\\"Home_Office_Quality\\\") <> ''\\n AND \\\"Innovation_Score\\\" IS NOT NULL\\n AND TRIM(\\\"Innovation_Score\\\") <> ''\\n GROUP BY \\\"Home_Office_Quality\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"Home_Office_Quality\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\", \"columns\": [\"Home_Office_Quality\", \"group_value\"], \"rows\": [{\"Home_Office_Quality\": \"Good\", \"group_value\": 43517.1}, {\"Home_Office_Quality\": \"Average\", \"group_value\": 32614.2}, {\"Home_Office_Quality\": \"Excellent\", \"group_value\": 25505.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.35}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..5ae99f94f99c4edebc1ab6db0e37f484794555ae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:49:17.873833+00:00", + "ended_at": "2026-05-19T15:49:34.084606+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_33fc53a063f7684e", + "problem_id": "v2p_m1_172019caf2ca4e53", + "dataset_id": "m1", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "filtered_stable_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Home_Office_Quality, measure_col=Innovation_Score.", + "bindings": { + "group_col": "Home_Office_Quality", + "measure_col": "Innovation_Score", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 84.2, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=6", + "sql_variant_index=1/2", + "binding_index=77" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 6, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_33fc53a063f7684e.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_33fc53a063f7684e", + "api_calls": 0, + "input_tokens": 16827, + "cached_input_tokens": 15744, + "output_tokens": 775, + "total_tokens": 17602, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16200.08, + "sql_execution_elapsed_ms_total": 3.35, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..41361644f2242dcaf78f93ecaa446493bc3a2a7f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:49:34.078025+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 16200.08, "started_at": "2026-05-19T15:49:17.877114+00:00", "ended_at": "2026-05-19T15:49:34.077228+00:00", "prompt_metrics": {"chars": 16911, "bytes_utf8": 16911, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 931, "bytes_utf8": 931, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16827, "cached_input_tokens": 15744, "output_tokens": 775, "reasoning_output_tokens": 514}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"Home_Office_Quality\\\",\\n SUM(CAST(\\\"Innovation_Score\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Home_Office_Quality\\\" IS NOT NULL\\n AND TRIM(\\\"Home_Office_Quality\\\") <> ''\\n AND \\\"Innovation_Score\\\" IS NOT NULL\\n AND TRIM(\\\"Innovation_Score\\\") <> ''\\n GROUP BY \\\"Home_Office_Quality\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"Home_Office_Quality\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the planned relative-to-total threshold template with group_col=Home_Office_Quality and measure_col=Innovation_Score, casting the text measure to REAL and filtering blank/null inputs for stability.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..0b14be90947ccd64ea93a8f810c7d33027414f54 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_33fc53a063f7684e", + "api_calls": 0, + "input_tokens": 16827, + "cached_input_tokens": 15744, + "output_tokens": 775, + "total_tokens": 17602, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16200.08, + "sql_execution_elapsed_ms_total": 3.35, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_33fc53a063f7684e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..603a577895da75b6baafeb503011235a2d5b6243 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16647, "bytes_utf8": 16647, "lines": 460, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 500, "bytes_utf8": 500, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16778, "cached_input_tokens": 12032, "output_tokens": 407, "reasoning_output_tokens": 272}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..bbee5b45a8d9a61de0596e7d55b3e1fbfa7de415 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_3630c87ccc27f385", + "api_calls": 0, + "input_tokens": 16778, + "cached_input_tokens": 12032, + "output_tokens": 407, + "total_tokens": 17185, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15454.39, + "sql_execution_elapsed_ms_total": 1.42, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ad3c46d6e3150b8c915944013715c38e263739d5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:43:44.352568+00:00", + "ended_at": "2026-05-19T15:43:59.807031+00:00", + "elapsed_ms": 15454.39, + "prompt_metrics": { + "chars": 16647, + "bytes_utf8": 16647, + "lines": 460, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1204, + "bytes_utf8": 1204, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 500, + "bytes_utf8": 500, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16778, + "cached_input_tokens": 12032, + "output_tokens": 407, + "reasoning_output_tokens": 272 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..10e2cc9a805de43d14c762b08b6ab878e636e774 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,460 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=Has_Children, group_col_2=Stress_Level.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "Has_Children", + "group_col_2": "Stress_Level", + "predicate_col": "Stress_Level", + "predicate_op": ">=", + "predicate_value": 7.0, + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..52015fddff2a9419ebc9549dc4d1191b57449e14 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40e8-4601-75e3-80fc-d096f050b605"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 17413. Please try again in 522ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"Has_Children\\\", \\\"Stress_Level\\\", COUNT(*) AS row_count\\nFROM \\\"m1\\\"\\nWHERE CAST(\\\"Stress_Level\\\" AS REAL) >= 7.0\\nGROUP BY \\\"Has_Children\\\", \\\"Stress_Level\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Used the planned filtered two-dimensional group count template with group_col=\\\"Has_Children\\\", group_col_2=\\\"Stress_Level\\\", and predicate on \\\"Stress_Level\\\" >= 7.0. Applied CAST to \\\"Stress_Level\\\" because the schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16778,"cached_input_tokens":12032,"output_tokens":407,"reasoning_output_tokens":272}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9330b1ed9f19d8e30187874bb50d954818cec0cb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"Has_Children\", \"Stress_Level\", COUNT(*) AS row_count\nFROM \"m1\"\nWHERE CAST(\"Stress_Level\" AS REAL) >= 7.0\nGROUP BY \"Has_Children\", \"Stress_Level\"\nORDER BY row_count DESC;","notes":"Used the planned filtered two-dimensional group count template with group_col=\"Has_Children\", group_col_2=\"Stress_Level\", and predicate on \"Stress_Level\" >= 7.0. Applied CAST to \"Stress_Level\" because the schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3630c87ccc27f385/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e0afa027b274082771386d555bae05e731e06d43 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16528, "bytes_utf8": 16528, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 2114, "bytes_utf8": 2114, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16729, "cached_input_tokens": 12032, "output_tokens": 3039, "reasoning_output_tokens": 2357}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..c743461ec0d61c5252d1a12fd1f9c2358fa3844a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_3e4a7433fc061142", + "api_calls": 0, + "input_tokens": 16729, + "cached_input_tokens": 12032, + "output_tokens": 3039, + "total_tokens": 19768, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 43344.99, + "sql_execution_elapsed_ms_total": 8.89, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..738743a2fcbcbd2f1b315112f8e06687d91e76f5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:53:53.956887+00:00", + "ended_at": "2026-05-19T15:54:37.301910+00:00", + "elapsed_ms": 43344.99, + "prompt_metrics": { + "chars": 16528, + "bytes_utf8": 16528, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2736, + "bytes_utf8": 2736, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 2114, + "bytes_utf8": 2114, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16729, + "cached_input_tokens": 12032, + "output_tokens": 3039, + "reasoning_output_tokens": 2357 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8c6c306062f7631fb0599ff59a8d748e50df17fd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=Education_Level, measure_col=WFH_Days_Per_Week.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Education_Level", + "measure_col": "WFH_Days_Per_Week", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 4.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..587d51df3a1a9b9f3ca0011d10ae92b0dca9079c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-9348-7e42-a690-3d6bd718e7ea"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"Education_Level\\\",\\n CAST(\\\"WFH_Days_Per_Week\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Education_Level\\\"\\n ORDER BY CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (PARTITION BY \\\"Education_Level\\\") AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Education_Level\\\" IS NOT NULL\\n AND TRIM(\\\"Education_Level\\\") <> ''\\n AND \\\"WFH_Days_Per_Week\\\" IS NOT NULL\\n AND TRIM(\\\"WFH_Days_Per_Week\\\") <> ''\\n),\\n\\\"group_positions\\\" AS (\\n SELECT DISTINCT\\n \\\"Education_Level\\\",\\n (((\\\"cnt\\\" - 1) * 0.95) + 1.0) AS \\\"pos\\\",\\n CAST((((\\\"cnt\\\" - 1) * 0.95) + 1.0) AS INTEGER) AS \\\"lower_rn\\\",\\n CASE\\n WHEN (((\\\"cnt\\\" - 1) * 0.95) + 1.0) = CAST((((\\\"cnt\\\" - 1) * 0.95) + 1.0) AS INTEGER)\\n THEN CAST((((\\\"cnt\\\" - 1) * 0.95) + 1.0) AS INTEGER)\\n ELSE CAST((((\\\"cnt\\\" - 1) * 0.95) + 1.0) AS INTEGER) + 1\\n END AS \\\"upper_rn\\\"\\n FROM \\\"ranked\\\"\\n WHERE \\\"cnt\\\" >= 5\\n),\\n\\\"percentile_inputs\\\" AS (\\n SELECT\\n gp.\\\"Education_Level\\\",\\n gp.\\\"pos\\\",\\n MAX(CASE WHEN r.\\\"rn\\\" = gp.\\\"lower_rn\\\" THEN r.\\\"measure_value\\\" END) AS \\\"lower_value\\\",\\n MAX(CASE WHEN r.\\\"rn\\\" = gp.\\\"upper_rn\\\" THEN r.\\\"measure_value\\\" END) AS \\\"upper_value\\\"\\n FROM \\\"group_positions\\\" AS gp\\n JOIN \\\"ranked\\\" AS r\\n ON r.\\\"Education_Level\\\" = gp.\\\"Education_Level\\\"\\n GROUP BY\\n gp.\\\"Education_Level\\\",\\n gp.\\\"pos\\\"\\n)\\nSELECT\\n \\\"Education_Level\\\",\\n CASE\\n WHEN \\\"lower_value\\\" IS NULL THEN NULL\\n WHEN \\\"upper_value\\\" IS NULL OR \\\"pos\\\" = CAST(\\\"pos\\\" AS INTEGER) THEN \\\"lower_value\\\"\\n ELSE \\\"lower_value\\\" + ((\\\"pos\\\" - CAST(\\\"pos\\\" AS INTEGER)) * (\\\"upper_value\\\" - \\\"lower_value\\\"))\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"percentile_inputs\\\"\\nORDER BY \\\"percentile_measure\\\" DESC, \\\"Education_Level\\\" ASC;\",\"notes\":\"SQLite-compatible emulation of the grouped 95th percentile for \\\"WFH_Days_Per_Week\\\" by \\\"Education_Level\\\" using linear interpolation between ranked rows; excludes groups with fewer than 5 records.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16729,"cached_input_tokens":12032,"output_tokens":3039,"reasoning_output_tokens":2357}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8829fcd878a53a5365523dd4ba82832f3ba21d82 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n SELECT\n \"Education_Level\",\n CAST(\"WFH_Days_Per_Week\" AS REAL) AS \"measure_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Education_Level\"\n ORDER BY CAST(\"WFH_Days_Per_Week\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (PARTITION BY \"Education_Level\") AS \"cnt\"\n FROM \"m1\"\n WHERE \"Education_Level\" IS NOT NULL\n AND TRIM(\"Education_Level\") <> ''\n AND \"WFH_Days_Per_Week\" IS NOT NULL\n AND TRIM(\"WFH_Days_Per_Week\") <> ''\n),\n\"group_positions\" AS (\n SELECT DISTINCT\n \"Education_Level\",\n (((\"cnt\" - 1) * 0.95) + 1.0) AS \"pos\",\n CAST((((\"cnt\" - 1) * 0.95) + 1.0) AS INTEGER) AS \"lower_rn\",\n CASE\n WHEN (((\"cnt\" - 1) * 0.95) + 1.0) = CAST((((\"cnt\" - 1) * 0.95) + 1.0) AS INTEGER)\n THEN CAST((((\"cnt\" - 1) * 0.95) + 1.0) AS INTEGER)\n ELSE CAST((((\"cnt\" - 1) * 0.95) + 1.0) AS INTEGER) + 1\n END AS \"upper_rn\"\n FROM \"ranked\"\n WHERE \"cnt\" >= 5\n),\n\"percentile_inputs\" AS (\n SELECT\n gp.\"Education_Level\",\n gp.\"pos\",\n MAX(CASE WHEN r.\"rn\" = gp.\"lower_rn\" THEN r.\"measure_value\" END) AS \"lower_value\",\n MAX(CASE WHEN r.\"rn\" = gp.\"upper_rn\" THEN r.\"measure_value\" END) AS \"upper_value\"\n FROM \"group_positions\" AS gp\n JOIN \"ranked\" AS r\n ON r.\"Education_Level\" = gp.\"Education_Level\"\n GROUP BY\n gp.\"Education_Level\",\n gp.\"pos\"\n)\nSELECT\n \"Education_Level\",\n CASE\n WHEN \"lower_value\" IS NULL THEN NULL\n WHEN \"upper_value\" IS NULL OR \"pos\" = CAST(\"pos\" AS INTEGER) THEN \"lower_value\"\n ELSE \"lower_value\" + ((\"pos\" - CAST(\"pos\" AS INTEGER)) * (\"upper_value\" - \"lower_value\"))\n END AS \"percentile_measure\"\nFROM \"percentile_inputs\"\nORDER BY \"percentile_measure\" DESC, \"Education_Level\" ASC;","notes":"SQLite-compatible emulation of the grouped 95th percentile for \"WFH_Days_Per_Week\" by \"Education_Level\" using linear interpolation between ranked rows; excludes groups with fewer than 5 records."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3e4a7433fc061142/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..083ddf86c7e35a9862bc256d31c1f35917ae544e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16897, "bytes_utf8": 16897, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 872, "bytes_utf8": 872, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16823, "cached_input_tokens": 15744, "output_tokens": 590, "reasoning_output_tokens": 361}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..79624084d8060018a89162c09c30f74567a40b55 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_3f7c3281c9980d67", + "api_calls": 0, + "input_tokens": 16823, + "cached_input_tokens": 15744, + "output_tokens": 590, + "total_tokens": 17413, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12446.82, + "sql_execution_elapsed_ms_total": 2.29, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9a7efec4239c7c5e30c33063bda0e860f92b55de --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:47:59.247812+00:00", + "ended_at": "2026-05-19T15:48:11.694683+00:00", + "elapsed_ms": 12446.82, + "prompt_metrics": { + "chars": 16897, + "bytes_utf8": 16897, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1259, + "bytes_utf8": 1259, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 872, + "bytes_utf8": 872, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16823, + "cached_input_tokens": 15744, + "output_tokens": 590, + "reasoning_output_tokens": 361 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..fd406093556be1ad55b05cb6ad562684e071cfba --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Job_Level, measure_col=Productivity_Score.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "Job_Level", + "measure_col": "Productivity_Score", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 94.8, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..61fbe70d6c6ecf635492f96977c19429bf84e33c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ec-2a29-77d2-a813-14f0cf54fd89"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Job_Level\\\", SUM(CAST(\\\"Productivity_Score\\\" AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n WHERE \\\"Job_Level\\\" IS NOT NULL\\n AND TRIM(\\\"Job_Level\\\") <> ''\\n AND \\\"Productivity_Score\\\" IS NOT NULL\\n AND TRIM(\\\"Productivity_Score\\\") <> ''\\n GROUP BY \\\"Job_Level\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Job_Level\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Used the required relative-to-total threshold template with group_col=\\\"Job_Level\\\" and measure_col=\\\"Productivity_Score\\\". Cast the measure to REAL because the SQLite schema stores it as TEXT, and filtered null/blank values for a stable grouped result.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16823,"cached_input_tokens":15744,"output_tokens":590,"reasoning_output_tokens":361}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c2f5602887ebb20e09ae6180ab81f41b4f113fd8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"Job_Level\", SUM(CAST(\"Productivity_Score\" AS REAL)) AS group_value\n FROM \"m1\"\n WHERE \"Job_Level\" IS NOT NULL\n AND TRIM(\"Job_Level\") <> ''\n AND \"Productivity_Score\" IS NOT NULL\n AND TRIM(\"Productivity_Score\") <> ''\n GROUP BY \"Job_Level\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"Job_Level\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Used the required relative-to-total threshold template with group_col=\"Job_Level\" and measure_col=\"Productivity_Score\". Cast the measure to REAL because the SQLite schema stores it as TEXT, and filtered null/blank values for a stable grouped result."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_3f7c3281c9980d67/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_41c2b25901cf37fd/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_41c2b25901cf37fd/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f34ba0d472c4866d86bda1b18ded57d8d427c335 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_41c2b25901cf37fd/run_manifest.json @@ -0,0 +1,72 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:04:54.275633+00:00", + "ended_at": "2026-05-19T16:05:04.246378+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_41c2b25901cf37fd", + "problem_id": "v2p_m1_8cf39c5e4147acb7", + "dataset_id": "m1", + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "dependency_strength_similarity", + "intended_facet_id": "pairwise_conditional_dependency", + "variant_semantic_role": "within_group_proportion", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Gender, condition_col=Team_Collaboration_Frequency.", + "bindings": { + "group_col": "Gender", + "condition_col": "Team_Collaboration_Frequency", + "condition_value": "Daily", + "positive_value": "Daily", + "negative_value": "Few times per week", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 98.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "notes": [ + "default_facets=pairwise_conditional_dependency", + "template_selection_mode=rule", + "problem_index_within_template=7", + "sql_variant_index=1/2", + "binding_index=102" + ], + "template_selection_mode": "rule", + "selected_template_rank": 9, + "problem_index_within_template": 7, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_41c2b25901cf37fd/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_41c2b25901cf37fd/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7e71906722ffcadba1b02c651a6c607e7e35cb25 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_41c2b25901cf37fd/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:04:59.935213+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 5657.12, "started_at": "2026-05-19T16:04:54.277302+00:00", "ended_at": "2026-05-19T16:04:59.934444+00:00", "prompt_metrics": {"chars": 16629, "bytes_utf8": 16629, "lines": 459, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fb-a683-7050-8975-c053cbf3ced1\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:05:04.246278+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3308.52, "started_at": "2026-05-19T16:05:00.936895+00:00", "ended_at": "2026-05-19T16:05:04.245465+00:00", "prompt_metrics": {"chars": 16629, "bytes_utf8": 16629, "lines": 459, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fb-c0a0-7073-944e-97a2759bf2c7\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4c6a104431f410c4b17456de49a90eeed24d33d4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16654, "bytes_utf8": 16654, "lines": 460, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 485, "bytes_utf8": 485, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16779, "cached_input_tokens": 12032, "output_tokens": 362, "reasoning_output_tokens": 234}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7a0daf65187778da28e040409851db2f2f692b73 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_426e721685818783", + "api_calls": 0, + "input_tokens": 16779, + "cached_input_tokens": 12032, + "output_tokens": 362, + "total_tokens": 17141, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9224.2, + "sql_execution_elapsed_ms_total": 0.87, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_attempt_1.metadata.json new file mode 100644 index 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"estimated_tokens": null + }, + "usage": { + "input_tokens": 16779, + "cached_input_tokens": 12032, + "output_tokens": 362, + "reasoning_output_tokens": 234 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..aa485b8c1c0c6b294fcb548fb1667ebd87f60a27 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,460 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=Has_Children, group_col_2=Company_Size.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "Has_Children", + "group_col_2": "Company_Size", + "predicate_col": "Quality_Score", + "predicate_op": ">=", + "predicate_value": 96.225, + "top_k": 13, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.225, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8f75a6048c8b26e7f06fd5d7e0e91069ce746b54 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e7-459d-7213-9996-d34fa3f90b27"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"Has_Children\\\", \\\"Company_Size\\\", COUNT(*) AS row_count\\nFROM \\\"m1\\\"\\nWHERE CAST(\\\"Quality_Score\\\" AS REAL) >= 96.225\\nGROUP BY \\\"Has_Children\\\", \\\"Company_Size\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Used the provided filtered two-dimensional group count template with bound roles Has_Children, Company_Size, and Quality_Score >= 96.225. CAST to REAL is necessary because the SQLite schema stores Quality_Score as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16779,"cached_input_tokens":12032,"output_tokens":362,"reasoning_output_tokens":234}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..3085957d6419887c098c631c7d4c72801516fdfe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"Has_Children\", \"Company_Size\", COUNT(*) AS row_count\nFROM \"m1\"\nWHERE CAST(\"Quality_Score\" AS REAL) >= 96.225\nGROUP BY \"Has_Children\", \"Company_Size\"\nORDER BY row_count DESC;","notes":"Used the provided filtered two-dimensional group count template with bound roles Has_Children, Company_Size, and Quality_Score >= 96.225. CAST to REAL is necessary because the SQLite schema stores Quality_Score as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_426e721685818783/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..534a916982ccad2b87b5ae815eeadaf51446ae35 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16321, "bytes_utf8": 16321, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 341, "bytes_utf8": 341, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16693, "cached_input_tokens": 12032, "output_tokens": 281, "reasoning_output_tokens": 184}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..87b4bf6c95a7692519ac23a8fbb0bfd34a5d6964 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_43ba685f953ecad6", + "api_calls": 0, + "input_tokens": 16693, + "cached_input_tokens": 12032, + "output_tokens": 281, + "total_tokens": 16974, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7626.22, + "sql_execution_elapsed_ms_total": 1.31, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c6bdc95c638bf742f8d1eb9c862c65ddcd2a6b54 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:26.459702+00:00", + "ended_at": "2026-05-19T16:07:34.085961+00:00", + "elapsed_ms": 7626.22, + "prompt_metrics": { + "chars": 16321, + "bytes_utf8": 16321, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 694, + "bytes_utf8": 694, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 341, + "bytes_utf8": 341, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16693, + "cached_input_tokens": 12032, + "output_tokens": 281, + "reasoning_output_tokens": 184 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1d018acce8fec21b1dd71af958e18651300f572b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=Marital_Status.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Marital_Status", + "top_k": 16, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 10.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..377ccbe1223f56e910bb04cfcf6f37c6b84da4a3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-f8ec-7451-a084-b671e9898255"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n \\\"Marital_Status\\\",\\n COUNT(*) AS support\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Marital_Status\\\"\\nORDER BY support ASC, \\\"Marital_Status\\\"\\nLIMIT 16;\",\"notes\":\"Applied the planned Low-Support Group Count template with group_col bound to \\\"Marital_Status\\\" and top_k set to 16.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16693,"cached_input_tokens":12032,"output_tokens":281,"reasoning_output_tokens":184}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6410dd225cc59abc8b8f7409a6c069687702a1e8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n \"Marital_Status\",\n COUNT(*) AS support\nFROM \"m1\"\nGROUP BY \"Marital_Status\"\nORDER BY support ASC, \"Marital_Status\"\nLIMIT 16;","notes":"Applied the planned Low-Support Group Count template with group_col bound to \"Marital_Status\" and top_k set to 16."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_43ba685f953ecad6/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..349c515fe9bb8baf0a9e33af8ebda55c8ad141d0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16305, "bytes_utf8": 16305, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 342, "bytes_utf8": 342, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16677, "cached_input_tokens": 12032, "output_tokens": 326, "reasoning_output_tokens": 232}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..1cb1a4881b5f3b72ebad28ed74810872a7367863 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_4438bd7ea2f58785", + "api_calls": 0, + "input_tokens": 16677, + "cached_input_tokens": 12032, + "output_tokens": 326, + "total_tokens": 17003, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8187.88, + "sql_execution_elapsed_ms_total": 1.91, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e26528beee8453da8ce8dfbef02db7e8d84720fa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:28:28.584732+00:00", + "ended_at": "2026-05-19T15:28:36.772648+00:00", + "elapsed_ms": 8187.88, + "prompt_metrics": { + "chars": 16305, + "bytes_utf8": 16305, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 704, + "bytes_utf8": 704, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 342, + "bytes_utf8": 342, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16677, + "cached_input_tokens": 12032, + "output_tokens": 326, + "reasoning_output_tokens": 232 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..00dc72d2bec8c28b7693fd94e37391a8437b98bc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Gender, measure_col=Age.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "Gender", + "measure_col": "Age", + "top_k": 15, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d6ff762add422b162c1cb1c04928c2ce4b91d530 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40da-4ca4-76b1-9456-0bd22888c05d"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Gender\\\", SUM(CAST(\\\"Age\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Gender\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the planned grouped-sum template with \\\"Gender\\\" as the grouping column and cast \\\"Age\\\" from TEXT to REAL before summing for SQLite compatibility.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16677,"cached_input_tokens":12032,"output_tokens":326,"reasoning_output_tokens":232}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a451f4955f869cbe5e880f94b37234b2cf5809d4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"Gender\", SUM(CAST(\"Age\" AS REAL)) AS \"total_measure\"\nFROM \"m1\"\nGROUP BY \"Gender\"\nORDER BY \"total_measure\" DESC;","notes":"Used the planned grouped-sum template with \"Gender\" as the grouping column and cast \"Age\" from TEXT to REAL before summing for SQLite compatibility."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_4438bd7ea2f58785/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..2c734a7b8aa489dc16e7d8f7dcebbca14dd462e4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Quality_Score. +Result preview: [{"Quality_Score": "100.0"}, {"Quality_Score": "100.0"}, {"Quality_Score": "100.0"}, {"Quality_Score": "100.0"}, {"Quality_Score": "100.0"}] Results were truncated. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..ee4b60ba9ac2c52dfdc5fd6440c47f18823a7373 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/generated_sql.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_m1_48f2c4cc35e98cf6 +-- problem_id: v2p_m1_190bd7834804544c +-- realization_mode: agent +-- source_kind: agent +WITH buckets AS ( + SELECT + "Quality_Score", + NTILE(10) OVER (ORDER BY CAST("Quality_Score" AS REAL) DESC) AS tail_bucket + FROM "m1" + WHERE "Quality_Score" IS NOT NULL +) +SELECT "Quality_Score" +FROM buckets +WHERE tail_bucket = 1 +ORDER BY CAST("Quality_Score" AS REAL) DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7339a9d8e7695bd09cc8e14c20bea8e608febb26 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_quantile_tail_slice\nWITH buckets AS (\n SELECT\n \"Quality_Score\",\n NTILE(10) OVER (ORDER BY CAST(\"Quality_Score\" AS REAL) DESC) AS tail_bucket\n FROM \"m1\"\n WHERE \"Quality_Score\" IS NOT NULL\n)\nSELECT \"Quality_Score\"\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY CAST(\"Quality_Score\" AS REAL) DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n SELECT\\n \\\"Quality_Score\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"Quality_Score\\\" AS REAL) DESC) AS tail_bucket\\n FROM \\\"m1\\\"\\n WHERE \\\"Quality_Score\\\" IS NOT NULL\\n)\\nSELECT \\\"Quality_Score\\\"\\nFROM buckets\\nWHERE tail_bucket = 1\\nORDER BY CAST(\\\"Quality_Score\\\" AS REAL) DESC;\", \"columns\": [\"Quality_Score\"], \"rows\": [{\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}, {\"Quality_Score\": \"100.0\"}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 2.89}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7a9671a58d06cf2a3f8331c2aa29f2305b8069bf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/run_manifest.json @@ -0,0 +1,87 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:45:06.991394+00:00", + "ended_at": "2026-05-19T15:45:24.403021+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_48f2c4cc35e98cf6", + "problem_id": "v2p_m1_190bd7834804544c", + "dataset_id": "m1", + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_set_consistency", + "intended_facet_id": "low_support_extremes", + "variant_semantic_role": "rare_extreme_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Quality_Score.", + "bindings": { + "measure_col": "Quality_Score", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.225, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "notes": [ + "default_facets=low_support_extremes", + "template_selection_mode=rule", + "problem_index_within_template=3", + "sql_variant_index=1/1", + "binding_index=62" + ], + "template_selection_mode": "rule", + "selected_template_rank": 6, + "problem_index_within_template": 3, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_48f2c4cc35e98cf6.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_48f2c4cc35e98cf6", + "api_calls": 0, + "input_tokens": 16736, + "cached_input_tokens": 12032, + "output_tokens": 358, + "total_tokens": 17094, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17404.23, + "sql_execution_elapsed_ms_total": 2.89, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7954bfad59035c8580daa129e749093e652c47fc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:45:24.398557+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 17404.23, "started_at": "2026-05-19T15:45:06.992662+00:00", "ended_at": "2026-05-19T15:45:24.396922+00:00", "prompt_metrics": {"chars": 16501, "bytes_utf8": 16501, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 524, "bytes_utf8": 524, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16736, "cached_input_tokens": 12032, "output_tokens": 358, "reasoning_output_tokens": 213}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n SELECT\\n \\\"Quality_Score\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"Quality_Score\\\" AS REAL) DESC) AS tail_bucket\\n FROM \\\"m1\\\"\\n WHERE \\\"Quality_Score\\\" IS NOT NULL\\n)\\nSELECT \\\"Quality_Score\\\"\\nFROM buckets\\nWHERE tail_bucket = 1\\nORDER BY CAST(\\\"Quality_Score\\\" AS REAL) DESC;\",\"notes\":\"Applied the Quantile Tail Slice template with \\\"Quality_Score\\\" as the measure column, casting it to REAL because the schema stores it as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..4309647f4f404e6dd66febc0e213192b2d0388c6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_48f2c4cc35e98cf6", + "api_calls": 0, + "input_tokens": 16736, + "cached_input_tokens": 12032, + "output_tokens": 358, + "total_tokens": 17094, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17404.23, + "sql_execution_elapsed_ms_total": 2.89, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_48f2c4cc35e98cf6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..746374c02f268d80d27b671fd2ecba5a4e99ee84 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16517, "bytes_utf8": 16517, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 16517, "bytes_utf8": 16517, "lines": 456, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 1879, "bytes_utf8": 1879, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16721, "cached_input_tokens": 12032, "output_tokens": 3294, "reasoning_output_tokens": 2683}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..b7699909ea8f10b272aaaffeb71025e837a83cec --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_508eb46f6a079dec", + "api_calls": 0, + "input_tokens": 16721, + "cached_input_tokens": 12032, + "output_tokens": 3294, + "total_tokens": 20015, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 51518.42, + "sql_execution_elapsed_ms_total": 5.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..387a7ab25e216c6f987c4bc861605fa22ebdfc34 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:59:11.632524+00:00", + "ended_at": "2026-05-19T15:59:15.011637+00:00", + "elapsed_ms": 3379.09, + "returncode": 1, + "prompt_metrics": { + "chars": 16517, + "bytes_utf8": 16517, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..69946b0d7b7a81f1640219c99f0f47779859c75e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:59:16.014121+00:00", + "ended_at": "2026-05-19T16:00:04.153493+00:00", + "elapsed_ms": 48139.33, + "prompt_metrics": { + "chars": 16517, + "bytes_utf8": 16517, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2493, + "bytes_utf8": 2493, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1879, + "bytes_utf8": 1879, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16721, + "cached_input_tokens": 12032, + "output_tokens": 3294, + "reasoning_output_tokens": 2683 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b82dbe3fad90945b694350a6eb8543058e1d9d06 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=Job_Level, measure_col=Innovation_Score.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Job_Level", + "measure_col": "Innovation_Score", + "top_k": 16, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 80.9, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..b82dbe3fad90945b694350a6eb8543058e1d9d06 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=Job_Level, measure_col=Innovation_Score.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Job_Level", + "measure_col": "Innovation_Score", + "top_k": 16, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 80.9, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..af2293fee46c86f335d78df350d7072b0e68b472 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-6c3d-7b72-b13b-a8dbd03fb8d7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0df3a1b6b7deb0cabb5728850814fab063b53691 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-6c3d-7b72-b13b-a8dbd03fb8d7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..904d1ac16727230982afcd21aae6f888f22ef91f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-7d3c-7770-9e3b-9ae4a5da39bf"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"Job_Level\\\",\\n CAST(\\\"Innovation_Score\\\" AS REAL) AS \\\"Innovation_Score\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Job_Level\\\"\\n ORDER BY CAST(\\\"Innovation_Score\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"Job_Level\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Job_Level\\\" IS NOT NULL\\n AND \\\"Job_Level\\\" <> ''\\n AND \\\"Innovation_Score\\\" IS NOT NULL\\n AND \\\"Innovation_Score\\\" <> ''\\n),\\n\\\"percentile_params\\\" AS (\\n SELECT\\n \\\"Job_Level\\\",\\n \\\"Innovation_Score\\\",\\n \\\"rn\\\",\\n \\\"cnt\\\",\\n 1 + CAST((((\\\"cnt\\\" - 1) * 95) / 100) AS INTEGER) AS \\\"lower_rn\\\",\\n 1 + CAST((((\\\"cnt\\\" - 1) * 95) / 100) AS INTEGER) + CASE\\n WHEN (((\\\"cnt\\\" - 1) * 95) % 100) > 0 THEN 1\\n ELSE 0\\n END AS \\\"upper_rn\\\",\\n (1 + CAST((((\\\"cnt\\\" - 1) * 95) / 100) AS INTEGER)) + ((((\\\"cnt\\\" - 1) * 95) % 100) / 100.0) AS \\\"pos\\\"\\n FROM \\\"ranked\\\"\\n),\\n\\\"bounds\\\" AS (\\n SELECT\\n \\\"Job_Level\\\",\\n MAX(CASE WHEN \\\"rn\\\" = \\\"lower_rn\\\" THEN \\\"Innovation_Score\\\" END) AS \\\"lower_value\\\",\\n MAX(CASE WHEN \\\"rn\\\" = \\\"upper_rn\\\" THEN \\\"Innovation_Score\\\" END) AS \\\"upper_value\\\",\\n MAX(\\\"lower_rn\\\") AS \\\"lower_rn\\\",\\n MAX(\\\"upper_rn\\\") AS \\\"upper_rn\\\",\\n MAX(\\\"pos\\\") AS \\\"pos\\\"\\n FROM \\\"percentile_params\\\"\\n GROUP BY \\\"Job_Level\\\"\\n)\\nSELECT\\n \\\"Job_Level\\\",\\n CASE\\n WHEN \\\"lower_rn\\\" = \\\"upper_rn\\\" THEN \\\"lower_value\\\"\\n ELSE \\\"lower_value\\\" + (\\\"pos\\\" - \\\"lower_rn\\\") * (\\\"upper_value\\\" - \\\"lower_value\\\")\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"bounds\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this emulates the 95th percentile for each Job_Level using window functions and linear interpolation, casting \\\"Innovation_Score\\\" from TEXT to REAL.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16721,"cached_input_tokens":12032,"output_tokens":3294,"reasoning_output_tokens":2683}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..d746503b6f339b9638346cf69fad313e1202188c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n SELECT\n \"Job_Level\",\n CAST(\"Innovation_Score\" AS REAL) AS \"Innovation_Score\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Job_Level\"\n ORDER BY CAST(\"Innovation_Score\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"Job_Level\"\n ) AS \"cnt\"\n FROM \"m1\"\n WHERE \"Job_Level\" IS NOT NULL\n AND \"Job_Level\" <> ''\n AND \"Innovation_Score\" IS NOT NULL\n AND \"Innovation_Score\" <> ''\n),\n\"percentile_params\" AS (\n SELECT\n \"Job_Level\",\n \"Innovation_Score\",\n \"rn\",\n \"cnt\",\n 1 + CAST((((\"cnt\" - 1) * 95) / 100) AS INTEGER) AS \"lower_rn\",\n 1 + CAST((((\"cnt\" - 1) * 95) / 100) AS INTEGER) + CASE\n WHEN (((\"cnt\" - 1) * 95) % 100) > 0 THEN 1\n ELSE 0\n END AS \"upper_rn\",\n (1 + CAST((((\"cnt\" - 1) * 95) / 100) AS INTEGER)) + ((((\"cnt\" - 1) * 95) % 100) / 100.0) AS \"pos\"\n FROM \"ranked\"\n),\n\"bounds\" AS (\n SELECT\n \"Job_Level\",\n MAX(CASE WHEN \"rn\" = \"lower_rn\" THEN \"Innovation_Score\" END) AS \"lower_value\",\n MAX(CASE WHEN \"rn\" = \"upper_rn\" THEN \"Innovation_Score\" END) AS \"upper_value\",\n MAX(\"lower_rn\") AS \"lower_rn\",\n MAX(\"upper_rn\") AS \"upper_rn\",\n MAX(\"pos\") AS \"pos\"\n FROM \"percentile_params\"\n GROUP BY \"Job_Level\"\n)\nSELECT\n \"Job_Level\",\n CASE\n WHEN \"lower_rn\" = \"upper_rn\" THEN \"lower_value\"\n ELSE \"lower_value\" + (\"pos\" - \"lower_rn\") * (\"upper_value\" - \"lower_value\")\n END AS \"percentile_measure\"\nFROM \"bounds\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this emulates the 95th percentile for each Job_Level using window functions and linear interpolation, casting \"Innovation_Score\" from TEXT to REAL."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_508eb46f6a079dec/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4494170182f9d1fa19287334b3bab3815ba8a258 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16348, "bytes_utf8": 16348, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 401, "bytes_utf8": 401, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16693, "cached_input_tokens": 12032, "output_tokens": 257, "reasoning_output_tokens": 146}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..2ee9651822029ede822808427f3bb00fa7269ada --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_52a490c1e8e4c3c6", + "api_calls": 0, + "input_tokens": 16693, + "cached_input_tokens": 12032, + "output_tokens": 257, + "total_tokens": 16950, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8323.82, + "sql_execution_elapsed_ms_total": 1.6, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8bfa30a35c28aa43397f52f9df7b29d149f8457d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:29:03.856928+00:00", + "ended_at": "2026-05-19T15:29:12.180774+00:00", + "elapsed_ms": 8323.82, + "prompt_metrics": { + "chars": 16348, + "bytes_utf8": 16348, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 755, + "bytes_utf8": 755, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 401, + "bytes_utf8": 401, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16693, + "cached_input_tokens": 12032, + "output_tokens": 257, + "reasoning_output_tokens": 146 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e84fde3a12a7827e8eca2cbaf7d1dc5712cfbe34 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Marital_Status, measure_col=WFH_Days_Per_Week.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "Marital_Status", + "measure_col": "WFH_Days_Per_Week", + "top_k": 17, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 4.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..bf4408e771b73959df769c861622975e61b83f9d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40da-d67f-7cc0-83a0-c0008d2e9520"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Marital_Status\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS total_measure\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Marital_Status\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the provided grouped numeric sum template with group_col=\\\"Marital_Status\\\" and measure_col=\\\"WFH_Days_Per_Week\\\". Cast the measure to REAL because the SQLite schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16693,"cached_input_tokens":12032,"output_tokens":257,"reasoning_output_tokens":146}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6ec235aab0b257c882441ece7c5571b6c4d2438e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"Marital_Status\", SUM(CAST(\"WFH_Days_Per_Week\" AS REAL)) AS total_measure\nFROM \"m1\"\nGROUP BY \"Marital_Status\"\nORDER BY total_measure DESC;","notes":"Used the provided grouped numeric sum template with group_col=\"Marital_Status\" and measure_col=\"WFH_Days_Per_Week\". Cast the measure to REAL because the SQLite schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..d7cb6524567fa5a5ce1c88f5cb912b1a0e4e15c0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Marital_Status, measure_col=WFH_Days_Per_Week. +Result preview: [{"Marital_Status": "Married", "total_measure": 2187.0}, {"Marital_Status": "Single", "total_measure": 1202.0}, {"Marital_Status": "Divorced", "total_measure": 560.0}, {"Marital_Status": "In Relationship", "total_measure": 337.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..30ada82b69053b3950fe6391f250b033a9af529a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/generated_sql.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_m1_52a490c1e8e4c3c6 +-- problem_id: v2p_m1_c5dd094ea5d6978c +-- realization_mode: agent +-- source_kind: agent +SELECT "Marital_Status", SUM(CAST("WFH_Days_Per_Week" AS REAL)) AS total_measure +FROM "m1" +GROUP BY "Marital_Status" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c6786401cc8e432dadb5fab6fb01c3488a7781cf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_h2o_group_sum\nSELECT \"Marital_Status\", SUM(CAST(\"WFH_Days_Per_Week\" AS REAL)) AS total_measure\nFROM \"m1\"\nGROUP BY \"Marital_Status\"\nORDER BY total_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Marital_Status\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS total_measure\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Marital_Status\\\"\\nORDER BY total_measure DESC;\", \"columns\": [\"Marital_Status\", \"total_measure\"], \"rows\": [{\"Marital_Status\": \"Married\", \"total_measure\": 2187.0}, {\"Marital_Status\": \"Single\", \"total_measure\": 1202.0}, {\"Marital_Status\": \"Divorced\", \"total_measure\": 560.0}, {\"Marital_Status\": \"In Relationship\", \"total_measure\": 337.0}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.6}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7bbf875dba75b1e456ee5f548c743b06de51931b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:29:03.855590+00:00", + "ended_at": "2026-05-19T15:29:12.184560+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_52a490c1e8e4c3c6", + "problem_id": "v2p_m1_c5dd094ea5d6978c", + "dataset_id": "m1", + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "family_id": "subgroup_structure", + "canonical_subitem_id": "internal_profile_stability", + "intended_facet_id": "subgroup_distribution_shift", + "variant_semantic_role": "collapsed_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Marital_Status, measure_col=WFH_Days_Per_Week.", + "bindings": { + "group_col": "Marital_Status", + "measure_col": "WFH_Days_Per_Week", + "top_k": 17, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 4.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "notes": [ + "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast", + "template_selection_mode=rule", + "problem_index_within_template=3", + "sql_variant_index=2/2", + "binding_index=2" + ], + "template_selection_mode": "rule", + "selected_template_rank": 1, + "problem_index_within_template": 3, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_52a490c1e8e4c3c6.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_52a490c1e8e4c3c6", + "api_calls": 0, + "input_tokens": 16693, + "cached_input_tokens": 12032, + "output_tokens": 257, + "total_tokens": 16950, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8323.82, + "sql_execution_elapsed_ms_total": 1.6, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..afd730bf2df29ef80b812c9a35dbf6f520f2c7b1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:29:12.181583+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 8323.82, "started_at": "2026-05-19T15:29:03.856928+00:00", "ended_at": "2026-05-19T15:29:12.180774+00:00", "prompt_metrics": {"chars": 16348, "bytes_utf8": 16348, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 401, "bytes_utf8": 401, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16693, "cached_input_tokens": 12032, "output_tokens": 257, "reasoning_output_tokens": 146}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Marital_Status\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS total_measure\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Marital_Status\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the provided grouped numeric sum template with group_col=\\\"Marital_Status\\\" and measure_col=\\\"WFH_Days_Per_Week\\\". Cast the measure to REAL because the SQLite schema stores it as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..250a753df258973e7d2f5f951db97d3222b94d6a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_52a490c1e8e4c3c6", + "api_calls": 0, + "input_tokens": 16693, + "cached_input_tokens": 12032, + "output_tokens": 257, + "total_tokens": 16950, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8323.82, + "sql_execution_elapsed_ms_total": 1.6, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_52a490c1e8e4c3c6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2877f9e571bb174f5caf80b2f7605cc6546f7531 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16766, "bytes_utf8": 16766, "lines": 458, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 766, "bytes_utf8": 766, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16807, "cached_input_tokens": 15744, "output_tokens": 1883, "reasoning_output_tokens": 1671}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..215c1dc192ac7fbf005cf75f0d7ee16e0dfa54fe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_53277d01856342f6", + "api_calls": 0, + "input_tokens": 16807, + "cached_input_tokens": 15744, + "output_tokens": 1883, + "total_tokens": 18690, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 29436.29, + "sql_execution_elapsed_ms_total": 9.01, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a804620d4f20aba183185ef1a56cdb069518b68f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:38:06.629381+00:00", + "ended_at": "2026-05-19T15:38:36.065706+00:00", + "elapsed_ms": 29436.29, + "prompt_metrics": { + "chars": 16766, + "bytes_utf8": 16766, + "lines": 458, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1169, + "bytes_utf8": 1169, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 766, + "bytes_utf8": 766, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16807, + "cached_input_tokens": 15744, + "output_tokens": 1883, + "reasoning_output_tokens": 1671 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5537be45b844826d71ece4ff6a338df0b12d59a8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,458 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Survey_Date, measure_col=Productivity_Score.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "Survey_Date", + "measure_col": "Productivity_Score", + "item_col": "Commute_Time_Minutes", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 98.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..aa351361ed839dae271c2b7e34d76c712503169f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e3-1ead-7941-93db-6e1817199232"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"Survey_Date\\\",\\n \\\"Commute_Time_Minutes\\\",\\n \\\"total_measure\\\",\\n \\\"total_measure\\\" * 100.0 / SUM(\\\"total_measure\\\") OVER (PARTITION BY \\\"Survey_Date\\\") AS \\\"share_within_group\\\"\\nFROM (\\n SELECT\\n \\\"Survey_Date\\\",\\n \\\"Commute_Time_Minutes\\\",\\n SUM(CAST(\\\"Productivity_Score\\\" AS REAL)) AS \\\"total_measure\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Survey_Date\\\", \\\"Commute_Time_Minutes\\\"\\n)\\nORDER BY \\\"share_within_group\\\" DESC\\nLIMIT 12;\",\"notes\":\"Applied the Within-Group Share of Total template with Survey_Date as the group, Commute_Time_Minutes as the item, and CAST(Productivity_Score AS REAL) because the schema stores numeric values as TEXT. Included LIMIT 12 from the provided bindings.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16807,"cached_input_tokens":15744,"output_tokens":1883,"reasoning_output_tokens":1671}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..bdc22a2b1781ab406236bee384b28a24b4371b28 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"Survey_Date\",\n \"Commute_Time_Minutes\",\n \"total_measure\",\n \"total_measure\" * 100.0 / SUM(\"total_measure\") OVER (PARTITION BY \"Survey_Date\") AS \"share_within_group\"\nFROM (\n SELECT\n \"Survey_Date\",\n \"Commute_Time_Minutes\",\n SUM(CAST(\"Productivity_Score\" AS REAL)) AS \"total_measure\"\n FROM \"m1\"\n GROUP BY \"Survey_Date\", \"Commute_Time_Minutes\"\n)\nORDER BY \"share_within_group\" DESC\nLIMIT 12;","notes":"Applied the Within-Group Share of Total template with Survey_Date as the group, Commute_Time_Minutes as the item, and CAST(Productivity_Score AS REAL) because the schema stores numeric values as TEXT. Included LIMIT 12 from the provided bindings."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_53277d01856342f6/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_54ba1dab53703388/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_54ba1dab53703388/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b136e9970fd787f390aa1c696b9604cf4af32946 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_54ba1dab53703388/run_manifest.json @@ -0,0 +1,69 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:09:59.819810+00:00", + "ended_at": "2026-05-19T16:10:07.947814+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_54ba1dab53703388", + "problem_id": "v2p_m1_4043f64e94b91b11", + "dataset_id": "m1", + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "ranked_signal_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=Response_Quality, measure_col=Meetings_Per_Week.", + "bindings": { + "group_col": "Response_Quality", + "measure_col": "Meetings_Per_Week", + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 10.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=4", + "sql_variant_index=1/2", + "binding_index=135" + ], + "template_selection_mode": "rule", + "selected_template_rank": 12, + "problem_index_within_template": 4, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_54ba1dab53703388/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_54ba1dab53703388/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..39931c00a94a52de0f37e36ce3570b7c11c26906 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_54ba1dab53703388/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:10:04.001336+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 4178.78, "started_at": "2026-05-19T16:09:59.821554+00:00", "ended_at": "2026-05-19T16:10:04.000368+00:00", "prompt_metrics": {"chars": 16433, "bytes_utf8": 16433, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-5002-7372-9a7d-387e6ff8c783\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:10:07.947721+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2943.78, "started_at": "2026-05-19T16:10:05.003125+00:00", "ended_at": "2026-05-19T16:10:07.946947+00:00", "prompt_metrics": {"chars": 16433, "bytes_utf8": 16433, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-6457-7133-9436-d475b06f3c51\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..705177fe7d2280b695046b16f6ba468705a4621e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16511, "bytes_utf8": 16511, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1819, "bytes_utf8": 1819, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16719, "cached_input_tokens": 15744, "output_tokens": 1083, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..27d306ec54b556fd6a6a69bba20028f6796872c2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_55fd02977f7294b3", + "api_calls": 0, + "input_tokens": 16719, + "cached_input_tokens": 15744, + "output_tokens": 1083, + "total_tokens": 17802, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 19848.35, + "sql_execution_elapsed_ms_total": 6.5, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..6a53457beb1a6941cbe6e7e61f6d1d88e37753b1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:58:28.324047+00:00", + "ended_at": "2026-05-19T15:58:48.172439+00:00", + "elapsed_ms": 19848.35, + "prompt_metrics": { + "chars": 16511, + "bytes_utf8": 16511, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2437, + "bytes_utf8": 2437, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1819, + "bytes_utf8": 1819, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16719, + "cached_input_tokens": 15744, + "output_tokens": 1083, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2d93b425193e3ec29307065c2e2813c38f4dc533 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Department, measure_col=Quality_Score.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Department", + "measure_col": "Quality_Score", + "top_k": 15, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 93.6, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..c43effa9176aa2cb6d2147374c875dd98b70a6a9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f5-c2e7-7670-bc02-bf0621a763e5"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"Department\\\",\\n CAST(\\\"Quality_Score\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Department\\\"\\n ORDER BY CAST(\\\"Quality_Score\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (PARTITION BY \\\"Department\\\") AS \\\"n\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Department\\\" IS NOT NULL\\n AND \\\"Quality_Score\\\" IS NOT NULL\\n),\\n\\\"params\\\" AS (\\n SELECT\\n \\\"Department\\\",\\n \\\"n\\\",\\n (1.0 + 0.9 * (\\\"n\\\" - 1)) AS \\\"r\\\"\\n FROM \\\"ordered\\\"\\n GROUP BY \\\"Department\\\", \\\"n\\\"\\n HAVING \\\"n\\\" >= 5\\n),\\n\\\"bounds\\\" AS (\\n SELECT\\n \\\"Department\\\",\\n \\\"n\\\",\\n \\\"r\\\",\\n CAST(\\\"r\\\" AS INTEGER) AS \\\"lo_rn\\\",\\n CASE\\n WHEN \\\"r\\\" = CAST(\\\"r\\\" AS INTEGER) THEN CAST(\\\"r\\\" AS INTEGER)\\n ELSE CAST(\\\"r\\\" AS INTEGER) + 1\\n END AS \\\"hi_rn\\\"\\n FROM \\\"params\\\"\\n)\\nSELECT\\n b.\\\"Department\\\",\\n CASE\\n WHEN b.\\\"lo_rn\\\" = b.\\\"hi_rn\\\" THEN MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lo_rn\\\" THEN o.\\\"measure_value\\\" END)\\n ELSE\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lo_rn\\\" THEN o.\\\"measure_value\\\" END) * (b.\\\"hi_rn\\\" - b.\\\"r\\\") +\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"hi_rn\\\" THEN o.\\\"measure_value\\\" END) * (b.\\\"r\\\" - b.\\\"lo_rn\\\")\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"bounds\\\" AS b\\nJOIN \\\"ordered\\\" AS o\\n ON o.\\\"Department\\\" = b.\\\"Department\\\"\\nGROUP BY b.\\\"Department\\\", b.\\\"r\\\", b.\\\"lo_rn\\\", b.\\\"hi_rn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"Uses the planned grouped-percentile template with group_col=\\\"Department\\\" and measure_col=\\\"Quality_Score\\\". Because SQLite does not support PERCENTILE_CONT, the query emulates the continuous 90th percentile via window functions and linear interpolation, casting the TEXT score column to REAL and enforcing min_group_size >= 5.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16719,"cached_input_tokens":15744,"output_tokens":1083,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c5fc1ef454e24e8d65e42beefe7630792973114b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n SELECT\n \"Department\",\n CAST(\"Quality_Score\" AS REAL) AS \"measure_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Department\"\n ORDER BY CAST(\"Quality_Score\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (PARTITION BY \"Department\") AS \"n\"\n FROM \"m1\"\n WHERE \"Department\" IS NOT NULL\n AND \"Quality_Score\" IS NOT NULL\n),\n\"params\" AS (\n SELECT\n \"Department\",\n \"n\",\n (1.0 + 0.9 * (\"n\" - 1)) AS \"r\"\n FROM \"ordered\"\n GROUP BY \"Department\", \"n\"\n HAVING \"n\" >= 5\n),\n\"bounds\" AS (\n SELECT\n \"Department\",\n \"n\",\n \"r\",\n CAST(\"r\" AS INTEGER) AS \"lo_rn\",\n CASE\n WHEN \"r\" = CAST(\"r\" AS INTEGER) THEN CAST(\"r\" AS INTEGER)\n ELSE CAST(\"r\" AS INTEGER) + 1\n END AS \"hi_rn\"\n FROM \"params\"\n)\nSELECT\n b.\"Department\",\n CASE\n WHEN b.\"lo_rn\" = b.\"hi_rn\" THEN MAX(CASE WHEN o.\"rn\" = b.\"lo_rn\" THEN o.\"measure_value\" END)\n ELSE\n MAX(CASE WHEN o.\"rn\" = b.\"lo_rn\" THEN o.\"measure_value\" END) * (b.\"hi_rn\" - b.\"r\") +\n MAX(CASE WHEN o.\"rn\" = b.\"hi_rn\" THEN o.\"measure_value\" END) * (b.\"r\" - b.\"lo_rn\")\n END AS \"percentile_measure\"\nFROM \"bounds\" AS b\nJOIN \"ordered\" AS o\n ON o.\"Department\" = b.\"Department\"\nGROUP BY b.\"Department\", b.\"r\", b.\"lo_rn\", b.\"hi_rn\"\nORDER BY \"percentile_measure\" DESC;","notes":"Uses the planned grouped-percentile template with group_col=\"Department\" and measure_col=\"Quality_Score\". Because SQLite does not support PERCENTILE_CONT, the query emulates the continuous 90th percentile via window functions and linear interpolation, casting the TEXT score column to REAL and enforcing min_group_size >= 5."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_55fd02977f7294b3/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ecb07cab7b0ce43a65729d194c0c618030c9711a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16344, "bytes_utf8": 16344, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 369, "bytes_utf8": 369, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16683, "cached_input_tokens": 12032, "output_tokens": 326, "reasoning_output_tokens": 232}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..f41f3df13f899a43a9ab2fa38c3c8cf3ff4d070b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_563c0bca293b80fd", + "api_calls": 0, + "input_tokens": 16683, + "cached_input_tokens": 12032, + "output_tokens": 326, + "total_tokens": 17009, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10329.08, + "sql_execution_elapsed_ms_total": 1.24, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..861dcfcf3fb645d9e29c01484988ebdc3ca43ccd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:31:19.225220+00:00", + "ended_at": "2026-05-19T15:31:29.554325+00:00", + "elapsed_ms": 10329.08, + "prompt_metrics": { + "chars": 16344, + "bytes_utf8": 16344, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 723, + "bytes_utf8": 723, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 369, + "bytes_utf8": 369, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16683, + "cached_input_tokens": 12032, + "output_tokens": 326, + "reasoning_output_tokens": 232 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..67de12d8ecdeb632f1496721a64d44e7112f4a96 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Company_Size, measure_col=Innovation_Score.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "Company_Size", + "measure_col": "Innovation_Score", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 80.9, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d8b7f25f3ba2aee00c4196ed9a372bd7283b0c10 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dc-e731-7a62-af8f-e76f530550be"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Company_Size\\\", SUM(CAST(\\\"Innovation_Score\\\" AS REAL)) AS total_measure\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Company_Size\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Uses the required Grouped Numeric Sum template with \\\"Company_Size\\\" as the grouping column and casts \\\"Innovation_Score\\\" to REAL for SQLite numeric aggregation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16683,"cached_input_tokens":12032,"output_tokens":326,"reasoning_output_tokens":232}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d8ecbe157488e0f9a036f4e9b19868e5cfc81f3e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"Company_Size\", SUM(CAST(\"Innovation_Score\" AS REAL)) AS total_measure\nFROM \"m1\"\nGROUP BY \"Company_Size\"\nORDER BY total_measure DESC;","notes":"Uses the required Grouped Numeric Sum template with \"Company_Size\" as the grouping column and casts \"Innovation_Score\" to REAL for SQLite numeric aggregation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_563c0bca293b80fd/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fb07df3b0521c82ef0a39f788341941873306b9c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:27.046863+00:00", + "ended_at": "2026-05-19T16:08:30.079459+00:00", + "elapsed_ms": 3032.57, + "returncode": 1, + "prompt_metrics": { + "chars": 16310, + "bytes_utf8": 16310, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..cb508921b45ed59185bcabd333bd4b96baf811fe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:31.081286+00:00", + "ended_at": "2026-05-19T16:08:33.811040+00:00", + "elapsed_ms": 2729.72, + "returncode": 1, + "prompt_metrics": { + "chars": 16310, + "bytes_utf8": 16310, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2e6b1e65556de03544c22e92b2101f674d6873f3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=Job_Level.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Job_Level", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 8.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..2e6b1e65556de03544c22e92b2101f674d6873f3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=Job_Level.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Job_Level", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 8.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0cc165a3d326c14718db4d61e4b2dadf1c026358 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-e596-7e02-b729-8f8c71ee59dd"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d909e23255f51421d7c9fc2a8057330fed12bd9b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-e596-7e02-b729-8f8c71ee59dd"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..65ea62fef300a9a9f5b9c6b7b3a13384c24fbac8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-f55e-7912-aafa-42b8319228fd"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..93f76f00148188e9c547b4b18ad35b36c27b64e5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-f55e-7912-aafa-42b8319228fd"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_56cda67062eb4e6b/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..027662c55e8f5e90869484539ba29949a766396b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16597, "bytes_utf8": 16597, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 472, "bytes_utf8": 472, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16754, "cached_input_tokens": 12032, "output_tokens": 299, "reasoning_output_tokens": 168}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..f484f9c95aac901f9b24c07e57033d69a142c933 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_571064e388aefa2b", + "api_calls": 0, + "input_tokens": 16754, + "cached_input_tokens": 12032, + "output_tokens": 299, + "total_tokens": 17053, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10696.96, + "sql_execution_elapsed_ms_total": 2.28, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..05e8321a421a1f1301211bad48076ca553ce0b54 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:42.124270+00:00", + "ended_at": "2026-05-19T16:05:52.821260+00:00", + "elapsed_ms": 10696.96, + "prompt_metrics": { + "chars": 16597, + "bytes_utf8": 16597, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 819, + "bytes_utf8": 819, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 472, + "bytes_utf8": 472, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16754, + "cached_input_tokens": 12032, + "output_tokens": 299, + "reasoning_output_tokens": 168 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..bf98b3f66d5cb75e2fe41bfb1f61be1c50626661 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Marital_Status, condition_col=WFH_Days_Per_Week.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Marital_Status", + "condition_col": "WFH_Days_Per_Week", + "condition_value": "3", + "positive_value": "4", + "negative_value": "3", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 96.225, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..80766f8e371f63bbb92b92940011e166192aaf18 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-6168-7ca2-9c61-09e7bde4cbf3"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"Marital_Status\\\",\\n AVG(CASE WHEN \\\"WFH_Days_Per_Week\\\" = '3' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Marital_Status\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Uses the Grouped Condition Rate template with group_col bound to Marital_Status and condition_col bound to WFH_Days_Per_Week, measuring the share of rows where WFH_Days_Per_Week equals 3 within each marital-status group.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16754,"cached_input_tokens":12032,"output_tokens":299,"reasoning_output_tokens":168}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..04df6c5191c71e610f103e5622e5359d6da545c0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"Marital_Status\",\n AVG(CASE WHEN \"WFH_Days_Per_Week\" = '3' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"m1\"\nGROUP BY \"Marital_Status\"\nORDER BY condition_rate DESC;","notes":"Uses the Grouped Condition Rate template with group_col bound to Marital_Status and condition_col bound to WFH_Days_Per_Week, measuring the share of rows where WFH_Days_Per_Week equals 3 within each marital-status group."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_571064e388aefa2b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0419dd6c37d4665c12bb7aaeb89d7d2c6c35d5f9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16533, "bytes_utf8": 16533, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 939, "bytes_utf8": 939, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16725, "cached_input_tokens": 15744, "output_tokens": 1683, "reasoning_output_tokens": 1416}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..fc8e1d4dddf4e7ef540fb041f0d7508f2a94cfdb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_576c9e03ba393e17", + "api_calls": 0, + "input_tokens": 16725, + "cached_input_tokens": 15744, + "output_tokens": 1683, + "total_tokens": 18408, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 30399.37, + "sql_execution_elapsed_ms_total": 3.96, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fd76e5de052f3a4ac4f05fb5af9d7ea74de15b4c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:57:23.738435+00:00", + "ended_at": "2026-05-19T15:57:54.137825+00:00", + "elapsed_ms": 30399.37, + "prompt_metrics": { + "chars": 16533, + "bytes_utf8": 16533, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1377, + "bytes_utf8": 1377, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 939, + "bytes_utf8": 939, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16725, + "cached_input_tokens": 15744, + "output_tokens": 1683, + "reasoning_output_tokens": 1416 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f8e2de7a94d9bdcb7d0b4d2bd0c91f1503e22ebf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=Location_Type, measure_col=Task_Completion_Rate.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Location_Type", + "measure_col": "Task_Completion_Rate", + "top_k": 19, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 92.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..66dada9c1b7cda2cbffba511fdab09711c04a43e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-c6b2-7c62-b7a5-9b94c49f320a"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"Location_Type\\\",\\n CAST(\\\"Task_Completion_Rate\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Location_Type\\\"\\n ORDER BY CAST(\\\"Task_Completion_Rate\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"Location_Type\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Task_Completion_Rate\\\" IS NOT NULL\\n AND \\\"Task_Completion_Rate\\\" <> ''\\n)\\nSELECT\\n \\\"Location_Type\\\",\\n MIN(\\\"measure_value\\\") AS \\\"percentile_measure\\\"\\nFROM \\\"ranked\\\"\\nWHERE \\\"rn\\\" >= CAST(0.95 * \\\"cnt\\\" + 0.999999999 AS INTEGER)\\nGROUP BY \\\"Location_Type\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 95th percentile approximation with window functions, grouped by \\\"Location_Type\\\" and casting \\\"Task_Completion_Rate\\\" from TEXT to REAL.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16725,"cached_input_tokens":15744,"output_tokens":1683,"reasoning_output_tokens":1416}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b42d9836136fa791a4a54b645322493cb221fff7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n SELECT\n \"Location_Type\",\n CAST(\"Task_Completion_Rate\" AS REAL) AS \"measure_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Location_Type\"\n ORDER BY CAST(\"Task_Completion_Rate\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"Location_Type\"\n ) AS \"cnt\"\n FROM \"m1\"\n WHERE \"Task_Completion_Rate\" IS NOT NULL\n AND \"Task_Completion_Rate\" <> ''\n)\nSELECT\n \"Location_Type\",\n MIN(\"measure_value\") AS \"percentile_measure\"\nFROM \"ranked\"\nWHERE \"rn\" >= CAST(0.95 * \"cnt\" + 0.999999999 AS INTEGER)\nGROUP BY \"Location_Type\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 95th percentile approximation with window functions, grouped by \"Location_Type\" and casting \"Task_Completion_Rate\" from TEXT to REAL."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..ca3baa835bc61dc043a56e4c2c4deb3c7783ea63 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=Location_Type, measure_col=Task_Completion_Rate. +Result preview: [{"Location_Type": "Urban", "percentile_measure": 100.0}, {"Location_Type": "Suburban", "percentile_measure": 100.0}, {"Location_Type": "Rural", "percentile_measure": 100.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..9e04116195c0bac50e3dcfa38ce8e79d71877d2f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/generated_sql.sql @@ -0,0 +1,35 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: focused_target_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_m1_576c9e03ba393e17 +-- problem_id: v2p_m1_19b650ab7931c028 +-- realization_mode: agent +-- source_kind: agent +WITH "ranked" AS ( + SELECT + "Location_Type", + CAST("Task_Completion_Rate" AS REAL) AS "measure_value", + ROW_NUMBER() OVER ( + PARTITION BY "Location_Type" + ORDER BY CAST("Task_Completion_Rate" AS REAL) + ) AS "rn", + COUNT(*) OVER ( + PARTITION BY "Location_Type" + ) AS "cnt" + FROM "m1" + WHERE "Task_Completion_Rate" IS NOT NULL + AND "Task_Completion_Rate" <> '' +) +SELECT + "Location_Type", + MIN("measure_value") AS "percentile_measure" +FROM "ranked" +WHERE "rn" >= CAST(0.95 * "cnt" + 0.999999999 AS INTEGER) +GROUP BY "Location_Type" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1143fe6eabb2376cde26bc6e8d31c87832638605 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n SELECT\n \"Location_Type\",\n CAST(\"Task_Completion_Rate\" AS REAL) AS \"measure_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Location_Type\"\n ORDER BY CAST(\"Task_Completion_Rate\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"Location_Type\"\n ) AS \"cnt\"\n FROM \"m1\"\n WHERE \"Task_Completion_Rate\" IS NOT NULL\n AND \"Task_Completion_Rate\" <> ''\n)\nSELECT\n \"Location_Type\",\n MIN(\"measure_value\") AS \"percentile_measure\"\nFROM \"ranked\"\nWHERE \"rn\" >= CAST(0.95 * \"cnt\" + 0.999999999 AS INTEGER)\nGROUP BY \"Location_Type\"\nORDER BY \"percentile_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"Location_Type\\\",\\n CAST(\\\"Task_Completion_Rate\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Location_Type\\\"\\n ORDER BY CAST(\\\"Task_Completion_Rate\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"Location_Type\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Task_Completion_Rate\\\" IS NOT NULL\\n AND \\\"Task_Completion_Rate\\\" <> ''\\n)\\nSELECT\\n \\\"Location_Type\\\",\\n MIN(\\\"measure_value\\\") AS \\\"percentile_measure\\\"\\nFROM \\\"ranked\\\"\\nWHERE \\\"rn\\\" >= CAST(0.95 * \\\"cnt\\\" + 0.999999999 AS INTEGER)\\nGROUP BY \\\"Location_Type\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\", \"columns\": [\"Location_Type\", \"percentile_measure\"], \"rows\": [{\"Location_Type\": \"Urban\", \"percentile_measure\": 100.0}, {\"Location_Type\": \"Suburban\", \"percentile_measure\": 100.0}, {\"Location_Type\": \"Rural\", \"percentile_measure\": 100.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.96}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..39c7f1a82bc468c791ac032ada962c2f99867350 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:57:23.736942+00:00", + "ended_at": "2026-05-19T15:57:54.145505+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_576c9e03ba393e17", + "problem_id": "v2p_m1_19b650ab7931c028", + "dataset_id": "m1", + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_concentration_consistency", + "intended_facet_id": "rare_target_concentration", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=Location_Type, measure_col=Task_Completion_Rate.", + "bindings": { + "group_col": "Location_Type", + "measure_col": "Task_Completion_Rate", + "top_k": 19, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 92.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "notes": [ + "default_facets=rare_target_concentration", + "template_selection_mode=rule", + "problem_index_within_template=6", + "sql_variant_index=2/2", + "binding_index=89" + ], + "template_selection_mode": "rule", + "selected_template_rank": 8, + "problem_index_within_template": 6, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_576c9e03ba393e17.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_576c9e03ba393e17", + "api_calls": 0, + "input_tokens": 16725, + "cached_input_tokens": 15744, + "output_tokens": 1683, + "total_tokens": 18408, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 30399.37, + "sql_execution_elapsed_ms_total": 3.96, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..db8640213a7e69e9a9c310fdd60b6dddc2890828 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:57:54.138676+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 30399.37, "started_at": "2026-05-19T15:57:23.738435+00:00", "ended_at": "2026-05-19T15:57:54.137825+00:00", "prompt_metrics": {"chars": 16533, "bytes_utf8": 16533, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 939, "bytes_utf8": 939, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16725, "cached_input_tokens": 15744, "output_tokens": 1683, "reasoning_output_tokens": 1416}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"Location_Type\\\",\\n CAST(\\\"Task_Completion_Rate\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Location_Type\\\"\\n ORDER BY CAST(\\\"Task_Completion_Rate\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"Location_Type\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Task_Completion_Rate\\\" IS NOT NULL\\n AND \\\"Task_Completion_Rate\\\" <> ''\\n)\\nSELECT\\n \\\"Location_Type\\\",\\n MIN(\\\"measure_value\\\") AS \\\"percentile_measure\\\"\\nFROM \\\"ranked\\\"\\nWHERE \\\"rn\\\" >= CAST(0.95 * \\\"cnt\\\" + 0.999999999 AS INTEGER)\\nGROUP BY \\\"Location_Type\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 95th percentile approximation with window functions, grouped by \\\"Location_Type\\\" and casting \\\"Task_Completion_Rate\\\" from TEXT to REAL.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..fc3df2b2552b2b780a28d60239197ca5f51e9af1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_576c9e03ba393e17", + "api_calls": 0, + "input_tokens": 16725, + "cached_input_tokens": 15744, + "output_tokens": 1683, + "total_tokens": 18408, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 30399.37, + "sql_execution_elapsed_ms_total": 3.96, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_576c9e03ba393e17/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..aa59c36acf91813ab193ff477ea2ac98512b2895 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:04.599371+00:00", + "ended_at": "2026-05-19T16:08:07.717327+00:00", + "elapsed_ms": 3117.93, + "returncode": 1, + "prompt_metrics": { + "chars": 16310, + "bytes_utf8": 16310, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..64730e58e084d595f3cf43cbe0245da05d88030b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:08.719257+00:00", + "ended_at": "2026-05-19T16:08:12.028226+00:00", + "elapsed_ms": 3308.93, + "returncode": 1, + "prompt_metrics": { + "chars": 16310, + "bytes_utf8": 16310, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4876444e4aa58e9a6280a5eb9852d0aa2a3cdff4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=Department.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Department", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..4876444e4aa58e9a6280a5eb9852d0aa2a3cdff4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=Department.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Department", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..7745e8b454354c58dcb8421fc619494eb5010416 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-8e0d-7272-9dc8-eacf51fe89b2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..021df3adca372775954781ff4d95650942ea9bc1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-8e0d-7272-9dc8-eacf51fe89b2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0a2b928d7e445bbbf62e18b6f39bce0e6599fb06 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-9e1b-7e21-969e-ddceb7780944"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..4bae2ac3bcc805516cfe632891b126da9014604f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-9e1b-7e21-969e-ddceb7780944"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_588fd4d411b3cb8b/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..71d13de2aa8fd734bde84c2594dc2e545937a74c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"value_label": "Engineering", "support": 455, "support_share": 0.30333333333333334, "support_rank": 1}, {"value_label": "Sales", "support": 202, "support_share": 0.13466666666666666, "support_rank": 2}, {"value_label": "Marketing", "support": 176, "support_share": 0.11733333333333333, "support_rank": 3}, {"value_label": "Operations", "support": 137, "support_share": 0.09133333333333334, "support_rank": 4}, {"value_label": "Customer Success", "support": 120, "support_share": 0.08, "support_rank": 5}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..cedd3c09b374fe8d77c188177794b3eecce0497e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/generated_sql.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_m1_58f8908ba50571a5 +-- problem_id: v2p_m1_945bf0c5f759a6e2 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "Department" AS value_label, COUNT(*) AS support + FROM "m1" + GROUP BY "Department" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4637bfdb74ffe7270b7f41c81592e05b1cfbd2e9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_a\n-- sql_source_dataset_id: m1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: support_concentration\n-- variant_semantic_role: count_distribution\n-- template_id: tpl_cardinality_support_rank_profile\n-- query_record_id: v2q_m1_58f8908ba50571a5\n-- problem_id: v2p_m1_945bf0c5f759a6e2\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n SELECT \"Department\" AS value_label, COUNT(*) AS support\n FROM \"m1\"\n GROUP BY \"Department\"\n)\nSELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_a\\n-- sql_source_dataset_id: m1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: support_concentration\\n-- variant_semantic_role: count_distribution\\n-- template_id: tpl_cardinality_support_rank_profile\\n-- query_record_id: v2q_m1_58f8908ba50571a5\\n-- problem_id: v2p_m1_945bf0c5f759a6e2\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n SELECT \\\"Department\\\" AS value_label, COUNT(*) AS support\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Department\\\"\\n)\\nSELECT\\n value_label,\\n support,\\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\\nFROM grouped\\nORDER BY support DESC, value_label;\", \"columns\": [\"value_label\", \"support\", \"support_share\", \"support_rank\"], \"rows\": [{\"value_label\": \"Engineering\", \"support\": 455, \"support_share\": 0.30333333333333334, \"support_rank\": 1}, {\"value_label\": \"Sales\", \"support\": 202, \"support_share\": 0.13466666666666666, \"support_rank\": 2}, {\"value_label\": \"Marketing\", \"support\": 176, \"support_share\": 0.11733333333333333, \"support_rank\": 3}, {\"value_label\": \"Operations\", \"support\": 137, \"support_share\": 0.09133333333333334, \"support_rank\": 4}, {\"value_label\": \"Customer Success\", \"support\": 120, \"support_share\": 0.08, \"support_rank\": 5}, {\"value_label\": \"Finance\", \"support\": 112, \"support_share\": 0.07466666666666667, \"support_rank\": 6}, {\"value_label\": \"HR\", \"support\": 101, \"support_share\": 0.06733333333333333, \"support_rank\": 7}, {\"value_label\": \"Product\", \"support\": 81, \"support_share\": 0.054, \"support_rank\": 8}, {\"value_label\": \"Design\", \"support\": 60, \"support_share\": 0.04, \"support_rank\": 9}, {\"value_label\": \"Data Science\", \"support\": 56, \"support_share\": 0.037333333333333336, \"support_rank\": 10}], \"row_count_returned\": 10, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.04}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f9f427eb45b436836d0db97327f86d7dfaab71eb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/run_manifest.json @@ -0,0 +1,57 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:11:34.287136+00:00", + "ended_at": "2026-05-19T16:11:34.288821+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_58f8908ba50571a5", + "problem_id": "v2p_m1_945bf0c5f759a6e2", + "dataset_id": "m1", + "template_id": "tpl_cardinality_support_rank_profile", + "template_name": "Cardinality Support Rank Profile", + "family_id": "cardinality_structure", + "canonical_subitem_id": "support_rank_profile_consistency", + "intended_facet_id": "support_concentration", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template Cardinality Support Rank Profile to probe support_rank_profile_consistency with semantic role count_distribution. Focus on group_col=Department.", + "bindings": { + "group_col": "Department" + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col} AS value_label, COUNT(*) AS support\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", + "notes": [ + "default_facets=support_concentration,value_imbalance_profile", + "template_selection_mode=deterministic", + "problem_index_within_template=5", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 5, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_58f8908ba50571a5.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_58f8908ba50571a5/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b47a730a7bd775b4fe39c16a0dab18b7d30ba45a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 17184, "bytes_utf8": 17184, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 697, "bytes_utf8": 697, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16893, "cached_input_tokens": 15744, "output_tokens": 503, "reasoning_output_tokens": 329}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..122663b8325daa7a9cac8ca02114d588b83c4c6c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_5e630050646101f7", + "api_calls": 0, + "input_tokens": 16893, + "cached_input_tokens": 15744, + "output_tokens": 503, + "total_tokens": 17396, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17163.16, + "sql_execution_elapsed_ms_total": 2.75, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..4e391e3284574994dc3cc129b118bb03f6f80d65 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:41:41.017083+00:00", + "ended_at": "2026-05-19T15:41:58.180274+00:00", + "elapsed_ms": 17163.16, + "prompt_metrics": { + "chars": 17184, + "bytes_utf8": 17184, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1058, + "bytes_utf8": 1058, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 697, + "bytes_utf8": 697, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16893, + "cached_input_tokens": 15744, + "output_tokens": 503, + "reasoning_output_tokens": 329 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d5f1e16f0849c6fcd0811ede936d0e2e47d9ff95 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=Company_Size, condition_col=Education_Level.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "Company_Size", + "condition_col": "Education_Level", + "condition_value": "Bachelor Degree", + "positive_value": "Bachelor Degree", + "negative_value": "Master Degree", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 8.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..74a3526f5914a5b70285bbad8e947bd818f58674 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e6-6414-7f52-a822-a79094183ac2"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"Company_Size\\\",\\n SUM(CASE WHEN \\\"Education_Level\\\" = 'Bachelor Degree' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"Education_Level\\\" = 'Master Degree' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Company_Size\\\"\\n)\\nSELECT \\\"Company_Size\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Computes the ratio of Bachelor Degree to Master Degree records within each Company_Size group; groups with zero Master Degree rows return NULL for the ratio.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16893,"cached_input_tokens":15744,"output_tokens":503,"reasoning_output_tokens":329}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ae79dc09ebc27ccbd04e9ddcff384753e26698c3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"Company_Size\",\n SUM(CASE WHEN \"Education_Level\" = 'Bachelor Degree' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"Education_Level\" = 'Master Degree' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"m1\"\n GROUP BY \"Company_Size\"\n)\nSELECT \"Company_Size\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Computes the ratio of Bachelor Degree to Master Degree records within each Company_Size group; groups with zero Master Degree rows return NULL for the ratio."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_5e630050646101f7/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..123506e56853432d09b79a320639ff942836fe53 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16269, "bytes_utf8": 16269, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 386, "bytes_utf8": 386, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16672, "cached_input_tokens": 15744, "output_tokens": 253, "reasoning_output_tokens": 145}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7da76fa975e4dda47f8c2ea59a3a0a91c8afe263 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_61933d4a3f5a053d", + "api_calls": 0, + "input_tokens": 16672, + "cached_input_tokens": 15744, + "output_tokens": 253, + "total_tokens": 16925, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7825.23, + "sql_execution_elapsed_ms_total": 0.79, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b94cbde92ea22ed056c7c1c513d100f678feddfd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:30.863125+00:00", + "ended_at": "2026-05-19T16:06:38.688380+00:00", + "elapsed_ms": 7825.23, + "prompt_metrics": { + "chars": 16269, + "bytes_utf8": 16269, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 730, + "bytes_utf8": 730, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 386, + "bytes_utf8": 386, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16672, + "cached_input_tokens": 15744, + "output_tokens": 253, + "reasoning_output_tokens": 145 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0903dcae868b791885cc71e3581f5e5cdbe8b56 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Years_Experience.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "Years_Experience", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..386656ff1c11edf018f25d9e25153b7842005d25 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-1fe6-7310-9b7d-8a8a38bdaf7f"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN CAST(\\\"Years_Experience\\\" AS REAL) <= 7.0 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"m1\\\";\",\"notes\":\"Applied the provided Threshold Rarity CDF template with measure_col bound to \\\"Years_Experience\\\" and threshold 7.0. Cast to REAL because the schema snapshot shows this column is stored as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16672,"cached_input_tokens":15744,"output_tokens":253,"reasoning_output_tokens":145}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..bcaf9339118a911477dafc28a16226155767f128 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN CAST(\"Years_Experience\" AS REAL) <= 7.0 THEN 1 ELSE 0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"m1\";","notes":"Applied the provided Threshold Rarity CDF template with measure_col bound to \"Years_Experience\" and threshold 7.0. Cast to REAL because the schema snapshot shows this column is stored as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_61933d4a3f5a053d/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..2a043ea047d281c184d91bbbe625a29143466ad9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=Education_Level. +Result preview: [{"Education_Level": "Bachelor Degree", "row_count": 673}, {"Education_Level": "Master Degree", "row_count": 489}, {"Education_Level": "Associate Degree", "row_count": 153}, {"Education_Level": "PhD", "row_count": 88}, {"Education_Level": "High School", "row_count": 72}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..ae99be39a0cb41255bfdf4e7a3aa192161c2ad8b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/generated_sql.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_m1_62f33e22c3fbc6af +-- problem_id: v2p_m1_af0e20ed5d79eb97 +-- realization_mode: agent +-- source_kind: agent +SELECT "Education_Level", COUNT(*) AS "row_count" +FROM "m1" +GROUP BY "Education_Level" +ORDER BY "row_count" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a6696b1c91bada58323d33e8fb13e034cfd49d5c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_clickbench_group_count\nSELECT \"Education_Level\", COUNT(*) AS \"row_count\"\nFROM \"m1\"\nGROUP BY \"Education_Level\"\nORDER BY \"row_count\" DESC;", "result": "{\"query\": \"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"Education_Level\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Education_Level\\\"\\nORDER BY \\\"row_count\\\" DESC;\", \"columns\": [\"Education_Level\", \"row_count\"], \"rows\": [{\"Education_Level\": \"Bachelor Degree\", \"row_count\": 673}, {\"Education_Level\": \"Master Degree\", \"row_count\": 489}, {\"Education_Level\": \"Associate Degree\", \"row_count\": 153}, {\"Education_Level\": \"PhD\", \"row_count\": 88}, {\"Education_Level\": \"High School\", \"row_count\": 72}, {\"Education_Level\": \"Professional Degree\", \"row_count\": 25}], \"row_count_returned\": 6, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 0.92}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2792d6ec69dd8fa440bdbbe18b5a2c8d7b170be7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/run_manifest.json @@ -0,0 +1,87 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:33:22.857704+00:00", + "ended_at": "2026-05-19T15:33:37.509093+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_62f33e22c3fbc6af", + "problem_id": "v2p_m1_af0e20ed5d79eb97", + "dataset_id": "m1", + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "family_id": "subgroup_structure", + "canonical_subitem_id": "subgroup_size_stability", + "intended_facet_id": "subgroup_distribution_shift", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=Education_Level.", + "bindings": { + "group_col": "Education_Level", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 98.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "notes": [ + "default_facets=subgroup_distribution_shift", + "template_selection_mode=rule", + "problem_index_within_template=7", + "sql_variant_index=1/1", + "binding_index=18" + ], + "template_selection_mode": "rule", + "selected_template_rank": 2, + "problem_index_within_template": 7, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_62f33e22c3fbc6af.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_62f33e22c3fbc6af", + "api_calls": 0, + "input_tokens": 16654, + "cached_input_tokens": 15744, + "output_tokens": 205, + "total_tokens": 16859, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14646.23, + "sql_execution_elapsed_ms_total": 0.92, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..531ff3f516b72a147678466553f4583a4703b62d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:33:37.506905+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 14646.23, "started_at": "2026-05-19T15:33:22.859902+00:00", "ended_at": "2026-05-19T15:33:37.506159+00:00", "prompt_metrics": {"chars": 16242, "bytes_utf8": 16242, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 303, "bytes_utf8": 303, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16654, "cached_input_tokens": 15744, "output_tokens": 205, "reasoning_output_tokens": 124}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"Education_Level\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Education_Level\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the provided grouped-count template with group_col bound to \\\"Education_Level\\\" on the single table \\\"m1\\\".\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8955d8943febfc056a01b93e0c117e4be271d12b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_62f33e22c3fbc6af", + "api_calls": 0, + "input_tokens": 16654, + "cached_input_tokens": 15744, + "output_tokens": 205, + "total_tokens": 16859, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14646.23, + "sql_execution_elapsed_ms_total": 0.92, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_62f33e22c3fbc6af/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b82f9864c655236fcd4a979a553a2208cc5bd5e4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:57.401019+00:00", + "ended_at": "2026-05-19T16:08:00.309743+00:00", + "elapsed_ms": 2908.69, + "returncode": 1, + "prompt_metrics": { + "chars": 16320, + "bytes_utf8": 16320, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..71a584292e25d497a854f725d71d0e6ce6c5b05f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:01.311967+00:00", + "ended_at": "2026-05-19T16:08:04.596099+00:00", + "elapsed_ms": 3284.1, + "returncode": 1, + "prompt_metrics": { + "chars": 16320, + "bytes_utf8": 16320, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..51bbeab190035250f2392683a605fdba09696e5d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=Location_Type.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Location_Type", + "top_k": 18, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..51bbeab190035250f2392683a605fdba09696e5d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=Location_Type.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Location_Type", + "top_k": 18, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..06f38033b6fc12aabb4a55f0016e506f8925c828 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-71ee-7282-9b70-72a4847a154a"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9206c899946bdbe52209482fdf7c8fa27a39c616 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-71ee-7282-9b70-72a4847a154a"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..bc7ee04794c96b7cb98c432159f16bfb2effbaa3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-8125-7623-970b-dcb36ca9b2cd"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..afd5a16cf3907046a239be9eeead6d49e989c7c9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-8125-7623-970b-dcb36ca9b2cd"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_63eec9d01f0a78f0/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7799804500c10e66d38e628d87565f9042d79c74 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16763, "bytes_utf8": 16763, "lines": 458, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 634, "bytes_utf8": 634, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16806, "cached_input_tokens": 15744, "output_tokens": 1348, "reasoning_output_tokens": 1171}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..08824e767711dae238fa6906256f707be278ed32 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_66bccadb790a3123", + "api_calls": 0, + "input_tokens": 16806, + "cached_input_tokens": 15744, + "output_tokens": 1348, + "total_tokens": 18154, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 22011.17, + "sql_execution_elapsed_ms_total": 4.87, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f519b610b7e0369b3f2c2e3c9353708b5fcc0377 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:38:36.083527+00:00", + "ended_at": "2026-05-19T15:38:58.094742+00:00", + "elapsed_ms": 22011.17, + "prompt_metrics": { + "chars": 16763, + "bytes_utf8": 16763, + "lines": 458, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1006, + "bytes_utf8": 1006, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 634, + "bytes_utf8": 634, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16806, + "cached_input_tokens": 15744, + "output_tokens": 1348, + "reasoning_output_tokens": 1171 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f04184da8599781de8e6a5d115a2eb87923f5a51 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,458 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Survey_Date, measure_col=Productivity_Score.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "Survey_Date", + "measure_col": "Productivity_Score", + "item_col": "Commute_Time_Minutes", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 94.8, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4f55a28e6e1bf37882189beefe80b0f849500505 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e3-91ba-7e80-93db-0cac1f335b67"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share.\\nSELECT\\n \\\"Survey_Date\\\",\\n \\\"Commute_Time_Minutes\\\",\\n SUM(CAST(\\\"Productivity_Score\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Productivity_Score\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Productivity_Score\\\" AS REAL))) OVER (PARTITION BY \\\"Survey_Date\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Survey_Date\\\", \\\"Commute_Time_Minutes\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the planned Within-Group Share of Total template with Survey_Date as group_col, Commute_Time_Minutes as item_col, and cast Productivity_Score from TEXT to REAL for SQLite aggregation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16806,"cached_input_tokens":15744,"output_tokens":1348,"reasoning_output_tokens":1171}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4cf4a28d653f6423ee8f807af8a9c0f3c13db543 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share.\nSELECT\n \"Survey_Date\",\n \"Commute_Time_Minutes\",\n SUM(CAST(\"Productivity_Score\" AS REAL)) AS total_measure,\n SUM(CAST(\"Productivity_Score\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"Productivity_Score\" AS REAL))) OVER (PARTITION BY \"Survey_Date\") AS share_within_group\nFROM \"m1\"\nGROUP BY \"Survey_Date\", \"Commute_Time_Minutes\"\nORDER BY share_within_group DESC;","notes":"Used the planned Within-Group Share of Total template with Survey_Date as group_col, Commute_Time_Minutes as item_col, and cast Productivity_Score from TEXT to REAL for SQLite aggregation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_66bccadb790a3123/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3cdb457b6e96c61a0db76c1589882e25b5d096d7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:06.615690+00:00", + "ended_at": "2026-05-19T16:07:09.585622+00:00", + "elapsed_ms": 2969.91, + "returncode": 1, + "prompt_metrics": { + "chars": 16323, + "bytes_utf8": 16323, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a3fb650288189b8a48939663351f6107239fe073 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:10.588288+00:00", + "ended_at": "2026-05-19T16:07:13.830607+00:00", + "elapsed_ms": 3242.28, + "returncode": 1, + "prompt_metrics": { + "chars": 16323, + "bytes_utf8": 16323, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b36c69f1f7819e84423fce57b0f69cd4afd2430 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=Education_Level.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Education_Level", + "top_k": 15, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b36c69f1f7819e84423fce57b0f69cd4afd2430 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=Education_Level.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Education_Level", + "top_k": 15, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..dac5b5f2c676ba760c85e71d6098ace2d7b57e06 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-ab81-7ba1-ae92-b8babdbe5d16"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5a257a35a6d1eee0be0cc1ea668edf829b269577 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-ab81-7ba1-ae92-b8babdbe5d16"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4a545b9e56864642b7d17b0074e501e1ab1b4567 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-bb0c-77c0-b27e-c1d03bf35364"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..0f6133371966d2d6724536021b958c9185bbfe2f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-bb0c-77c0-b27e-c1d03bf35364"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_67696c2d3dd72808/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6c53d8cb9ed416852aec81b6e5bccde50a0413b8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 17146, "bytes_utf8": 17146, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 763, "bytes_utf8": 763, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16898, "cached_input_tokens": 15744, "output_tokens": 698, "reasoning_output_tokens": 480}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..f906ab8cb58c83e1ef4aa7c0117338fad87ee9e4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_68d2671f1c6b9080", + "api_calls": 0, + "input_tokens": 16898, + "cached_input_tokens": 15744, + "output_tokens": 698, + "total_tokens": 17596, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16924.64, + "sql_execution_elapsed_ms_total": 2.04, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..4c19e631bddb5c5d5c8e9b23ab554acffb9ffaa6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:41:09.671033+00:00", + "ended_at": "2026-05-19T15:41:26.595702+00:00", + "elapsed_ms": 16924.64, + "prompt_metrics": { + "chars": 17146, + "bytes_utf8": 17146, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1156, + "bytes_utf8": 1156, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 763, + "bytes_utf8": 763, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16898, + "cached_input_tokens": 15744, + "output_tokens": 698, + "reasoning_output_tokens": 480 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..035d2cbbb9a20c39aa6ca3901714c621f26bca26 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=Department, condition_col=WFH_Days_Per_Week.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "Department", + "condition_col": "WFH_Days_Per_Week", + "condition_value": "4", + "positive_value": "4", + "negative_value": "3", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..13b09695f1de377f0a8394cdb6f53092e5cba0eb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e5-e998-7a51-ac11-5bc6d8e7781d"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"Department\\\",\\n SUM(CASE WHEN \\\"WFH_Days_Per_Week\\\" = '4' THEN 1 ELSE 0 END) AS \\\"numerator_count\\\",\\n SUM(CASE WHEN \\\"WFH_Days_Per_Week\\\" = '3' THEN 1 ELSE 0 END) AS \\\"denominator_count\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Department\\\"\\n)\\nSELECT \\\"Department\\\",\\n CAST(\\\"numerator_count\\\" AS FLOAT) / NULLIF(\\\"denominator_count\\\", 0) AS \\\"condition_ratio\\\"\\nFROM \\\"grouped\\\"\\nORDER BY \\\"condition_ratio\\\" DESC;\",\"notes\":\"Applies the grouped ratio template with Department as the group and computes count(WFH_Days_Per_Week='4') divided by count(WFH_Days_Per_Week='3'). String literals are used because the column is stored as TEXT in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16898,"cached_input_tokens":15744,"output_tokens":698,"reasoning_output_tokens":480}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..77bea01340788f352125c96ec3cae1efca66b72a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH \"grouped\" AS (\n SELECT \"Department\",\n SUM(CASE WHEN \"WFH_Days_Per_Week\" = '4' THEN 1 ELSE 0 END) AS \"numerator_count\",\n SUM(CASE WHEN \"WFH_Days_Per_Week\" = '3' THEN 1 ELSE 0 END) AS \"denominator_count\"\n FROM \"m1\"\n GROUP BY \"Department\"\n)\nSELECT \"Department\",\n CAST(\"numerator_count\" AS FLOAT) / NULLIF(\"denominator_count\", 0) AS \"condition_ratio\"\nFROM \"grouped\"\nORDER BY \"condition_ratio\" DESC;","notes":"Applies the grouped ratio template with Department as the group and computes count(WFH_Days_Per_Week='4') divided by count(WFH_Days_Per_Week='3'). String literals are used because the column is stored as TEXT in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68d2671f1c6b9080/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..861ad38e4890b95867faf445847b485b3baefa01 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16753, "bytes_utf8": 16753, "lines": 458, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 660, "bytes_utf8": 660, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16804, "cached_input_tokens": 12288, "output_tokens": 697, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..fc7c4ac8ff5469dbcce0014ac63e73c57cbc04d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_68e5472b127cd84d", + "api_calls": 0, + "input_tokens": 16804, + "cached_input_tokens": 12288, + "output_tokens": 697, + "total_tokens": 17501, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14538.41, + "sql_execution_elapsed_ms_total": 7.39, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..17c48b2a701a81d2ab79c1fc00cfef473f3365c1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:36:13.857909+00:00", + "ended_at": "2026-05-19T15:36:28.396369+00:00", + "elapsed_ms": 14538.41, + "prompt_metrics": { + "chars": 16753, + "bytes_utf8": 16753, + "lines": 458, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1044, + "bytes_utf8": 1044, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 660, + "bytes_utf8": 660, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16804, + "cached_input_tokens": 12288, + "output_tokens": 697, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..10202231666e67613548d5c36c1f51006f55c996 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,458 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Manager_Support_Level, measure_col=Age.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "Manager_Support_Level", + "measure_col": "Age", + "item_col": "Task_Completion_Rate", + "top_k": 18, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d545f91ea2fbf4bfcf2168b42c387de48b0807cf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e1-661c-7600-a090-a8f2d812f980"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"Manager_Support_Level\\\", \\\"Task_Completion_Rate\\\",\\n SUM(CAST(\\\"Age\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Age\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Age\\\" AS REAL))) OVER (PARTITION BY \\\"Manager_Support_Level\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Manager_Support_Level\\\", \\\"Task_Completion_Rate\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Uses the requested Within-Group Share of Total template with group_col=\\\"Manager_Support_Level\\\", item_col=\\\"Task_Completion_Rate\\\", and measure_col=\\\"Age\\\". \\\"Age\\\" is cast to REAL because the SQLite schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16804,"cached_input_tokens":12288,"output_tokens":697,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a31e93531609025b32a3683e30c7bf3d29a254f6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"Manager_Support_Level\", \"Task_Completion_Rate\",\n SUM(CAST(\"Age\" AS REAL)) AS total_measure,\n SUM(CAST(\"Age\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"Age\" AS REAL))) OVER (PARTITION BY \"Manager_Support_Level\") AS share_within_group\nFROM \"m1\"\nGROUP BY \"Manager_Support_Level\", \"Task_Completion_Rate\"\nORDER BY share_within_group DESC;","notes":"Uses the requested Within-Group Share of Total template with group_col=\"Manager_Support_Level\", item_col=\"Task_Completion_Rate\", and measure_col=\"Age\". \"Age\" is cast to REAL because the SQLite schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_68e5472b127cd84d/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..0e3a9058b683cbbc48e9f0439ef644c52f74845f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"value_label": "Married", "support": 775, "support_share": 0.5166666666666667, "cumulative_support": 775}, {"value_label": "Single", "support": 417, "support_share": 0.278, "cumulative_support": 1192}, {"value_label": "Divorced", "support": 186, "support_share": 0.124, "cumulative_support": 1378}, {"value_label": "In Relationship", "support": 122, "support_share": 0.08133333333333333, "cumulative_support": 1500}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..744376c7239a17fdd06aeb23398ca17f4746083c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/generated_sql.sql @@ -0,0 +1,28 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_cardinality_distinct_share_profile +-- query_record_id: v2q_m1_69e30604e3eef174 +-- problem_id: v2p_m1_fe9c2c608e278fe5 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "Marital_Status" AS value_label, COUNT(*) AS support + FROM "m1" + GROUP BY "Marital_Status" +), ranked AS ( + SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support + FROM grouped +) +SELECT * +FROM ranked +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9123c0c4e2686ed092809d15db35fcf860c5fc49 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_a\n-- sql_source_dataset_id: m1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: support_concentration\n-- variant_semantic_role: ranked_signal_view\n-- template_id: tpl_cardinality_distinct_share_profile\n-- query_record_id: v2q_m1_69e30604e3eef174\n-- problem_id: v2p_m1_fe9c2c608e278fe5\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n SELECT \"Marital_Status\" AS value_label, COUNT(*) AS support\n FROM \"m1\"\n GROUP BY \"Marital_Status\"\n), ranked AS (\n SELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\n FROM grouped\n)\nSELECT *\nFROM ranked\nORDER BY support DESC, value_label;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_a\\n-- sql_source_dataset_id: m1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: support_concentration\\n-- variant_semantic_role: ranked_signal_view\\n-- template_id: tpl_cardinality_distinct_share_profile\\n-- query_record_id: v2q_m1_69e30604e3eef174\\n-- problem_id: v2p_m1_fe9c2c608e278fe5\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n SELECT \\\"Marital_Status\\\" AS value_label, COUNT(*) AS support\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Marital_Status\\\"\\n), ranked AS (\\n SELECT\\n value_label,\\n support,\\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\\n SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\\n FROM grouped\\n)\\nSELECT *\\nFROM ranked\\nORDER BY support DESC, value_label;\", \"columns\": [\"value_label\", \"support\", \"support_share\", \"cumulative_support\"], \"rows\": [{\"value_label\": \"Married\", \"support\": 775, \"support_share\": 0.5166666666666667, \"cumulative_support\": 775}, {\"value_label\": \"Single\", \"support\": 417, \"support_share\": 0.278, \"cumulative_support\": 1192}, {\"value_label\": \"Divorced\", \"support\": 186, \"support_share\": 0.124, \"cumulative_support\": 1378}, {\"value_label\": \"In Relationship\", \"support\": 122, \"support_share\": 0.08133333333333333, \"cumulative_support\": 1500}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 0.9}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..41bcda4be3e9a64671f47b3c9e85a1f000709a15 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/run_manifest.json @@ -0,0 +1,57 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:11:34.254964+00:00", + "ended_at": "2026-05-19T16:11:34.256734+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_69e30604e3eef174", + "problem_id": "v2p_m1_fe9c2c608e278fe5", + "dataset_id": "m1", + "template_id": "tpl_cardinality_distinct_share_profile", + "template_name": "Cardinality Distinct Share Profile", + "family_id": "cardinality_structure", + "canonical_subitem_id": "support_rank_profile_consistency", + "intended_facet_id": "support_concentration", + "variant_semantic_role": "ranked_signal_view", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template Cardinality Distinct Share Profile to probe support_rank_profile_consistency with semantic role ranked_signal_view. Focus on group_col=Marital_Status.", + "bindings": { + "group_col": "Marital_Status" + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col} AS value_label, COUNT(*) AS support\n FROM {table}\n GROUP BY {group_col}\n), ranked AS (\n SELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\n FROM grouped\n)\nSELECT *\nFROM ranked\nORDER BY support DESC, value_label;", + "notes": [ + "default_facets=support_concentration,value_imbalance_profile", + "template_selection_mode=deterministic", + "problem_index_within_template=3", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 3, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_69e30604e3eef174.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_69e30604e3eef174/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7926818ac1563f70a46d09297e8e1adece187ffa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:10:15.124185+00:00", + "ended_at": "2026-05-19T16:10:18.361964+00:00", + "elapsed_ms": 3237.75, + "returncode": 1, + "prompt_metrics": { + "chars": 16424, + "bytes_utf8": 16424, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..82b87fae4756b102260f98ac9dce64f26fe74ec4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:10:19.364569+00:00", + "ended_at": "2026-05-19T16:10:22.785421+00:00", + "elapsed_ms": 3420.81, + "returncode": 1, + "prompt_metrics": { + "chars": 16424, + "bytes_utf8": 16424, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6437c02c402625d6803195caaecd0c6dc7901a42 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=Gender, measure_col=Commute_Time_Minutes.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Gender", + "measure_col": "Commute_Time_Minutes", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..6437c02c402625d6803195caaecd0c6dc7901a42 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=Gender, measure_col=Commute_Time_Minutes.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Gender", + "measure_col": "Commute_Time_Minutes", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..1d9f869785a1f054f3e1e9512ef8e9c332f8c79e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4100-8bca-74d2-babd-c8b9fb687743"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..74ccee5e1af9ab255d1dcd90739b3debcbacb98d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4100-8bca-74d2-babd-c8b9fb687743"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..63b45d1f234f4d95ea34a178ead261e8ddc4070d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4100-9c6c-7d03-9597-770aa889a080"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a6afac3b6f2fc86e7683e2d362028894bef30495 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4100-9c6c-7d03-9597-770aa889a080"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6a0d40c722a3e6f6/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..5c1f718b7222a58680e7557df9ac68d268a1ac5c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Job_Level, measure_col=Productivity_Score. +Result preview: [{"Job_Level": "Junior", "group_value": 42360.7}, {"Job_Level": "Mid-Level", "group_value": 34885.7}, {"Job_Level": "Senior", "group_value": 28375.5}, {"Job_Level": "Lead", "group_value": 12911.5}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..27556fdc6e9db08e46f9dfad4ba86a20a03f8c29 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/generated_sql.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_m1_6cdd84097e92fbfc +-- problem_id: v2p_m1_3f5ba8de22da416c +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT + "Job_Level", + SUM(CAST("Productivity_Score" AS REAL)) AS "group_value" + FROM "m1" + GROUP BY "Job_Level" +), total AS ( + SELECT SUM("group_value") AS "total_value" + FROM grouped +) +SELECT + g."Job_Level", + g."group_value" +FROM grouped AS g +CROSS JOIN total AS t +WHERE g."group_value" > t."total_value" * 0.1 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..26fe7fa4254566d2f61dcc4c96fb21b533f71235 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT\n \"Job_Level\",\n SUM(CAST(\"Productivity_Score\" AS REAL)) AS \"group_value\"\n FROM \"m1\"\n GROUP BY \"Job_Level\"\n), total AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM grouped\n)\nSELECT\n g.\"Job_Level\",\n g.\"group_value\"\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT\\n \\\"Job_Level\\\",\\n SUM(CAST(\\\"Productivity_Score\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Job_Level\\\"\\n), total AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM grouped\\n)\\nSELECT\\n g.\\\"Job_Level\\\",\\n g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\", \"columns\": [\"Job_Level\", \"group_value\"], \"rows\": [{\"Job_Level\": \"Junior\", \"group_value\": 42360.7}, {\"Job_Level\": \"Mid-Level\", \"group_value\": 34885.7}, {\"Job_Level\": \"Senior\", \"group_value\": 28375.5}, {\"Job_Level\": \"Lead\", \"group_value\": 12911.5}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.12}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..8a891987c7f3308480b258b86ff90eac83a427bc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:47:47.433840+00:00", + "ended_at": "2026-05-19T15:47:59.243361+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_6cdd84097e92fbfc", + "problem_id": "v2p_m1_3f5ba8de22da416c", + "dataset_id": "m1", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Job_Level, measure_col=Productivity_Score.", + "bindings": { + "group_col": "Job_Level", + "measure_col": "Productivity_Score", + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 98.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=3", + "sql_variant_index=1/2", + "binding_index=74" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 3, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_6cdd84097e92fbfc.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_6cdd84097e92fbfc", + "api_calls": 0, + "input_tokens": 16821, + "cached_input_tokens": 15744, + "output_tokens": 593, + "total_tokens": 17414, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11798.35, + "sql_execution_elapsed_ms_total": 3.12, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0beb47e60f146cadc70761893bbb42c9337d34a5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:47:59.236878+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11798.35, "started_at": "2026-05-19T15:47:47.436472+00:00", "ended_at": "2026-05-19T15:47:59.234883+00:00", "prompt_metrics": {"chars": 16894, "bytes_utf8": 16894, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 701, "bytes_utf8": 701, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16821, "cached_input_tokens": 15744, "output_tokens": 593, "reasoning_output_tokens": 399}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT\\n \\\"Job_Level\\\",\\n SUM(CAST(\\\"Productivity_Score\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Job_Level\\\"\\n), total AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM grouped\\n)\\nSELECT\\n g.\\\"Job_Level\\\",\\n g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Used the provided Relative-to-Total Extreme Threshold template with group_col=\\\"Job_Level\\\" and measure_col=\\\"Productivity_Score\\\". CAST to REAL is applied because the schema stores numeric fields as TEXT in SQLite.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..023ba0f87813422c79e6b2e9886c4b55554908d3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_6cdd84097e92fbfc", + "api_calls": 0, + "input_tokens": 16821, + "cached_input_tokens": 15744, + "output_tokens": 593, + "total_tokens": 17414, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11798.35, + "sql_execution_elapsed_ms_total": 3.12, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_6cdd84097e92fbfc/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3940470b6bd63d8a322dfc029b754fdc61aae254 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16523, "bytes_utf8": 16523, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 2070, "bytes_utf8": 2070, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16721, "cached_input_tokens": 12032, "output_tokens": 2836, "reasoning_output_tokens": 2179}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..1aac5e1e428f747509d0b7ed594a4ee0544b3fa0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_72a042837e3c3c9b", + "api_calls": 0, + "input_tokens": 16721, + "cached_input_tokens": 12032, + "output_tokens": 2836, + "total_tokens": 19557, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 41477.18, + "sql_execution_elapsed_ms_total": 7.98, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3bdec47f982b7098d793dfe635a33300637359a4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:00:04.163147+00:00", + "ended_at": "2026-05-19T16:00:45.640358+00:00", + "elapsed_ms": 41477.18, + "prompt_metrics": { + "chars": 16523, + "bytes_utf8": 16523, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2699, + "bytes_utf8": 2699, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 2070, + "bytes_utf8": 2070, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16721, + "cached_input_tokens": 12032, + "output_tokens": 2836, + "reasoning_output_tokens": 2179 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6c16a9169a45c9668b608a307b766d4a427cf9f2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=Company_Size, measure_col=Efficiency_Rating.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Company_Size", + "measure_col": "Efficiency_Rating", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..35eed33296b410dfaa9aa60b9df2da5e1760961a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-3973-7e70-8ef4-fe78b59ee2db"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n SELECT\\n \\\"Company_Size\\\",\\n CAST(\\\"Efficiency_Rating\\\" AS REAL) AS \\\"Efficiency_Rating_num\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Company_Size\\\" IS NOT NULL\\n AND \\\"Efficiency_Rating\\\" IS NOT NULL\\n AND TRIM(\\\"Efficiency_Rating\\\") <> ''\\n),\\n\\\"ranked\\\" AS (\\n SELECT\\n \\\"Company_Size\\\",\\n \\\"Efficiency_Rating_num\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Company_Size\\\"\\n ORDER BY \\\"Efficiency_Rating_num\\\"\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (PARTITION BY \\\"Company_Size\\\") AS \\\"cnt\\\"\\n FROM \\\"base\\\"\\n),\\n\\\"positions\\\" AS (\\n SELECT\\n \\\"Company_Size\\\",\\n \\\"Efficiency_Rating_num\\\",\\n \\\"rn\\\",\\n \\\"cnt\\\",\\n ((0.95 * (\\\"cnt\\\" - 1)) + 1.0) AS \\\"pos\\\",\\n CAST(((0.95 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER) AS \\\"lower_rn\\\",\\n CASE\\n WHEN ((0.95 * (\\\"cnt\\\" - 1)) + 1.0) = CAST(((0.95 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER)\\n THEN CAST(((0.95 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER)\\n ELSE CAST(((0.95 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER) + 1\\n END AS \\\"upper_rn\\\"\\n FROM \\\"ranked\\\"\\n WHERE \\\"cnt\\\" >= 5\\n),\\n\\\"percentiles\\\" AS (\\n SELECT\\n \\\"Company_Size\\\",\\n MAX(CASE WHEN \\\"rn\\\" = \\\"lower_rn\\\" THEN \\\"Efficiency_Rating_num\\\" END) AS \\\"lower_val\\\",\\n MAX(CASE WHEN \\\"rn\\\" = \\\"upper_rn\\\" THEN \\\"Efficiency_Rating_num\\\" END) AS \\\"upper_val\\\",\\n MAX(\\\"pos\\\") AS \\\"pos\\\"\\n FROM \\\"positions\\\"\\n GROUP BY \\\"Company_Size\\\"\\n)\\nSELECT\\n \\\"Company_Size\\\",\\n CASE\\n WHEN \\\"pos\\\" = CAST(\\\"pos\\\" AS INTEGER) THEN \\\"lower_val\\\"\\n ELSE \\\"lower_val\\\" + (\\\"pos\\\" - CAST(\\\"pos\\\" AS INTEGER)) * (\\\"upper_val\\\" - \\\"lower_val\\\")\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"percentiles\\\"\\nORDER BY \\\"percentile_measure\\\" DESC, \\\"Company_Size\\\";\",\"notes\":\"Computes the 95th percentile of \\\"Efficiency_Rating\\\" for each \\\"Company_Size\\\" group in SQLite by casting the TEXT measure to REAL and emulating PERCENTILE_CONT with window-based linear interpolation. Null/blank measure rows are excluded and groups smaller than 5 rows are filtered out.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16721,"cached_input_tokens":12032,"output_tokens":2836,"reasoning_output_tokens":2179}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a83d4a32b173ba0050d93d181f33dbfe1507180 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"base\" AS (\n SELECT\n \"Company_Size\",\n CAST(\"Efficiency_Rating\" AS REAL) AS \"Efficiency_Rating_num\"\n FROM \"m1\"\n WHERE \"Company_Size\" IS NOT NULL\n AND \"Efficiency_Rating\" IS NOT NULL\n AND TRIM(\"Efficiency_Rating\") <> ''\n),\n\"ranked\" AS (\n SELECT\n \"Company_Size\",\n \"Efficiency_Rating_num\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Company_Size\"\n ORDER BY \"Efficiency_Rating_num\"\n ) AS \"rn\",\n COUNT(*) OVER (PARTITION BY \"Company_Size\") AS \"cnt\"\n FROM \"base\"\n),\n\"positions\" AS (\n SELECT\n \"Company_Size\",\n \"Efficiency_Rating_num\",\n \"rn\",\n \"cnt\",\n ((0.95 * (\"cnt\" - 1)) + 1.0) AS \"pos\",\n CAST(((0.95 * (\"cnt\" - 1)) + 1.0) AS INTEGER) AS \"lower_rn\",\n CASE\n WHEN ((0.95 * (\"cnt\" - 1)) + 1.0) = CAST(((0.95 * (\"cnt\" - 1)) + 1.0) AS INTEGER)\n THEN CAST(((0.95 * (\"cnt\" - 1)) + 1.0) AS INTEGER)\n ELSE CAST(((0.95 * (\"cnt\" - 1)) + 1.0) AS INTEGER) + 1\n END AS \"upper_rn\"\n FROM \"ranked\"\n WHERE \"cnt\" >= 5\n),\n\"percentiles\" AS (\n SELECT\n \"Company_Size\",\n MAX(CASE WHEN \"rn\" = \"lower_rn\" THEN \"Efficiency_Rating_num\" END) AS \"lower_val\",\n MAX(CASE WHEN \"rn\" = \"upper_rn\" THEN \"Efficiency_Rating_num\" END) AS \"upper_val\",\n MAX(\"pos\") AS \"pos\"\n FROM \"positions\"\n GROUP BY \"Company_Size\"\n)\nSELECT\n \"Company_Size\",\n CASE\n WHEN \"pos\" = CAST(\"pos\" AS INTEGER) THEN \"lower_val\"\n ELSE \"lower_val\" + (\"pos\" - CAST(\"pos\" AS INTEGER)) * (\"upper_val\" - \"lower_val\")\n END AS \"percentile_measure\"\nFROM \"percentiles\"\nORDER BY \"percentile_measure\" DESC, \"Company_Size\";","notes":"Computes the 95th percentile of \"Efficiency_Rating\" for each \"Company_Size\" group in SQLite by casting the TEXT measure to REAL and emulating PERCENTILE_CONT with window-based linear interpolation. Null/blank measure rows are excluded and groups smaller than 5 rows are filtered out."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_72a042837e3c3c9b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..c988c4090ada94f61cad3dbeddb0f4179a40a2fe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=Gender. +Result preview: [{"Gender": "Female", "row_count": 737}, {"Gender": "Male", "row_count": 703}, {"Gender": "Non-binary", "row_count": 60}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..ae7b678a774bdf2271d51446c4942d1c4c6c8a50 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/generated_sql.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_m1_7644ac366e675269 +-- problem_id: v2p_m1_44e71beddd8fa2fe +-- realization_mode: agent +-- source_kind: agent +SELECT "Gender", COUNT(*) AS "row_count" +FROM "m1" +GROUP BY "Gender" +ORDER BY "row_count" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1e7e0137944f4260fd1393562b092ebffef93f81 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_clickbench_group_count\nSELECT \"Gender\", COUNT(*) AS \"row_count\"\nFROM \"m1\"\nGROUP BY \"Gender\"\nORDER BY \"row_count\" DESC;", "result": "{\"query\": \"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"Gender\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Gender\\\"\\nORDER BY \\\"row_count\\\" DESC;\", \"columns\": [\"Gender\", \"row_count\"], \"rows\": [{\"Gender\": \"Female\", \"row_count\": 737}, {\"Gender\": \"Male\", \"row_count\": 703}, {\"Gender\": \"Non-binary\", \"row_count\": 60}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.49}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..41e7c21019a615b4a05c4486ccdea79042b1ac42 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/run_manifest.json @@ -0,0 +1,87 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:33:15.053565+00:00", + "ended_at": "2026-05-19T15:33:22.856732+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_7644ac366e675269", + "problem_id": "v2p_m1_44e71beddd8fa2fe", + "dataset_id": "m1", + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "family_id": "subgroup_structure", + "canonical_subitem_id": "subgroup_size_stability", + "intended_facet_id": "subgroup_distribution_shift", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=Gender.", + "bindings": { + "group_col": "Gender", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 46.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "notes": [ + "default_facets=subgroup_distribution_shift", + "template_selection_mode=rule", + "problem_index_within_template=6", + "sql_variant_index=1/1", + "binding_index=17" + ], + "template_selection_mode": "rule", + "selected_template_rank": 2, + "problem_index_within_template": 6, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_7644ac366e675269.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_7644ac366e675269", + "api_calls": 0, + "input_tokens": 16652, + "cached_input_tokens": 12032, + "output_tokens": 183, + "total_tokens": 16835, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7796.46, + "sql_execution_elapsed_ms_total": 1.49, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..cf82905aaf3007563026b38bb4729eff25c9f1be --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:33:22.853078+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 7796.46, "started_at": "2026-05-19T15:33:15.055217+00:00", "ended_at": "2026-05-19T15:33:22.851706+00:00", "prompt_metrics": {"chars": 16223, "bytes_utf8": 16223, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 288, "bytes_utf8": 288, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16652, "cached_input_tokens": 12032, "output_tokens": 183, "reasoning_output_tokens": 108}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"Gender\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Gender\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the provided grouped-count template with group_col bound to \\\"Gender\\\" to measure subgroup count distribution.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..20b059425999ad72a1d2d8ef722389dc4ddbdf73 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_7644ac366e675269", + "api_calls": 0, + "input_tokens": 16652, + "cached_input_tokens": 12032, + "output_tokens": 183, + "total_tokens": 16835, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7796.46, + "sql_execution_elapsed_ms_total": 1.49, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7644ac366e675269/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_76edc40b3c5fcfd0/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_76edc40b3c5fcfd0/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e38319d5c654e28cf9a506c9251f0cdbf8c0da0e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_76edc40b3c5fcfd0/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:36:13.840307+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 13948.55, "started_at": "2026-05-19T15:35:59.890016+00:00", "ended_at": "2026-05-19T15:36:13.838605+00:00", "prompt_metrics": {"chars": 16756, "bytes_utf8": 16756, "lines": 458, "estimated_tokens": null}, "response_metrics": {"chars": 685, "bytes_utf8": 685, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16805, "cached_input_tokens": 12032, "output_tokens": 708, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"Manager_Support_Level\\\",\\n \\\"Task_Completion_Rate\\\",\\n SUM(CAST(\\\"Age\\\" AS REAL)) AS \\\"total_measure\\\",\\n SUM(CAST(\\\"Age\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Age\\\" AS REAL))) OVER (PARTITION BY \\\"Manager_Support_Level\\\") AS \\\"share_within_group\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Manager_Support_Level\\\", \\\"Task_Completion_Rate\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Used the provided Within-Group Share of Total template with bound roles: group_col=\\\"Manager_Support_Level\\\", item_col=\\\"Task_Completion_Rate\\\", measure_col=\\\"Age\\\". Cast \\\"Age\\\" to REAL because the SQLite schema stores numeric fields as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..c9ff007bdc1a2ea1675a0ac4be7a5e253fa0d629 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Job_Level, measure_col=Quality_Score. +Result preview: [{"Job_Level": "Junior", "total_measure": 43265.7}, {"Job_Level": "Mid-Level", "total_measure": 35516.0}, {"Job_Level": "Senior", "total_measure": 28583.0}, {"Job_Level": "Lead", "total_measure": 13107.6}, {"Job_Level": "Manager", "total_measure": 5539.6}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..c7a807ba6c87e9a2f301a366d0e8d39089838927 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/generated_sql.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_m1_77ca51d9786db7c5 +-- problem_id: v2p_m1_be14ad0454a84e81 +-- realization_mode: agent +-- source_kind: agent +SELECT "Job_Level", SUM(CAST("Quality_Score" AS REAL)) AS "total_measure" +FROM "m1" +GROUP BY "Job_Level" +ORDER BY "total_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..abfc93940f707c2ec36a6554929f4ef0095bdc1a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_h2o_group_sum\nSELECT \"Job_Level\", SUM(CAST(\"Quality_Score\" AS REAL)) AS \"total_measure\"\nFROM \"m1\"\nGROUP BY \"Job_Level\"\nORDER BY \"total_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Job_Level\\\", SUM(CAST(\\\"Quality_Score\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Job_Level\\\"\\nORDER BY \\\"total_measure\\\" DESC;\", \"columns\": [\"Job_Level\", \"total_measure\"], \"rows\": [{\"Job_Level\": \"Junior\", \"total_measure\": 43265.7}, {\"Job_Level\": \"Mid-Level\", \"total_measure\": 35516.0}, {\"Job_Level\": \"Senior\", \"total_measure\": 28583.0}, {\"Job_Level\": \"Lead\", \"total_measure\": 13107.6}, {\"Job_Level\": \"Manager\", \"total_measure\": 5539.6}, {\"Job_Level\": \"Director\", \"total_measure\": 604.5}], \"row_count_returned\": 6, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.11}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..367034a9526e780ce015656d73426aa28135a279 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:30:25.273255+00:00", + "ended_at": "2026-05-19T15:30:45.450542+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_77ca51d9786db7c5", + "problem_id": "v2p_m1_be14ad0454a84e81", + "dataset_id": "m1", + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "family_id": "subgroup_structure", + "canonical_subitem_id": "internal_profile_stability", + "intended_facet_id": "subgroup_distribution_shift", + "variant_semantic_role": "collapsed_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Job_Level, measure_col=Quality_Score.", + "bindings": { + "group_col": "Job_Level", + "measure_col": "Quality_Score", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.225, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "notes": [ + "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast", + "template_selection_mode=rule", + "problem_index_within_template=7", + "sql_variant_index=1/2", + "binding_index=6" + ], + "template_selection_mode": "rule", + "selected_template_rank": 1, + "problem_index_within_template": 7, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_77ca51d9786db7c5.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_77ca51d9786db7c5", + "api_calls": 0, + "input_tokens": 16683, + "cached_input_tokens": 12032, + "output_tokens": 356, + "total_tokens": 17039, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 20172.19, + "sql_execution_elapsed_ms_total": 1.11, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9788216d702cf362a1160fba8dd596ad53370cd3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:30:45.448204+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 20172.19, "started_at": "2026-05-19T15:30:25.275162+00:00", "ended_at": "2026-05-19T15:30:45.447383+00:00", "prompt_metrics": {"chars": 16332, "bytes_utf8": 16332, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 406, "bytes_utf8": 406, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16683, "cached_input_tokens": 12032, "output_tokens": 356, "reasoning_output_tokens": 249}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Job_Level\\\", SUM(CAST(\\\"Quality_Score\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Job_Level\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the required Grouped Numeric Sum template with group_col=\\\"Job_Level\\\" and measure_col=\\\"Quality_Score\\\". CAST to REAL is included because the SQLite schema stores numeric-looking fields as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..5fc8fc80fc1166c3ec7c82221b8fe58fc2c41c3f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_77ca51d9786db7c5", + "api_calls": 0, + "input_tokens": 16683, + "cached_input_tokens": 12032, + "output_tokens": 356, + "total_tokens": 17039, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 20172.19, + "sql_execution_elapsed_ms_total": 1.11, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_77ca51d9786db7c5/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..686350699d2d21c661c34eb0e790c276f2ee3d82 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16438, "bytes_utf8": 16438, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 16438, "bytes_utf8": 16438, "lines": 456, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 844, "bytes_utf8": 844, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16700, "cached_input_tokens": 12032, "output_tokens": 768, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..f380c154224b72f674d2b9d114758557e1c51ea2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_7879ba783503fa04", + "api_calls": 0, + "input_tokens": 16700, + "cached_input_tokens": 12032, + "output_tokens": 768, + "total_tokens": 17468, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18125.19, + "sql_execution_elapsed_ms_total": 4.46, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5bf9ba2ee41f53dbb746f75328e7a81a69737e11 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:09:26.086983+00:00", + "ended_at": "2026-05-19T16:09:29.157796+00:00", + "elapsed_ms": 3070.77, + "returncode": 1, + "prompt_metrics": { + "chars": 16438, + "bytes_utf8": 16438, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7f607e2baa94771ea16ece9a59916e8a9ee4cb6f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:09:30.160778+00:00", + "ended_at": "2026-05-19T16:09:45.215250+00:00", + "elapsed_ms": 15054.42, + "prompt_metrics": { + "chars": 16438, + "bytes_utf8": 16438, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1241, + "bytes_utf8": 1241, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 844, + "bytes_utf8": 844, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16700, + "cached_input_tokens": 12032, + "output_tokens": 768, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4406ecdc7c1395fdf1b99bc9d2ba1365ac6493c0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=Work_Life_Balance, measure_col=Innovation_Score.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Work_Life_Balance", + "measure_col": "Innovation_Score", + "top_k": 18, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 80.9, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..4406ecdc7c1395fdf1b99bc9d2ba1365ac6493c0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=Work_Life_Balance, measure_col=Innovation_Score.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Work_Life_Balance", + "measure_col": "Innovation_Score", + "top_k": 18, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 80.9, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..ef2387a34557877d1ba5c3a5da8a26894374104e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-cc4f-7cb1-ad05-3d24bcf7157d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7f1604da5c50a20247548d5964bad807376a2eb9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-cc4f-7cb1-ad05-3d24bcf7157d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..79b964a349f7a9b4db8725fab728c85f428d9bce --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-dc52-7221-a650-4145dd906903"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n \\\"Work_Life_Balance\\\",\\n AVG(CAST(\\\"Innovation_Score\\\" AS REAL)) OVER (PARTITION BY \\\"Work_Life_Balance\\\") AS avg_measure\\nFROM \\\"m1\\\"\\nWHERE TRIM(COALESCE(\\\"Work_Life_Balance\\\", '')) <> ''\\n AND TRIM(COALESCE(\\\"Innovation_Score\\\", '')) <> ''\\n AND \\\"Work_Life_Balance\\\" IN (\\n SELECT \\\"Work_Life_Balance\\\"\\n FROM \\\"m1\\\"\\n WHERE TRIM(COALESCE(\\\"Work_Life_Balance\\\", '')) <> ''\\n AND TRIM(COALESCE(\\\"Innovation_Score\\\", '')) <> ''\\n GROUP BY \\\"Work_Life_Balance\\\"\\n HAVING COUNT(*) >= 5\\n )\\nORDER BY avg_measure DESC;\",\"notes\":\"Uses the required Window Partition Average template with group_col=\\\"Work_Life_Balance\\\" and measure_col=\\\"Innovation_Score\\\", casting the measure to REAL for SQLite and filtering to stable groups with at least 5 rows.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16700,"cached_input_tokens":12032,"output_tokens":768,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..0073743b042633270e8e6b43bf82ef31b44b6d1e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n \"Work_Life_Balance\",\n AVG(CAST(\"Innovation_Score\" AS REAL)) OVER (PARTITION BY \"Work_Life_Balance\") AS avg_measure\nFROM \"m1\"\nWHERE TRIM(COALESCE(\"Work_Life_Balance\", '')) <> ''\n AND TRIM(COALESCE(\"Innovation_Score\", '')) <> ''\n AND \"Work_Life_Balance\" IN (\n SELECT \"Work_Life_Balance\"\n FROM \"m1\"\n WHERE TRIM(COALESCE(\"Work_Life_Balance\", '')) <> ''\n AND TRIM(COALESCE(\"Innovation_Score\", '')) <> ''\n GROUP BY \"Work_Life_Balance\"\n HAVING COUNT(*) >= 5\n )\nORDER BY avg_measure DESC;","notes":"Uses the required Window Partition Average template with group_col=\"Work_Life_Balance\" and measure_col=\"Innovation_Score\", casting the measure to REAL for SQLite and filtering to stable groups with at least 5 rows."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7879ba783503fa04/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..56a14b4fee58a8369886883804c57b4f3ef1d1c0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 17156, "bytes_utf8": 17156, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 729, "bytes_utf8": 729, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16898, "cached_input_tokens": 15744, "output_tokens": 506, "reasoning_output_tokens": 306}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..3b6b43b629f223441b133bc89805be96f1b4989f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_7aff9a4eb56d4661", + "api_calls": 0, + "input_tokens": 16898, + "cached_input_tokens": 15744, + "output_tokens": 506, + "total_tokens": 17404, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11146.89, + "sql_execution_elapsed_ms_total": 1.35, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7dfd630f2ef10ebda03faf9338dc3a823481f354 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:42:12.687212+00:00", + "ended_at": "2026-05-19T15:42:23.834145+00:00", + "elapsed_ms": 11146.89, + "prompt_metrics": { + "chars": 17156, + "bytes_utf8": 17156, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1100, + "bytes_utf8": 1100, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 729, + "bytes_utf8": 729, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16898, + "cached_input_tokens": 15744, + "output_tokens": 506, + "reasoning_output_tokens": 306 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..90b6a34cfc517a1e1cbead70c3286f6e53d7ee24 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=Home_Office_Quality, condition_col=Has_Children.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "Home_Office_Quality", + "condition_col": "Has_Children", + "condition_value": "No", + "positive_value": "No", + "negative_value": "Yes", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..3090a76a3a0c660a04528784ff47490b590b3d16 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e6-dfe7-7bd1-85ee-41d309460c1e"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT\\n \\\"Home_Office_Quality\\\",\\n SUM(CASE WHEN \\\"Has_Children\\\" = 'No' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"Has_Children\\\" = 'Yes' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Home_Office_Quality\\\"\\n)\\nSELECT\\n \\\"Home_Office_Quality\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Uses the requested grouped ratio template with group_col=\\\"Home_Office_Quality\\\" and condition_col=\\\"Has_Children\\\", computing the ratio of 'No' to 'Yes' within each home office quality group.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16898,"cached_input_tokens":15744,"output_tokens":506,"reasoning_output_tokens":306}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ac5e896f95bb5077bc8bdbc5900a6364cb74b083 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT\n \"Home_Office_Quality\",\n SUM(CASE WHEN \"Has_Children\" = 'No' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"Has_Children\" = 'Yes' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"m1\"\n GROUP BY \"Home_Office_Quality\"\n)\nSELECT\n \"Home_Office_Quality\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Uses the requested grouped ratio template with group_col=\"Home_Office_Quality\" and condition_col=\"Has_Children\", computing the ratio of 'No' to 'Yes' within each home office quality group."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7aff9a4eb56d4661/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a485931b8957062508b563aa4168d7ef5e0b3429 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16654, "bytes_utf8": 16654, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 434, "bytes_utf8": 434, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16761, "cached_input_tokens": 15744, "output_tokens": 509, "reasoning_output_tokens": 388}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..681913ea2c88653899cad802558f5505027caaae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_7b0c01e142f826fb", + "api_calls": 0, + "input_tokens": 16761, + "cached_input_tokens": 15744, + "output_tokens": 509, + "total_tokens": 17270, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11116.1, + "sql_execution_elapsed_ms_total": 1.63, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..98d835b656a491d7ae4c88c56c2c153382a541a7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:04:19.398035+00:00", + "ended_at": "2026-05-19T16:04:30.514160+00:00", + "elapsed_ms": 11116.1, + "prompt_metrics": { + "chars": 16654, + "bytes_utf8": 16654, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 790, + "bytes_utf8": 790, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 434, + "bytes_utf8": 434, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16761, + "cached_input_tokens": 15744, + "output_tokens": 509, + "reasoning_output_tokens": 388 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..70eb417fcce6cb3df3660b5a09274af5d3275c4e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Survey_Date, condition_col=Internet_Speed_Category.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Survey_Date", + "condition_col": "Internet_Speed_Category", + "condition_value": "Fast (50-100 Mbps)", + "positive_value": "Very Fast (100+ Mbps)", + "negative_value": "Fast (50-100 Mbps)", + "top_k": 15, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 4.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..93d11f16f554393b543063a84a47612b3721dce3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-1e61-7c12-b154-1fd3276e4657"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT\\n \\\"Survey_Date\\\",\\n AVG(CASE WHEN \\\"Internet_Speed_Category\\\" = 'Fast (50-100 Mbps)' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Survey_Date\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Uses the requested Grouped Condition Rate template: groups by Survey_Date and computes the share of rows where Internet_Speed_Category is 'Fast (50-100 Mbps)'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16761,"cached_input_tokens":15744,"output_tokens":509,"reasoning_output_tokens":388}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..63ab176f70893bb2c971f8ee2f5ded1a9c281fa1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT\n \"Survey_Date\",\n AVG(CASE WHEN \"Internet_Speed_Category\" = 'Fast (50-100 Mbps)' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m1\"\nGROUP BY \"Survey_Date\"\nORDER BY \"condition_rate\" DESC;","notes":"Uses the requested Grouped Condition Rate template: groups by Survey_Date and computes the share of rows where Internet_Speed_Category is 'Fast (50-100 Mbps)'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7b0c01e142f826fb/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..e9e4976fd9551ed6826a026a2cf977fd872af77d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Company_Size, measure_col=Task_Completion_Rate. +Result preview: [{"Company_Size": "Large (1001-5000)", "group_value": 31847.0}, {"Company_Size": "Medium (201-1000)", "group_value": 31123.7}, {"Company_Size": "Small (51-200)", "group_value": 29358.5}, {"Company_Size": "Startup (1-50)", "group_value": 18661.0}, {"Company_Size": "Enterprise (5000+)", "group_value": 12770.5}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..c5cc22a2e9d5b66ae0b0d610f18008e0c7721527 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/generated_sql.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_m1_7c50fcbb8b0ad962 +-- problem_id: v2p_m1_991ed6755e5292f3 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "Company_Size", SUM(CAST(NULLIF("Task_Completion_Rate", '') AS REAL)) AS group_value + FROM "m1" + GROUP BY "Company_Size" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."Company_Size", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.05 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7abf4e8f7577a9cd3976b6e1dad7eb1ba5751a3d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"Company_Size\", SUM(CAST(NULLIF(\"Task_Completion_Rate\", '') AS REAL)) AS group_value\n FROM \"m1\"\n GROUP BY \"Company_Size\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"Company_Size\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Company_Size\\\", SUM(CAST(NULLIF(\\\"Task_Completion_Rate\\\", '') AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Company_Size\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Company_Size\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\", \"columns\": [\"Company_Size\", \"group_value\"], \"rows\": [{\"Company_Size\": \"Large (1001-5000)\", \"group_value\": 31847.0}, {\"Company_Size\": \"Medium (201-1000)\", \"group_value\": 31123.7}, {\"Company_Size\": \"Small (51-200)\", \"group_value\": 29358.5}, {\"Company_Size\": \"Startup (1-50)\", \"group_value\": 18661.0}, {\"Company_Size\": \"Enterprise (5000+)\", \"group_value\": 12770.5}], \"row_count_returned\": 5, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.29}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0e8cbc42cbcc07c89a6ed3f20289f232c008a55b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:48:29.424339+00:00", + "ended_at": "2026-05-19T15:48:45.556384+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_7c50fcbb8b0ad962", + "problem_id": "v2p_m1_991ed6755e5292f3", + "dataset_id": "m1", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Company_Size, measure_col=Task_Completion_Rate.", + "bindings": { + "group_col": "Company_Size", + "measure_col": "Task_Completion_Rate", + "top_k": 15, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 92.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=4", + "sql_variant_index=2/2", + "binding_index=75" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 4, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_7c50fcbb8b0ad962.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_7c50fcbb8b0ad962", + "api_calls": 0, + "input_tokens": 16823, + "cached_input_tokens": 12032, + "output_tokens": 619, + "total_tokens": 17442, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16125.38, + "sql_execution_elapsed_ms_total": 1.29, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..48fa7e04f8e31ac58beeb9a6919a8c5acb591f62 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:48:45.553645+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 16125.38, "started_at": "2026-05-19T15:48:29.427419+00:00", "ended_at": "2026-05-19T15:48:45.552832+00:00", "prompt_metrics": {"chars": 16906, "bytes_utf8": 16906, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 669, "bytes_utf8": 669, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16823, "cached_input_tokens": 12032, "output_tokens": 619, "reasoning_output_tokens": 445}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Company_Size\\\", SUM(CAST(NULLIF(\\\"Task_Completion_Rate\\\", '') AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Company_Size\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Company_Size\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Uses the required template with group_col=\\\"Company_Size\\\" and measure_col=\\\"Task_Completion_Rate\\\". Casts the TEXT measure to REAL and treats empty strings as NULL for SQLite-safe aggregation.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..c2487d74d5bb98cbf6982dcb144968eafe9b4fcc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_7c50fcbb8b0ad962", + "api_calls": 0, + "input_tokens": 16823, + "cached_input_tokens": 12032, + "output_tokens": 619, + "total_tokens": 17442, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16125.38, + "sql_execution_elapsed_ms_total": 1.29, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7c50fcbb8b0ad962/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..885e7b90e1398c6e17287fd9159a0f2ee945095a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16900, "bytes_utf8": 16900, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 639, "bytes_utf8": 639, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16823, "cached_input_tokens": 12032, "output_tokens": 402, "reasoning_output_tokens": 231}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..a7adb4ccb8ebf15e1546ea361c51e27957c12c4a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_7e18b1f6e1301941", + "api_calls": 0, + "input_tokens": 16823, + "cached_input_tokens": 12032, + "output_tokens": 402, + "total_tokens": 17225, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9883.47, + "sql_execution_elapsed_ms_total": 3.13, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..dbecb65a64e9e9535496d754d2e9bca9413bf878 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:47:37.541391+00:00", + "ended_at": "2026-05-19T15:47:47.424910+00:00", + "elapsed_ms": 9883.47, + "prompt_metrics": { + "chars": 16900, + "bytes_utf8": 16900, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1010, + "bytes_utf8": 1010, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 639, + "bytes_utf8": 639, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16823, + "cached_input_tokens": 12032, + "output_tokens": 402, + "reasoning_output_tokens": 231 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c620d380cdac73869d20e69b129892775ca79254 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Department, measure_col=Work_Hours_Per_Week.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "Department", + "measure_col": "Work_Hours_Per_Week", + "top_k": 18, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 45.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4f71ca3f085c0335222eee28f7ee4b99b235067d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40eb-d538-7493-9141-149e8c94b23a"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Department\\\", SUM(CAST(\\\"Work_Hours_Per_Week\\\" AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Department\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Department\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided template with group_col=\\\"Department\\\" and measure_col=\\\"Work_Hours_Per_Week\\\". Cast \\\"Work_Hours_Per_Week\\\" to REAL because the SQLite schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16823,"cached_input_tokens":12032,"output_tokens":402,"reasoning_output_tokens":231}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d19be9a94c57322f77c0d1b225385a882cb9fa7e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"Department\", SUM(CAST(\"Work_Hours_Per_Week\" AS REAL)) AS group_value\n FROM \"m1\"\n GROUP BY \"Department\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"Department\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Applied the provided template with group_col=\"Department\" and measure_col=\"Work_Hours_Per_Week\". Cast \"Work_Hours_Per_Week\" to REAL because the SQLite schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_7e18b1f6e1301941/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..b4c9681b8049e3e69287cd4fbb8419c57b10e4f6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": []} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..42aa1a14e18c2f616e3cdec373018149aec7fc0b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_m1_817f747525c5fb34 +-- problem_id: v2p_m1_8f1a6895c0e29d28 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "Employee_ID", + COUNT(*) AS support, + AVG("Age") AS avg_response +FROM "m1" +GROUP BY "Employee_ID" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..abcddae36015cf024a49a63bdc3046f078982a70 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_a\n-- sql_source_dataset_id: m1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_m1_817f747525c5fb34\n-- problem_id: v2p_m1_8f1a6895c0e29d28\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n \"Employee_ID\",\n COUNT(*) AS support,\n AVG(\"Age\") AS avg_response\nFROM \"m1\"\nGROUP BY \"Employee_ID\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_a\\n-- sql_source_dataset_id: m1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_m1_817f747525c5fb34\\n-- problem_id: v2p_m1_8f1a6895c0e29d28\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n \\\"Employee_ID\\\",\\n COUNT(*) AS support,\\n AVG(\\\"Age\\\") AS avg_response\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Employee_ID\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"Employee_ID\", \"support\", \"avg_response\"], \"rows\": [], \"row_count_returned\": 0, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 0.95}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..95d7f0fa88c93ef017e7439c284de9077a81cbe5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/run_manifest.json @@ -0,0 +1,60 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:11:34.303226+00:00", + "ended_at": "2026-05-19T16:11:34.304760+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_817f747525c5fb34", + "problem_id": "v2p_m1_8f1a6895c0e29d28", + "dataset_id": "m1", + "template_id": "tpl_cardinality_high_card_response_stability", + "template_name": "High-Cardinality Response Stability", + "family_id": "cardinality_structure", + "canonical_subitem_id": "high_cardinality_response_stability", + "intended_facet_id": "target_cardinality_cross_section", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=Age, key_col=Employee_ID.", + "bindings": { + "key_col": "Employee_ID", + "measure_col": "Age", + "min_support": 5 + }, + "binding_roles": [ + "key_col", + "target_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "SELECT\n {key_col},\n COUNT(*) AS support,\n AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;", + "notes": [ + "default_facets=target_cardinality_cross_section", + "template_selection_mode=deterministic", + "problem_index_within_template=1", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 1, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_817f747525c5fb34.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_817f747525c5fb34/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..38d81d0f73d9bc000b52b42561c4845aff18f344 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:52.832735+00:00", + "ended_at": "2026-05-19T16:05:56.402222+00:00", + "elapsed_ms": 3569.44, + "returncode": 1, + "prompt_metrics": { + "chars": 16279, + "bytes_utf8": 16279, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fbbfce504183c334727fbcf779758a035c44a9f7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:57.404027+00:00", + "ended_at": "2026-05-19T16:06:00.793332+00:00", + "elapsed_ms": 3389.27, + "returncode": 1, + "prompt_metrics": { + "chars": 16279, + "bytes_utf8": 16279, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..54a4cf4f6b6d93e0200f27697022d0e5929f4d9f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Commute_Time_Minutes.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "Commute_Time_Minutes", + "top_k": 13, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..54a4cf4f6b6d93e0200f27697022d0e5929f4d9f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Commute_Time_Minutes.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "Commute_Time_Minutes", + "top_k": 13, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0f689e0af928aeeb38dea894e8f69c9592827a6b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-8b53-78b2-a62b-017bc533c7e3"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f2bac5261d23021a3c3990d6a19350695393ba70 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-8b53-78b2-a62b-017bc533c7e3"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..aec2e34d71f4015fe8ab9e80434ab2a7b5416fd8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-9d2f-7f80-a4e3-e1e13abf8542"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..6300b48e81f040409fcd1022ec3c8aabe769f80b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-9d2f-7f80-a4e3-e1e13abf8542"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_867500e27b23478e/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..336b819f27ab1f85cef16ebf9a42e8538793f54c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16605, "bytes_utf8": 16605, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 16605, "bytes_utf8": 16605, "lines": 459, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 491, "bytes_utf8": 491, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16754, "cached_input_tokens": 12032, "output_tokens": 426, "reasoning_output_tokens": 288}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..fbbcbfe93c2a610a5261fd1ee99d6866249b6d85 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_87ce556639d1b4eb", + "api_calls": 0, + "input_tokens": 16754, + "cached_input_tokens": 12032, + "output_tokens": 426, + "total_tokens": 17180, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16489.51, + "sql_execution_elapsed_ms_total": 1.28, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ddf22d1b313063e64c8b994bc11903d05eb9a81e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:41.421617+00:00", + "ended_at": "2026-05-19T16:03:44.590412+00:00", + "elapsed_ms": 3168.77, + "returncode": 1, + "prompt_metrics": { + "chars": 16605, + "bytes_utf8": 16605, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..470526b064cbfd30a498c8b031f748cf2b16290b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:45.593034+00:00", + "ended_at": "2026-05-19T16:03:58.913809+00:00", + "elapsed_ms": 13320.74, + "prompt_metrics": { + "chars": 16605, + "bytes_utf8": 16605, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 854, + "bytes_utf8": 854, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 491, + "bytes_utf8": 491, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16754, + "cached_input_tokens": 12032, + "output_tokens": 426, + "reasoning_output_tokens": 288 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0c69a8665f1970a700b8f3ee4bd0fa16277cca3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=Work_Life_Balance, condition_col=Home_Office_Quality.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Work_Life_Balance", + "condition_col": "Home_Office_Quality", + "condition_value": "Good", + "positive_value": "Good", + "negative_value": "Average", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0c69a8665f1970a700b8f3ee4bd0fa16277cca3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=Work_Life_Balance, condition_col=Home_Office_Quality.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Work_Life_Balance", + "condition_col": "Home_Office_Quality", + "condition_value": "Good", + "positive_value": "Good", + "negative_value": "Average", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..bcf10ccfee321a04e42eaece742fa7cb65f44d85 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-89ef-7d42-a273-dd5a74abe29c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1466593c0649f976ad7de813a80487981b13983a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-89ef-7d42-a273-dd5a74abe29c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a76fa731add5e2eddbee5dc265668634140e3d82 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-9a48-70b0-a526-006d8ddd1650"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"Work_Life_Balance\\\",\\n AVG(CASE WHEN \\\"Home_Office_Quality\\\" = 'Good' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Work_Life_Balance\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Uses the Grouped Condition Rate template with group_col=\\\"Work_Life_Balance\\\" and condition_col=\\\"Home_Office_Quality\\\", measuring the rate of rows where Home_Office_Quality is 'Good' within each Work_Life_Balance group.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16754,"cached_input_tokens":12032,"output_tokens":426,"reasoning_output_tokens":288}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..780519dbfe9ab78497e2f96cbc16c2e44860fb9c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"Work_Life_Balance\",\n AVG(CASE WHEN \"Home_Office_Quality\" = 'Good' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m1\"\nGROUP BY \"Work_Life_Balance\"\nORDER BY \"condition_rate\" DESC;","notes":"Uses the Grouped Condition Rate template with group_col=\"Work_Life_Balance\" and condition_col=\"Home_Office_Quality\", measuring the rate of rows where Home_Office_Quality is 'Good' within each Work_Life_Balance group."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_87ce556639d1b4eb/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e3ad9b26cb2d6e0451f782fd3ff68a12f025e749 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16771, "bytes_utf8": 16771, "lines": 458, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 722, "bytes_utf8": 722, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16808, "cached_input_tokens": 12032, "output_tokens": 513, "reasoning_output_tokens": 325}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..aeba0fd7cfd37f465b3486d97ec5e24097e8989d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_8938c70caedfbf2c", + "api_calls": 0, + "input_tokens": 16808, + "cached_input_tokens": 12032, + "output_tokens": 513, + "total_tokens": 17321, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11221.26, + "sql_execution_elapsed_ms_total": 2.33, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..89501b87c35a14bf8c83ae20e52f2e1d6b0d044b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:38:58.104397+00:00", + "ended_at": "2026-05-19T15:39:09.325686+00:00", + "elapsed_ms": 11221.26, + "prompt_metrics": { + "chars": 16771, + "bytes_utf8": 16771, + "lines": 458, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1102, + "bytes_utf8": 1102, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 722, + "bytes_utf8": 722, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16808, + "cached_input_tokens": 12032, + "output_tokens": 513, + "reasoning_output_tokens": 325 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c17dfb44be869792ad580c0f53af987c96a1d699 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,458 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Response_Quality, measure_col=Task_Completion_Rate.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "Response_Quality", + "measure_col": "Task_Completion_Rate", + "item_col": "Job_Satisfaction", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e0df0917fa4cf9a9f4475634aca9be84dc3ac2b1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e3-e7c4-79d0-a09c-0af96a49b54f"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"Response_Quality\\\", \\\"Job_Satisfaction\\\",\\n SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL))) OVER (PARTITION BY \\\"Response_Quality\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Response_Quality\\\", \\\"Job_Satisfaction\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the provided Within-Group Share of Total template with group_col=\\\"Response_Quality\\\", item_col=\\\"Job_Satisfaction\\\", and measure_col=\\\"Task_Completion_Rate\\\". CAST to REAL is included because the schema stores numeric-looking fields as TEXT in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16808,"cached_input_tokens":12032,"output_tokens":513,"reasoning_output_tokens":325}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..3775b1d4871b1398f0a7b08ecdd5bd5ef90c8bf7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"Response_Quality\", \"Job_Satisfaction\",\n SUM(CAST(\"Task_Completion_Rate\" AS REAL)) AS total_measure,\n SUM(CAST(\"Task_Completion_Rate\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"Task_Completion_Rate\" AS REAL))) OVER (PARTITION BY \"Response_Quality\") AS share_within_group\nFROM \"m1\"\nGROUP BY \"Response_Quality\", \"Job_Satisfaction\"\nORDER BY share_within_group DESC;","notes":"Applied the provided Within-Group Share of Total template with group_col=\"Response_Quality\", item_col=\"Job_Satisfaction\", and measure_col=\"Task_Completion_Rate\". CAST to REAL is included because the schema stores numeric-looking fields as TEXT in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8938c70caedfbf2c/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..884acff17c89f0ea674f5ae9984537a52fdacd3b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16529, "bytes_utf8": 16529, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 16529, "bytes_utf8": 16529, "lines": 456, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 1014, "bytes_utf8": 1014, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16729, "cached_input_tokens": 15744, "output_tokens": 2227, "reasoning_output_tokens": 1933}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..781c3c0fa5db5f3d69f20a5aaa773ac73f695ec6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_8b5292ded8624754", + "api_calls": 0, + "input_tokens": 16729, + "cached_input_tokens": 15744, + "output_tokens": 2227, + "total_tokens": 18956, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 35407.12, + "sql_execution_elapsed_ms_total": 5.26, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0f549841cc8ec3935e5e2a61bc71fa46e7331aae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:55:27.831084+00:00", + "ended_at": "2026-05-19T15:55:30.708064+00:00", + "elapsed_ms": 2876.96, + "returncode": 1, + "prompt_metrics": { + "chars": 16529, + "bytes_utf8": 16529, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3af847d0f99c61c06bfd28b0be1fbf4916202844 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:55:31.709743+00:00", + "ended_at": "2026-05-19T15:56:04.239941+00:00", + "elapsed_ms": 32530.16, + "prompt_metrics": { + "chars": 16529, + "bytes_utf8": 16529, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1455, + "bytes_utf8": 1455, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1014, + "bytes_utf8": 1014, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16729, + "cached_input_tokens": 15744, + "output_tokens": 2227, + "reasoning_output_tokens": 1933 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..fe1623e66bf22905a0355970af68b12412a87ed4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Marital_Status, measure_col=Work_Hours_Per_Week.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Marital_Status", + "measure_col": "Work_Hours_Per_Week", + "top_k": 12, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 46.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..fe1623e66bf22905a0355970af68b12412a87ed4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Marital_Status, measure_col=Work_Hours_Per_Week.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Marital_Status", + "measure_col": "Work_Hours_Per_Week", + "top_k": 12, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 46.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..7e819aa46f61794a894bbac67a02f9b5566b9519 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f3-01df-7431-bd12-cda30688cec7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..17ad7d291f0c8d2c8f0192c9a225604e4a1dae8e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f3-01df-7431-bd12-cda30688cec7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..2e0e0ade21cec8f7089e7e91e1228feaa016e1f6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f3-1105-7a53-af5c-d687b63c3936"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"Marital_Status\\\",\\n CAST(\\\"Work_Hours_Per_Week\\\" AS REAL) AS \\\"measure_value\\\",\\n CUME_DIST() OVER (\\n PARTITION BY \\\"Marital_Status\\\"\\n ORDER BY CAST(\\\"Work_Hours_Per_Week\\\" AS REAL)\\n ) AS \\\"cum_dist\\\",\\n COUNT(*) OVER (PARTITION BY \\\"Marital_Status\\\") AS \\\"group_size\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Marital_Status\\\" IS NOT NULL\\n AND \\\"Work_Hours_Per_Week\\\" IS NOT NULL\\n)\\nSELECT\\n \\\"Marital_Status\\\",\\n MIN(\\\"measure_value\\\") AS \\\"percentile_measure\\\"\\nFROM \\\"ranked\\\"\\nWHERE \\\"cum_dist\\\" >= 0.9\\n AND \\\"group_size\\\" >= 5\\nGROUP BY \\\"Marital_Status\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses CUME_DIST to return the nearest-rank 90th percentile of \\\"Work_Hours_Per_Week\\\" by \\\"Marital_Status\\\". \\\"Work_Hours_Per_Week\\\" is cast from TEXT to REAL, and groups smaller than 5 rows are excluded using the provided minimum group size.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16729,"cached_input_tokens":15744,"output_tokens":2227,"reasoning_output_tokens":1933}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..13767d490a2b9a79bbcaa80ff9fc221651d7b29f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n SELECT\n \"Marital_Status\",\n CAST(\"Work_Hours_Per_Week\" AS REAL) AS \"measure_value\",\n CUME_DIST() OVER (\n PARTITION BY \"Marital_Status\"\n ORDER BY CAST(\"Work_Hours_Per_Week\" AS REAL)\n ) AS \"cum_dist\",\n COUNT(*) OVER (PARTITION BY \"Marital_Status\") AS \"group_size\"\n FROM \"m1\"\n WHERE \"Marital_Status\" IS NOT NULL\n AND \"Work_Hours_Per_Week\" IS NOT NULL\n)\nSELECT\n \"Marital_Status\",\n MIN(\"measure_value\") AS \"percentile_measure\"\nFROM \"ranked\"\nWHERE \"cum_dist\" >= 0.9\n AND \"group_size\" >= 5\nGROUP BY \"Marital_Status\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses CUME_DIST to return the nearest-rank 90th percentile of \"Work_Hours_Per_Week\" by \"Marital_Status\". \"Work_Hours_Per_Week\" is cast from TEXT to REAL, and groups smaller than 5 rows are excluded using the provided minimum group size."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8b5292ded8624754/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..d0a21a6caf31dd2bf4d0d79d125411af980e0ac2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Commute_Time_Minutes. +Result preview: [{"Commute_Time_Minutes": 104.0}, {"Commute_Time_Minutes": 101.0}, {"Commute_Time_Minutes": 99.0}, {"Commute_Time_Minutes": 97.0}, {"Commute_Time_Minutes": 94.0}] Results were truncated. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..e59ac069b2aa733549f5e2c55560e45ece376ad3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/generated_sql.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_m1_8ed1fe529e96b24b +-- problem_id: v2p_m1_b4a857dd469d63db +-- realization_mode: agent +-- source_kind: agent +WITH "buckets" AS ( + SELECT + CAST("Commute_Time_Minutes" AS REAL) AS "Commute_Time_Minutes", + NTILE(10) OVER (ORDER BY CAST("Commute_Time_Minutes" AS REAL) DESC) AS "tail_bucket" + FROM "m1" + WHERE "Commute_Time_Minutes" IS NOT NULL + AND TRIM("Commute_Time_Minutes") <> '' +) +SELECT "Commute_Time_Minutes" +FROM "buckets" +WHERE "tail_bucket" = 1 +ORDER BY "Commute_Time_Minutes" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..25b32272eb5e7c54640a1b5c1d80ba91e31a61f2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n SELECT\n CAST(\"Commute_Time_Minutes\" AS REAL) AS \"Commute_Time_Minutes\",\n NTILE(10) OVER (ORDER BY CAST(\"Commute_Time_Minutes\" AS REAL) DESC) AS \"tail_bucket\"\n FROM \"m1\"\n WHERE \"Commute_Time_Minutes\" IS NOT NULL\n AND TRIM(\"Commute_Time_Minutes\") <> ''\n)\nSELECT \"Commute_Time_Minutes\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY \"Commute_Time_Minutes\" DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT\\n CAST(\\\"Commute_Time_Minutes\\\" AS REAL) AS \\\"Commute_Time_Minutes\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"Commute_Time_Minutes\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Commute_Time_Minutes\\\" IS NOT NULL\\n AND TRIM(\\\"Commute_Time_Minutes\\\") <> ''\\n)\\nSELECT \\\"Commute_Time_Minutes\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"Commute_Time_Minutes\\\" DESC;\", \"columns\": [\"Commute_Time_Minutes\"], \"rows\": [{\"Commute_Time_Minutes\": 104.0}, {\"Commute_Time_Minutes\": 101.0}, {\"Commute_Time_Minutes\": 99.0}, {\"Commute_Time_Minutes\": 97.0}, {\"Commute_Time_Minutes\": 94.0}, {\"Commute_Time_Minutes\": 90.0}, {\"Commute_Time_Minutes\": 88.0}, {\"Commute_Time_Minutes\": 81.0}, {\"Commute_Time_Minutes\": 80.0}, {\"Commute_Time_Minutes\": 80.0}, {\"Commute_Time_Minutes\": 79.0}, {\"Commute_Time_Minutes\": 79.0}, {\"Commute_Time_Minutes\": 79.0}, {\"Commute_Time_Minutes\": 79.0}, {\"Commute_Time_Minutes\": 77.0}, {\"Commute_Time_Minutes\": 76.0}, {\"Commute_Time_Minutes\": 76.0}, {\"Commute_Time_Minutes\": 76.0}, {\"Commute_Time_Minutes\": 75.0}, {\"Commute_Time_Minutes\": 74.0}, {\"Commute_Time_Minutes\": 73.0}, {\"Commute_Time_Minutes\": 73.0}, {\"Commute_Time_Minutes\": 73.0}, {\"Commute_Time_Minutes\": 73.0}, {\"Commute_Time_Minutes\": 72.0}, {\"Commute_Time_Minutes\": 72.0}, {\"Commute_Time_Minutes\": 72.0}, {\"Commute_Time_Minutes\": 71.0}, {\"Commute_Time_Minutes\": 71.0}, {\"Commute_Time_Minutes\": 71.0}, {\"Commute_Time_Minutes\": 69.0}, {\"Commute_Time_Minutes\": 68.0}, {\"Commute_Time_Minutes\": 68.0}, {\"Commute_Time_Minutes\": 68.0}, {\"Commute_Time_Minutes\": 68.0}, {\"Commute_Time_Minutes\": 67.0}, {\"Commute_Time_Minutes\": 66.0}, {\"Commute_Time_Minutes\": 65.0}, {\"Commute_Time_Minutes\": 65.0}, {\"Commute_Time_Minutes\": 65.0}, {\"Commute_Time_Minutes\": 65.0}, {\"Commute_Time_Minutes\": 65.0}, {\"Commute_Time_Minutes\": 65.0}, {\"Commute_Time_Minutes\": 64.0}, {\"Commute_Time_Minutes\": 64.0}, {\"Commute_Time_Minutes\": 64.0}, {\"Commute_Time_Minutes\": 64.0}, {\"Commute_Time_Minutes\": 63.0}, {\"Commute_Time_Minutes\": 63.0}, {\"Commute_Time_Minutes\": 63.0}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 2.76}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b7d1dce68cfc88d8c3f3987fa4877579c0c2a366 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/run_manifest.json @@ -0,0 +1,87 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:46:13.279328+00:00", + "ended_at": "2026-05-19T15:46:27.091081+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_8ed1fe529e96b24b", + "problem_id": "v2p_m1_b4a857dd469d63db", + "dataset_id": "m1", + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_set_consistency", + "intended_facet_id": "low_support_extremes", + "variant_semantic_role": "rare_extreme_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Commute_Time_Minutes.", + "bindings": { + "measure_col": "Commute_Time_Minutes", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "notes": [ + "default_facets=low_support_extremes", + "template_selection_mode=rule", + "problem_index_within_template=7", + "sql_variant_index=1/1", + "binding_index=66" + ], + "template_selection_mode": "rule", + "selected_template_rank": 6, + "problem_index_within_template": 7, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_8ed1fe529e96b24b.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_8ed1fe529e96b24b", + "api_calls": 0, + "input_tokens": 16740, + "cached_input_tokens": 12032, + "output_tokens": 595, + "total_tokens": 17335, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13804.51, + "sql_execution_elapsed_ms_total": 2.76, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..13218d653261e62d4f787b9d8d2674f53b647310 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:46:27.086855+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 13804.51, "started_at": "2026-05-19T15:46:13.281422+00:00", "ended_at": "2026-05-19T15:46:27.085966+00:00", "prompt_metrics": {"chars": 16513, "bytes_utf8": 16513, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 680, "bytes_utf8": 680, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16740, "cached_input_tokens": 12032, "output_tokens": 595, "reasoning_output_tokens": 400}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT\\n CAST(\\\"Commute_Time_Minutes\\\" AS REAL) AS \\\"Commute_Time_Minutes\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"Commute_Time_Minutes\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Commute_Time_Minutes\\\" IS NOT NULL\\n AND TRIM(\\\"Commute_Time_Minutes\\\") <> ''\\n)\\nSELECT \\\"Commute_Time_Minutes\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"Commute_Time_Minutes\\\" DESC;\",\"notes\":\"Applied the Quantile Tail Slice template with measure_col bound to \\\"Commute_Time_Minutes\\\" and num_tiles=10. Cast to REAL because the schema stores numeric fields as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..a8e208956c8a47d75538abcd924ee0c813394ee3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_8ed1fe529e96b24b", + "api_calls": 0, + "input_tokens": 16740, + "cached_input_tokens": 12032, + "output_tokens": 595, + "total_tokens": 17335, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13804.51, + "sql_execution_elapsed_ms_total": 2.76, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_8ed1fe529e96b24b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..986f9ddecd0aa7dbe5c68a4f3d4ea87753cec0d2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16504, "bytes_utf8": 16504, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 542, "bytes_utf8": 542, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16736, "cached_input_tokens": 15744, "output_tokens": 511, "reasoning_output_tokens": 355}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8c196d022338211c27b3bb236aed056e2435dad3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_98563080c9a3cf68", + "api_calls": 0, + "input_tokens": 16736, + "cached_input_tokens": 15744, + "output_tokens": 511, + "total_tokens": 17247, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13576.66, + "sql_execution_elapsed_ms_total": 4.94, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..94e51fad3d98d8629308eff80df6bd9f01f0ebc6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:45:24.404946+00:00", + "ended_at": "2026-05-19T15:45:37.981638+00:00", + "elapsed_ms": 13576.66, + "prompt_metrics": { + "chars": 16504, + "bytes_utf8": 16504, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 923, + "bytes_utf8": 923, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 542, + "bytes_utf8": 542, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16736, + "cached_input_tokens": 15744, + "output_tokens": 511, + "reasoning_output_tokens": 355 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a2c2cb28dc47de688d9286b20173e415419789b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Innovation_Score.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "Innovation_Score", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 84.2, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5469c705a2286041aed34e07e64d92873acb4761 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e9-ccc3-7e83-acb8-6aec90ec8c1f"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT\\n \\\"Innovation_Score\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"Innovation_Score\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Innovation_Score\\\" IS NOT NULL\\n)\\nSELECT \\\"Innovation_Score\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY CAST(\\\"Innovation_Score\\\" AS REAL) DESC;\",\"notes\":\"Applies the Quantile Tail Slice template to \\\"Innovation_Score\\\", casting from TEXT to REAL so the top decile is computed numerically in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16736,"cached_input_tokens":15744,"output_tokens":511,"reasoning_output_tokens":355}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0684e5dc8e4bd46e912f4cf9ba7738624c43fe5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n SELECT\n \"Innovation_Score\",\n NTILE(10) OVER (ORDER BY CAST(\"Innovation_Score\" AS REAL) DESC) AS \"tail_bucket\"\n FROM \"m1\"\n WHERE \"Innovation_Score\" IS NOT NULL\n)\nSELECT \"Innovation_Score\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY CAST(\"Innovation_Score\" AS REAL) DESC;","notes":"Applies the Quantile Tail Slice template to \"Innovation_Score\", casting from TEXT to REAL so the top decile is computed numerically in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_98563080c9a3cf68/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9d795e59b326498f/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9d795e59b326498f/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..db411b65d8b662c059f8dd5bcb02c3bc581fb907 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9d795e59b326498f/run_manifest.json @@ -0,0 +1,69 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:10:22.786853+00:00", + "ended_at": "2026-05-19T16:10:31.702797+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_9d795e59b326498f", + "problem_id": "v2p_m1_e75de2be66b3cfdc", + "dataset_id": "m1", + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "slice_level_consistency", + "intended_facet_id": "conditional_interaction_hotspots", + "variant_semantic_role": "ranked_signal_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=Gender, measure_col=Commute_Time_Minutes.", + "bindings": { + "group_col": "Gender", + "measure_col": "Commute_Time_Minutes", + "top_k": 16, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 33.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "notes": [ + "default_facets=conditional_interaction_hotspots", + "template_selection_mode=rule", + "problem_index_within_template=5", + "sql_variant_index=2/2", + "binding_index=136" + ], + "template_selection_mode": "rule", + "selected_template_rank": 12, + "problem_index_within_template": 5, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9d795e59b326498f/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9d795e59b326498f/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9f11c2af5f31a83027066a3d60b4bacd884b09b2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9d795e59b326498f/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:10:27.653964+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 4864.0, "started_at": "2026-05-19T16:10:22.789116+00:00", "ended_at": "2026-05-19T16:10:27.653151+00:00", "prompt_metrics": {"chars": 16423, "bytes_utf8": 16423, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-a9d8-7be3-9534-67e1d9eacd52\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:10:31.702708+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3046.11, "started_at": "2026-05-19T16:10:28.655757+00:00", "ended_at": "2026-05-19T16:10:31.701906+00:00", "prompt_metrics": {"chars": 16423, "bytes_utf8": 16423, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-c0d0-77a3-b454-37a60efb6236\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..29b8a39edfb68a8de7da829a841fbb549bd538cc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:52.697582+00:00", + "ended_at": "2026-05-19T16:06:55.514578+00:00", + "elapsed_ms": 2816.97, + "returncode": 1, + "prompt_metrics": { + "chars": 16275, + "bytes_utf8": 16275, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f82ecd983e7132dc9e926fdb4575f09a3cc2551c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:56.517121+00:00", + "ended_at": "2026-05-19T16:06:59.842472+00:00", + "elapsed_ms": 3325.3, + "returncode": 1, + "prompt_metrics": { + "chars": 16275, + "bytes_utf8": 16275, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5e46e6e6e08e78a8bd6340682a297ea53195a11c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Productivity_Score.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "Productivity_Score", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 98.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..5e46e6e6e08e78a8bd6340682a297ea53195a11c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Productivity_Score.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "Productivity_Score", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 98.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..1ac9d8546af17d45f1677737ce33b2292f590af8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-7532-79c3-b85b-cfdcc22273c6"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..31e84b9fcdf2251943181da500111a20a44c37d6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-7532-79c3-b85b-cfdcc22273c6"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..84d109e745ae65c6b55f3b1e07e1756caef8d180 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-841e-7cb0-9859-9d855d7bf85e"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..decebe8f3b3fd442918701b337ab89ce659f8f58 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-841e-7cb0-9859-9d855d7bf85e"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0b72ee5c5cb3e5/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..39679f2a82a66e9b97cd424f8c0a0ce4563490fa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:50.045480+00:00", + "ended_at": "2026-05-19T16:07:53.446541+00:00", + "elapsed_ms": 3401.03, + "returncode": 1, + "prompt_metrics": { + "chars": 16318, + "bytes_utf8": 16318, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..4a3dddd5b145616f2df5cde1841c65e42e7f5768 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:54.449152+00:00", + "ended_at": "2026-05-19T16:07:57.398223+00:00", + "elapsed_ms": 2949.03, + "returncode": 1, + "prompt_metrics": { + "chars": 16318, + "bytes_utf8": 16318, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7b2000808c7cfe0bd089e9b441bd4b91003c8b68 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Location_Type.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Location_Type", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..7b2000808c7cfe0bd089e9b441bd4b91003c8b68 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Location_Type.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Location_Type", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..2303c0454b3a50f4e3e0f74a2366b3da999d84f2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-5519-7283-b848-ad08deb518a7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0f76477e7f76126772fc9ef18fcb43e694147ff7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-5519-7283-b848-ad08deb518a7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..c963f71cf941c8afa629d3e6694289a4a4e15cd6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-665e-7f42-8a72-1ff8e10a9f7a"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..550c8f5c49279f71e15a4f4f572f59464c08050d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-665e-7f42-8a72-1ff8e10a9f7a"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_9e0c2505f500d820/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..69ec02828ae9aeb6083f1d03b565d2345a6811ae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:41.088044+00:00", + "ended_at": "2026-05-19T16:08:44.499093+00:00", + "elapsed_ms": 3411.02, + "returncode": 1, + "prompt_metrics": { + "chars": 16317, + "bytes_utf8": 16317, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e6ef18b9b3cecea160eee66b1f0971f236d4faf5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:45.501927+00:00", + "ended_at": "2026-05-19T16:08:48.352360+00:00", + "elapsed_ms": 2850.39, + "returncode": 1, + "prompt_metrics": { + "chars": 16317, + "bytes_utf8": 16317, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b3001e20b3d718dab80ea836053e983b149b9bd3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=Company_Size.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Company_Size", + "top_k": 16, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 41.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..b3001e20b3d718dab80ea836053e983b149b9bd3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=Company_Size.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Company_Size", + "top_k": 16, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 41.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..cb71c4f003639bee8e415146d4fbedd1869a119f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-1c9f-7e01-af0c-2209bef466be"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9773ed953b442eef38193b866e596573dca2c089 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-1c9f-7e01-af0c-2209bef466be"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..2502fc80fbf6dbc504f81c21894e71082a7d9221 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-2dbc-7d71-8ce4-089dcd569edd"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..2bb81d7127fe3959cf93539bb21437249dbf0e94 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-2dbc-7d71-8ce4-089dcd569edd"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a2df9b1f89c21ffe/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..32c2ae04278952e177cab3549b1e971aff19c290 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Job_Satisfaction.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "Job_Satisfaction", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..21f9d581b1c58c2662d1177cae6d837254d77553 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-aabc-72d0-bdd0-b75c5eed0cd3"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..b720d7b8a037412e71ebaf0c751af6179f5e03eb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-bac6-7d52-9ff0-cac53cfef1eb"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..830bc924dae77059b371ddee167556eefc622fed --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/run_manifest.json @@ -0,0 +1,67 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:06:00.794689+00:00", + "ended_at": "2026-05-19T16:06:08.060634+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_a3405e74ec685e5f", + "problem_id": "v2p_m1_6f389c1c755b2b71", + "dataset_id": "m1", + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_set_consistency", + "intended_facet_id": "low_support_extremes", + "variant_semantic_role": "rare_extreme_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Job_Satisfaction.", + "bindings": { + "measure_col": "Job_Satisfaction", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "notes": [ + "default_facets=low_support_extremes", + "template_selection_mode=rule", + "problem_index_within_template=2", + "sql_variant_index=1/1", + "binding_index=109" + ], + "template_selection_mode": "rule", + "selected_template_rank": 10, + "problem_index_within_template": 2, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..19740c8866d5e8350315dae66b63e37a44cc427a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a3405e74ec685e5f/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:06:03.963474+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3166.06, "started_at": "2026-05-19T16:06:00.796573+00:00", "ended_at": "2026-05-19T16:06:03.962662+00:00", "prompt_metrics": {"chars": 16271, "bytes_utf8": 16271, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fc-aabc-72d0-bdd0-b75c5eed0cd3\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:06:08.060531+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3095.1, "started_at": "2026-05-19T16:06:04.964535+00:00", "ended_at": "2026-05-19T16:06:08.059668+00:00", "prompt_metrics": {"chars": 16271, "bytes_utf8": 16271, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fc-bac6-7d52-9ff0-cac53cfef1eb\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d737a266685e31924c718f4289816d46ec7a3105 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16923, "bytes_utf8": 16923, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 744, "bytes_utf8": 744, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16825, "cached_input_tokens": 15744, "output_tokens": 341, "reasoning_output_tokens": 142}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..296a743b02e6493f3b2afbbc216d8cd93f49848a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_a46a36a7def755f1", + "api_calls": 0, + "input_tokens": 16825, + "cached_input_tokens": 15744, + "output_tokens": 341, + "total_tokens": 17166, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8775.19, + "sql_execution_elapsed_ms_total": 1.5, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f9f852ed98341444a750651ff29e728f13045575 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:49:55.303585+00:00", + "ended_at": "2026-05-19T15:50:04.078808+00:00", + "elapsed_ms": 8775.19, + "prompt_metrics": { + "chars": 16923, + "bytes_utf8": 16923, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1151, + "bytes_utf8": 1151, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 744, + "bytes_utf8": 744, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16825, + "cached_input_tokens": 15744, + "output_tokens": 341, + "reasoning_output_tokens": 142 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f511cd6b22025c7c79cd2e4aebb339116e9c0d5e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Internet_Speed_Category, measure_col=Efficiency_Rating.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "Internet_Speed_Category", + "measure_col": "Efficiency_Rating", + "top_k": 18, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a199c8c130fb97e3ce2f4191e01c01713fc13225 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ed-ef09-7ad0-a689-11b42893d766"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"Internet_Speed_Category\\\", SUM(CAST(\\\"Efficiency_Rating\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Internet_Speed_Category\\\"\\n), \\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"Internet_Speed_Category\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Applied the provided relative-to-total threshold template with group_col=Internet_Speed_Category and measure_col=Efficiency_Rating. CAST to REAL is used because the schema stores numeric values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16825,"cached_input_tokens":15744,"output_tokens":341,"reasoning_output_tokens":142}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1cfb485a0a7e69adff2b7bc54fb305056cd6160d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT \"Internet_Speed_Category\", SUM(CAST(\"Efficiency_Rating\" AS REAL)) AS \"group_value\"\n FROM \"m1\"\n GROUP BY \"Internet_Speed_Category\"\n), \"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT g.\"Internet_Speed_Category\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;","notes":"Applied the provided relative-to-total threshold template with group_col=Internet_Speed_Category and measure_col=Efficiency_Rating. CAST to REAL is used because the schema stores numeric values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a46a36a7def755f1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9ed8ef6e06bf8cce286f0028b9319ca245b1a314 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:48.355105+00:00", + "ended_at": "2026-05-19T16:08:51.599817+00:00", + "elapsed_ms": 3244.69, + "returncode": 1, + "prompt_metrics": { + "chars": 16306, + "bytes_utf8": 16306, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2e888439cf55a7009e824405fa457c5658fb882b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:52.601643+00:00", + "ended_at": "2026-05-19T16:08:55.912173+00:00", + "elapsed_ms": 3310.5, + "returncode": 1, + "prompt_metrics": { + "chars": 16306, + "bytes_utf8": 16306, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5c97d2d88d0f2bfd5ef5f8ad4b26bceb585484b9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Industry.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Industry", + "top_k": 12, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..5c97d2d88d0f2bfd5ef5f8ad4b26bceb585484b9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Industry.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Industry", + "top_k": 12, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ba58df50be3fd34b0061c69a973eb1fcf429cce --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-38dc-7230-bc3d-08a30f092866"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d0a6dc2f3378030443274f2bee0a46a0703d1834 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-38dc-7230-bc3d-08a30f092866"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..068150d0b627d60703709274e77d1bc7399a324f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-497f-7340-aec3-b130766228ab"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c403724a53a4ea42ca83f3fbc06e25b02da8c048 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-497f-7340-aec3-b130766228ab"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a5e7d46f13575f17/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4d9f6bcf7b7898e042f9f40c5602a7d12ef2579b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16331, "bytes_utf8": 16331, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 404, "bytes_utf8": 404, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16683, "cached_input_tokens": 12032, "output_tokens": 394, "reasoning_output_tokens": 285}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..ff43577e49084d9f7c2d83692be79e8c9643ccc2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_a7f3a45c099a8422", + "api_calls": 0, + "input_tokens": 16683, + "cached_input_tokens": 12032, + "output_tokens": 394, + "total_tokens": 17077, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13809.14, + "sql_execution_elapsed_ms_total": 1.11, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8738d9f755d3595af59bb1dcd1de4dceab29c212 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:30:45.452251+00:00", + "ended_at": "2026-05-19T15:30:59.261410+00:00", + "elapsed_ms": 13809.14, + "prompt_metrics": { + "chars": 16331, + "bytes_utf8": 16331, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 768, + "bytes_utf8": 768, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 404, + "bytes_utf8": 404, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16683, + "cached_input_tokens": 12032, + "output_tokens": 394, + "reasoning_output_tokens": 285 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ad9a08626a554e6ebf24af657bf25c30486a3323 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Job_Level, measure_col=Quality_Score.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "Job_Level", + "measure_col": "Quality_Score", + "top_k": 16, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 93.6, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..cbc73fbebd560fdea573bc7e6a7506dc9ea5af42 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dc-6357-7653-bb50-88d3b40210b3"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT\\n \\\"Job_Level\\\",\\n SUM(CAST(\\\"Quality_Score\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Job_Level\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the planned grouped numeric sum template with group_col=\\\"Job_Level\\\" and measure_col=\\\"Quality_Score\\\". CAST to REAL is included because the schema stores numeric-looking fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16683,"cached_input_tokens":12032,"output_tokens":394,"reasoning_output_tokens":285}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e2ef1bf44d43498e1b126d2695d96ed5002498cf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT\n \"Job_Level\",\n SUM(CAST(\"Quality_Score\" AS REAL)) AS \"total_measure\"\nFROM \"m1\"\nGROUP BY \"Job_Level\"\nORDER BY \"total_measure\" DESC;","notes":"Used the planned grouped numeric sum template with group_col=\"Job_Level\" and measure_col=\"Quality_Score\". CAST to REAL is included because the schema stores numeric-looking fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_a7f3a45c099a8422/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..013f3654e823ff92a0d2d8afe59d3769e0edf824 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 17153, "bytes_utf8": 17153, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 715, "bytes_utf8": 715, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16892, "cached_input_tokens": 15744, "output_tokens": 705, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..613496ec9806f1c080271aa069c9600ca093c647 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_aa25f1891be005fe", + "api_calls": 0, + "input_tokens": 16892, + "cached_input_tokens": 15744, + "output_tokens": 705, + "total_tokens": 17597, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14483.48, + "sql_execution_elapsed_ms_total": 2.72, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ffe1a587be35ac0c8e4e2bd361cff1691e8cf323 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:41:58.191594+00:00", + "ended_at": "2026-05-19T15:42:12.675124+00:00", + "elapsed_ms": 14483.48, + "prompt_metrics": { + "chars": 17153, + "bytes_utf8": 17153, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1100, + "bytes_utf8": 1100, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 715, + "bytes_utf8": 715, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16892, + "cached_input_tokens": 15744, + "output_tokens": 705, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0ccb99db6e3b40538fe5208b5c25057b2d47ec1a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=Industry, condition_col=Marital_Status.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "Industry", + "condition_col": "Marital_Status", + "condition_value": "Married", + "positive_value": "Married", + "negative_value": "Single", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 41.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa5c4082980dda147dcf919716de15caaf015e9c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e6-a741-74d2-b39f-f2e6e650b513"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"Industry\\\",\\n SUM(CASE WHEN \\\"Marital_Status\\\" = 'Married' THEN 1 ELSE 0 END) AS \\\"numerator_count\\\",\\n SUM(CASE WHEN \\\"Marital_Status\\\" = 'Single' THEN 1 ELSE 0 END) AS \\\"denominator_count\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Industry\\\"\\n)\\nSELECT \\\"Industry\\\",\\n CAST(\\\"numerator_count\\\" AS FLOAT) / NULLIF(\\\"denominator_count\\\", 0) AS \\\"condition_ratio\\\"\\nFROM grouped\\nORDER BY \\\"condition_ratio\\\" DESC;\",\"notes\":\"Computes, for each industry, the ratio of Married to Single employees based on the provided template. Industries with no Single records return NULL for the ratio because of NULLIF.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16892,"cached_input_tokens":15744,"output_tokens":705,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e4f129dc9a6aa455800d85c46e4572fc61589811 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"Industry\",\n SUM(CASE WHEN \"Marital_Status\" = 'Married' THEN 1 ELSE 0 END) AS \"numerator_count\",\n SUM(CASE WHEN \"Marital_Status\" = 'Single' THEN 1 ELSE 0 END) AS \"denominator_count\"\n FROM \"m1\"\n GROUP BY \"Industry\"\n)\nSELECT \"Industry\",\n CAST(\"numerator_count\" AS FLOAT) / NULLIF(\"denominator_count\", 0) AS \"condition_ratio\"\nFROM grouped\nORDER BY \"condition_ratio\" DESC;","notes":"Computes, for each industry, the ratio of Married to Single employees based on the provided template. Industries with no Single records return NULL for the ratio because of NULLIF."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aa25f1891be005fe/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d30a67ecf630157815ae13f572ce53281abafd9f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16765, "bytes_utf8": 16765, "lines": 458, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 687, "bytes_utf8": 687, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16809, "cached_input_tokens": 15744, "output_tokens": 608, "reasoning_output_tokens": 416}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d96277037d605c7e190fa9e08ca2be172056ec61 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_aadc3c1faafeb752", + "api_calls": 0, + "input_tokens": 16809, + "cached_input_tokens": 15744, + "output_tokens": 608, + "total_tokens": 17417, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14854.33, + "sql_execution_elapsed_ms_total": 4.37, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..78f98111a540b5469a5186df95818b6610a6cd90 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:35:20.329483+00:00", + "ended_at": "2026-05-19T15:35:35.183850+00:00", + "elapsed_ms": 14854.33, + "prompt_metrics": { + "chars": 16765, + "bytes_utf8": 16765, + "lines": 458, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1071, + "bytes_utf8": 1071, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 687, + "bytes_utf8": 687, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16809, + "cached_input_tokens": 15744, + "output_tokens": 608, + "reasoning_output_tokens": 416 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0b9c4f1029a1b63aa0744d3d53271e69bde8ef92 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,458 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Home_Office_Quality, measure_col=Stress_Level.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "Home_Office_Quality", + "measure_col": "Stress_Level", + "item_col": "Work_Hours_Per_Week", + "top_k": 16, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..59b18d94df33ce3ff4d518f4da71db5e5b02e88d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e0-94f4-76c0-874c-03eb982769ba"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share.\\nSELECT \\\"Home_Office_Quality\\\", \\\"Work_Hours_Per_Week\\\",\\n SUM(CAST(\\\"Stress_Level\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Stress_Level\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Stress_Level\\\" AS REAL))) OVER (PARTITION BY \\\"Home_Office_Quality\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Home_Office_Quality\\\", \\\"Work_Hours_Per_Week\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the provided Within-Group Share of Total template with group_col=\\\"Home_Office_Quality\\\", item_col=\\\"Work_Hours_Per_Week\\\", and measure_col=\\\"Stress_Level\\\". Cast \\\"Stress_Level\\\" to REAL because the schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16809,"cached_input_tokens":15744,"output_tokens":608,"reasoning_output_tokens":416}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c59c2ed8a4064854db9fa27b6f3f1eda19692590 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share.\nSELECT \"Home_Office_Quality\", \"Work_Hours_Per_Week\",\n SUM(CAST(\"Stress_Level\" AS REAL)) AS total_measure,\n SUM(CAST(\"Stress_Level\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"Stress_Level\" AS REAL))) OVER (PARTITION BY \"Home_Office_Quality\") AS share_within_group\nFROM \"m1\"\nGROUP BY \"Home_Office_Quality\", \"Work_Hours_Per_Week\"\nORDER BY share_within_group DESC;","notes":"Applied the provided Within-Group Share of Total template with group_col=\"Home_Office_Quality\", item_col=\"Work_Hours_Per_Week\", and measure_col=\"Stress_Level\". Cast \"Stress_Level\" to REAL because the schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_aadc3c1faafeb752/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e830af4c2669d178f747a5503b01360d3b04dc82 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:10:38.979862+00:00", + "ended_at": "2026-05-19T16:10:41.840292+00:00", + "elapsed_ms": 2860.4, + "returncode": 1, + "prompt_metrics": { + "chars": 16435, + "bytes_utf8": 16435, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ce26ff97bd9e69b27fbafe5bd9630c13ae8a27f8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:10:42.842129+00:00", + "ended_at": "2026-05-19T16:10:45.566809+00:00", + "elapsed_ms": 2724.65, + "returncode": 1, + "prompt_metrics": { + "chars": 16435, + "bytes_utf8": 16435, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..61650d1a2f0e65279a59d0d8607d396a95937d1e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=Education_Level, measure_col=Job_Satisfaction.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Education_Level", + "measure_col": "Job_Satisfaction", + "top_k": 17, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..61650d1a2f0e65279a59d0d8607d396a95937d1e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=Education_Level, measure_col=Job_Satisfaction.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Education_Level", + "measure_col": "Job_Satisfaction", + "top_k": 17, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f033762075bfbe0929087102b0b30040c9cfa8ff --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4100-e902-71b1-ad39-67cb114bcb5d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. 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Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_acd5edd6276f7df4/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..8eac6ae5ae2921f58e23e86930daff6c37f5930f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Home_Office_Quality, measure_col=Innovation_Score. +Result preview: [{"Home_Office_Quality": "Good", "group_value": 43517.1}, {"Home_Office_Quality": "Average", "group_value": 32614.2}, {"Home_Office_Quality": "Excellent", "group_value": 25505.0}, {"Home_Office_Quality": "Poor", "group_value": 7596.2}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..26d706ceb1b022d5fc78e7e2796f06c81f2a2597 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/generated_sql.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_m1_ae154c66cfcfc8db +-- problem_id: v2p_m1_0700623b46bbf458 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "Home_Office_Quality", SUM(CAST("Innovation_Score" AS REAL)) AS group_value + FROM "m1" + GROUP BY "Home_Office_Quality" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."Home_Office_Quality", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.05 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5383a74936acb053f1e8a242b62582ca16349ef6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"Home_Office_Quality\", SUM(CAST(\"Innovation_Score\" AS REAL)) AS group_value\n FROM \"m1\"\n GROUP BY \"Home_Office_Quality\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"Home_Office_Quality\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Home_Office_Quality\\\", SUM(CAST(\\\"Innovation_Score\\\" AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Home_Office_Quality\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Home_Office_Quality\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\", \"columns\": [\"Home_Office_Quality\", \"group_value\"], \"rows\": [{\"Home_Office_Quality\": \"Good\", \"group_value\": 43517.1}, {\"Home_Office_Quality\": \"Average\", \"group_value\": 32614.2}, {\"Home_Office_Quality\": \"Excellent\", \"group_value\": 25505.0}, {\"Home_Office_Quality\": \"Poor\", \"group_value\": 7596.2}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.2}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..5c1ac6955a8a97f17b211ea67855cdd6bbdfd73a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:49:34.085928+00:00", + "ended_at": "2026-05-19T15:49:45.137113+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_ae154c66cfcfc8db", + "problem_id": "v2p_m1_0700623b46bbf458", + "dataset_id": "m1", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Home_Office_Quality, measure_col=Innovation_Score.", + "bindings": { + "group_col": "Home_Office_Quality", + "measure_col": "Innovation_Score", + "top_k": 17, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 80.9, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=6", + "sql_variant_index=2/2", + "binding_index=77" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 6, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_ae154c66cfcfc8db.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_ae154c66cfcfc8db", + "api_calls": 0, + "input_tokens": 16825, + "cached_input_tokens": 12032, + "output_tokens": 510, + "total_tokens": 17335, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11044.77, + "sql_execution_elapsed_ms_total": 1.2, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bd4cba445e699f013abc16248d7d4c76e8c0ac6b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:49:45.134567+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11044.77, "started_at": "2026-05-19T15:49:34.088819+00:00", "ended_at": "2026-05-19T15:49:45.133627+00:00", "prompt_metrics": {"chars": 16912, "bytes_utf8": 16912, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 701, "bytes_utf8": 701, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16825, "cached_input_tokens": 12032, "output_tokens": 510, "reasoning_output_tokens": 328}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Home_Office_Quality\\\", SUM(CAST(\\\"Innovation_Score\\\" AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Home_Office_Quality\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Home_Office_Quality\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the planned Relative-to-Total Extreme Threshold template with group_col=\\\"Home_Office_Quality\\\" and measure_col=\\\"Innovation_Score\\\". CAST to REAL is used because the schema stores numeric-looking values as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..b91dbda9a441d877beed9b7721f306c97b0bfd1f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_ae154c66cfcfc8db", + "api_calls": 0, + "input_tokens": 16825, + "cached_input_tokens": 12032, + "output_tokens": 510, + "total_tokens": 17335, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11044.77, + "sql_execution_elapsed_ms_total": 1.2, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae154c66cfcfc8db/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..59f59a466053dc17d7c6055f5e0a3bb52762596d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16267, "bytes_utf8": 16267, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 345, "bytes_utf8": 345, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16662, "cached_input_tokens": 15744, "output_tokens": 226, "reasoning_output_tokens": 133}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..0d79de3934ad142f4305c44730bbe7712ddbadc1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_ae904dc31842a947", + "api_calls": 0, + "input_tokens": 16662, + "cached_input_tokens": 15744, + "output_tokens": 226, + "total_tokens": 16888, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7348.76, + "sql_execution_elapsed_ms_total": 2.21, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f3e642fc40ae649506f3f3b94493aecdc3ee9424 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:32:02.437325+00:00", + "ended_at": "2026-05-19T15:32:09.786124+00:00", + "elapsed_ms": 7348.76, + "prompt_metrics": { + "chars": 16267, + "bytes_utf8": 16267, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 703, + "bytes_utf8": 703, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 345, + "bytes_utf8": 345, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16662, + "cached_input_tokens": 15744, + "output_tokens": 226, + "reasoning_output_tokens": 133 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ba629f404742b77f1724a0cabe687e477bdc1e7d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=Team_Collaboration_Frequency.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "Team_Collaboration_Frequency", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..9b17ef185def1314aa010e3cebe15874b0c16973 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dd-9015-7651-95c0-4d0223df8f87"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"Team_Collaboration_Frequency\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Team_Collaboration_Frequency\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Applied the provided grouped count template with group_col bound to \\\"Team_Collaboration_Frequency\\\" on the single table \\\"m1\\\".\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16662,"cached_input_tokens":15744,"output_tokens":226,"reasoning_output_tokens":133}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..491c5a495971977d60c8ea361c97b723a42e6895 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"Team_Collaboration_Frequency\", COUNT(*) AS \"row_count\"\nFROM \"m1\"\nGROUP BY \"Team_Collaboration_Frequency\"\nORDER BY \"row_count\" DESC;","notes":"Applied the provided grouped count template with group_col bound to \"Team_Collaboration_Frequency\" on the single table \"m1\"."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ae904dc31842a947/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c83e6146e94d52b24fad8c3b79461fa2a143fa14 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:09:03.858950+00:00", + "ended_at": "2026-05-19T16:09:07.002093+00:00", + "elapsed_ms": 3143.11, + "returncode": 1, + "prompt_metrics": { + "chars": 16424, + "bytes_utf8": 16424, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..603bd0c835136c5d388c653d1316b1d10e22c06c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:09:08.003968+00:00", + "ended_at": "2026-05-19T16:09:11.224890+00:00", + "elapsed_ms": 3220.89, + "returncode": 1, + "prompt_metrics": { + "chars": 16424, + "bytes_utf8": 16424, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a5198e0c0c5f43fa27ac79c21df84696c6987b37 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=Stress_Level, measure_col=Quality_Score.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Stress_Level", + "measure_col": "Quality_Score", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.225, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a5198e0c0c5f43fa27ac79c21df84696c6987b37 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=Stress_Level, measure_col=Quality_Score.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Stress_Level", + "measure_col": "Quality_Score", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.225, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..31fb725764fd4df6c742a3d460b90e9ce67cd462 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-758f-7db2-8c36-c34ce49448b4"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..58f83748ad3bd657b1c48ddc24fb6f3325a8165c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-758f-7db2-8c36-c34ce49448b4"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d408df9699fc8c69dba9a4389c1a51f54954164d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-85a1-7141-a103-5268677adee7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c0ea2416c1ea8832679ae40f38cda99f390c0c4a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-85a1-7141-a103-5268677adee7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b233ce118c77ef0b/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..edb007dad0805763c219d21ebdce0833f9c54180 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16348, "bytes_utf8": 16348, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 370, "bytes_utf8": 370, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16685, "cached_input_tokens": 12032, "output_tokens": 342, "reasoning_output_tokens": 245}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..03267b3fac7f073f7c41493e36a6dadeb93f34e8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_b2a704c3a211637e", + "api_calls": 0, + "input_tokens": 16685, + "cached_input_tokens": 12032, + "output_tokens": 342, + "total_tokens": 17027, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10054.43, + "sql_execution_elapsed_ms_total": 1.82, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2db8a799c27aa205e73bb9ee20daaabfff16787b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:30:15.212814+00:00", + "ended_at": "2026-05-19T15:30:25.267270+00:00", + "elapsed_ms": 10054.43, + "prompt_metrics": { + "chars": 16348, + "bytes_utf8": 16348, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 724, + "bytes_utf8": 724, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 370, + "bytes_utf8": 370, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16685, + "cached_input_tokens": 12032, + "output_tokens": 342, + "reasoning_output_tokens": 245 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..bbf46a56acae7c4f5888e5229a0a02bd714a292e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Department, measure_col=Task_Completion_Rate.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "Department", + "measure_col": "Task_Completion_Rate", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 92.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..c2be66d5d265f1f396191f928608a9bbc7ee5f35 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40db-ed49-78c1-891a-91f65cd6d689"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Department\\\", SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL)) AS total_measure\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Department\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Applied the Grouped Numeric Sum template with \\\"Department\\\" as the grouping column and cast \\\"Task_Completion_Rate\\\" to REAL because the schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16685,"cached_input_tokens":12032,"output_tokens":342,"reasoning_output_tokens":245}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..55196bc9597d1850383843528c1f75734a5b4cf3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"Department\", SUM(CAST(\"Task_Completion_Rate\" AS REAL)) AS total_measure\nFROM \"m1\"\nGROUP BY \"Department\"\nORDER BY total_measure DESC;","notes":"Applied the Grouped Numeric Sum template with \"Department\" as the grouping column and cast \"Task_Completion_Rate\" to REAL because the schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b2a704c3a211637e/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..554301777b31eb34eb28b6b43d10f3bb02d8e061 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=Education_Level, condition_col=Response_Quality. +Result preview: [{"Education_Level": "High School", "condition_rate": 0.7638888888888888}, {"Education_Level": "Master Degree", "condition_rate": 0.7137014314928425}, {"Education_Level": "Bachelor Degree", "condition_rate": 0.6998514115898959}, {"Education_Level": "Associate Degree", "condition_rate": 0.6862745098039216}, {"Education_Level": "Professional Degree", "condition_rate": 0.68}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..20b6a94147e4366d7412ba87d8d9b7d31ba55052 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/generated_sql.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_m1_b77815aeee05cd58 +-- problem_id: v2p_m1_db224dd3d8b4b4f8 +-- realization_mode: agent +-- source_kind: agent +SELECT "Education_Level", + AVG(CASE WHEN "Response_Quality" = 'High' THEN 1 ELSE 0 END) AS condition_rate +FROM "m1" +GROUP BY "Education_Level" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..45c2988b0c5724c3975cd07dbbfeb20598a99067 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT \"Education_Level\",\n AVG(CASE WHEN \"Response_Quality\" = 'High' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"m1\"\nGROUP BY \"Education_Level\"\nORDER BY condition_rate DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"Education_Level\\\",\\n AVG(CASE WHEN \\\"Response_Quality\\\" = 'High' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Education_Level\\\"\\nORDER BY condition_rate DESC;\", \"columns\": [\"Education_Level\", \"condition_rate\"], \"rows\": [{\"Education_Level\": \"High School\", \"condition_rate\": 0.7638888888888888}, {\"Education_Level\": \"Master Degree\", \"condition_rate\": 0.7137014314928425}, {\"Education_Level\": \"Bachelor Degree\", \"condition_rate\": 0.6998514115898959}, {\"Education_Level\": \"Associate Degree\", \"condition_rate\": 0.6862745098039216}, {\"Education_Level\": \"Professional Degree\", \"condition_rate\": 0.68}, {\"Education_Level\": \"PhD\", \"condition_rate\": 0.6363636363636364}], \"row_count_returned\": 6, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.19}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..d8a7ace542249a4c2b7b8d4186efb7e2b3585e97 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/run_manifest.json @@ -0,0 +1,92 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:05:12.056066+00:00", + "ended_at": "2026-05-19T16:05:23.997891+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_b77815aeee05cd58", + "problem_id": "v2p_m1_db224dd3d8b4b4f8", + "dataset_id": "m1", + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=Education_Level, condition_col=Response_Quality.", + "bindings": { + "group_col": "Education_Level", + "condition_col": "Response_Quality", + "condition_value": "High", + "positive_value": "High", + "negative_value": "Medium", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=8", + "sql_variant_index=1/2", + "binding_index=103" + ], + "template_selection_mode": "rule", + "selected_template_rank": 9, + "problem_index_within_template": 8, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_b77815aeee05cd58.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_b77815aeee05cd58", + "api_calls": 0, + "input_tokens": 16744, + "cached_input_tokens": 15744, + "output_tokens": 296, + "total_tokens": 17040, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11936.31, + "sql_execution_elapsed_ms_total": 1.19, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2e3a6c2f8e7c156115cf0a31e637557da7dbae5d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T16:05:23.994968+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11936.31, "started_at": "2026-05-19T16:05:12.057592+00:00", "ended_at": "2026-05-19T16:05:23.993934+00:00", "prompt_metrics": {"chars": 16595, "bytes_utf8": 16595, "lines": 459, "estimated_tokens": null}, "response_metrics": {"chars": 446, "bytes_utf8": 446, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16744, "cached_input_tokens": 15744, "output_tokens": 296, "reasoning_output_tokens": 185}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"Education_Level\\\",\\n AVG(CASE WHEN \\\"Response_Quality\\\" = 'High' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Education_Level\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Uses the Grouped Condition Rate template with group_col=\\\"Education_Level\\\" and condition_col=\\\"Response_Quality\\\", measuring the rate of 'High' response quality within each education level.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..2b86ba3a86a41bf3592ae989f4e9ffeefe6fbca9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_b77815aeee05cd58", + "api_calls": 0, + "input_tokens": 16744, + "cached_input_tokens": 15744, + "output_tokens": 296, + "total_tokens": 17040, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11936.31, + "sql_execution_elapsed_ms_total": 1.19, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_b77815aeee05cd58/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1278deffe90bc6ce6265fa1e07ca92167f580d4c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:04:41.322947+00:00", + "ended_at": "2026-05-19T16:04:47.078248+00:00", + "elapsed_ms": 5755.27, + "returncode": 1, + "prompt_metrics": { + "chars": 16616, + "bytes_utf8": 16616, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e08fa4ecec2e3aeef0d0276e5f6b59ea55544ddf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:04:48.080190+00:00", + "ended_at": "2026-05-19T16:04:54.273883+00:00", + "elapsed_ms": 6193.66, + "returncode": 1, + "prompt_metrics": { + "chars": 16616, + "bytes_utf8": 16616, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ae5dbf74d677287ba8560eb686da31ec76b3e384 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=Response_Quality, condition_col=Manager_Support_Level.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Response_Quality", + "condition_col": "Manager_Support_Level", + "condition_value": "High", + "positive_value": "Moderate", + "negative_value": "High", + "top_k": 16, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 46.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..ae5dbf74d677287ba8560eb686da31ec76b3e384 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=Response_Quality, condition_col=Manager_Support_Level.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Response_Quality", + "condition_col": "Manager_Support_Level", + "condition_value": "High", + "positive_value": "Moderate", + "negative_value": "High", + "top_k": 16, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 46.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..957ea72b01f1b67963f518abb1ca63abe3b13352 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-73fe-7242-a7d1-b30fa85a78b2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4bf6204c53b6e41a32cc33704b120f8e5ba9d6a9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-73fe-7242-a7d1-b30fa85a78b2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..781331997c51e2f49d293f8686ba6c91aba1596f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-8e46-7033-a835-facb2b88009e"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..44c63dde3b20cdeb0a074259eb7cb6de0bf8560c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-8e46-7033-a835-facb2b88009e"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bac2152358fff1e1/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d2b3731b1c0ba2890baf919b5ef573e0fc19d642 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16337, "bytes_utf8": 16337, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 403, "bytes_utf8": 403, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16681, "cached_input_tokens": 12032, "output_tokens": 245, "reasoning_output_tokens": 141}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7acc8d3d54b9c3b2c5ceaa225f0abeb1738dd51d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_bbde35724f878391", + "api_calls": 0, + "input_tokens": 16681, + "cached_input_tokens": 12032, + "output_tokens": 245, + "total_tokens": 16926, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11933.85, + "sql_execution_elapsed_ms_total": 1.24, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7823ab5140daa1f52353d6d67919ff24a1a87df5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:31:50.497855+00:00", + "ended_at": "2026-05-19T15:32:02.431740+00:00", + "elapsed_ms": 11933.85, + "prompt_metrics": { + "chars": 16337, + "bytes_utf8": 16337, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 769, + "bytes_utf8": 769, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 403, + "bytes_utf8": 403, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16681, + "cached_input_tokens": 12032, + "output_tokens": 245, + "reasoning_output_tokens": 141 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c80077fbf16bcebe3d793741f90c806c2ed9e65c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Industry, measure_col=Efficiency_Rating.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "Industry", + "measure_col": "Efficiency_Rating", + "top_k": 18, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f04e580b8e2a937cdc6cdaa3900f6ab8c2ad854e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dd-6177-7b90-a9f5-ea18f9282618"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Industry\\\", SUM(CAST(\\\"Efficiency_Rating\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Industry\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Applied the planned grouped numeric sum template with group_col=\\\"Industry\\\" and measure_col=\\\"Efficiency_Rating\\\". Cast \\\"Efficiency_Rating\\\" to REAL because the SQLite schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16681,"cached_input_tokens":12032,"output_tokens":245,"reasoning_output_tokens":141}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..29fe56d1a61c575b77e688ea2ef8c9fe772a939d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"Industry\", SUM(CAST(\"Efficiency_Rating\" AS REAL)) AS \"total_measure\"\nFROM \"m1\"\nGROUP BY \"Industry\"\nORDER BY \"total_measure\" DESC;","notes":"Applied the planned grouped numeric sum template with group_col=\"Industry\" and measure_col=\"Efficiency_Rating\". Cast \"Efficiency_Rating\" to REAL because the SQLite schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bbde35724f878391/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bd5caf91611a2686/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bd5caf91611a2686/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9a3c3ff5a8a4a2ed54a35eb015ccae00ff13695d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bd5caf91611a2686/run_manifest.json @@ -0,0 +1,69 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:10:07.948195+00:00", + "ended_at": "2026-05-19T16:10:15.122050+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_bd5caf91611a2686", + "problem_id": "v2p_m1_c907f81971100ee2", + "dataset_id": "m1", + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "filtered_stable_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=Response_Quality, measure_col=Meetings_Per_Week.", + "bindings": { + "group_col": "Response_Quality", + "measure_col": "Meetings_Per_Week", + "top_k": 15, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 9.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=4", + "sql_variant_index=2/2", + "binding_index=135" + ], + "template_selection_mode": "rule", + "selected_template_rank": 12, + "problem_index_within_template": 4, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bd5caf91611a2686/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bd5caf91611a2686/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4d33c9a62fca6e644f2709ec4f7ef80a8bcdd1b7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bd5caf91611a2686/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:10:10.996913+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3046.36, "started_at": "2026-05-19T16:10:07.949717+00:00", "ended_at": "2026-05-19T16:10:10.996102+00:00", "prompt_metrics": {"chars": 16437, "bytes_utf8": 16437, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-6fcf-74c1-b3fe-280d5083c433\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:10:15.121916+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3122.96, "started_at": "2026-05-19T16:10:11.997948+00:00", "ended_at": "2026-05-19T16:10:15.120942+00:00", "prompt_metrics": {"chars": 16437, "bytes_utf8": 16437, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-7f97-7551-9212-71aef24537e8\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..59922b75223a03909a0322d5f0a91ff1994e2a50 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=Education_Level, condition_col=Response_Quality. +Result preview: [{"Education_Level": "PhD", "condition_rate": 0.32954545454545453}, {"Education_Level": "Bachelor Degree", "condition_rate": 0.2526002971768202}, {"Education_Level": "Master Degree", "condition_rate": 0.2392638036809816}, {"Education_Level": "Associate Degree", "condition_rate": 0.21568627450980393}, {"Education_Level": "High School", "condition_rate": 0.19444444444444445}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..7cb80761ac6d960f465c46b0530417ed93140d95 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/generated_sql.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_m1_bdbfc29aa9bd9eb0 +-- problem_id: v2p_m1_696aab537cd6b6c2 +-- realization_mode: agent +-- source_kind: agent +SELECT "Education_Level", + AVG(CASE WHEN "Response_Quality" = 'Medium' THEN 1 ELSE 0 END) AS "condition_rate" +FROM "m1" +GROUP BY "Education_Level" +ORDER BY "condition_rate" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ee5939972d644299a400fdb55e6c16ac8aabf5a2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT \"Education_Level\",\n AVG(CASE WHEN \"Response_Quality\" = 'Medium' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m1\"\nGROUP BY \"Education_Level\"\nORDER BY \"condition_rate\" DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"Education_Level\\\",\\n AVG(CASE WHEN \\\"Response_Quality\\\" = 'Medium' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Education_Level\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\", \"columns\": [\"Education_Level\", \"condition_rate\"], \"rows\": [{\"Education_Level\": \"PhD\", \"condition_rate\": 0.32954545454545453}, {\"Education_Level\": \"Bachelor Degree\", \"condition_rate\": 0.2526002971768202}, {\"Education_Level\": \"Master Degree\", \"condition_rate\": 0.2392638036809816}, {\"Education_Level\": \"Associate Degree\", \"condition_rate\": 0.21568627450980393}, {\"Education_Level\": \"High School\", \"condition_rate\": 0.19444444444444445}, {\"Education_Level\": \"Professional Degree\", \"condition_rate\": 0.16}], \"row_count_returned\": 6, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.98}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a1b61d410a7a02282aa945443d9105956c34de57 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/run_manifest.json @@ -0,0 +1,92 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:05:23.998450+00:00", + "ended_at": "2026-05-19T16:05:32.879031+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_bdbfc29aa9bd9eb0", + "problem_id": "v2p_m1_696aab537cd6b6c2", + "dataset_id": "m1", + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "within_group_proportion", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=Education_Level, condition_col=Response_Quality.", + "bindings": { + "group_col": "Education_Level", + "condition_col": "Response_Quality", + "condition_value": "Medium", + "positive_value": "High", + "negative_value": "Medium", + "top_k": 18, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 96.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=8", + "sql_variant_index=2/2", + "binding_index=103" + ], + "template_selection_mode": "rule", + "selected_template_rank": 9, + "problem_index_within_template": 8, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_bdbfc29aa9bd9eb0.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_bdbfc29aa9bd9eb0", + "api_calls": 0, + "input_tokens": 16745, + "cached_input_tokens": 12032, + "output_tokens": 339, + "total_tokens": 17084, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8873.61, + "sql_execution_elapsed_ms_total": 1.98, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..87d90167d6c40e094d16e8adb4dca5d477cb9311 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T16:05:32.874713+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 8873.61, "started_at": "2026-05-19T16:05:23.999741+00:00", "ended_at": "2026-05-19T16:05:32.873386+00:00", "prompt_metrics": {"chars": 16604, "bytes_utf8": 16604, "lines": 459, "estimated_tokens": null}, "response_metrics": {"chars": 435, "bytes_utf8": 435, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16745, "cached_input_tokens": 12032, "output_tokens": 339, "reasoning_output_tokens": 227}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"Education_Level\\\",\\n AVG(CASE WHEN \\\"Response_Quality\\\" = 'Medium' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Education_Level\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Computes the within-group proportion of records with \\\"Response_Quality\\\" = 'Medium' for each \\\"Education_Level\\\", following the planned Grouped Condition Rate template.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..b9c099d96b4143fadf68c61d105ab58df9f355db --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_bdbfc29aa9bd9eb0", + "api_calls": 0, + "input_tokens": 16745, + "cached_input_tokens": 12032, + "output_tokens": 339, + "total_tokens": 17084, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8873.61, + "sql_execution_elapsed_ms_total": 1.98, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_bdbfc29aa9bd9eb0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..9c755c1f3981e48114fd4d5a1f40359dcfbae384 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Company_Size, measure_col=Task_Completion_Rate. +Result preview: [{"Company_Size": "Large (1001-5000)", "group_value": 31847.0}, {"Company_Size": "Medium (201-1000)", "group_value": 31123.7}, {"Company_Size": "Small (51-200)", "group_value": 29358.5}, {"Company_Size": "Startup (1-50)", "group_value": 18661.0}, {"Company_Size": "Enterprise (5000+)", "group_value": 12770.5}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..afa76f3534a2a227112a6c0755afab130f7e3396 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/generated_sql.sql @@ -0,0 +1,35 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_m1_c157c1bffc2a5f53 +-- problem_id: v2p_m1_a54a42add1a68556 +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT + "Company_Size", + SUM(CAST("Task_Completion_Rate" AS REAL)) AS "group_value" + FROM "m1" + WHERE "Company_Size" IS NOT NULL + AND "Company_Size" <> '' + AND "Task_Completion_Rate" IS NOT NULL + AND "Task_Completion_Rate" <> '' + GROUP BY "Company_Size" +), +"total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT + "g"."Company_Size", + "g"."group_value" +FROM "grouped" AS "g" +CROSS JOIN "total" AS "t" +WHERE "g"."group_value" > "t"."total_value" * 0.1 +ORDER BY "g"."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..449c12f0a27663a628ab5045e25e8f5b7876ed8c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT\n \"Company_Size\",\n SUM(CAST(\"Task_Completion_Rate\" AS REAL)) AS \"group_value\"\n FROM \"m1\"\n WHERE \"Company_Size\" IS NOT NULL\n AND \"Company_Size\" <> ''\n AND \"Task_Completion_Rate\" IS NOT NULL\n AND \"Task_Completion_Rate\" <> ''\n GROUP BY \"Company_Size\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT\n \"g\".\"Company_Size\",\n \"g\".\"group_value\"\nFROM \"grouped\" AS \"g\"\nCROSS JOIN \"total\" AS \"t\"\nWHERE \"g\".\"group_value\" > \"t\".\"total_value\" * 0.1\nORDER BY \"g\".\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"Company_Size\\\",\\n SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Company_Size\\\" IS NOT NULL\\n AND \\\"Company_Size\\\" <> ''\\n AND \\\"Task_Completion_Rate\\\" IS NOT NULL\\n AND \\\"Task_Completion_Rate\\\" <> ''\\n GROUP BY \\\"Company_Size\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT\\n \\\"g\\\".\\\"Company_Size\\\",\\n \\\"g\\\".\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS \\\"g\\\"\\nCROSS JOIN \\\"total\\\" AS \\\"t\\\"\\nWHERE \\\"g\\\".\\\"group_value\\\" > \\\"t\\\".\\\"total_value\\\" * 0.1\\nORDER BY \\\"g\\\".\\\"group_value\\\" DESC;\", \"columns\": [\"Company_Size\", \"group_value\"], \"rows\": [{\"Company_Size\": \"Large (1001-5000)\", \"group_value\": 31847.0}, {\"Company_Size\": \"Medium (201-1000)\", \"group_value\": 31123.7}, {\"Company_Size\": \"Small (51-200)\", \"group_value\": 29358.5}, {\"Company_Size\": \"Startup (1-50)\", \"group_value\": 18661.0}, {\"Company_Size\": \"Enterprise (5000+)\", \"group_value\": 12770.5}], \"row_count_returned\": 5, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.79}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..5a31481d0bd1933e955c64f170abed18a6c24214 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:48:11.701354+00:00", + "ended_at": "2026-05-19T15:48:29.423022+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_c157c1bffc2a5f53", + "problem_id": "v2p_m1_a54a42add1a68556", + "dataset_id": "m1", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "filtered_stable_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Company_Size, measure_col=Task_Completion_Rate.", + "bindings": { + "group_col": "Company_Size", + "measure_col": "Task_Completion_Rate", + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=4", + "sql_variant_index=1/2", + "binding_index=75" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 4, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_c157c1bffc2a5f53.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_c157c1bffc2a5f53", + "api_calls": 0, + "input_tokens": 16825, + "cached_input_tokens": 15744, + "output_tokens": 803, + "total_tokens": 17628, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17704.06, + "sql_execution_elapsed_ms_total": 4.79, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c48af3e7ce000530db556f1e51ad586b5b810e6b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:48:29.413530+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 17704.06, "started_at": "2026-05-19T15:48:11.703292+00:00", "ended_at": "2026-05-19T15:48:29.407394+00:00", "prompt_metrics": {"chars": 16905, "bytes_utf8": 16905, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 1005, "bytes_utf8": 1005, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16825, "cached_input_tokens": 15744, "output_tokens": 803, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"Company_Size\\\",\\n SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Company_Size\\\" IS NOT NULL\\n AND \\\"Company_Size\\\" <> ''\\n AND \\\"Task_Completion_Rate\\\" IS NOT NULL\\n AND \\\"Task_Completion_Rate\\\" <> ''\\n GROUP BY \\\"Company_Size\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT\\n \\\"g\\\".\\\"Company_Size\\\",\\n \\\"g\\\".\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS \\\"g\\\"\\nCROSS JOIN \\\"total\\\" AS \\\"t\\\"\\nWHERE \\\"g\\\".\\\"group_value\\\" > \\\"t\\\".\\\"total_value\\\" * 0.1\\nORDER BY \\\"g\\\".\\\"group_value\\\" DESC;\",\"notes\":\"Applied the planned Relative-to-Total Extreme Threshold template with group_col=Company_Size, measure_col=Task_Completion_Rate, and fraction_threshold=0.1. Added null/empty filtering and CAST(... AS REAL) because Task_Completion_Rate is stored as TEXT in the SQLite sche"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..51985998d52c8ca6921e0ef88a2d65486171a8d6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_c157c1bffc2a5f53", + "api_calls": 0, + "input_tokens": 16825, + "cached_input_tokens": 15744, + "output_tokens": 803, + "total_tokens": 17628, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17704.06, + "sql_execution_elapsed_ms_total": 4.79, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c157c1bffc2a5f53/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..995e7aff3a7c7bbf4a8ad29b4b62320ee2197fa5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16939, "bytes_utf8": 16939, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 999, "bytes_utf8": 999, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16833, "cached_input_tokens": 15744, "output_tokens": 777, "reasoning_output_tokens": 491}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..3bbecbe1243cd77b9832829a7bedffa03f0fa1ce --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_c19b4d1740ce0bf4", + "api_calls": 0, + "input_tokens": 16833, + "cached_input_tokens": 15744, + "output_tokens": 777, + "total_tokens": 17610, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18386.96, + "sql_execution_elapsed_ms_total": 1.45, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..14733f61b015fbe05be80307fe6b943bdfcfa239 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:50:38.493747+00:00", + "ended_at": "2026-05-19T15:50:56.880743+00:00", + "elapsed_ms": 18386.96, + "prompt_metrics": { + "chars": 16939, + "bytes_utf8": 16939, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1439, + "bytes_utf8": 1439, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 999, + "bytes_utf8": 999, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16833, + "cached_input_tokens": 15744, + "output_tokens": 777, + "reasoning_output_tokens": 491 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..25ec1d810128883aa7564b5f58cc4e484b69cbfe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Team_Collaboration_Frequency, measure_col=Commute_Time_Minutes.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "Team_Collaboration_Frequency", + "measure_col": "Commute_Time_Minutes", + "top_k": 15, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 33.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..1bd3c79967aff4bf925c33e5068af54ee243cd27 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ee-97a4-7df2-aa52-32de01ea3d93"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"Team_Collaboration_Frequency\\\",\\n SUM(CAST(\\\"Commute_Time_Minutes\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Team_Collaboration_Frequency\\\" IS NOT NULL\\n AND \\\"Team_Collaboration_Frequency\\\" <> ''\\n AND \\\"Commute_Time_Minutes\\\" IS NOT NULL\\n AND \\\"Commute_Time_Minutes\\\" <> ''\\n GROUP BY \\\"Team_Collaboration_Frequency\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT\\n g.\\\"Team_Collaboration_Frequency\\\",\\n g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the planned Relative-to-Total Extreme Threshold template with group_col=\\\"Team_Collaboration_Frequency\\\" and measure_col=\\\"Commute_Time_Minutes\\\". Casts the text-typed measure to REAL and filters null/blank values for a stable grouped sum.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16833,"cached_input_tokens":15744,"output_tokens":777,"reasoning_output_tokens":491}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0da3cb8cea91d9b0ed46485fffb2f926b72fc7b6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT\n \"Team_Collaboration_Frequency\",\n SUM(CAST(\"Commute_Time_Minutes\" AS REAL)) AS \"group_value\"\n FROM \"m1\"\n WHERE \"Team_Collaboration_Frequency\" IS NOT NULL\n AND \"Team_Collaboration_Frequency\" <> ''\n AND \"Commute_Time_Minutes\" IS NOT NULL\n AND \"Commute_Time_Minutes\" <> ''\n GROUP BY \"Team_Collaboration_Frequency\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT\n g.\"Team_Collaboration_Frequency\",\n g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;","notes":"Uses the planned Relative-to-Total Extreme Threshold template with group_col=\"Team_Collaboration_Frequency\" and measure_col=\"Commute_Time_Minutes\". Casts the text-typed measure to REAL and filters null/blank values for a stable grouped sum."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c19b4d1740ce0bf4/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bcd11bc0efde0a64a45a273326bf4da2630b9684 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16341, "bytes_utf8": 16341, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 396, "bytes_utf8": 396, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16683, "cached_input_tokens": 12032, "output_tokens": 388, "reasoning_output_tokens": 284}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..5ef89cbe94746d1a849aa62833b3fadc538abc86 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_c24c10c5a88f3e89", + "api_calls": 0, + "input_tokens": 16683, + "cached_input_tokens": 12032, + "output_tokens": 388, + "total_tokens": 17071, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 19953.81, + "sql_execution_elapsed_ms_total": 1.2, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ce16cdeec8cbd9114f4c2d09df40bd9f685d48b2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:30:59.266289+00:00", + "ended_at": "2026-05-19T15:31:19.220121+00:00", + "elapsed_ms": 19953.81, + "prompt_metrics": { + "chars": 16341, + "bytes_utf8": 16341, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1755, + "bytes_utf8": 1755, + "lines": 7, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 396, + "bytes_utf8": 396, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16683, + "cached_input_tokens": 12032, + "output_tokens": 388, + "reasoning_output_tokens": 284 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0722f352b14b5c83b114a4bbe8c97af1c2aacb5e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Company_Size, measure_col=Innovation_Score.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "Company_Size", + "measure_col": "Innovation_Score", + "top_k": 12, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 84.2, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6e8df4cab28554fd811437259d4e2c6999e239df --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,7 @@ +{"type":"thread.started","thread_id":"019e40dc-9945-7c00-8cde-fdceb270e762"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1995677, Requested 17318. Please try again in 389ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 17318. Please try again in 519ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1982807, Requested 17318. Please try again in 3ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Company_Size\\\", SUM(CAST(\\\"Innovation_Score\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Company_Size\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Applied the Grouped Numeric Sum template with group_col=\\\"Company_Size\\\" and measure_col=\\\"Innovation_Score\\\". CAST to REAL is used because the schema stores numeric values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16683,"cached_input_tokens":12032,"output_tokens":388,"reasoning_output_tokens":284}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..24a9107fdf60b7d862cc81f429e56ad9d341a27f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"Company_Size\", SUM(CAST(\"Innovation_Score\" AS REAL)) AS \"total_measure\"\nFROM \"m1\"\nGROUP BY \"Company_Size\"\nORDER BY \"total_measure\" DESC;","notes":"Applied the Grouped Numeric Sum template with group_col=\"Company_Size\" and measure_col=\"Innovation_Score\". CAST to REAL is used because the schema stores numeric values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c24c10c5a88f3e89/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..c276529ec6401f6c0b19d5bda35a7e3622f93561 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Industry, measure_col=Quality_Score. +Result preview: [{"Industry": "Technology", "group_value": 39951.9}, {"Industry": "Finance", "group_value": 19014.6}, {"Industry": "Healthcare", "group_value": 15927.6}, {"Industry": "Education", "group_value": 12109.5}, {"Industry": "Retail", "group_value": 9026.8}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..0e58d5149db7b3af6183e279a95fbcb15d5dada2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/generated_sql.sql @@ -0,0 +1,27 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_m1_c38da853d6c799f6 +-- problem_id: v2p_m1_2dfe55a422e2c976 +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT "Industry", SUM(CAST("Quality_Score" AS REAL)) AS "group_value" + FROM "m1" + GROUP BY "Industry" +), +"total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT g."Industry", g."group_value" +FROM "grouped" AS g +CROSS JOIN "total" AS t +WHERE g."group_value" > t."total_value" * 0.05 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4ab9e557efe2db7a2f6c0dd051d97845cade28a9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT \"Industry\", SUM(CAST(\"Quality_Score\" AS REAL)) AS \"group_value\"\n FROM \"m1\"\n GROUP BY \"Industry\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT g.\"Industry\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"Industry\\\", SUM(CAST(\\\"Quality_Score\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Industry\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"Industry\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\", \"columns\": [\"Industry\", \"group_value\"], \"rows\": [{\"Industry\": \"Technology\", \"group_value\": 39951.9}, {\"Industry\": \"Finance\", \"group_value\": 19014.6}, {\"Industry\": \"Healthcare\", \"group_value\": 15927.6}, {\"Industry\": \"Education\", \"group_value\": 12109.5}, {\"Industry\": \"Retail\", \"group_value\": 9026.8}, {\"Industry\": \"Manufacturing\", \"group_value\": 7880.5}, {\"Industry\": \"Consulting\", \"group_value\": 7362.7}], \"row_count_returned\": 7, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.68}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..bd683467336827d644ae27e9c34fa285b6a1d94f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:48:59.775382+00:00", + "ended_at": "2026-05-19T15:49:17.872536+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_c38da853d6c799f6", + "problem_id": "v2p_m1_2dfe55a422e2c976", + "dataset_id": "m1", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "filtered_stable_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Industry, measure_col=Quality_Score.", + "bindings": { + "group_col": "Industry", + "measure_col": "Quality_Score", + "top_k": 16, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 93.6, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=5", + "sql_variant_index=2/2", + "binding_index=76" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 5, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_c38da853d6c799f6.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_c38da853d6c799f6", + "api_calls": 0, + "input_tokens": 16819, + "cached_input_tokens": 15744, + "output_tokens": 471, + "total_tokens": 17290, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18086.43, + "sql_execution_elapsed_ms_total": 2.68, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d946ff683bbeea587967b42f47e508226deb7a49 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:49:17.866889+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 18086.43, "started_at": "2026-05-19T15:48:59.778214+00:00", "ended_at": "2026-05-19T15:49:17.864683+00:00", "prompt_metrics": {"chars": 16885, "bytes_utf8": 16885, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 702, "bytes_utf8": 702, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16819, "cached_input_tokens": 15744, "output_tokens": 471, "reasoning_output_tokens": 277}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"Industry\\\", SUM(CAST(\\\"Quality_Score\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Industry\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"Industry\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Used the required Relative-to-Total Extreme Threshold template with group_col=\\\"Industry\\\" and measure_col=\\\"Quality_Score\\\". CAST to REAL is included because the schema stores numeric-looking values as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..dbc0d6110b813dcdc770404389672670c6db0160 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_c38da853d6c799f6", + "api_calls": 0, + "input_tokens": 16819, + "cached_input_tokens": 15744, + "output_tokens": 471, + "total_tokens": 17290, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18086.43, + "sql_execution_elapsed_ms_total": 2.68, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38da853d6c799f6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..631fe3a995805ac0ce7de767e8fc52e21a1b2e35 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16761, "bytes_utf8": 16761, "lines": 458, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 849, "bytes_utf8": 849, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16811, "cached_input_tokens": 15744, "output_tokens": 1256, "reasoning_output_tokens": 1003}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..4a57f398f5e73dfd2e7dedb9a9cd5df4b439077e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_c38ddb99037a01c0", + "api_calls": 0, + "input_tokens": 16811, + "cached_input_tokens": 15744, + "output_tokens": 1256, + "total_tokens": 18067, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 23003.68, + "sql_execution_elapsed_ms_total": 4.72, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..749d867c547d7047ad67040038b16e73cb890b44 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:36:55.984831+00:00", + "ended_at": "2026-05-19T15:37:18.988548+00:00", + "elapsed_ms": 23003.68, + "prompt_metrics": { + "chars": 16761, + "bytes_utf8": 16761, + "lines": 458, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1253, + "bytes_utf8": 1253, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 849, + "bytes_utf8": 849, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16811, + "cached_input_tokens": 15744, + "output_tokens": 1256, + "reasoning_output_tokens": 1003 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e0d8c46771c65d3b1bbef4553ce5c4ded530f5dc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,458 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Stress_Level, measure_col=WFH_Days_Per_Week.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "Stress_Level", + "measure_col": "WFH_Days_Per_Week", + "item_col": "Innovation_Score", + "top_k": 10, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 4.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5ccca6ddbb80c8e030319da190d4daedf5924cc8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e2-0acc-70b1-b52f-50ab2398361f"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"Stress_Level\\\", \\\"Innovation_Score\\\",\\n SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL))) OVER (PARTITION BY \\\"Stress_Level\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nWHERE \\\"Stress_Level\\\" IS NOT NULL\\n AND \\\"Innovation_Score\\\" IS NOT NULL\\n AND \\\"WFH_Days_Per_Week\\\" IS NOT NULL\\n AND TRIM(\\\"Stress_Level\\\") <> ''\\n AND TRIM(\\\"Innovation_Score\\\") <> ''\\n AND TRIM(\\\"WFH_Days_Per_Week\\\") <> ''\\nGROUP BY \\\"Stress_Level\\\", \\\"Innovation_Score\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Uses the planned Within-Group Share of Total template with the provided bindings. \\\"WFH_Days_Per_Week\\\" is CAST to REAL because the schema stores numeric-like fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16811,"cached_input_tokens":15744,"output_tokens":1256,"reasoning_output_tokens":1003}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..531fe0feb55c210958f1c297524e7d7d8f25c62d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"Stress_Level\", \"Innovation_Score\",\n SUM(CAST(\"WFH_Days_Per_Week\" AS REAL)) AS total_measure,\n SUM(CAST(\"WFH_Days_Per_Week\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"WFH_Days_Per_Week\" AS REAL))) OVER (PARTITION BY \"Stress_Level\") AS share_within_group\nFROM \"m1\"\nWHERE \"Stress_Level\" IS NOT NULL\n AND \"Innovation_Score\" IS NOT NULL\n AND \"WFH_Days_Per_Week\" IS NOT NULL\n AND TRIM(\"Stress_Level\") <> ''\n AND TRIM(\"Innovation_Score\") <> ''\n AND TRIM(\"WFH_Days_Per_Week\") <> ''\nGROUP BY \"Stress_Level\", \"Innovation_Score\"\nORDER BY share_within_group DESC;","notes":"Uses the planned Within-Group Share of Total template with the provided bindings. \"WFH_Days_Per_Week\" is CAST to REAL because the schema stores numeric-like fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c38ddb99037a01c0/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e52d9a8232f9c95e5928494f606624802cef6601 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:09:18.673343+00:00", + "ended_at": "2026-05-19T16:09:22.316847+00:00", + "elapsed_ms": 3643.48, + "returncode": 1, + "prompt_metrics": { + "chars": 16433, + "bytes_utf8": 16433, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f70f9cc355718b17c0ea5cf1326469b08b4a3cf1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:09:23.321312+00:00", + "ended_at": "2026-05-19T16:09:26.083792+00:00", + "elapsed_ms": 2762.44, + "returncode": 1, + "prompt_metrics": { + "chars": 16433, + "bytes_utf8": 16433, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f754fff23b137f43de6be1703b77903321b87247 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=Work_Life_Balance, measure_col=Innovation_Score.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Work_Life_Balance", + "measure_col": "Innovation_Score", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 84.2, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..f754fff23b137f43de6be1703b77903321b87247 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=Work_Life_Balance, measure_col=Innovation_Score.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Work_Life_Balance", + "measure_col": "Innovation_Score", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 84.2, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..63dccb78d121c878ee71df3161345a8571f39d2f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-af59-79e1-a1b0-8b97bd71c71d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..efb1449ea8b02abc9b6a586c488bba37a8cf8f1f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-af59-79e1-a1b0-8b97bd71c71d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..73073b36b437711f8620984ffeea46d13d584db9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-c18d-7ca2-bbeb-c41c90ebad5f"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..addc35b0cb3024e23363a127baf261d995fe9869 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-c18d-7ca2-bbeb-c41c90ebad5f"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c3b5b83f2de3db92/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..299c203975520fa1d3d874dba9ec38a46fd62449 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:09:11.227728+00:00", + "ended_at": "2026-05-19T16:09:14.752973+00:00", + "elapsed_ms": 3525.22, + "returncode": 1, + "prompt_metrics": { + "chars": 16421, + "bytes_utf8": 16421, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c7fa2b7a07e43565562e244f9671bf3ea937975a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:09:15.754696+00:00", + "ended_at": "2026-05-19T16:09:18.670449+00:00", + "elapsed_ms": 2915.72, + "returncode": 1, + "prompt_metrics": { + "chars": 16421, + "bytes_utf8": 16421, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1750be9693cced53c5582c0a0d375c226b293357 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=Stress_Level, measure_col=Quality_Score.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Stress_Level", + "measure_col": "Quality_Score", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 93.6, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..1750be9693cced53c5582c0a0d375c226b293357 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=Stress_Level, measure_col=Quality_Score.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Stress_Level", + "measure_col": "Quality_Score", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 93.6, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..9a993c8d448766a2b870241c33c179e27aef4cc3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-9237-75d0-a4e2-03ff0898ff72"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..edefb1079e569baad2456c71a45213db992b1a24 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-9237-75d0-a4e2-03ff0898ff72"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..90f34009a46ca92008644f494b95c7b1666cbc52 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-a3ea-7272-b500-e9be4f32eecf"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..65da79469b81fdc54f4c8d1c1a4eecc5aeebd34e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-a3ea-7272-b500-e9be4f32eecf"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c47a38d2b51e254e/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2dd5edb3736d63510e83b19a6ec2afb7f06c22b4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16239, "bytes_utf8": 16239, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 326, "bytes_utf8": 326, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16656, "cached_input_tokens": 15744, "output_tokens": 600, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..dc5b7335c746e0a35245ea13f20d196a8f4f36c1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_c7c146b9ba6a1ca1", + "api_calls": 0, + "input_tokens": 16656, + "cached_input_tokens": 15744, + "output_tokens": 600, + "total_tokens": 17256, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15453.99, + "sql_execution_elapsed_ms_total": 1.05, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b4b167486620f90ded1df6a42097aaa0919e4165 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:33:37.510748+00:00", + "ended_at": "2026-05-19T15:33:52.964770+00:00", + "elapsed_ms": 15453.99, + "prompt_metrics": { + "chars": 16239, + "bytes_utf8": 16239, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 680, + "bytes_utf8": 680, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 326, + "bytes_utf8": 326, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16656, + "cached_input_tokens": 15744, + "output_tokens": 600, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ebb84dcd1e5bd5614b77eb4e18c917f9994d3147 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=Marital_Status.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "Marital_Status", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..b4acf720f417c5d92485b70efb2f7508d26c354b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40df-0368-7ae3-b418-5b8e3a47ecfa"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"Marital_Status\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Marital_Status\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the grouped-count template with \\\"Marital_Status\\\" as the category column to return subgroup sizes ordered from largest to smallest.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16656,"cached_input_tokens":15744,"output_tokens":600,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..55d1803b34b7fe39ba02107b1a6b6e3647ff0473 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"Marital_Status\", COUNT(*) AS \"row_count\"\nFROM \"m1\"\nGROUP BY \"Marital_Status\"\nORDER BY \"row_count\" DESC;","notes":"Uses the grouped-count template with \"Marital_Status\" as the category column to return subgroup sizes ordered from largest to smallest."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_c7c146b9ba6a1ca1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6c4e8ff400cd8bbe508b445d20ca47cf545aaac8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16513, "bytes_utf8": 16513, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1096, "bytes_utf8": 1096, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16723, "cached_input_tokens": 12032, "output_tokens": 2901, "reasoning_output_tokens": 2588}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d96f1a98838d7be82bd1fd00d1e9a9e309951080 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_cedf08d47958fc8f", + "api_calls": 0, + "input_tokens": 16723, + "cached_input_tokens": 12032, + "output_tokens": 2901, + "total_tokens": 19624, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 41920.32, + "sql_execution_elapsed_ms_total": 3.75, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..cdf23dde32debc687e4e77766d7d04c34f5b745b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:03.288527+00:00", + "ended_at": "2026-05-19T16:01:45.208879+00:00", + "elapsed_ms": 41920.32, + "prompt_metrics": { + "chars": 16513, + "bytes_utf8": 16513, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1565, + "bytes_utf8": 1565, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1096, + "bytes_utf8": 1096, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16723, + "cached_input_tokens": 12032, + "output_tokens": 2901, + "reasoning_output_tokens": 2588 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6bb555a030c39a4beea82969603813755b293f7c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n SELECT\n \"Industry\",\n CAST(\"Meetings_Per_Week\" AS REAL) AS \"measure_value\",\n COUNT(*) OVER (PARTITION BY \"Industry\") AS \"group_size\",\n CUME_DIST() OVER (\n PARTITION BY \"Industry\"\n ORDER BY CAST(\"Meetings_Per_Week\" AS REAL)\n ) AS \"cume_dist_value\"\n FROM \"m1\"\n WHERE \"Industry\" IS NOT NULL\n AND \"Meetings_Per_Week\" IS NOT NULL\n),\n\"percentiles\" AS (\n SELECT\n \"Industry\",\n MIN(\"measure_value\") AS \"percentile_measure\",\n MAX(\"group_size\") AS \"group_size\"\n FROM \"ranked\"\n WHERE \"cume_dist_value\" >= 0.9\n GROUP BY \"Industry\"\n)\nSELECT\n \"Industry\",\n \"percentile_measure\"\nFROM \"percentiles\"\nWHERE \"group_size\" >= 5\nORDER BY \"percentile_measure\" DESC\nLIMIT 13;","notes":"SQLite does not support PERCENTILE_CONT, so this uses a CUME_DIST-based discrete 90th-percentile surrogate for \"Meetings_Per_Week\" within each \"Industry\", filters out groups with fewer than 5 rows, and ranks the results descending."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_cedf08d47958fc8f/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e021dc038cf676d09a72d4a6391cd1c62a423421 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16675, "bytes_utf8": 16675, "lines": 460, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 488, "bytes_utf8": 488, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16785, "cached_input_tokens": 15744, "output_tokens": 365, "reasoning_output_tokens": 239}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..44c29edb99900722f7b1255a90d3fe954d818c57 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_d0a659e45057df42", + "api_calls": 0, + "input_tokens": 16785, + "cached_input_tokens": 15744, + "output_tokens": 365, + "total_tokens": 17150, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14264.83, + "sql_execution_elapsed_ms_total": 1.41, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0b163b1c6732f1720024ecc553796b9a3fd45a03 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:43:17.487959+00:00", + "ended_at": "2026-05-19T15:43:31.752827+00:00", + "elapsed_ms": 14264.83, + "prompt_metrics": { + "chars": 16675, + "bytes_utf8": 16675, + "lines": 460, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1176, + "bytes_utf8": 1176, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 488, + "bytes_utf8": 488, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16785, + "cached_input_tokens": 15744, + "output_tokens": 365, + "reasoning_output_tokens": 239 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4e5022b019f4b1ef8cb598ae3a836dfbda8cb9bc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,460 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=Has_Children, group_col_2=Manager_Support_Level.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "Has_Children", + "group_col_2": "Manager_Support_Level", + "predicate_col": "Commute_Time_Minutes", + "predicate_op": ">=", + "predicate_value": 38.0, + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 38.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5c7253865d232a24ad2ca24bb02d5ec73b2ab4a1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40e7-dcfa-78b3-b637-b1381bcabde4"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 17420. Please try again in 522ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"Has_Children\\\", \\\"Manager_Support_Level\\\", COUNT(*) AS row_count\\nFROM \\\"m1\\\"\\nWHERE CAST(\\\"Commute_Time_Minutes\\\" AS REAL) >= 38.0\\nGROUP BY \\\"Has_Children\\\", \\\"Manager_Support_Level\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Used the planned filtered two-dimensional group count template with Has_Children and Manager_Support_Level. Cast Commute_Time_Minutes to REAL because the schema stores numeric-looking fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16785,"cached_input_tokens":15744,"output_tokens":365,"reasoning_output_tokens":239}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..055248a6c003d7763e0ff3409c09de86844ef920 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"Has_Children\", \"Manager_Support_Level\", COUNT(*) AS row_count\nFROM \"m1\"\nWHERE CAST(\"Commute_Time_Minutes\" AS REAL) >= 38.0\nGROUP BY \"Has_Children\", \"Manager_Support_Level\"\nORDER BY row_count DESC;","notes":"Used the planned filtered two-dimensional group count template with Has_Children and Manager_Support_Level. Cast Commute_Time_Minutes to REAL because the schema stores numeric-looking fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0a659e45057df42/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..1608f84fdcb07ce95b91abe2fbd7730f145a934d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Job_Level, measure_col=Innovation_Score. +Result preview: [{"Job_Level": "Manager", "percentile_measure": 95.0}, {"Job_Level": "Senior", "percentile_measure": 94.3}, {"Job_Level": "Director", "percentile_measure": 92.96}, {"Job_Level": "Mid-Level", "percentile_measure": 92.95000000000002}, {"Job_Level": "Lead", "percentile_measure": 92.94000000000003}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..18d2d177f457acb6ba04926059e5a352976f8981 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/generated_sql.sql @@ -0,0 +1,65 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_m1_d0aa13aa38e488cc +-- problem_id: v2p_m1_a5bdfab82dab486b +-- realization_mode: agent +-- source_kind: agent +WITH "ordered" AS ( + SELECT + "Job_Level", + CAST("Innovation_Score" AS REAL) AS "measure_value", + ROW_NUMBER() OVER ( + PARTITION BY "Job_Level" + ORDER BY CAST("Innovation_Score" AS REAL) + ) AS "rn", + COUNT(*) OVER ( + PARTITION BY "Job_Level" + ) AS "cnt" + FROM "m1" + WHERE "Job_Level" IS NOT NULL + AND "Innovation_Score" IS NOT NULL +), +"positions" AS ( + SELECT DISTINCT + "Job_Level", + "cnt", + (("cnt" - 1) * 0.9) AS "raw_pos", + CAST((("cnt" - 1) * 0.9) AS INTEGER) + 1 AS "lower_rn", + CASE + WHEN (("cnt" - 1) * 0.9) = CAST((("cnt" - 1) * 0.9) AS INTEGER) THEN CAST((("cnt" - 1) * 0.9) AS INTEGER) + 1 + ELSE CAST((("cnt" - 1) * 0.9) AS INTEGER) + 2 + END AS "upper_rn" + FROM "ordered" +), +"picked" AS ( + SELECT + o."Job_Level", + p."raw_pos", + p."lower_rn", + p."upper_rn", + MAX(CASE WHEN o."rn" = p."lower_rn" THEN o."measure_value" END) AS "lower_value", + MAX(CASE WHEN o."rn" = p."upper_rn" THEN o."measure_value" END) AS "upper_value" + FROM "ordered" AS o + JOIN "positions" AS p + ON o."Job_Level" = p."Job_Level" + GROUP BY + o."Job_Level", + p."raw_pos", + p."lower_rn", + p."upper_rn" +) +SELECT + "Job_Level", + CASE + WHEN "lower_rn" = "upper_rn" THEN "lower_value" + ELSE "lower_value" + ("raw_pos" - CAST("raw_pos" AS INTEGER)) * ("upper_value" - "lower_value") + END AS "percentile_measure" +FROM "picked" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9bfa65b0893b2aaa84248e18e0df890e00dc3d23 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 2, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n SELECT\n \"Job_Level\",\n CAST(\"Innovation_Score\" AS REAL) AS \"measure_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Job_Level\"\n ORDER BY CAST(\"Innovation_Score\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"Job_Level\"\n ) AS \"cnt\"\n FROM \"m1\"\n WHERE \"Job_Level\" IS NOT NULL\n AND \"Innovation_Score\" IS NOT NULL\n),\n\"positions\" AS (\n SELECT DISTINCT\n \"Job_Level\",\n \"cnt\",\n ((\"cnt\" - 1) * 0.9) AS \"raw_pos\",\n CAST(((\"cnt\" - 1) * 0.9) AS INTEGER) + 1 AS \"lower_rn\",\n CASE\n WHEN ((\"cnt\" - 1) * 0.9) = CAST(((\"cnt\" - 1) * 0.9) AS INTEGER) THEN CAST(((\"cnt\" - 1) * 0.9) AS INTEGER) + 1\n ELSE CAST(((\"cnt\" - 1) * 0.9) AS INTEGER) + 2\n END AS \"upper_rn\"\n FROM \"ordered\"\n),\n\"picked\" AS (\n SELECT\n o.\"Job_Level\",\n p.\"raw_pos\",\n p.\"lower_rn\",\n p.\"upper_rn\",\n MAX(CASE WHEN o.\"rn\" = p.\"lower_rn\" THEN o.\"measure_value\" END) AS \"lower_value\",\n MAX(CASE WHEN o.\"rn\" = p.\"upper_rn\" THEN o.\"measure_value\" END) AS \"upper_value\"\n FROM \"ordered\" AS o\n JOIN \"positions\" AS p\n ON o.\"Job_Level\" = p.\"Job_Level\"\n GROUP BY\n o.\"Job_Level\",\n p.\"raw_pos\",\n p.\"lower_rn\",\n p.\"upper_rn\"\n)\nSELECT\n \"Job_Level\",\n CASE\n WHEN \"lower_rn\" = \"upper_rn\" THEN \"lower_value\"\n ELSE \"lower_value\" + (\"raw_pos\" - CAST(\"raw_pos\" AS INTEGER)) * (\"upper_value\" - \"lower_value\")\n END AS \"percentile_measure\"\nFROM \"picked\"\nORDER BY \"percentile_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"Job_Level\\\",\\n CAST(\\\"Innovation_Score\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Job_Level\\\"\\n ORDER BY CAST(\\\"Innovation_Score\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"Job_Level\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Job_Level\\\" IS NOT NULL\\n AND \\\"Innovation_Score\\\" IS NOT NULL\\n),\\n\\\"positions\\\" AS (\\n SELECT DISTINCT\\n \\\"Job_Level\\\",\\n \\\"cnt\\\",\\n ((\\\"cnt\\\" - 1) * 0.9) AS \\\"raw_pos\\\",\\n CAST(((\\\"cnt\\\" - 1) * 0.9) AS INTEGER) + 1 AS \\\"lower_rn\\\",\\n CASE\\n WHEN ((\\\"cnt\\\" - 1) * 0.9) = CAST(((\\\"cnt\\\" - 1) * 0.9) AS INTEGER) THEN CAST(((\\\"cnt\\\" - 1) * 0.9) AS INTEGER) + 1\\n ELSE CAST(((\\\"cnt\\\" - 1) * 0.9) AS INTEGER) + 2\\n END AS \\\"upper_rn\\\"\\n FROM \\\"ordered\\\"\\n),\\n\\\"picked\\\" AS (\\n SELECT\\n o.\\\"Job_Level\\\",\\n p.\\\"raw_pos\\\",\\n p.\\\"lower_rn\\\",\\n p.\\\"upper_rn\\\",\\n MAX(CASE WHEN o.\\\"rn\\\" = p.\\\"lower_rn\\\" THEN o.\\\"measure_value\\\" END) AS \\\"lower_value\\\",\\n MAX(CASE WHEN o.\\\"rn\\\" = p.\\\"upper_rn\\\" THEN o.\\\"measure_value\\\" END) AS \\\"upper_value\\\"\\n FROM \\\"ordered\\\" AS o\\n JOIN \\\"positions\\\" AS p\\n ON o.\\\"Job_Level\\\" = p.\\\"Job_Level\\\"\\n GROUP BY\\n o.\\\"Job_Level\\\",\\n p.\\\"raw_pos\\\",\\n p.\\\"lower_rn\\\",\\n p.\\\"upper_rn\\\"\\n)\\nSELECT\\n \\\"Job_Level\\\",\\n CASE\\n WHEN \\\"lower_rn\\\" = \\\"upper_rn\\\" THEN \\\"lower_value\\\"\\n ELSE \\\"lower_value\\\" + (\\\"raw_pos\\\" - CAST(\\\"raw_pos\\\" AS INTEGER)) * (\\\"upper_value\\\" - \\\"lower_value\\\")\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"picked\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\", \"columns\": [\"Job_Level\", \"percentile_measure\"], \"rows\": [{\"Job_Level\": \"Manager\", \"percentile_measure\": 95.0}, {\"Job_Level\": \"Senior\", \"percentile_measure\": 94.3}, {\"Job_Level\": \"Director\", \"percentile_measure\": 92.96}, {\"Job_Level\": \"Mid-Level\", \"percentile_measure\": 92.95000000000002}, {\"Job_Level\": \"Lead\", \"percentile_measure\": 92.94000000000003}, {\"Job_Level\": \"Junior\", \"percentile_measure\": 87.83999999999999}], \"row_count_returned\": 6, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 6.09}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..c06e212af46f5bc3b9386f2ee3bd708dda32d40a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:58:48.181978+00:00", + "ended_at": "2026-05-19T15:59:11.630755+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_d0aa13aa38e488cc", + "problem_id": "v2p_m1_a5bdfab82dab486b", + "dataset_id": "m1", + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_concentration_consistency", + "intended_facet_id": "rare_target_concentration", + "variant_semantic_role": "ranked_signal_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Job_Level, measure_col=Innovation_Score.", + "bindings": { + "group_col": "Job_Level", + "measure_col": "Innovation_Score", + "top_k": 11, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 84.2, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "notes": [ + "default_facets=rare_target_concentration", + "template_selection_mode=rule", + "problem_index_within_template=8", + "sql_variant_index=1/2", + "binding_index=91" + ], + "template_selection_mode": "rule", + "selected_template_rank": 8, + "problem_index_within_template": 8, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_d0aa13aa38e488cc.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_d0aa13aa38e488cc", + "api_calls": 0, + "input_tokens": 16721, + "cached_input_tokens": 15744, + "output_tokens": 1124, + "total_tokens": 17845, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 22435.79, + "sql_execution_elapsed_ms_total": 6.09, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..078a56610e28f756e5dd178a29895f63188b1568 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T15:58:51.101243+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2916.91, "started_at": "2026-05-19T15:58:48.183557+00:00", "ended_at": "2026-05-19T15:58:51.100501+00:00", "prompt_metrics": {"chars": 16513, "bytes_utf8": 16513, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f6-1087-73b3-9f60-f19272ddd5e3\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T15:59:11.623172+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 19518.88, "started_at": "2026-05-19T15:58:52.103407+00:00", "ended_at": "2026-05-19T15:59:11.622329+00:00", "prompt_metrics": {"chars": 16513, "bytes_utf8": 16513, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 1836, "bytes_utf8": 1836, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16721, "cached_input_tokens": 15744, "output_tokens": 1124, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"Job_Level\\\",\\n CAST(\\\"Innovation_Score\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Job_Level\\\"\\n ORDER BY CAST(\\\"Innovation_Score\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"Job_Level\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Job_Level\\\" IS NOT NULL\\n AND \\\"Innovation_Score\\\" IS NOT NULL\\n),\\n\\\"positions\\\" AS (\\n SELECT DISTINCT\\n \\\"Job_Level\\\",\\n \\\"cnt\\\",\\n ((\\\"cnt\\\" - 1) * 0.9) AS \\\"raw_pos\\\",\\n CAST(((\\\"cnt\\\" - 1) * 0.9) AS INTEGER) + 1 AS \\\"lower_rn\\\",\\n CASE\\n WHEN ((\\\"cnt\\\" - 1) * 0.9) = CAST(((\\\"cnt\\\" - 1) * 0.9) AS INTEGER) THEN CAST(((\\\"cnt\\\" - 1) * 0.9) AS INTEGER) + 1\\n ELSE CAST(((\\\"cnt\\\" - 1) * 0.9) AS INTEGER) + 2\\n END AS \\\"upper_rn\\\"\\n FROM \\\"ordered\\\"\\n),\\n\\\"picked\\\" AS (\\n SELECT\\n o.\\\"Job_Level\\\",\\n p.\\\"raw_pos\\\",\\n p.\\\"lower_rn\\\",\\n p.\\\"upper_rn\\\",\\n M"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..db8c17015802980c01341da3262301c7f22e82c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_d0aa13aa38e488cc", + "api_calls": 0, + "input_tokens": 16721, + "cached_input_tokens": 15744, + "output_tokens": 1124, + "total_tokens": 17845, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 22435.79, + "sql_execution_elapsed_ms_total": 6.09, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d0aa13aa38e488cc/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e4d901000b47e778d19f915b65a0f5752980827d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:33.815413+00:00", + "ended_at": "2026-05-19T16:08:37.101531+00:00", + "elapsed_ms": 3286.08, + "returncode": 1, + "prompt_metrics": { + "chars": 16315, + "bytes_utf8": 16315, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..95844796cc63b94fb74ee538906534a659af408e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:38.104041+00:00", + "ended_at": "2026-05-19T16:08:41.083532+00:00", + "elapsed_ms": 2979.44, + "returncode": 1, + "prompt_metrics": { + "chars": 16315, + "bytes_utf8": 16315, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1c84f40bf09b9cb1dcc1de636729fc9cf61961b7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=Company_Size.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Company_Size", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 41.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..1c84f40bf09b9cb1dcc1de636729fc9cf61961b7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=Company_Size.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Company_Size", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 41.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..75f0f2729b735c24b6769c573754928e101cf060 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-0027-7763-8a0a-4394d60818f9"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..086233b092a848ce0d2f3e6274b381e0c4c0ae39 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-0027-7763-8a0a-4394d60818f9"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..35a0c5e8230aa3864c518fbbca10bce8e836661c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-10d7-79b3-a11d-01940429a884"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f5c079d8aa3b0f6bcef567b03bf3ad8230c634a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-10d7-79b3-a11d-01940429a884"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3f12db71fc48823e5148c68bd8983f48ff45434e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d3e3bc47147f9b33/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:08:37.102335+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3286.08, "started_at": "2026-05-19T16:08:33.815413+00:00", "ended_at": "2026-05-19T16:08:37.101531+00:00", "prompt_metrics": {"chars": 16315, "bytes_utf8": 16315, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40ff-0027-7763-8a0a-4394d60818f9\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:08:41.084328+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2979.44, "started_at": "2026-05-19T16:08:38.104041+00:00", "ended_at": "2026-05-19T16:08:41.083532+00:00", "prompt_metrics": {"chars": 16315, "bytes_utf8": 16315, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40ff-10d7-79b3-a11d-01940429a884\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..0f239c0dd2e1867a97fa30f5f1b860cf169a91e9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"value_label": "Large (1001-5000)", "support": 386, "support_share": 0.25733333333333336, "support_rank": 1}, {"value_label": "Medium (201-1000)", "support": 384, "support_share": 0.256, "support_rank": 2}, {"value_label": "Small (51-200)", "support": 352, "support_share": 0.23466666666666666, "support_rank": 3}, {"value_label": "Startup (1-50)", "support": 225, "support_share": 0.15, "support_rank": 4}, {"value_label": "Enterprise (5000+)", "support": 153, "support_share": 0.102, "support_rank": 5}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..2858ab2d04dbf73b6566c4024168a87720c3fd33 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/generated_sql.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: value_imbalance_profile +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_m1_d944891a5e1d3c06 +-- problem_id: v2p_m1_e6b9c172ddd31594 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "Company_Size" AS value_label, COUNT(*) AS support + FROM "m1" + GROUP BY "Company_Size" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5378fe7928d1891032c3763e2162c465eb793f40 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_a\n-- sql_source_dataset_id: m1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: value_imbalance_profile\n-- variant_semantic_role: count_distribution\n-- template_id: tpl_cardinality_support_rank_profile\n-- query_record_id: v2q_m1_d944891a5e1d3c06\n-- problem_id: v2p_m1_e6b9c172ddd31594\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n SELECT \"Company_Size\" AS value_label, COUNT(*) AS support\n FROM \"m1\"\n GROUP BY \"Company_Size\"\n)\nSELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_a\\n-- sql_source_dataset_id: m1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: value_imbalance_profile\\n-- variant_semantic_role: count_distribution\\n-- template_id: tpl_cardinality_support_rank_profile\\n-- query_record_id: v2q_m1_d944891a5e1d3c06\\n-- problem_id: v2p_m1_e6b9c172ddd31594\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n SELECT \\\"Company_Size\\\" AS value_label, COUNT(*) AS support\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Company_Size\\\"\\n)\\nSELECT\\n value_label,\\n support,\\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\\nFROM grouped\\nORDER BY support DESC, value_label;\", \"columns\": [\"value_label\", \"support\", \"support_share\", \"support_rank\"], \"rows\": [{\"value_label\": \"Large (1001-5000)\", \"support\": 386, \"support_share\": 0.25733333333333336, \"support_rank\": 1}, {\"value_label\": \"Medium (201-1000)\", \"support\": 384, \"support_share\": 0.256, \"support_rank\": 2}, {\"value_label\": \"Small (51-200)\", \"support\": 352, \"support_share\": 0.23466666666666666, \"support_rank\": 3}, {\"value_label\": \"Startup (1-50)\", \"support\": 225, \"support_share\": 0.15, \"support_rank\": 4}, {\"value_label\": \"Enterprise (5000+)\", \"support\": 153, \"support_share\": 0.102, \"support_rank\": 5}], \"row_count_returned\": 5, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 0.83}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..c10c58999d022e1222967c8f442ca05f7934a615 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/run_manifest.json @@ -0,0 +1,57 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:11:34.289288+00:00", + "ended_at": "2026-05-19T16:11:34.290726+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_d944891a5e1d3c06", + "problem_id": "v2p_m1_e6b9c172ddd31594", + "dataset_id": "m1", + "template_id": "tpl_cardinality_support_rank_profile", + "template_name": "Cardinality Support Rank Profile", + "family_id": "cardinality_structure", + "canonical_subitem_id": "support_rank_profile_consistency", + "intended_facet_id": "value_imbalance_profile", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template Cardinality Support Rank Profile to probe support_rank_profile_consistency with semantic role count_distribution. Focus on group_col=Company_Size.", + "bindings": { + "group_col": "Company_Size" + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col} AS value_label, COUNT(*) AS support\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", + "notes": [ + "default_facets=support_concentration,value_imbalance_profile", + "template_selection_mode=deterministic", + "problem_index_within_template=6", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 6, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_d944891a5e1d3c06.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d944891a5e1d3c06/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b77c8e75d861d892e0440fcfba6c93a9df603068 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:09:52.842426+00:00", + "ended_at": "2026-05-19T16:09:56.038502+00:00", + "elapsed_ms": 3196.05, + "returncode": 1, + "prompt_metrics": { + "chars": 16427, + "bytes_utf8": 16427, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..54be7e1b28f5800a83b6d8b0a526705b981877e5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:09:57.041164+00:00", + "ended_at": "2026-05-19T16:09:59.817910+00:00", + "elapsed_ms": 2776.7, + "returncode": 1, + "prompt_metrics": { + "chars": 16427, + "bytes_utf8": 16427, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..952b47620e3e6a2f49b6030d2d275d76a0b79699 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=Survey_Date, measure_col=Efficiency_Rating.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Survey_Date", + "measure_col": "Efficiency_Rating", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..952b47620e3e6a2f49b6030d2d275d76a0b79699 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=Survey_Date, measure_col=Efficiency_Rating.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "Survey_Date", + "measure_col": "Efficiency_Rating", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a6fdaf369c1e26542c5cc440b34c7cbb6559a980 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4100-34f9-7c60-84ac-df72c23786bf"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..189128a3205fb59a082b226421c7dbc3ce9a1490 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4100-34f9-7c60-84ac-df72c23786bf"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5efcb9482bf66b8c928bdc28640531fca0daffc9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4100-452a-7473-ba6d-bc798e40c526"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..286d738f6565099810b84de9862930a181bc0915 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e4100-452a-7473-ba6d-bc798e40c526"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b5dadc7e1e8842a1ce7981968da528e469d6cc5a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/run_manifest.json @@ -0,0 +1,69 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:09:52.840775+00:00", + "ended_at": "2026-05-19T16:09:59.819067+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_d9f60e9bf54cb7d9", + "problem_id": "v2p_m1_9ecb48598c151e5b", + "dataset_id": "m1", + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "slice_level_consistency", + "intended_facet_id": "conditional_interaction_hotspots", + "variant_semantic_role": "ranked_signal_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=Survey_Date, measure_col=Efficiency_Rating.", + "bindings": { + "group_col": "Survey_Date", + "measure_col": "Efficiency_Rating", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "notes": [ + "default_facets=conditional_interaction_hotspots", + "template_selection_mode=rule", + "problem_index_within_template=3", + "sql_variant_index=2/2", + "binding_index=134" + ], + "template_selection_mode": "rule", + "selected_template_rank": 12, + "problem_index_within_template": 3, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..13c731b4c971dd1a09a7e9de9675071aed63a003 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_d9f60e9bf54cb7d9/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:09:56.039259+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3196.05, "started_at": "2026-05-19T16:09:52.842426+00:00", "ended_at": "2026-05-19T16:09:56.038502+00:00", "prompt_metrics": {"chars": 16427, "bytes_utf8": 16427, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-34f9-7c60-84ac-df72c23786bf\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:09:59.818934+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2776.7, "started_at": "2026-05-19T16:09:57.041164+00:00", "ended_at": "2026-05-19T16:09:59.817910+00:00", "prompt_metrics": {"chars": 16427, "bytes_utf8": 16427, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-452a-7473-ba6d-bc798e40c526\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..882c95c0f921eb3a3492fcac684e2aeb64cd256d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Response_Quality, measure_col=Task_Completion_Rate. +Result preview: [{"Response_Quality": "High", "Job_Satisfaction": "100.0", "total_measure": 65923.0, "share_within_group": 76.18399353759852}, {"Response_Quality": "Low", "Job_Satisfaction": "100.0", "total_measure": 4932.2, "share_within_group": 75.67625623321825}, {"Response_Quality": "Medium", "Job_Satisfaction": "100.0", "total_measure": 22513.1, "share_within_group": 73.3041589742087}, {"Response_Quality": "Low", "Job_Satisfaction": "97.0", "total_measure": 98.3, "share_within_group": 1.508247027234369}, {"Response_Quality": "Low", "Job_Satisfaction": "85.6", "total_measure": 98.2, "share_within_group": 1.5067126965861144}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..4a99716df620656cbca686ed1e50d7a15d00cf27 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/generated_sql.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_m1_db58c55ec04c817e +-- problem_id: v2p_m1_e27fd24edc12cbf7 +-- realization_mode: agent +-- source_kind: agent +SELECT + "Response_Quality", + "Job_Satisfaction", + SUM(CAST("Task_Completion_Rate" AS REAL)) AS total_measure, + SUM(CAST("Task_Completion_Rate" AS REAL)) * 100.0 / SUM(SUM(CAST("Task_Completion_Rate" AS REAL))) OVER (PARTITION BY "Response_Quality") AS share_within_group +FROM "m1" +WHERE "Response_Quality" IS NOT NULL + AND "Job_Satisfaction" IS NOT NULL + AND "Task_Completion_Rate" IS NOT NULL +GROUP BY "Response_Quality", "Job_Satisfaction" +ORDER BY share_within_group DESC +LIMIT 18; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b1bff061f9270c5c83f6b0a67ec3cfe9dde332de --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"Response_Quality\",\n \"Job_Satisfaction\",\n SUM(CAST(\"Task_Completion_Rate\" AS REAL)) AS total_measure,\n SUM(CAST(\"Task_Completion_Rate\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"Task_Completion_Rate\" AS REAL))) OVER (PARTITION BY \"Response_Quality\") AS share_within_group\nFROM \"m1\"\nWHERE \"Response_Quality\" IS NOT NULL\n AND \"Job_Satisfaction\" IS NOT NULL\n AND \"Task_Completion_Rate\" IS NOT NULL\nGROUP BY \"Response_Quality\", \"Job_Satisfaction\"\nORDER BY share_within_group DESC\nLIMIT 18;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"Response_Quality\\\",\\n \\\"Job_Satisfaction\\\",\\n SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL))) OVER (PARTITION BY \\\"Response_Quality\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nWHERE \\\"Response_Quality\\\" IS NOT NULL\\n AND \\\"Job_Satisfaction\\\" IS NOT NULL\\n AND \\\"Task_Completion_Rate\\\" IS NOT NULL\\nGROUP BY \\\"Response_Quality\\\", \\\"Job_Satisfaction\\\"\\nORDER BY share_within_group DESC\\nLIMIT 18;\", \"columns\": [\"Response_Quality\", \"Job_Satisfaction\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"Response_Quality\": \"High\", \"Job_Satisfaction\": \"100.0\", \"total_measure\": 65923.0, \"share_within_group\": 76.18399353759852}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"100.0\", \"total_measure\": 4932.2, \"share_within_group\": 75.67625623321825}, {\"Response_Quality\": \"Medium\", \"Job_Satisfaction\": \"100.0\", \"total_measure\": 22513.1, \"share_within_group\": 73.3041589742087}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"97.0\", \"total_measure\": 98.3, \"share_within_group\": 1.508247027234369}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"85.6\", \"total_measure\": 98.2, \"share_within_group\": 1.5067126965861144}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"78.4\", \"total_measure\": 89.1, \"share_within_group\": 1.3670886075949367}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"91.1\", \"total_measure\": 88.3, \"share_within_group\": 1.354813962408899}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"83.5\", \"total_measure\": 85.8, \"share_within_group\": 1.3164556962025316}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"84.6\", \"total_measure\": 81.5, \"share_within_group\": 1.2504794783275797}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"97.7\", \"total_measure\": 79.3, \"share_within_group\": 1.2167242040659763}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"97.3\", \"total_measure\": 77.5, \"share_within_group\": 1.1891062523973916}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"89.8\", \"total_measure\": 77.2, \"share_within_group\": 1.1845032604526275}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"67.4\", \"total_measure\": 75.6, \"share_within_group\": 1.1599539700805521}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"93.0\", \"total_measure\": 74.2, \"share_within_group\": 1.1384733410049865}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"80.7\", \"total_measure\": 71.2, \"share_within_group\": 1.0924434215573455}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"87.2\", \"total_measure\": 70.6, \"share_within_group\": 1.0832374376678173}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"93.8\", \"total_measure\": 70.3, \"share_within_group\": 1.0786344457230532}, {\"Response_Quality\": \"Low\", \"Job_Satisfaction\": \"81.6\", \"total_measure\": 68.9, \"share_within_group\": 1.0571538166474876}], \"row_count_returned\": 18, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.67}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4c36c3b9aa2be724c6378c7c5b778f6c98752975 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/run_manifest.json @@ -0,0 +1,91 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:39:09.330755+00:00", + "ended_at": "2026-05-19T15:39:27.274304+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_db58c55ec04c817e", + "problem_id": "v2p_m1_e27fd24edc12cbf7", + "dataset_id": "m1", + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "dependency_strength_similarity", + "intended_facet_id": "pairwise_conditional_dependency", + "variant_semantic_role": "within_group_proportion", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Response_Quality, measure_col=Task_Completion_Rate.", + "bindings": { + "group_col": "Response_Quality", + "measure_col": "Task_Completion_Rate", + "item_col": "Job_Satisfaction", + "top_k": 18, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 92.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "item_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "notes": [ + "default_facets=pairwise_conditional_dependency", + "template_selection_mode=rule", + "problem_index_within_template=10", + "sql_variant_index=2/2", + "binding_index=33" + ], + "template_selection_mode": "rule", + "selected_template_rank": 3, + "problem_index_within_template": 10, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_db58c55ec04c817e.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_db58c55ec04c817e", + "api_calls": 0, + "input_tokens": 16809, + "cached_input_tokens": 12032, + "output_tokens": 957, + "total_tokens": 17766, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17936.9, + "sql_execution_elapsed_ms_total": 2.67, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6ce1d0f72e12dfaef28c90d7dcf111e01b5d8604 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:39:27.269920+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 17936.9, "started_at": "2026-05-19T15:39:09.332193+00:00", "ended_at": "2026-05-19T15:39:27.269119+00:00", "prompt_metrics": {"chars": 16778, "bytes_utf8": 16778, "lines": 458, "estimated_tokens": null}, "response_metrics": {"chars": 857, "bytes_utf8": 857, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16809, "cached_input_tokens": 12032, "output_tokens": 957, "reasoning_output_tokens": 724}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"Response_Quality\\\",\\n \\\"Job_Satisfaction\\\",\\n SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Task_Completion_Rate\\\" AS REAL))) OVER (PARTITION BY \\\"Response_Quality\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nWHERE \\\"Response_Quality\\\" IS NOT NULL\\n AND \\\"Job_Satisfaction\\\" IS NOT NULL\\n AND \\\"Task_Completion_Rate\\\" IS NOT NULL\\nGROUP BY \\\"Response_Quality\\\", \\\"Job_Satisfaction\\\"\\nORDER BY share_within_group DESC\\nLIMIT 18;\",\"notes\":\"Used the bound template roles exactly: group_col=\\\"Response_Quality\\\", item_col=\\\"Job_Satisfaction\\\", measure_col=\\\"Task_Completion_Rate\\\". CAST is required because the schema stores numeric fields as TEXT. Grouping on \\\"Job_Satisfaction\\\" uses exact stored values.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..216ded74adec55cabc6e5083b90a1012342faa93 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_db58c55ec04c817e", + "api_calls": 0, + "input_tokens": 16809, + "cached_input_tokens": 12032, + "output_tokens": 957, + "total_tokens": 17766, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17936.9, + "sql_execution_elapsed_ms_total": 2.67, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_db58c55ec04c817e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..014d6c3d3746c345c291601a41dc582db79175bb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:12.031248+00:00", + "ended_at": "2026-05-19T16:08:15.289475+00:00", + "elapsed_ms": 3258.19, + "returncode": 1, + "prompt_metrics": { + "chars": 16312, + "bytes_utf8": 16312, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..6cae370bd6a3a5f089d95603040b04b0d6c9c83b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:16.291526+00:00", + "ended_at": "2026-05-19T16:08:19.663231+00:00", + "elapsed_ms": 3371.67, + "returncode": 1, + "prompt_metrics": { + "chars": 16312, + "bytes_utf8": 16312, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a3ff148d7134f9b2f5186f0e32f72a6753bda099 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=Department.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Department", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a3ff148d7134f9b2f5186f0e32f72a6753bda099 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=Department.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Department", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a72f7c46173cffc277c80ac7b738403bb702d6c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-ab01-7c72-87bf-af6cfb82c5f9"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9929e9f66a23781816c0632560813d2c778fb93d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-ab01-7c72-87bf-af6cfb82c5f9"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e30469c3bcaa52a67ee058acfb28f75dc03fa189 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-bbe5-72e2-8ac9-be2cc7656bfc"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..bf8dfa31679cf1907a5350b1eae66f9415c18bbf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-bbe5-72e2-8ac9-be2cc7656bfc"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_dd4960f03ec33bc6/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..93fee5310174f1ea7aceef287052d6932a56b3c9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16613, "bytes_utf8": 16613, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 368, "bytes_utf8": 368, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16750, "cached_input_tokens": 12032, "output_tokens": 255, "reasoning_output_tokens": 154}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..24806d1dd5764808a13f43d29a8c7eb5275d4aa5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_de76b4a85eebf400", + "api_calls": 0, + "input_tokens": 16750, + "cached_input_tokens": 12032, + "output_tokens": 255, + "total_tokens": 17005, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10796.97, + "sql_execution_elapsed_ms_total": 1.15, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..432e0582cc7bec1e0e8087cf8f97eaec39e3e11d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:04:30.520211+00:00", + "ended_at": "2026-05-19T16:04:41.317207+00:00", + "elapsed_ms": 10796.97, + "prompt_metrics": { + "chars": 16613, + "bytes_utf8": 16613, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 715, + "bytes_utf8": 715, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 368, + "bytes_utf8": 368, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16750, + "cached_input_tokens": 12032, + "output_tokens": 255, + "reasoning_output_tokens": 154 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..47b01cd81c841123e13d3cc9df4cf5123d2a3c56 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=Response_Quality, condition_col=Manager_Support_Level.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Response_Quality", + "condition_col": "Manager_Support_Level", + "condition_value": "Moderate", + "positive_value": "Moderate", + "negative_value": "High", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 46.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..04bb0d91ee14317583158f8e910f82059c46773a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-49c6-7772-ac3a-0848fc91268b"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"Response_Quality\\\",\\n AVG(CASE WHEN \\\"Manager_Support_Level\\\" = 'Moderate' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Response_Quality\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Grouped by Response_Quality and computed the share of rows where Manager_Support_Level is 'Moderate'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16750,"cached_input_tokens":12032,"output_tokens":255,"reasoning_output_tokens":154}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..87aa031d2ead64ab2939dcb6738f78438d939917 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"Response_Quality\",\n AVG(CASE WHEN \"Manager_Support_Level\" = 'Moderate' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"m1\"\nGROUP BY \"Response_Quality\"\nORDER BY condition_rate DESC;","notes":"Grouped by Response_Quality and computed the share of rows where Manager_Support_Level is 'Moderate'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_de76b4a85eebf400/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a36a089d4f29649285294a1052c0dab24863cbe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Efficiency_Rating. +Result preview: [{"Efficiency_Rating": "95.0"}, {"Efficiency_Rating": "95.0"}, {"Efficiency_Rating": "95.0"}, {"Efficiency_Rating": "95.0"}, {"Efficiency_Rating": "95.0"}] Results were truncated. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..e8d5df6bd1e50bea819ccbffe9f061f7526099d9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/generated_sql.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_m1_e1588fcccccc4ac0 +-- problem_id: v2p_m1_8ce786a0e18450bb +-- realization_mode: agent +-- source_kind: agent +WITH buckets AS ( + SELECT "Efficiency_Rating", + NTILE(10) OVER (ORDER BY CAST("Efficiency_Rating" AS REAL) DESC) AS tail_bucket + FROM "m1" +) +SELECT "Efficiency_Rating" +FROM buckets +WHERE tail_bucket = 1 +ORDER BY CAST("Efficiency_Rating" AS REAL) DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0ef74cd90452ef63d6166042152219e37785f13b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_quantile_tail_slice\nWITH buckets AS (\n SELECT \"Efficiency_Rating\",\n NTILE(10) OVER (ORDER BY CAST(\"Efficiency_Rating\" AS REAL) DESC) AS tail_bucket\n FROM \"m1\"\n)\nSELECT \"Efficiency_Rating\"\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY CAST(\"Efficiency_Rating\" AS REAL) DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n SELECT \\\"Efficiency_Rating\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"Efficiency_Rating\\\" AS REAL) DESC) AS tail_bucket\\n FROM \\\"m1\\\"\\n)\\nSELECT \\\"Efficiency_Rating\\\"\\nFROM buckets\\nWHERE tail_bucket = 1\\nORDER BY CAST(\\\"Efficiency_Rating\\\" AS REAL) DESC;\", \"columns\": [\"Efficiency_Rating\"], \"rows\": [{\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}, {\"Efficiency_Rating\": \"95.0\"}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 5.83}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f21fd8b4f0889c500f82168b39471db601a0c18c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/run_manifest.json @@ -0,0 +1,87 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:45:37.991533+00:00", + "ended_at": "2026-05-19T15:46:01.617252+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_e1588fcccccc4ac0", + "problem_id": "v2p_m1_8ce786a0e18450bb", + "dataset_id": "m1", + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_set_consistency", + "intended_facet_id": "low_support_extremes", + "variant_semantic_role": "rare_extreme_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Efficiency_Rating.", + "bindings": { + "measure_col": "Efficiency_Rating", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "notes": [ + "default_facets=low_support_extremes", + "template_selection_mode=rule", + "problem_index_within_template=5", + "sql_variant_index=1/1", + "binding_index=64" + ], + "template_selection_mode": "rule", + "selected_template_rank": 6, + "problem_index_within_template": 5, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_e1588fcccccc4ac0.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_e1588fcccccc4ac0", + "api_calls": 0, + "input_tokens": 16736, + "cached_input_tokens": 15744, + "output_tokens": 497, + "total_tokens": 17233, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 23612.82, + "sql_execution_elapsed_ms_total": 5.83, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a08e5bf83f7b31050c61d0309d70e9c3d3bc7f5d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:46:01.608383+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 23612.82, "started_at": "2026-05-19T15:45:37.993814+00:00", "ended_at": "2026-05-19T15:46:01.606677+00:00", "prompt_metrics": {"chars": 16507, "bytes_utf8": 16507, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 543, "bytes_utf8": 543, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16736, "cached_input_tokens": 15744, "output_tokens": 497, "reasoning_output_tokens": 356}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n SELECT \\\"Efficiency_Rating\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"Efficiency_Rating\\\" AS REAL) DESC) AS tail_bucket\\n FROM \\\"m1\\\"\\n)\\nSELECT \\\"Efficiency_Rating\\\"\\nFROM buckets\\nWHERE tail_bucket = 1\\nORDER BY CAST(\\\"Efficiency_Rating\\\" AS REAL) DESC;\",\"notes\":\"Applied the Quantile Tail Slice template with measure_col bound to \\\"Efficiency_Rating\\\" and num_tiles=10. Cast to REAL for correct numeric ordering because the SQLite column is stored as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..037c2937de3400b09f9755213595344920217591 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_e1588fcccccc4ac0", + "api_calls": 0, + "input_tokens": 16736, + "cached_input_tokens": 15744, + "output_tokens": 497, + "total_tokens": 17233, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 23612.82, + "sql_execution_elapsed_ms_total": 5.83, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e1588fcccccc4ac0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..5afeb6520f7243d519be1671681433b8e0b66f0b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"value_label": "Technology", "support": 475, "support_share": 0.31666666666666665, "support_rank": 1}, {"value_label": "Finance", "support": 223, "support_share": 0.14866666666666667, "support_rank": 2}, {"value_label": "Healthcare", "support": 185, "support_share": 0.12333333333333334, "support_rank": 3}, {"value_label": "Education", "support": 144, "support_share": 0.096, "support_rank": 4}, {"value_label": "Retail", "support": 106, "support_share": 0.07066666666666667, "support_rank": 5}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..46f9d9fa6d5bac88811bf2f2fe0ba33b7f295563 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/generated_sql.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_m1_e4eee0305f038aaa +-- problem_id: v2p_m1_9f06775f54aa4087 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "Industry" AS value_label, COUNT(*) AS support + FROM "m1" + GROUP BY "Industry" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c033d30e4eb0ea17bb5b1517343d1df820f96aef --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_a\n-- sql_source_dataset_id: m1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: support_concentration\n-- variant_semantic_role: count_distribution\n-- template_id: tpl_cardinality_support_rank_profile\n-- query_record_id: v2q_m1_e4eee0305f038aaa\n-- problem_id: v2p_m1_9f06775f54aa4087\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n SELECT \"Industry\" AS value_label, COUNT(*) AS support\n FROM \"m1\"\n GROUP BY \"Industry\"\n)\nSELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_a\\n-- sql_source_dataset_id: m1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: support_concentration\\n-- variant_semantic_role: count_distribution\\n-- template_id: tpl_cardinality_support_rank_profile\\n-- query_record_id: v2q_m1_e4eee0305f038aaa\\n-- problem_id: v2p_m1_9f06775f54aa4087\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n SELECT \\\"Industry\\\" AS value_label, COUNT(*) AS support\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Industry\\\"\\n)\\nSELECT\\n value_label,\\n support,\\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\\nFROM grouped\\nORDER BY support DESC, value_label;\", \"columns\": [\"value_label\", \"support\", \"support_share\", \"support_rank\"], \"rows\": [{\"value_label\": \"Technology\", \"support\": 475, \"support_share\": 0.31666666666666665, \"support_rank\": 1}, {\"value_label\": \"Finance\", \"support\": 223, \"support_share\": 0.14866666666666667, \"support_rank\": 2}, {\"value_label\": \"Healthcare\", \"support\": 185, \"support_share\": 0.12333333333333334, \"support_rank\": 3}, {\"value_label\": \"Education\", \"support\": 144, \"support_share\": 0.096, \"support_rank\": 4}, {\"value_label\": \"Retail\", \"support\": 106, \"support_share\": 0.07066666666666667, \"support_rank\": 5}, {\"value_label\": \"Manufacturing\", \"support\": 93, \"support_share\": 0.062, \"support_rank\": 6}, {\"value_label\": \"Consulting\", \"support\": 88, \"support_share\": 0.058666666666666666, \"support_rank\": 7}, {\"value_label\": \"Government\", \"support\": 74, \"support_share\": 0.04933333333333333, \"support_rank\": 8}, {\"value_label\": \"Media\", \"support\": 72, \"support_share\": 0.048, \"support_rank\": 9}, {\"value_label\": \"Non-profit\", \"support\": 40, \"support_share\": 0.02666666666666667, \"support_rank\": 10}], \"row_count_returned\": 10, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 0.85}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..bdd84c4302b586c4d43fe6f1a80b6cd2ac18f915 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/run_manifest.json @@ -0,0 +1,57 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:11:34.291174+00:00", + "ended_at": "2026-05-19T16:11:34.292639+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_e4eee0305f038aaa", + "problem_id": "v2p_m1_9f06775f54aa4087", + "dataset_id": "m1", + "template_id": "tpl_cardinality_support_rank_profile", + "template_name": "Cardinality Support Rank Profile", + "family_id": "cardinality_structure", + "canonical_subitem_id": "support_rank_profile_consistency", + "intended_facet_id": "support_concentration", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template Cardinality Support Rank Profile to probe support_rank_profile_consistency with semantic role count_distribution. Focus on group_col=Industry.", + "bindings": { + "group_col": "Industry" + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col} AS value_label, COUNT(*) AS support\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", + "notes": [ + "default_facets=support_concentration,value_imbalance_profile", + "template_selection_mode=deterministic", + "problem_index_within_template=7", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 7, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_e4eee0305f038aaa.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e4eee0305f038aaa/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3b76b63c268c96bc4834438a53a924e4f6d4e8f5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16525, "bytes_utf8": 16525, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1855, "bytes_utf8": 1855, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16725, "cached_input_tokens": 15744, "output_tokens": 1124, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..4bd59a6689cf319c7f22ed40f983662fd7a3fda5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_e63bd12587827a46", + "api_calls": 0, + "input_tokens": 16725, + "cached_input_tokens": 15744, + "output_tokens": 1124, + "total_tokens": 17849, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 20089.83, + "sql_execution_elapsed_ms_total": 10.91, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5d0e52f8e799ae53c4429dc5a9cb2c8c17c68c3e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:56:38.460068+00:00", + "ended_at": "2026-05-19T15:56:58.549940+00:00", + "elapsed_ms": 20089.83, + "prompt_metrics": { + "chars": 16525, + "bytes_utf8": 16525, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2499, + "bytes_utf8": 2499, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1855, + "bytes_utf8": 1855, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16725, + "cached_input_tokens": 15744, + "output_tokens": 1124, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d124370e6b68403de6caa2f9a4b82034f21ca01d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=Has_Children, measure_col=Productivity_Score.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "Has_Children", + "measure_col": "Productivity_Score", + "top_k": 13, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 98.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0cbe7f196458fb1ca85fa04a5f952c7db61880e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-15bc-7a01-8314-2efbbca56548"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n SELECT\\n \\\"Has_Children\\\",\\n CAST(\\\"Productivity_Score\\\" AS REAL) AS \\\"measure\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Has_Children\\\" IS NOT NULL\\n AND \\\"Productivity_Score\\\" IS NOT NULL\\n),\\n\\\"ordered\\\" AS (\\n SELECT\\n \\\"Has_Children\\\",\\n \\\"measure\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"Has_Children\\\"\\n ORDER BY \\\"measure\\\"\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"Has_Children\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"base\\\"\\n),\\n\\\"percentile_pos\\\" AS (\\n SELECT DISTINCT\\n \\\"Has_Children\\\",\\n \\\"cnt\\\",\\n (1.0 + (\\\"cnt\\\" - 1) * 0.95) AS \\\"pos\\\"\\n FROM \\\"ordered\\\"\\n),\\n\\\"bounds\\\" AS (\\n SELECT\\n \\\"Has_Children\\\",\\n \\\"cnt\\\",\\n \\\"pos\\\",\\n CAST(\\\"pos\\\" AS INTEGER) AS \\\"lo_rn\\\",\\n CASE\\n WHEN \\\"pos\\\" = CAST(\\\"pos\\\" AS INTEGER) THEN CAST(\\\"pos\\\" AS INTEGER)\\n ELSE CAST(\\\"pos\\\" AS INTEGER) + 1\\n END AS \\\"hi_rn\\\"\\n FROM \\\"percentile_pos\\\"\\n)\\nSELECT\\n b.\\\"Has_Children\\\",\\n CASE\\n WHEN b.\\\"lo_rn\\\" = b.\\\"hi_rn\\\" THEN MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lo_rn\\\" THEN o.\\\"measure\\\" END)\\n ELSE\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lo_rn\\\" THEN o.\\\"measure\\\" END) +\\n (b.\\\"pos\\\" - b.\\\"lo_rn\\\") * (\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"hi_rn\\\" THEN o.\\\"measure\\\" END) -\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lo_rn\\\" THEN o.\\\"measure\\\" END)\\n )\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"bounds\\\" AS b\\nJOIN \\\"ordered\\\" AS o\\n ON o.\\\"Has_Children\\\" = b.\\\"Has_Children\\\"\\nGROUP BY\\n b.\\\"Has_Children\\\",\\n b.\\\"cnt\\\",\\n b.\\\"pos\\\",\\n b.\\\"lo_rn\\\",\\n b.\\\"hi_rn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses window functions plus linear interpolation to emulate the 95th percentile of \\\"Productivity_Score\\\" within each \\\"Has_Children\\\" group.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16725,"cached_input_tokens":15744,"output_tokens":1124,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..37e82b552aea811fa81d9b659ad52705053dcf90 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"base\" AS (\n SELECT\n \"Has_Children\",\n CAST(\"Productivity_Score\" AS REAL) AS \"measure\"\n FROM \"m1\"\n WHERE \"Has_Children\" IS NOT NULL\n AND \"Productivity_Score\" IS NOT NULL\n),\n\"ordered\" AS (\n SELECT\n \"Has_Children\",\n \"measure\",\n ROW_NUMBER() OVER (\n PARTITION BY \"Has_Children\"\n ORDER BY \"measure\"\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"Has_Children\"\n ) AS \"cnt\"\n FROM \"base\"\n),\n\"percentile_pos\" AS (\n SELECT DISTINCT\n \"Has_Children\",\n \"cnt\",\n (1.0 + (\"cnt\" - 1) * 0.95) AS \"pos\"\n FROM \"ordered\"\n),\n\"bounds\" AS (\n SELECT\n \"Has_Children\",\n \"cnt\",\n \"pos\",\n CAST(\"pos\" AS INTEGER) AS \"lo_rn\",\n CASE\n WHEN \"pos\" = CAST(\"pos\" AS INTEGER) THEN CAST(\"pos\" AS INTEGER)\n ELSE CAST(\"pos\" AS INTEGER) + 1\n END AS \"hi_rn\"\n FROM \"percentile_pos\"\n)\nSELECT\n b.\"Has_Children\",\n CASE\n WHEN b.\"lo_rn\" = b.\"hi_rn\" THEN MAX(CASE WHEN o.\"rn\" = b.\"lo_rn\" THEN o.\"measure\" END)\n ELSE\n MAX(CASE WHEN o.\"rn\" = b.\"lo_rn\" THEN o.\"measure\" END) +\n (b.\"pos\" - b.\"lo_rn\") * (\n MAX(CASE WHEN o.\"rn\" = b.\"hi_rn\" THEN o.\"measure\" END) -\n MAX(CASE WHEN o.\"rn\" = b.\"lo_rn\" THEN o.\"measure\" END)\n )\n END AS \"percentile_measure\"\nFROM \"bounds\" AS b\nJOIN \"ordered\" AS o\n ON o.\"Has_Children\" = b.\"Has_Children\"\nGROUP BY\n b.\"Has_Children\",\n b.\"cnt\",\n b.\"pos\",\n b.\"lo_rn\",\n b.\"hi_rn\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses window functions plus linear interpolation to emulate the 95th percentile of \"Productivity_Score\" within each \"Has_Children\" group."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_e63bd12587827a46/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..428683fe66d6471dbb9db362778cb79e38ac5291 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16615, "bytes_utf8": 16615, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 436, "bytes_utf8": 436, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16755, "cached_input_tokens": 12032, "output_tokens": 478, "reasoning_output_tokens": 358}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..31fda523dea0f0be414b18bd248db2286d4b848a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_eaa2a0e330571993", + "api_calls": 0, + "input_tokens": 16755, + "cached_input_tokens": 12032, + "output_tokens": 478, + "total_tokens": 17233, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11916.79, + "sql_execution_elapsed_ms_total": 1.22, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..4bab997ed39ca5b0485f5b39440de2bdc8d300c3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:58.919129+00:00", + "ended_at": "2026-05-19T16:04:10.835949+00:00", + "elapsed_ms": 11916.79, + "prompt_metrics": { + "chars": 16615, + "bytes_utf8": 16615, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 783, + "bytes_utf8": 783, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 436, + "bytes_utf8": 436, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16755, + "cached_input_tokens": 12032, + "output_tokens": 478, + "reasoning_output_tokens": 358 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..85a337ed2b57d473e80924567875f7bd17834cb3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=Work_Life_Balance, condition_col=Home_Office_Quality.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Work_Life_Balance", + "condition_col": "Home_Office_Quality", + "condition_value": "Average", + "positive_value": "Good", + "negative_value": "Average", + "top_k": 19, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..7ae4de734d9ad4d96591f509bcf4315207897d8c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-ce5a-7e60-81e9-9eb5d76ff884"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"Work_Life_Balance\\\",\\n AVG(CASE WHEN \\\"Home_Office_Quality\\\" = 'Average' THEN 1.0 ELSE 0 END) AS condition_rate\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Work_Life_Balance\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Uses the planned Grouped Condition Rate template to compute the within-group proportion of rows where Home_Office_Quality is 'Average' for each Work_Life_Balance value.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16755,"cached_input_tokens":12032,"output_tokens":478,"reasoning_output_tokens":358}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ef27a54ada0c661a74df19df73e986dee934fab --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"Work_Life_Balance\",\n AVG(CASE WHEN \"Home_Office_Quality\" = 'Average' THEN 1.0 ELSE 0 END) AS condition_rate\nFROM \"m1\"\nGROUP BY \"Work_Life_Balance\"\nORDER BY condition_rate DESC;","notes":"Uses the planned Grouped Condition Rate template to compute the within-group proportion of rows where Home_Office_Quality is 'Average' for each Work_Life_Balance value."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eaa2a0e330571993/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ad0ecb698f4bc193f1196bcc154f6b36e00caf57 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16512, "bytes_utf8": 16512, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 617, "bytes_utf8": 617, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16740, "cached_input_tokens": 12032, "output_tokens": 590, "reasoning_output_tokens": 416}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..065928a798ab0b70ebd9956a1f5f27cf6748924a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_eab928ed73fc2917", + "api_calls": 0, + "input_tokens": 16740, + "cached_input_tokens": 12032, + "output_tokens": 590, + "total_tokens": 17330, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15300.99, + "sql_execution_elapsed_ms_total": 2.97, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..74033ff797d5271b7a68358fe8be0445879b9f89 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:44:51.684589+00:00", + "ended_at": "2026-05-19T15:45:06.985607+00:00", + "elapsed_ms": 15300.99, + "prompt_metrics": { + "chars": 16512, + "bytes_utf8": 16512, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1001, + "bytes_utf8": 1001, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 617, + "bytes_utf8": 617, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16740, + "cached_input_tokens": 12032, + "output_tokens": 590, + "reasoning_output_tokens": 416 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6dfda1c10944d177d5eca866775fe5fa8277302a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Task_Completion_Rate.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "Task_Completion_Rate", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.1, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..13fcb5a13f313f997671a9046bf676f5378d1890 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e9-4cfa-7520-a8a1-d591329e00fd"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT CAST(\\\"Task_Completion_Rate\\\" AS REAL) AS \\\"Task_Completion_Rate\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"Task_Completion_Rate\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"m1\\\"\\n WHERE \\\"Task_Completion_Rate\\\" IS NOT NULL\\n)\\nSELECT \\\"Task_Completion_Rate\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"Task_Completion_Rate\\\" DESC;\",\"notes\":\"Used the requested Quantile Tail Slice template with \\\"Task_Completion_Rate\\\" as the measure column. Cast to REAL because the schema stores numeric values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16740,"cached_input_tokens":12032,"output_tokens":590,"reasoning_output_tokens":416}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..400421b495f9fb9ebe98f6be819b9deb9bbeb84b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n SELECT CAST(\"Task_Completion_Rate\" AS REAL) AS \"Task_Completion_Rate\",\n NTILE(10) OVER (ORDER BY CAST(\"Task_Completion_Rate\" AS REAL) DESC) AS \"tail_bucket\"\n FROM \"m1\"\n WHERE \"Task_Completion_Rate\" IS NOT NULL\n)\nSELECT \"Task_Completion_Rate\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY \"Task_Completion_Rate\" DESC;","notes":"Used the requested Quantile Tail Slice template with \"Task_Completion_Rate\" as the measure column. Cast to REAL because the schema stores numeric values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eab928ed73fc2917/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..419647f69511223a0143b7b9911d33cbb91d00e2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16246, "bytes_utf8": 16246, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 310, "bytes_utf8": 310, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16660, "cached_input_tokens": 12032, "output_tokens": 431, "reasoning_output_tokens": 341}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..6c77d007dbfbd2201762d7871bb118017c9e7d88 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_eb0b2e475105a31c", + "api_calls": 0, + "input_tokens": 16660, + "cached_input_tokens": 12032, + "output_tokens": 431, + "total_tokens": 17091, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 24720.67, + "sql_execution_elapsed_ms_total": 1.21, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..85a2b0602066bfba868176e1f121127f7d587501 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:32:27.039250+00:00", + "ended_at": "2026-05-19T15:32:51.759963+00:00", + "elapsed_ms": 24720.67, + "prompt_metrics": { + "chars": 16246, + "bytes_utf8": 16246, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1666, + "bytes_utf8": 1666, + "lines": 7, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 310, + "bytes_utf8": 310, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16660, + "cached_input_tokens": 12032, + "output_tokens": 431, + "reasoning_output_tokens": 341 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a16f50f538eb36a84eb119e9ca9145ee9d10b7b5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=Work_Life_Balance.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "Work_Life_Balance", + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 41.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..61ad86d8e91828cc6ae8db79e30d38dcb5b1ff8a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,7 @@ +{"type":"thread.started","thread_id":"019e40dd-f033-7af3-b97b-f7fa07ae336e"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1983071, Requested 17295. Please try again in 10ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1990308, Requested 17295. Please try again in 228ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 17295. Please try again in 518ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"Work_Life_Balance\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Work_Life_Balance\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the grouped count template with \\\"Work_Life_Balance\\\" as the bound category column on the single table \\\"m1\\\".\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16660,"cached_input_tokens":12032,"output_tokens":431,"reasoning_output_tokens":341}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..db0d669aec2feac3ff3af3d91ca1ca3609a0b2ae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"Work_Life_Balance\", COUNT(*) AS \"row_count\"\nFROM \"m1\"\nGROUP BY \"Work_Life_Balance\"\nORDER BY \"row_count\" DESC;","notes":"Uses the grouped count template with \"Work_Life_Balance\" as the bound category column on the single table \"m1\"."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eb0b2e475105a31c/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..01f7479575b1a1c4ff7a04c4bf104c87c32062ae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:32.882305+00:00", + "ended_at": "2026-05-19T16:05:37.951804+00:00", + "elapsed_ms": 5069.47, + "returncode": 1, + "prompt_metrics": { + "chars": 16600, + "bytes_utf8": 16600, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9c8db9c6963f469d477da19cee7eb01b4d5ba85e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:38.953811+00:00", + "ended_at": "2026-05-19T16:05:42.121624+00:00", + "elapsed_ms": 3167.78, + "returncode": 1, + "prompt_metrics": { + "chars": 16600, + "bytes_utf8": 16600, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0bdba4b4d26c333d21928a203814a24988eebb27 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Marital_Status, condition_col=WFH_Days_Per_Week.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Marital_Status", + "condition_col": "WFH_Days_Per_Week", + "condition_value": "4", + "positive_value": "4", + "negative_value": "3", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.225, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..0bdba4b4d26c333d21928a203814a24988eebb27 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Marital_Status, condition_col=WFH_Days_Per_Week.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Marital_Status", + "condition_col": "WFH_Days_Per_Week", + "condition_value": "4", + "positive_value": "4", + "negative_value": "3", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 96.225, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f49349600d55ace823849d5c64aa23e4316548c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-3d61-7652-96d0-0896e9901f39"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..079fb3cd763289ac27a4f32e0279c85b167c2f39 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-3d61-7652-96d0-0896e9901f39"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..48ef755e175cfdf3495891e75c602f70fb41c219 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-550a-7413-88b9-b08a141fc195"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..806b378417ac04bd946395302c07af4a1c0ae2c8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-550a-7413-88b9-b08a141fc195"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ed1ba06fea9912a0/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e0ba736786ab57f4237a858e3265b3b8c13a0c6f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16615, "bytes_utf8": 16615, "lines": 459, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 16615, "bytes_utf8": 16615, "lines": 459, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 495, "bytes_utf8": 495, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16747, "cached_input_tokens": 12032, "output_tokens": 347, "reasoning_output_tokens": 223}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..064a47d2b97cc1901c50f0de8aa9cfff5b1dd25d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_eda05bbe275e9b00", + "api_calls": 0, + "input_tokens": 16747, + "cached_input_tokens": 12032, + "output_tokens": 347, + "total_tokens": 17094, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12967.49, + "sql_execution_elapsed_ms_total": 2.04, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d059772d35d08cf93b242a1063e81e1a9190fe0f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:50.704832+00:00", + "ended_at": "2026-05-19T16:02:54.250612+00:00", + "elapsed_ms": 3545.75, + "returncode": 1, + "prompt_metrics": { + "chars": 16615, + "bytes_utf8": 16615, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..83b3e97fbfc32cbb82b359940d6fc2b8f43acc24 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:55.253633+00:00", + "ended_at": "2026-05-19T16:03:04.675423+00:00", + "elapsed_ms": 9421.74, + "prompt_metrics": { + "chars": 16615, + "bytes_utf8": 16615, + "lines": 459, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 858, + "bytes_utf8": 858, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 495, + "bytes_utf8": 495, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16747, + "cached_input_tokens": 12032, + "output_tokens": 347, + "reasoning_output_tokens": 223 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..3bdceb9297d3b807d1b2a7f1ac841b083b6382e7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Manager_Support_Level, condition_col=Location_Type.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Manager_Support_Level", + "condition_col": "Location_Type", + "condition_value": "Urban", + "positive_value": "Suburban", + "negative_value": "Urban", + "top_k": 16, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..3bdceb9297d3b807d1b2a7f1ac841b083b6382e7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,459 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Manager_Support_Level, condition_col=Location_Type.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "Manager_Support_Level", + "condition_col": "Location_Type", + "condition_value": "Urban", + "positive_value": "Suburban", + "negative_value": "Urban", + "top_k": 16, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e8a125b868fb6b9ffb47318e437807763bf93746 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-c3d0-7340-86e9-1162dd7d2db2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c5f0665895ea70fc70e3dc73cc79fd15aa290c2d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-c3d0-7340-86e9-1162dd7d2db2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..33e11b729e9fc27685c68d674792172b64d72cea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-d59d-7bf0-a45d-6c717bc805dd"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"Manager_Support_Level\\\",\\n AVG(CASE WHEN \\\"Location_Type\\\" = 'Urban' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Manager_Support_Level\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Uses the provided Grouped Condition Rate template with group_col=\\\"Manager_Support_Level\\\" and condition_col=\\\"Location_Type\\\", measuring the share of rows where Location_Type is 'Urban' within each manager support group.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16747,"cached_input_tokens":12032,"output_tokens":347,"reasoning_output_tokens":223}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..bfff76f0e6736aa2faf97ca4128cb5b390840c5c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"Manager_Support_Level\",\n AVG(CASE WHEN \"Location_Type\" = 'Urban' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m1\"\nGROUP BY \"Manager_Support_Level\"\nORDER BY \"condition_rate\" DESC;","notes":"Uses the provided Grouped Condition Rate template with group_col=\"Manager_Support_Level\" and condition_col=\"Location_Type\", measuring the share of rows where Location_Type is 'Urban' within each manager support group."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eda05bbe275e9b00/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..4613cdc3555ed1f33d9aba707533549e2e681f00 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Industry, measure_col=Job_Satisfaction. +Result preview: [{"Industry": "Retail", "Years_Experience": "0", "total_measure": 2348.6, "share_within_group": 23.19879886998953}, {"Industry": "Media", "Years_Experience": "0", "total_measure": 1591.9, "share_within_group": 23.17074945781116}, {"Industry": "Retail", "Years_Experience": "1", "total_measure": 2289.7, "share_within_group": 22.61700152116794}, {"Industry": "Manufacturing", "Years_Experience": "0", "total_measure": 1715.6, "share_within_group": 19.41646483623441}, {"Industry": "Consulting", "Years_Experience": "0", "total_measure": 1582.7, "share_within_group": 18.746816701214094}] Results were truncated. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..daf5ebdc55083b693c6211457966119c23e705f7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_m1_ee7e37d3f8e9fc22 +-- problem_id: v2p_m1_9f670a67346461d0 +-- realization_mode: agent +-- source_kind: agent +SELECT + "Industry", + "Years_Experience", + SUM(CAST("Job_Satisfaction" AS REAL)) AS total_measure, + SUM(CAST("Job_Satisfaction" AS REAL)) * 100.0 / SUM(SUM(CAST("Job_Satisfaction" AS REAL))) OVER (PARTITION BY "Industry") AS share_within_group +FROM "m1" +GROUP BY "Industry", "Years_Experience" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b35591abdb3304b5c735f2ec47df35d5641a0e27 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"Industry\",\n \"Years_Experience\",\n SUM(CAST(\"Job_Satisfaction\" AS REAL)) AS total_measure,\n SUM(CAST(\"Job_Satisfaction\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"Job_Satisfaction\" AS REAL))) OVER (PARTITION BY \"Industry\") AS share_within_group\nFROM \"m1\"\nGROUP BY \"Industry\", \"Years_Experience\"\nORDER BY share_within_group DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"Industry\\\",\\n \\\"Years_Experience\\\",\\n SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL))) OVER (PARTITION BY \\\"Industry\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Industry\\\", \\\"Years_Experience\\\"\\nORDER BY share_within_group DESC;\", \"columns\": [\"Industry\", \"Years_Experience\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"Industry\": \"Retail\", \"Years_Experience\": \"0\", \"total_measure\": 2348.6, \"share_within_group\": 23.19879886998953}, {\"Industry\": \"Media\", \"Years_Experience\": \"0\", \"total_measure\": 1591.9, \"share_within_group\": 23.17074945781116}, {\"Industry\": \"Retail\", \"Years_Experience\": \"1\", \"total_measure\": 2289.7, \"share_within_group\": 22.61700152116794}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"0\", \"total_measure\": 1715.6, \"share_within_group\": 19.41646483623441}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"0\", \"total_measure\": 1582.7, \"share_within_group\": 18.746816701214094}, {\"Industry\": \"Non-profit\", \"Years_Experience\": \"1\", \"total_measure\": 674.7, \"share_within_group\": 18.10594675826535}, {\"Industry\": \"Government\", \"Years_Experience\": \"0\", \"total_measure\": 1263.9, \"share_within_group\": 18.096042609243458}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"0\", \"total_measure\": 3186.8, \"share_within_group\": 17.750002784925755}, {\"Industry\": \"Education\", \"Years_Experience\": \"0\", \"total_measure\": 2424.8, \"share_within_group\": 17.71789326006898}, {\"Industry\": \"Government\", \"Years_Experience\": \"1\", \"total_measure\": 1203.1, \"share_within_group\": 17.225531183780994}, {\"Industry\": \"Finance\", \"Years_Experience\": \"1\", \"total_measure\": 3259.6, \"share_within_group\": 15.297469037596032}, {\"Industry\": \"Technology\", \"Years_Experience\": \"1\", \"total_measure\": 6879.8, \"share_within_group\": 15.249169361577625}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"2\", \"total_measure\": 1254.0, \"share_within_group\": 14.853420195439739}, {\"Industry\": \"Finance\", \"Years_Experience\": \"0\", \"total_measure\": 3091.6, \"share_within_group\": 14.509036469699318}, {\"Industry\": \"Technology\", \"Years_Experience\": \"0\", \"total_measure\": 6241.2, \"share_within_group\": 13.833703860501508}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"3\", \"total_measure\": 1158.9, \"share_within_group\": 13.726976606455436}, {\"Industry\": \"Non-profit\", \"Years_Experience\": \"0\", \"total_measure\": 505.7, \"share_within_group\": 13.570738514383855}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"1\", \"total_measure\": 1143.0, \"share_within_group\": 13.538643766656795}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"2\", \"total_measure\": 1196.0, \"share_within_group\": 13.535842821249917}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"1\", \"total_measure\": 1180.3, \"share_within_group\": 13.35815659023518}, {\"Industry\": \"Education\", \"Years_Experience\": \"1\", \"total_measure\": 1828.1, \"share_within_group\": 13.357835973578068}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"4\", \"total_measure\": 1171.1, \"share_within_group\": 13.25403472237941}, {\"Industry\": \"Media\", \"Years_Experience\": \"3\", \"total_measure\": 880.7, \"share_within_group\": 12.818945315342852}, {\"Industry\": \"Media\", \"Years_Experience\": \"6\", \"total_measure\": 873.5, \"share_within_group\": 12.714146398264996}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"1\", \"total_measure\": 2196.3, \"share_within_group\": 12.233064866490661}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"2\", \"total_measure\": 2162.8, \"share_within_group\": 12.046474840980741}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"3\", \"total_measure\": 1036.4, \"share_within_group\": 11.729554765838976}, {\"Industry\": \"Media\", \"Years_Experience\": \"1\", \"total_measure\": 750.1, \"share_within_group\": 10.918009402791727}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"3\", \"total_measure\": 1937.2, \"share_within_group\": 10.789916340830354}, {\"Industry\": \"Technology\", \"Years_Experience\": \"3\", \"total_measure\": 4761.2, \"share_within_group\": 10.55326392690825}, {\"Industry\": \"Education\", \"Years_Experience\": \"5\", \"total_measure\": 1425.7, \"share_within_group\": 10.417519144209972}, {\"Industry\": \"Retail\", \"Years_Experience\": \"5\", \"total_measure\": 1046.5, \"share_within_group\": 10.337027598332643}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"4\", \"total_measure\": 858.2, \"share_within_group\": 10.165235416049748}, {\"Industry\": \"Technology\", \"Years_Experience\": \"2\", \"total_measure\": 4563.1, \"share_within_group\": 10.114172608769858}, {\"Industry\": \"Education\", \"Years_Experience\": \"2\", \"total_measure\": 1329.5, \"share_within_group\": 9.714590518501199}, {\"Industry\": \"Technology\", \"Years_Experience\": \"4\", \"total_measure\": 4337.3, \"share_within_group\": 9.613683867550021}, {\"Industry\": \"Government\", \"Years_Experience\": \"2\", \"total_measure\": 664.7, \"share_within_group\": 9.516923429356853}, {\"Industry\": \"Education\", \"Years_Experience\": \"3\", \"total_measure\": 1290.4, \"share_within_group\": 9.428888758987549}, {\"Industry\": \"Finance\", \"Years_Experience\": \"2\", \"total_measure\": 1880.1, \"share_within_group\": 8.823405183944134}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"6\", \"total_measure\": 1556.0, \"share_within_group\": 8.666688946072698}, {\"Industry\": \"Finance\", \"Years_Experience\": \"4\", \"total_measure\": 1830.6, \"share_within_group\": 8.591099159474567}, {\"Industry\": \"Non-profit\", \"Years_Experience\": \"2\", \"total_measure\": 314.4, \"share_within_group\": 8.437097466723914}, {\"Industry\": \"Consulting\", \"Years_Experience\": \"5\", \"total_measure\": 699.5, \"share_within_group\": 8.285460467870891}, {\"Industry\": \"Non-profit\", \"Years_Experience\": \"7\", \"total_measure\": 300.0, \"share_within_group\": 8.050665521683126}, {\"Industry\": \"Manufacturing\", \"Years_Experience\": \"5\", \"total_measure\": 700.0, \"share_within_group\": 7.922316032504131}, {\"Industry\": \"Healthcare\", \"Years_Experience\": \"5\", \"total_measure\": 1350.3, \"share_within_group\": 7.520970490926712}, {\"Industry\": \"Finance\", \"Years_Experience\": \"7\", \"total_measure\": 1585.1, \"share_within_group\": 7.438955139125498}, {\"Industry\": \"Non-profit\", \"Years_Experience\": \"3\", \"total_measure\": 273.6, \"share_within_group\": 7.342206955775011}, {\"Industry\": \"Media\", \"Years_Experience\": \"4\", \"total_measure\": 500.0, \"share_within_group\": 7.277702574851171}, {\"Industry\": \"Technology\", \"Years_Experience\": \"5\", \"total_measure\": 3250.7, \"share_within_group\": 7.205220332521351}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 3.8}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1e70e0d3bc8739487325083d66d8a44faca552ea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/run_manifest.json @@ -0,0 +1,91 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:34:47.276017+00:00", + "ended_at": "2026-05-19T15:35:07.585801+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_ee7e37d3f8e9fc22", + "problem_id": "v2p_m1_9f670a67346461d0", + "dataset_id": "m1", + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "dependency_strength_similarity", + "intended_facet_id": "pairwise_conditional_dependency", + "variant_semantic_role": "within_group_proportion", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Industry, measure_col=Job_Satisfaction.", + "bindings": { + "group_col": "Industry", + "measure_col": "Job_Satisfaction", + "item_col": "Years_Experience", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 100.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "item_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "notes": [ + "default_facets=pairwise_conditional_dependency", + "template_selection_mode=rule", + "problem_index_within_template=2", + "sql_variant_index=2/2", + "binding_index=25" + ], + "template_selection_mode": "rule", + "selected_template_rank": 3, + "problem_index_within_template": 2, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_ee7e37d3f8e9fc22.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_ee7e37d3f8e9fc22", + "api_calls": 0, + "input_tokens": 16801, + "cached_input_tokens": 12032, + "output_tokens": 694, + "total_tokens": 17495, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 20299.02, + "sql_execution_elapsed_ms_total": 3.8, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..504894accd980f210dd69294860ff5ebfdf952ed --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:35:07.579444+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 20299.02, "started_at": "2026-05-19T15:34:47.279090+00:00", "ended_at": "2026-05-19T15:35:07.578154+00:00", "prompt_metrics": {"chars": 16755, "bytes_utf8": 16755, "lines": 458, "estimated_tokens": null}, "response_metrics": {"chars": 660, "bytes_utf8": 660, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16801, "cached_input_tokens": 12032, "output_tokens": 694, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"Industry\\\",\\n \\\"Years_Experience\\\",\\n SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Job_Satisfaction\\\" AS REAL))) OVER (PARTITION BY \\\"Industry\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Industry\\\", \\\"Years_Experience\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the Within-Group Share of Total template with group_col=\\\"Industry\\\", item_col=\\\"Years_Experience\\\", and measure_col=\\\"Job_Satisfaction\\\". CAST to REAL is used because \\\"Job_Satisfaction\\\" is stored as TEXT in the schema snapshot.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..32d1a99480fd0419b29e5be67e7937076052874a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_ee7e37d3f8e9fc22", + "api_calls": 0, + "input_tokens": 16801, + "cached_input_tokens": 12032, + "output_tokens": 694, + "total_tokens": 17495, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 20299.02, + "sql_execution_elapsed_ms_total": 3.8, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_ee7e37d3f8e9fc22/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ef09d5f43d13985e29c1e13d62cbbf990bcba49f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:59.845725+00:00", + "ended_at": "2026-05-19T16:07:02.601294+00:00", + "elapsed_ms": 2755.54, + "returncode": 1, + "prompt_metrics": { + "chars": 16321, + "bytes_utf8": 16321, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..cad9352fcaac47e96ef53d6e6c92241700fc505e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:03.603069+00:00", + "ended_at": "2026-05-19T16:07:06.611250+00:00", + "elapsed_ms": 3008.14, + "returncode": 1, + "prompt_metrics": { + "chars": 16321, + "bytes_utf8": 16321, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..05a9aaa1bd9947af9947a9d2bdfabc2b7de7e233 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=Education_Level.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Education_Level", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..05a9aaa1bd9947af9947a9d2bdfabc2b7de7e233 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=Education_Level.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Education_Level", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0bbe229634dc64f9653c328524cdc398dbe489a0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-911a-77d2-aad6-91109d0cb2f7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..12055ec52bee47fc50b279d5251d6ad648ec7762 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-911a-77d2-aad6-91109d0cb2f7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..ea12f25e5448a259f9e9f6a05818b1d95887e49a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-9fbe-77c1-abc7-f176b02edefb"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..2fd9d1fe1a0e702bdd2911827e593e2bfc83cdce --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-9fbe-77c1-abc7-f176b02edefb"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_eeafb0b1bd2aae82/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..ed1841dd957b16dc2b7d446966b94997f04d935e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"value_label": "6", "support": 303, "support_share": 0.202, "cumulative_support": 303}, {"value_label": "7", "support": 279, "support_share": 0.186, "cumulative_support": 582}, {"value_label": "8", "support": 217, "support_share": 0.14466666666666667, "cumulative_support": 799}, {"value_label": "5", "support": 194, "support_share": 0.12933333333333333, "cumulative_support": 993}, {"value_label": "4", "support": 132, "support_share": 0.088, "cumulative_support": 1125}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..61dc8a871e137603352571c38a64b15e476ebc41 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/generated_sql.sql @@ -0,0 +1,28 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_cardinality_distinct_share_profile +-- query_record_id: v2q_m1_f00e75c0ac1e792a +-- problem_id: v2p_m1_4b5de089e34e887b +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "Work_Life_Balance" AS value_label, COUNT(*) AS support + FROM "m1" + GROUP BY "Work_Life_Balance" +), ranked AS ( + SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support + FROM grouped +) +SELECT * +FROM ranked +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..778c1670a9f1c4bfe411700d82bb5f1f5a598e52 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_a\n-- sql_source_dataset_id: m1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: support_concentration\n-- variant_semantic_role: ranked_signal_view\n-- template_id: tpl_cardinality_distinct_share_profile\n-- query_record_id: v2q_m1_f00e75c0ac1e792a\n-- problem_id: v2p_m1_4b5de089e34e887b\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n SELECT \"Work_Life_Balance\" AS value_label, COUNT(*) AS support\n FROM \"m1\"\n GROUP BY \"Work_Life_Balance\"\n), ranked AS (\n SELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\n FROM grouped\n)\nSELECT *\nFROM ranked\nORDER BY support DESC, value_label;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_a\\n-- sql_source_dataset_id: m1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: support_concentration\\n-- variant_semantic_role: ranked_signal_view\\n-- template_id: tpl_cardinality_distinct_share_profile\\n-- query_record_id: v2q_m1_f00e75c0ac1e792a\\n-- problem_id: v2p_m1_4b5de089e34e887b\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n SELECT \\\"Work_Life_Balance\\\" AS value_label, COUNT(*) AS support\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Work_Life_Balance\\\"\\n), ranked AS (\\n SELECT\\n value_label,\\n support,\\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\\n SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\\n FROM grouped\\n)\\nSELECT *\\nFROM ranked\\nORDER BY support DESC, value_label;\", \"columns\": [\"value_label\", \"support\", \"support_share\", \"cumulative_support\"], \"rows\": [{\"value_label\": \"6\", \"support\": 303, \"support_share\": 0.202, \"cumulative_support\": 303}, {\"value_label\": \"7\", \"support\": 279, \"support_share\": 0.186, \"cumulative_support\": 582}, {\"value_label\": \"8\", \"support\": 217, \"support_share\": 0.14466666666666667, \"cumulative_support\": 799}, {\"value_label\": \"5\", \"support\": 194, \"support_share\": 0.12933333333333333, \"cumulative_support\": 993}, {\"value_label\": \"4\", \"support\": 132, \"support_share\": 0.088, \"cumulative_support\": 1125}, {\"value_label\": \"9\", \"support\": 130, \"support_share\": 0.08666666666666667, \"cumulative_support\": 1255}, {\"value_label\": \"3\", \"support\": 91, \"support_share\": 0.06066666666666667, \"cumulative_support\": 1346}, {\"value_label\": \"10\", \"support\": 76, \"support_share\": 0.050666666666666665, \"cumulative_support\": 1422}, {\"value_label\": \"2\", \"support\": 46, \"support_share\": 0.030666666666666665, \"cumulative_support\": 1468}, {\"value_label\": \"1\", \"support\": 32, \"support_share\": 0.021333333333333333, \"cumulative_support\": 1500}], \"row_count_returned\": 10, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.13}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..5072587fa4ac953b3cef436cea271cdb188cf598 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/run_manifest.json @@ -0,0 +1,57 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:11:34.274365+00:00", + "ended_at": "2026-05-19T16:11:34.276501+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_f00e75c0ac1e792a", + "problem_id": "v2p_m1_4b5de089e34e887b", + "dataset_id": "m1", + "template_id": "tpl_cardinality_distinct_share_profile", + "template_name": "Cardinality Distinct Share Profile", + "family_id": "cardinality_structure", + "canonical_subitem_id": "support_rank_profile_consistency", + "intended_facet_id": "support_concentration", + "variant_semantic_role": "ranked_signal_view", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template Cardinality Distinct Share Profile to probe support_rank_profile_consistency with semantic role ranked_signal_view. Focus on group_col=Work_Life_Balance.", + "bindings": { + "group_col": "Work_Life_Balance" + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col} AS value_label, COUNT(*) AS support\n FROM {table}\n GROUP BY {group_col}\n), ranked AS (\n SELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\n FROM grouped\n)\nSELECT *\nFROM ranked\nORDER BY support DESC, value_label;", + "notes": [ + "default_facets=support_concentration,value_imbalance_profile", + "template_selection_mode=deterministic", + "problem_index_within_template=11", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 11, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_f00e75c0ac1e792a.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f00e75c0ac1e792a/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..8ec07462f0fe9068fbb37dfd8c13f54afb922900 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16899, "bytes_utf8": 16899, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 703, "bytes_utf8": 703, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16825, "cached_input_tokens": 15744, "output_tokens": 636, "reasoning_output_tokens": 429}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..169edc73b45de90c72fea44f44fad7fe06f3a97f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_f0531f50454c2cb0", + "api_calls": 0, + "input_tokens": 16825, + "cached_input_tokens": 15744, + "output_tokens": 636, + "total_tokens": 17461, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12904.68, + "sql_execution_elapsed_ms_total": 1.79, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..63f088f5f2d5a147568a86bc30187f7cdc7740d2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:47:24.628533+00:00", + "ended_at": "2026-05-19T15:47:37.533273+00:00", + "elapsed_ms": 12904.68, + "prompt_metrics": { + "chars": 16899, + "bytes_utf8": 16899, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1123, + "bytes_utf8": 1123, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 703, + "bytes_utf8": 703, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16825, + "cached_input_tokens": 15744, + "output_tokens": 636, + "reasoning_output_tokens": 429 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7c3c3c38828e52d19013e3e818ccec4463f46b2d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Department, measure_col=Work_Hours_Per_Week.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "Department", + "measure_col": "Work_Hours_Per_Week", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 46.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5383580b1f56fbc242bf511049807d8e9604c799 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40eb-a2ed-7681-893f-d4a6205df60f"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold.\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"Department\\\",\\n SUM(CAST(\\\"Work_Hours_Per_Week\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Department\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT\\n g.\\\"Department\\\",\\n g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the requested template with group_col=\\\"Department\\\" and measure_col=\\\"Work_Hours_Per_Week\\\". Since the measure column is stored as TEXT in SQLite, it is cast to REAL before aggregation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16825,"cached_input_tokens":15744,"output_tokens":636,"reasoning_output_tokens":429}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0517194edd13dd81f4e3f15844dd3ee70ee83ecb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold.\nWITH \"grouped\" AS (\n SELECT\n \"Department\",\n SUM(CAST(\"Work_Hours_Per_Week\" AS REAL)) AS \"group_value\"\n FROM \"m1\"\n GROUP BY \"Department\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT\n g.\"Department\",\n g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;","notes":"Uses the requested template with group_col=\"Department\" and measure_col=\"Work_Hours_Per_Week\". Since the measure column is stored as TEXT in SQLite, it is cast to REAL before aggregation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f0531f50454c2cb0/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f9c1cd6ed2b55eaa94626600879dcb368b530cef --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16920, "bytes_utf8": 16920, "lines": 456, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 686, "bytes_utf8": 686, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16823, "cached_input_tokens": 15744, "output_tokens": 454, "reasoning_output_tokens": 279}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..0808d715780d6f184b80b1e378dd174fb243a7dd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_f1db6b76347295cf", + "api_calls": 0, + "input_tokens": 16823, + "cached_input_tokens": 15744, + "output_tokens": 454, + "total_tokens": 17277, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10158.99, + "sql_execution_elapsed_ms_total": 1.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b6d381df466e1e5b71fa089e9db58458b69ba2d7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:49:45.138903+00:00", + "ended_at": "2026-05-19T15:49:55.297922+00:00", + "elapsed_ms": 10158.99, + "prompt_metrics": { + "chars": 16920, + "bytes_utf8": 16920, + "lines": 456, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1057, + "bytes_utf8": 1057, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 686, + "bytes_utf8": 686, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16823, + "cached_input_tokens": 15744, + "output_tokens": 454, + "reasoning_output_tokens": 279 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2425946c2c5cf3d4f0489d0577f152316eb96151 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,456 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Internet_Speed_Category, measure_col=Efficiency_Rating.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "Internet_Speed_Category", + "measure_col": "Efficiency_Rating", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 95.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..38d3bf180a0caf8faae0a62ca2c4239ca19406df --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ed-c739-7dc0-a4f1-33b89f41c096"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"Internet_Speed_Category\\\", SUM(CAST(\\\"Efficiency_Rating\\\" AS REAL)) AS group_value\\n FROM \\\"m1\\\"\\n GROUP BY \\\"Internet_Speed_Category\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"Internet_Speed_Category\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided template with group_col=\\\"Internet_Speed_Category\\\" and measure_col=\\\"Efficiency_Rating\\\". Casted \\\"Efficiency_Rating\\\" to REAL because the SQLite schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16823,"cached_input_tokens":15744,"output_tokens":454,"reasoning_output_tokens":279}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..beb69da1ff3db61a77c73c5895387e538e702c3a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"Internet_Speed_Category\", SUM(CAST(\"Efficiency_Rating\" AS REAL)) AS group_value\n FROM \"m1\"\n GROUP BY \"Internet_Speed_Category\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"Internet_Speed_Category\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;","notes":"Applied the provided template with group_col=\"Internet_Speed_Category\" and measure_col=\"Efficiency_Rating\". Casted \"Efficiency_Rating\" to REAL because the SQLite schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f1db6b76347295cf/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..3eb13c9f822452008e35f776399babb285fabddb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Work_Life_Balance, measure_col=Work_Hours_Per_Week. +Result preview: [{"Work_Life_Balance": "1", "Efficiency_Rating": "95.0", "total_measure": 960.0, "share_within_group": 72.23476297968398}, {"Work_Life_Balance": "10", "Efficiency_Rating": "95.0", "total_measure": 2293.0, "share_within_group": 71.58913518576335}, {"Work_Life_Balance": "4", "Efficiency_Rating": "95.0", "total_measure": 3917.0, "share_within_group": 71.50419861263235}, {"Work_Life_Balance": "9", "Efficiency_Rating": "95.0", "total_measure": 3825.0, "share_within_group": 70.2608376193975}, {"Work_Life_Balance": "8", "Efficiency_Rating": "95.0", "total_measure": 6189.0, "share_within_group": 67.3449401523395}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..97798d007cf3414db2f7b5b5e99cab2400a24179 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/generated_sql.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: m1 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_m1_f683d596c73a902d +-- problem_id: v2p_m1_2dfa0367f15c1f68 +-- realization_mode: agent +-- source_kind: agent +SELECT + "Work_Life_Balance", + "Efficiency_Rating", + SUM(CAST("Work_Hours_Per_Week" AS REAL)) AS total_measure, + SUM(CAST("Work_Hours_Per_Week" AS REAL)) * 100.0 / SUM(SUM(CAST("Work_Hours_Per_Week" AS REAL))) OVER (PARTITION BY "Work_Life_Balance") AS share_within_group +FROM "m1" +GROUP BY "Work_Life_Balance", "Efficiency_Rating" +ORDER BY share_within_group DESC +LIMIT 16; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..fc334ce2b6d1b58273bce344154543d57cdb5b70 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"Work_Life_Balance\",\n \"Efficiency_Rating\",\n SUM(CAST(\"Work_Hours_Per_Week\" AS REAL)) AS total_measure,\n SUM(CAST(\"Work_Hours_Per_Week\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"Work_Hours_Per_Week\" AS REAL))) OVER (PARTITION BY \"Work_Life_Balance\") AS share_within_group\nFROM \"m1\"\nGROUP BY \"Work_Life_Balance\", \"Efficiency_Rating\"\nORDER BY share_within_group DESC\nLIMIT 16;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"Work_Life_Balance\\\",\\n \\\"Efficiency_Rating\\\",\\n SUM(CAST(\\\"Work_Hours_Per_Week\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Work_Hours_Per_Week\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Work_Hours_Per_Week\\\" AS REAL))) OVER (PARTITION BY \\\"Work_Life_Balance\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Work_Life_Balance\\\", \\\"Efficiency_Rating\\\"\\nORDER BY share_within_group DESC\\nLIMIT 16;\", \"columns\": [\"Work_Life_Balance\", \"Efficiency_Rating\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"Work_Life_Balance\": \"1\", \"Efficiency_Rating\": \"95.0\", \"total_measure\": 960.0, \"share_within_group\": 72.23476297968398}, {\"Work_Life_Balance\": \"10\", \"Efficiency_Rating\": \"95.0\", \"total_measure\": 2293.0, \"share_within_group\": 71.58913518576335}, {\"Work_Life_Balance\": \"4\", \"Efficiency_Rating\": \"95.0\", \"total_measure\": 3917.0, \"share_within_group\": 71.50419861263235}, {\"Work_Life_Balance\": \"9\", \"Efficiency_Rating\": \"95.0\", \"total_measure\": 3825.0, \"share_within_group\": 70.2608376193975}, {\"Work_Life_Balance\": \"8\", \"Efficiency_Rating\": \"95.0\", \"total_measure\": 6189.0, \"share_within_group\": 67.3449401523395}, {\"Work_Life_Balance\": \"6\", \"Efficiency_Rating\": \"95.0\", \"total_measure\": 8362.0, \"share_within_group\": 65.6770342444235}, {\"Work_Life_Balance\": \"5\", \"Efficiency_Rating\": \"95.0\", \"total_measure\": 5283.0, \"share_within_group\": 64.94959429554955}, {\"Work_Life_Balance\": \"3\", \"Efficiency_Rating\": \"95.0\", \"total_measure\": 2352.0, \"share_within_group\": 64.45601534667033}, {\"Work_Life_Balance\": \"7\", \"Efficiency_Rating\": \"95.0\", \"total_measure\": 7259.0, \"share_within_group\": 63.06141951177135}, {\"Work_Life_Balance\": \"2\", \"Efficiency_Rating\": \"95.0\", \"total_measure\": 1122.0, \"share_within_group\": 58.104609010875194}, {\"Work_Life_Balance\": \"1\", \"Efficiency_Rating\": \"63.1\", \"total_measure\": 53.0, \"share_within_group\": 3.9879608728367195}, {\"Work_Life_Balance\": \"1\", \"Efficiency_Rating\": \"93.5\", \"total_measure\": 48.0, \"share_within_group\": 3.6117381489841986}, {\"Work_Life_Balance\": \"1\", \"Efficiency_Rating\": \"78.5\", \"total_measure\": 45.0, \"share_within_group\": 3.386004514672686}, {\"Work_Life_Balance\": \"1\", \"Efficiency_Rating\": \"90.9\", \"total_measure\": 45.0, \"share_within_group\": 3.386004514672686}, {\"Work_Life_Balance\": \"1\", \"Efficiency_Rating\": \"89.4\", \"total_measure\": 44.0, \"share_within_group\": 3.310759969902182}, {\"Work_Life_Balance\": \"1\", \"Efficiency_Rating\": \"79.0\", \"total_measure\": 38.0, \"share_within_group\": 2.8592927012791574}], \"row_count_returned\": 16, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.73}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..dd2feb131905cd1c5b67f2b7a30f24b6958f088e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/run_manifest.json @@ -0,0 +1,91 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T15:37:48.801029+00:00", + "ended_at": "2026-05-19T15:38:06.627353+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_f683d596c73a902d", + "problem_id": "v2p_m1_2dfa0367f15c1f68", + "dataset_id": "m1", + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "dependency_strength_similarity", + "intended_facet_id": "pairwise_conditional_dependency", + "variant_semantic_role": "within_group_proportion", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Work_Life_Balance, measure_col=Work_Hours_Per_Week.", + "bindings": { + "group_col": "Work_Life_Balance", + "measure_col": "Work_Hours_Per_Week", + "item_col": "Efficiency_Rating", + "top_k": 16, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 45.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "item_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "notes": [ + "default_facets=pairwise_conditional_dependency", + "template_selection_mode=rule", + "problem_index_within_template=8", + "sql_variant_index=2/2", + "binding_index=31" + ], + "template_selection_mode": "rule", + "selected_template_rank": 3, + "problem_index_within_template": 8, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_f683d596c73a902d.sql", + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_f683d596c73a902d", + "api_calls": 0, + "input_tokens": 16815, + "cached_input_tokens": 15744, + "output_tokens": 992, + "total_tokens": 17807, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17819.76, + "sql_execution_elapsed_ms_total": 2.73, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4fac6174f1606e3f3ab8d15efc4c430ae7b50642 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:38:06.623081+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 17819.76, "started_at": "2026-05-19T15:37:48.802450+00:00", "ended_at": "2026-05-19T15:38:06.622240+00:00", "prompt_metrics": {"chars": 16779, "bytes_utf8": 16779, "lines": 458, "estimated_tokens": null}, "response_metrics": {"chars": 748, "bytes_utf8": 748, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16815, "cached_input_tokens": 15744, "output_tokens": 992, "reasoning_output_tokens": 777}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"Work_Life_Balance\\\",\\n \\\"Efficiency_Rating\\\",\\n SUM(CAST(\\\"Work_Hours_Per_Week\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Work_Hours_Per_Week\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Work_Hours_Per_Week\\\" AS REAL))) OVER (PARTITION BY \\\"Work_Life_Balance\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Work_Life_Balance\\\", \\\"Efficiency_Rating\\\"\\nORDER BY share_within_group DESC\\nLIMIT 16;\",\"notes\":\"Applied the Within-Group Share of Total template with group_col=\\\"Work_Life_Balance\\\", item_col=\\\"Efficiency_Rating\\\", and measure_col=\\\"Work_Hours_Per_Week\\\". CAST to REAL is used because the schema stores numeric fields as TEXT. Added LIMIT 16 from the provided top_k binding.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..cdcd3f8110911613c6a529f176b20d15e4e999c5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_f683d596c73a902d", + "api_calls": 0, + "input_tokens": 16815, + "cached_input_tokens": 15744, + "output_tokens": 992, + "total_tokens": 17807, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17819.76, + "sql_execution_elapsed_ms_total": 2.73, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f683d596c73a902d/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1360a3548eca4f4669d8c78ac52b113552ab8d67 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:08.062963+00:00", + "ended_at": "2026-05-19T16:06:11.850055+00:00", + "elapsed_ms": 3787.06, + "returncode": 1, + "prompt_metrics": { + "chars": 16262, + "bytes_utf8": 16262, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d8f72ab7daf6171bd6124b41f49b08ba1dcd9920 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:12.852209+00:00", + "ended_at": "2026-05-19T16:06:15.682877+00:00", + "elapsed_ms": 2830.63, + "returncode": 1, + "prompt_metrics": { + "chars": 16262, + "bytes_utf8": 16262, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f50c1bc4251fa458e24f9e0f7af1c597d0382a77 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Stress_Level.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "Stress_Level", + "top_k": 10, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..f50c1bc4251fa458e24f9e0f7af1c597d0382a77 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Stress_Level.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "Stress_Level", + "top_k": 10, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..29661516aca4692abf5c188f658c4c03a8a155f7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-c6bd-7b42-9f8f-e662237c94ae"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..25daf97107b3f21155d5032bf8d184b8f795bb1b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-c6bd-7b42-9f8f-e662237c94ae"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e4f9fc49082b020604b3a9c2cc4e023b0c1e896f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-d989-71e0-8abc-1468c313ff82"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0dafe4af96a086aa94509c0874b8308b34a3905 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-d989-71e0-8abc-1468c313ff82"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_f9651081b16bd014/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..492b6a6a1930fc693c9c90b1b1cb4fb365366328 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:55.914870+00:00", + "ended_at": "2026-05-19T16:08:59.041885+00:00", + "elapsed_ms": 3126.99, + "returncode": 1, + "prompt_metrics": { + "chars": 16308, + "bytes_utf8": 16308, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0da5a7c06cd086d2152192c598c8231259d13063 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:09:00.043800+00:00", + "ended_at": "2026-05-19T16:09:03.855672+00:00", + "elapsed_ms": 3811.84, + "returncode": 1, + "prompt_metrics": { + "chars": 16308, + "bytes_utf8": 16308, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0093536fa3793b0b1c3f9c48f663e0dcc93855a8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=Industry.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Industry", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..0093536fa3793b0b1c3f9c48f663e0dcc93855a8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=Industry.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "Industry", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..59cf66880dc930777117b583489a689b8584a7dc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-5669-7111-bf6a-aec74302be78"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2ce5fe3ec89ebe6856d7a7110e453c694ddc3473 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-5669-7111-bf6a-aec74302be78"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..00f07b0aeb82bdf7de96ff72dc1c64c3193023d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-668b-7cb2-9bfe-44e15fc649f0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..61ab76f67ae55bc3ce3fbbb5ef76de2b86e033c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-668b-7cb2-9bfe-44e15fc649f0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..89aa02bc216bd3509cff9b901917863066b22a61 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/run_manifest.json @@ -0,0 +1,67 @@ +{ + "run_id": "v2_cli_20260502_081223_a", + "dataset_id": "m1", + "started_at": "2026-05-19T16:08:55.913428+00:00", + "ended_at": "2026-05-19T16:09:03.856510+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_m1_fa4e137973f90d3a", + "problem_id": "v2p_m1_f5b4e7a61e993273", + "dataset_id": "m1", + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "rare_extreme_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=Industry.", + "bindings": { + "group_col": "Industry", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=8", + "sql_variant_index=2/2", + "binding_index=127" + ], + "template_selection_mode": "rule", + "selected_template_rank": 11, + "problem_index_within_template": 8, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..96abed4893145068da953cced310524b69454e30 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fa4e137973f90d3a/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:08:59.042802+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3126.99, "started_at": "2026-05-19T16:08:55.914870+00:00", "ended_at": "2026-05-19T16:08:59.041885+00:00", "prompt_metrics": {"chars": 16308, "bytes_utf8": 16308, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40ff-5669-7111-bf6a-aec74302be78\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:09:03.856425+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3811.84, "started_at": "2026-05-19T16:09:00.043800+00:00", "ended_at": "2026-05-19T16:09:03.855672+00:00", "prompt_metrics": {"chars": 16308, "bytes_utf8": 16308, "lines": 454, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40ff-668b-7cb2-9bfe-44e15fc649f0\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9c9e476baa39f93ad0160a6a116f0fd8aed6f92f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16243, "bytes_utf8": 16243, "lines": 454, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 309, "bytes_utf8": 309, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16656, "cached_input_tokens": 12032, "output_tokens": 288, "reasoning_output_tokens": 211}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..fffde38448532edb9f8e7237330f9101fba2068c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_fd050336ee4fb50c", + "api_calls": 0, + "input_tokens": 16656, + "cached_input_tokens": 12032, + "output_tokens": 288, + "total_tokens": 16944, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14168.67, + "sql_execution_elapsed_ms_total": 0.9, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..84b83b516b51777ad8d01f0478d63cc11e2056c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:33:00.881425+00:00", + "ended_at": "2026-05-19T15:33:15.050123+00:00", + "elapsed_ms": 14168.67, + "prompt_metrics": { + "chars": 16243, + "bytes_utf8": 16243, + "lines": 454, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 992, + "bytes_utf8": 992, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 309, + "bytes_utf8": 309, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16656, + "cached_input_tokens": 12032, + "output_tokens": 288, + "reasoning_output_tokens": 211 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..483718a87168addd2f5563d552a5934f9cd768b9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,454 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=Response_Quality.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "Response_Quality", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 4.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..2ab28a871db9f334baf52df5131b998a6e827afc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40de-7458-7043-8493-486e53d270af"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 17291. Please try again in 518ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"Response_Quality\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Response_Quality\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Grouped count distribution for the target subgroup column Response_Quality using the provided single-table template.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16656,"cached_input_tokens":12032,"output_tokens":288,"reasoning_output_tokens":211}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..19d03906fea50022f20a75fea00d1587b6439942 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"Response_Quality\", COUNT(*) AS \"row_count\"\nFROM \"m1\"\nGROUP BY \"Response_Quality\"\nORDER BY \"row_count\" DESC;","notes":"Grouped count distribution for the target subgroup column Response_Quality using the provided single-table template."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd050336ee4fb50c/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..51c5e0b889f2c3cbfaaa7a79e4b20823e21acd24 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16768, "bytes_utf8": 16768, "lines": 458, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 711, "bytes_utf8": 711, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16810, "cached_input_tokens": 12032, "output_tokens": 634, "reasoning_output_tokens": 437}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d02f2e7d3acec0d8ed6c6b931e8d5e55a96d1bd5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_fd5fd45ba8e018f9", + "api_calls": 0, + "input_tokens": 16810, + "cached_input_tokens": 12032, + "output_tokens": 634, + "total_tokens": 17444, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12731.87, + "sql_execution_elapsed_ms_total": 2.76, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..96f4a1ccf76ba1fe7f1c36e7216c2259ddcc2b42 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:35:07.588528+00:00", + "ended_at": "2026-05-19T15:35:20.320433+00:00", + "elapsed_ms": 12731.87, + "prompt_metrics": { + "chars": 16768, + "bytes_utf8": 16768, + "lines": 458, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1103, + "bytes_utf8": 1103, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 711, + "bytes_utf8": 711, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16810, + "cached_input_tokens": 12032, + "output_tokens": 634, + "reasoning_output_tokens": 437 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..417d2f74d625d73cc3cf468d75ffbdb4c05d18c2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,458 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=Home_Office_Quality, measure_col=Stress_Level.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "Home_Office_Quality", + "measure_col": "Stress_Level", + "item_col": "Work_Hours_Per_Week", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 7.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..04dae300c04e0a1b7e456bf7a9d8fd25d7e7fd2b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e0-6343-7fb0-bdc7-a4047f757324"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"Home_Office_Quality\\\", \\\"Work_Hours_Per_Week\\\",\\n SUM(CAST(\\\"Stress_Level\\\" AS REAL)) AS \\\"total_measure\\\",\\n SUM(CAST(\\\"Stress_Level\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Stress_Level\\\" AS REAL))) OVER (PARTITION BY \\\"Home_Office_Quality\\\") AS \\\"share_within_group\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Home_Office_Quality\\\", \\\"Work_Hours_Per_Week\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Used the planned Within-Group Share of Total template with group_col=\\\"Home_Office_Quality\\\", item_col=\\\"Work_Hours_Per_Week\\\", and measure_col=\\\"Stress_Level\\\". CAST to REAL is included because the schema stores numeric fields as TEXT in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16810,"cached_input_tokens":12032,"output_tokens":634,"reasoning_output_tokens":437}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8cc38d6e1ceaf731cae08605f9a8dc0ed69aad8c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"Home_Office_Quality\", \"Work_Hours_Per_Week\",\n SUM(CAST(\"Stress_Level\" AS REAL)) AS \"total_measure\",\n SUM(CAST(\"Stress_Level\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"Stress_Level\" AS REAL))) OVER (PARTITION BY \"Home_Office_Quality\") AS \"share_within_group\"\nFROM \"m1\"\nGROUP BY \"Home_Office_Quality\", \"Work_Hours_Per_Week\"\nORDER BY \"share_within_group\" DESC;","notes":"Used the planned Within-Group Share of Total template with group_col=\"Home_Office_Quality\", item_col=\"Work_Hours_Per_Week\", and measure_col=\"Stress_Level\". CAST to REAL is included because the schema stores numeric fields as TEXT in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fd5fd45ba8e018f9/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..404ad30663fc6d719a449854de1954d94ff5639f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16772, "bytes_utf8": 16772, "lines": 458, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 674, "bytes_utf8": 674, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16814, "cached_input_tokens": 15744, "output_tokens": 904, "reasoning_output_tokens": 713}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..5ce0078f647af11c6e1c14d873b1408df764018c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m1", + "model": "v2-cli:codex", + "run_id": "v2q_m1_fe534c3cbd7b9b91", + "api_calls": 0, + "input_tokens": 16814, + "cached_input_tokens": 15744, + "output_tokens": 904, + "total_tokens": 17718, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15549.56, + "sql_execution_elapsed_ms_total": 3.09, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..23e81e74de47f9120fecb7dec1111e388cd8f0d0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:37:33.243894+00:00", + "ended_at": "2026-05-19T15:37:48.793480+00:00", + "elapsed_ms": 15549.56, + "prompt_metrics": { + "chars": 16772, + "bytes_utf8": 16772, + "lines": 458, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1042, + "bytes_utf8": 1042, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 674, + "bytes_utf8": 674, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 16814, + "cached_input_tokens": 15744, + "output_tokens": 904, + "reasoning_output_tokens": 713 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..fb6c5dee833e37c7e5b36950be2128e80ce891e5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,458 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m1 +- dataset_name: Remote Worker Productivity +- table_name: m1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one employee survey/assessment record in a remote-work context. +- task_type: classification +- target_column: Response_Quality +- main_row_count: 1500 +- important_fields: +- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier. +- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years. +- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience. +- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week. +- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category. +- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category. +- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category. +- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children. +- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type. +- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function. +- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level. +- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket. +- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector. +- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality. +- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office. +- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week. +- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level. +- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration. +- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score. +- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score. +- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score. +- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score. +- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score. +- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week. +- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes. +- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score. +- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score). +- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled). +- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date. +- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label. +- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']] +- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction'] +- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity + +SQLite schema snapshot: +{ + "table_name": "m1", + "quoted_table_name": "\"m1\"", + "row_count": 1500, + "columns": [ + { + "name": "Employee_ID", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Age", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Years_Experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "WFH_Days_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Education_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Marital_Status", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Has_Children", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Location_Type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Department", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Company_Size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Industry", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Home_Office_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Internet_Speed_Category", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Hours_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Manager_Support_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Team_Collaboration_Frequency", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Productivity_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Task_Completion_Rate", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Quality_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Innovation_Score", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Efficiency_Rating", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Meetings_Per_Week", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Commute_Time_Minutes", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Job_Satisfaction", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Stress_Level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Work_Life_Balance", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Survey_Date", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "Response_Quality", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "Employee_ID": "EMP0001", + "Age": "39", + "Years_Experience": "10", + "WFH_Days_Per_Week": "2", + "Gender": "Female", + "Education_Level": "Associate Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Product", + "Job_Level": "Mid-Level", + "Company_Size": "Large (1001-5000)", + "Industry": "Finance", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "41", + "Manager_Support_Level": "Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "52.2", + "Task_Completion_Rate": "56.6", + "Quality_Score": "58.1", + "Innovation_Score": "52.1", + "Efficiency_Rating": "72.1", + "Meetings_Per_Week": "4", + "Commute_Time_Minutes": "48", + "Job_Satisfaction": "55.9", + "Stress_Level": "6", + "Work_Life_Balance": "8", + "Survey_Date": "2024-04-05", + "Response_Quality": "Medium" + }, + { + "Employee_ID": "EMP0002", + "Age": "33", + "Years_Experience": "4", + "WFH_Days_Per_Week": "5", + "Gender": "Female", + "Education_Level": "Master Degree", + "Marital_Status": "Married", + "Has_Children": "No", + "Location_Type": "Urban", + "Department": "Customer Success", + "Job_Level": "Senior", + "Company_Size": "Startup (1-50)", + "Industry": "Education", + "Home_Office_Quality": "Good", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "52", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Monthly", + "Productivity_Score": "81.5", + "Task_Completion_Rate": "70.8", + "Quality_Score": "93.3", + "Innovation_Score": "77.9", + "Efficiency_Rating": "89.5", + "Meetings_Per_Week": "12", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "96.1", + "Stress_Level": "3", + "Work_Life_Balance": "8", + "Survey_Date": "2024-01-29", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0003", + "Age": "40", + "Years_Experience": "3", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "PhD", + "Marital_Status": "Single", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Operations", + "Job_Level": "Mid-Level", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Fast (50-100 Mbps)", + "Work_Hours_Per_Week": "43", + "Manager_Support_Level": "Moderate", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "82.2", + "Task_Completion_Rate": "81.9", + "Quality_Score": "84.7", + "Innovation_Score": "63.2", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "15", + "Commute_Time_Minutes": "24", + "Job_Satisfaction": "90.4", + "Stress_Level": "5", + "Work_Life_Balance": "6", + "Survey_Date": "2024-01-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0004", + "Age": "48", + "Years_Experience": "14", + "WFH_Days_Per_Week": "3", + "Gender": "Male", + "Education_Level": "Bachelor Degree", + "Marital_Status": "Married", + "Has_Children": "Yes", + "Location_Type": "Urban", + "Department": "Finance", + "Job_Level": "Manager", + "Company_Size": "Medium (201-1000)", + "Industry": "Technology", + "Home_Office_Quality": "Excellent", + "Internet_Speed_Category": "Very Fast (100+ Mbps)", + "Work_Hours_Per_Week": "45", + "Manager_Support_Level": "High", + "Team_Collaboration_Frequency": "Daily", + "Productivity_Score": "75.6", + "Task_Completion_Rate": "70.2", + "Quality_Score": "67.8", + "Innovation_Score": "82.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "8", + "Commute_Time_Minutes": "8", + "Job_Satisfaction": "100.0", + "Stress_Level": "10", + "Work_Life_Balance": "5", + "Survey_Date": "2024-04-18", + "Response_Quality": "High" + }, + { + "Employee_ID": "EMP0005", + "Age": "32", + "Years_Experience": "6", + "WFH_Days_Per_Week": "5", + "Gender": "Male", + "Education_Level": "High School", + "Marital_Status": "Divorced", + "Has_Children": "Yes", + "Location_Type": "Rural", + "Department": "Engineering", + "Job_Level": "Senior", + "Company_Size": "Small (51-200)", + "Industry": "Technology", + "Home_Office_Quality": "Average", + "Internet_Speed_Category": "Moderate (25-50 Mbps)", + "Work_Hours_Per_Week": "42", + "Manager_Support_Level": "Very Low", + "Team_Collaboration_Frequency": "Few times per week", + "Productivity_Score": "98.0", + "Task_Completion_Rate": "98.2", + "Quality_Score": "86.4", + "Innovation_Score": "67.5", + "Efficiency_Rating": "95.0", + "Meetings_Per_Week": "10", + "Commute_Time_Minutes": "0", + "Job_Satisfaction": "100.0", + "Stress_Level": "3", + "Work_Life_Balance": "4", + "Survey_Date": "2024-02-19", + "Response_Quality": "High" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=Work_Life_Balance, measure_col=Work_Hours_Per_Week.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "Work_Life_Balance", + "measure_col": "Work_Hours_Per_Week", + "item_col": "Efficiency_Rating", + "top_k": 11, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 46.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..02541e48a3330f8724c9fea2572fff159111b033 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e2-9c4d-7a82-bc5d-67b7d5ec6073"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"Work_Life_Balance\\\", \\\"Efficiency_Rating\\\",\\n SUM(CAST(\\\"Work_Hours_Per_Week\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"Work_Hours_Per_Week\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"Work_Hours_Per_Week\\\" AS REAL))) OVER (PARTITION BY \\\"Work_Life_Balance\\\") AS share_within_group\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Work_Life_Balance\\\", \\\"Efficiency_Rating\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the provided Within-Group Share of Total template with group_col=Work_Life_Balance, item_col=Efficiency_Rating, and cast the TEXT measure column Work_Hours_Per_Week to REAL for SQLite-compatible summation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":16814,"cached_input_tokens":15744,"output_tokens":904,"reasoning_output_tokens":713}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..18c2f3c3295cd591741f7966a222378ce35c6b53 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"Work_Life_Balance\", \"Efficiency_Rating\",\n SUM(CAST(\"Work_Hours_Per_Week\" AS REAL)) AS total_measure,\n SUM(CAST(\"Work_Hours_Per_Week\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"Work_Hours_Per_Week\" AS REAL))) OVER (PARTITION BY \"Work_Life_Balance\") AS share_within_group\nFROM \"m1\"\nGROUP BY \"Work_Life_Balance\", \"Efficiency_Rating\"\nORDER BY share_within_group DESC;","notes":"Used the provided Within-Group Share of Total template with group_col=Work_Life_Balance, item_col=Efficiency_Rating, and cast the TEXT measure column Work_Hours_Per_Week to REAL for SQLite-compatible summation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_fe534c3cbd7b9b91/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391