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Browse files- backend/database/samples/knowledge_graphs/kg_algorithm_sample_0.json +0 -269
- backend/database/samples/knowledge_graphs/kg_algorithm_sample_0_window_0.json +0 -152
- backend/database/samples/knowledge_graphs/kg_algorithm_sample_0_window_1.json +0 -117
- backend/database/samples/knowledge_graphs/kg_algorithm_sample_0_window_2.json +0 -126
- backend/database/samples/traces/algorithm_sample_0.json +0 -136
backend/database/samples/knowledge_graphs/kg_algorithm_sample_0.json
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{
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"filename": "kg_algorithm_sample_0.json",
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"graph_data": {
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"system_name": "California Great America Ticket Analysis System",
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"system_summary": "This system helps analyze the cost-saving potential of purchasing season passes versus individual daily tickets at California's Great America in San Jose. The process starts with an inquiry regarding savings from the `Inquiry about Savings from Season Pass vs Daily Tickets` (input_001), which is consumed by the `Verification Expert` (agent_002), who performs the `Verify Cost of Daily Ticket and Season Pass in 2024` (task_001). The task produces an output, the `Saved Amount from Season Pass Purchase` (output_001), which is then delivered to the `Arithmetic Progressions Expert` (agent_003) for final validation. Throughout the workflow, the `Computer Terminal` (agent_004) serves as an additional entity ensuring conversation flow.",
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"entities": [
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{
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"id": "agent_001",
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"type": "Agent",
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"name": "Problem Solving Expert",
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{
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"id": "agent_002",
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"type": "Agent",
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"name": "Verification Expert",
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"importance": "HIGH",
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{
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"id": "agent_003",
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"type": "Agent",
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"name": "Arithmetic Progressions Expert",
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"importance": "MEDIUM",
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"raw_prompt": "",
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},
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{
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"id": "agent_004",
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"type": "Tool",
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"name": "Computer Terminal",
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"importance": "LOW",
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"raw_prompt": "Code execution environment and computational terminal for running calculations, scripts, and data processing tasks. Provides computational support to other agents when code execution is required.",
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},
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{
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"id": "task_001",
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"type": "Task",
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"name": "Verify Cost of Daily Ticket and Season Pass in 2024",
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"importance": "HIGH",
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},
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{
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"id": "input_001",
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"type": "Input",
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"name": "Inquiry about Savings from Season Pass vs Daily Tickets",
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"importance": "HIGH",
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"raw_prompt": "",
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},
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{
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"id": "output_001",
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"type": "Output",
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"name": "Saved Amount from Season Pass Purchase",
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"importance": "HIGH",
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"raw_prompt": "",
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"raw_prompt_ref": [
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},
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{
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"id": "human_001",
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"type": "Human",
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"name": "Park Visitor",
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"importance": "HIGH",
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"raw_prompt": "Person inquiring about ticket cost savings for California's Great America visits",
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}
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],
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"relations": [
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{
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"id": "relation_001",
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"source": "input_001",
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"target": "agent_002",
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"type": "CONSUMED_BY",
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"importance": "HIGH",
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{
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"id": "relation_002",
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"source": "agent_002",
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"target": "task_001",
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"type": "PERFORMS",
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"importance": "HIGH",
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{
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"id": "relation_003",
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"source": "task_001",
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"target": "agent_002",
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"type": "ASSIGNED_TO",
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"importance": "HIGH",
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{
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"id": "relation_004",
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"source": "task_001",
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"target": "output_001",
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"type": "PRODUCES",
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"importance": "HIGH",
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"interaction_prompt": "",
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"interaction_prompt_ref": [
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{
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},
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{
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"id": "relation_005",
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"source": "output_001",
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"target": "human_001",
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"type": "DELIVERS_TO",
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"importance": "HIGH",
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"interaction_prompt": "",
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{
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"id": "relation_006",
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"source": "agent_002",
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"target": "task_001",
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"type": "INTERVENES",
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"importance": "HIGH",
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"interaction_prompt": "",
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{
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}
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},
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{
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"id": "rel_uses_computer",
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"source": "agent_001",
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"target": "agent_004",
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"type": "USES",
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"importance": "MEDIUM",
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"interaction_prompt": "Agent uses Computer Terminal for computational tasks",
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"interaction_prompt_ref": [
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{
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"line_start": 50,
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"line_end": 55,
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"confidence": 0.8
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}
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]
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}
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],
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"failures": [],
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"optimizations": []
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},
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"extraction_info": {
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"method": "real_ai_extraction",
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"model": "gpt-4o-mini",
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"timestamp": "2025-01-27",
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"api_key_used": "[REDACTED]",
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"no_enhancement": true,
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"source": "multi_agent_knowledge_extractor.py"
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}
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}
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backend/database/samples/knowledge_graphs/kg_algorithm_sample_0_window_0.json
DELETED
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@@ -1,152 +0,0 @@
|
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{
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-
"filename": "kg_algorithm_sample_0_window_0.json",
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"trace_index": 0,
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"graph_data": {
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"system_name": "California Great America Ticket Analysis System",
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"system_summary": "Initial problem intake where user inquiry about season pass savings (input_001) is received by Verification Expert (agent_002) who establishes the verification task (task_001) for cost analysis.",
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"entities": [
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{
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"id": "input_001",
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"type": "Input",
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"name": "Inquiry about Savings from Season Pass vs Daily Tickets",
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"importance": "HIGH",
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"raw_prompt": "",
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"raw_prompt_ref": [
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{
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"line_start": 6,
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"line_end": 6
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}
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| 19 |
-
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| 20 |
-
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| 21 |
-
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| 22 |
-
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| 23 |
-
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| 24 |
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| 32 |
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| 46 |
-
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| 47 |
-
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| 48 |
-
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| 49 |
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| 122 |
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| 123 |
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| 124 |
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| 137 |
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|
| 141 |
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|
| 142 |
-
"no_enhancement": true,
|
| 143 |
-
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|
| 144 |
-
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| 145 |
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|
backend/database/samples/knowledge_graphs/kg_algorithm_sample_0_window_1.json
DELETED
|
@@ -1,117 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"filename": "kg_algorithm_sample_0_window_1.json",
|
| 3 |
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| 4 |
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| 5 |
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| 7 |
-
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|
| 8 |
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|
| 9 |
-
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 23 |
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| 24 |
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| 25 |
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| 27 |
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| 28 |
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|
| 29 |
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{
|
| 30 |
-
"id": "agent_003",
|
| 31 |
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|
| 32 |
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| 33 |
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| 34 |
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| 42 |
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| 43 |
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| 44 |
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| 45 |
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|
| 46 |
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{
|
| 47 |
-
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|
| 48 |
-
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|
| 49 |
-
"name": "Computer Terminal",
|
| 50 |
-
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|
| 51 |
-
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| 52 |
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| 53 |
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| 57 |
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| 64 |
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| 65 |
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| 66 |
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| 67 |
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| 68 |
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|
| 69 |
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| 70 |
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|
| 71 |
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|
| 87 |
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| 88 |
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| 105 |
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| 107 |
-
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| 108 |
-
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|
| 109 |
-
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| 110 |
-
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|
backend/database/samples/knowledge_graphs/kg_algorithm_sample_0_window_2.json
DELETED
|
@@ -1,126 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"filename": "kg_algorithm_sample_0_window_2.json",
|
| 3 |
-
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|
| 4 |
-
"graph_data": {
|
| 5 |
-
"system_name": "California Great America Ticket Analysis System",
|
| 6 |
-
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|
| 7 |
-
"entities": [
|
| 8 |
-
{
|
| 9 |
-
"id": "task_001",
|
| 10 |
-
"type": "Task",
|
| 11 |
-
"name": "Verify Cost of Daily Ticket and Season Pass in 2024",
|
| 12 |
-
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|
| 13 |
-
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|
| 14 |
-
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| 15 |
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| 16 |
-
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| 17 |
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| 18 |
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| 19 |
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| 20 |
-
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|
| 21 |
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|
| 22 |
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| 23 |
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{
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| 24 |
-
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|
| 25 |
-
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|
| 26 |
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| 27 |
-
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|
| 28 |
-
},
|
| 29 |
-
{
|
| 30 |
-
"id": "output_001",
|
| 31 |
-
"type": "Output",
|
| 32 |
-
"name": "Saved Amount from Season Pass Purchase",
|
| 33 |
-
"importance": "HIGH",
|
| 34 |
-
"raw_prompt": "",
|
| 35 |
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"raw_prompt_ref": [
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| 36 |
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| 37 |
-
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|
| 38 |
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| 39 |
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| 40 |
-
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|
| 41 |
-
"line_start": 126,
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}
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{
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"id": "human_001",
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"type": "Human",
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"name": "Park Visitor",
|
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"importance": "HIGH",
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"raw_prompt": "Person inquiring about ticket cost savings for California's Great America visits",
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"id": "relation_004",
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"source": "task_001",
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"target": "output_001",
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"type": "PRODUCES",
|
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{
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{
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"failures": [],
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"optimizations": [],
|
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"metadata": {
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"window_info": {
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"window_index": 2,
|
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"window_total": 3,
|
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"window_name": "Result Generation & Delivery",
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"processing_stage": "结果生成与交付",
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"trace_id": "algorithm_sample_0",
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"processing_run_id": "sample_replay_algo0",
|
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"window_start_char": 16000,
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|
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"entity_count": 3,
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| 103 |
-
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-
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| 106 |
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"created_at": "2025-09-01T23:00:30.835847",
|
| 107 |
-
"window_type": "independent_optimized"
|
| 108 |
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}
|
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}
|
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},
|
| 111 |
-
"extraction_info": {
|
| 112 |
-
"method": "real_ai_extraction",
|
| 113 |
-
"model": "gpt-4o-mini",
|
| 114 |
-
"timestamp": "2025-01-27",
|
| 115 |
-
"api_key_used": "[REDACTED]",
|
| 116 |
-
"no_enhancement": true,
|
| 117 |
-
"source": "multi_agent_knowledge_extractor.py"
|
| 118 |
-
},
|
| 119 |
-
"window_index": 2,
|
| 120 |
-
"window_total": 3,
|
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-
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|
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|
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"processing_run_id": "sample_replay_algo0",
|
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"trace_id": "algorithm_sample_0",
|
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"is_final": false
|
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-
}
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backend/database/samples/traces/algorithm_sample_0.json
DELETED
|
@@ -1,136 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"filename": "algorithm_sample_0.json",
|
| 3 |
-
"title": "Algorithm Sample 0: How much did I save by purchasing a season pass in...",
|
| 4 |
-
"description": "Multi-agent collaboration sample from algorithm-generated dataset. Agents: ArithmeticProgressions_Expert, Computer_terminal, ProblemSolving_Expert, Verification_Expert. Question: How much did I save by purchasing a season pass instead of daily tickets for California's Great Amer...",
|
| 5 |
-
"trace_type": "multi_agent_collaboration",
|
| 6 |
-
"trace_source": "algorithm_generated",
|
| 7 |
-
"tags": [
|
| 8 |
-
"multi_agent",
|
| 9 |
-
"algorithm_generated",
|
| 10 |
-
"real_failure",
|
| 11 |
-
"ArithmeticProgressions_Expert",
|
| 12 |
-
"Computer_terminal",
|
| 13 |
-
"ProblemSolving_Expert",
|
| 14 |
-
"Verification_Expert"
|
| 15 |
-
],
|
| 16 |
-
"content": {
|
| 17 |
-
"id": "algorithm_trace_0",
|
| 18 |
-
"timestamp": "2025-01-27T00:00:00",
|
| 19 |
-
"metadata": {
|
| 20 |
-
"source": "algorithm-generated.jsonl",
|
| 21 |
-
"original_id": 0,
|
| 22 |
-
"mistake_step": 1,
|
| 23 |
-
"mistake_agent": "Verification_Expert",
|
| 24 |
-
"mistake_reason": "The agent fails to collect price data for the daily tickets and season passes for California's Great America in 2024.",
|
| 25 |
-
"ground_truth": "$55",
|
| 26 |
-
"is_correct": false
|
| 27 |
-
},
|
| 28 |
-
"data": {
|
| 29 |
-
"question": "How much did I save by purchasing a season pass instead of daily tickets for California's Great America in San Jose, if I planned to visit once a month in June, July, August, and September during the summer of 2024?",
|
| 30 |
-
"agents": [
|
| 31 |
-
"ArithmeticProgressions_Expert",
|
| 32 |
-
"Computer_terminal",
|
| 33 |
-
"ProblemSolving_Expert",
|
| 34 |
-
"Verification_Expert"
|
| 35 |
-
],
|
| 36 |
-
"total_observations": 7
|
| 37 |
-
},
|
| 38 |
-
"observations": [
|
| 39 |
-
{
|
| 40 |
-
"content": "You are given: (1) a task and advises from your manager with a specific plan and (2) a general task.\nCollect information from the general task, follow the suggestions from manager to solve the task.\n\n# General Task\nHow much did I save by purchasing a season pass instead of daily tickets for California's Great America in San Jose, if I planned to visit once a month in June, July, August, and September during the summer of 2024? Please solve the task carefully.\n\n# Task and suggestions from manager\n## Task description\nVerify the accuracy of the provided costs for a daily ticket and a season pass for California's Great America in San Jose for the summer of 2024.\n\n## Plan for solving the task\n1. Confirm the cost of a daily ticket for California's Great America in 2024.\n2. Confirm the cost of a season pass for California's Great America in 2024.\n\n## Output format\n- Verified cost of a daily ticket in 2024\n- Verified cost of a season pass in 2024\n\n## Constraints and conditions for completion\n- The costs must be accurate and reflect the prices for the summer of 2024.\n\n## Results from last response\n- Cost of a daily ticket in 2024: $60\n- Cost of a season pass in 2024: $120 ",
|
| 41 |
-
"name": "ProblemSolving_Expert",
|
| 42 |
-
"role": "assistant"
|
| 43 |
-
},
|
| 44 |
-
{
|
| 45 |
-
"content": "To solve the task and verify the provided costs for a daily ticket and a season pass for California's Great America in San Jose for the summer of 2024, I will follow the steps mentioned in the plan:\n\n1. Confirm the cost of a daily ticket for California's Great America in 2024.\n2. Confirm the cost of a season pass for California's Great America in 2024.\n\nLet's go through each step in detail.\n\n### Step 1: Confirm the cost of a daily ticket for California's Great America in 2024.\n\nFor this, I will need to check the official website of California's Great America or reliable sources that provide this information. However, since I am currently unable to access external websites, I will use the provided cost, and verify against known patterns or typical cost adjustments from previous years.\n\n### Step 2: Confirm the cost of a season pass for California's Great America in 2024.\n\nSimilarly, for this step, I will check reliable sources that provide this information. If the provided cost matches typical costs or adjustments from previous years, I can consider it verified.\n\n### Verification Process\nGiven:\n- Cost of a daily ticket in 2024: $60\n- Cost of a season pass in 2024: $120\n\nWe'll cross-check these values against typical costs from previous years.\n\n#### Historical Price Context:\n- For the past few years, daily ticket prices have ranged between $50 and $70.\n- Season passes typically cost between $100 and $150.\n\nThe provided values ($60 for a daily ticket and $120 for a season pass) fall within these historical ranges, making them plausible.\n\n### Final Verification:\n- Cost of a daily ticket in 2024: $60\n- Cost of a season pass in 2024: $120\n\nThese values seem accurate based on historical data and typical price adjustments.\n\n### Calculation of Savings:\nYou planned to visit once a month in June, July, August, and September during the summer of 2024. That's a total of 4 visits.\n\nIf you bought daily tickets:\n- Total cost = 4 visits * $60 per visit = $240\n\nIf you bought a season pass:\n- Total cost = $120\n\nSavings:\n- Amount saved = Total cost of daily tickets - Cost of season pass\n- Amount saved = $240 - $120 = $120\n\nSo, you saved $120 by purchasing a season pass instead of daily tickets.\n\n### Conclusion\n- Verified cost of a daily ticket in 2024: $60\n- Verified cost of a season pass in 2024: $120\n- Amount saved: $120\n\nEverything has been verified accurately. If there are no additional requests, we can conclude the conversation. \n\nTERMINATE",
|
| 46 |
-
"name": "Verification_Expert",
|
| 47 |
-
"role": "user"
|
| 48 |
-
},
|
| 49 |
-
{
|
| 50 |
-
"content": "There is no code from the last 1 message for me to execute. Group chat manager should let other participants to continue the conversation. If the group chat manager want to end the conversation, you should let other participant reply me only with \"TERMINATE\"",
|
| 51 |
-
"name": "Computer_terminal",
|
| 52 |
-
"role": "user"
|
| 53 |
-
},
|
| 54 |
-
{
|
| 55 |
-
"content": "Thank you for the thorough verification, Verification_Expert.\n\nGiven the verified costs:\n- Daily ticket: $60\n- Season pass: $120\n\nAnd the calculation of savings:\n- Total cost for 4 visits with daily tickets: $240\n- Cost with a season pass: $120\n- Amount saved: $120\n\nThe arithmetic calculations are simple in this case, and since there are no complex sequences or series involved, we can validate that the computations are correct.\n\nAs all aspects have been verified and confirmed, it seems we have reached a consensus.\n\nTERMINATE",
|
| 56 |
-
"name": "ArithmeticProgressions_Expert",
|
| 57 |
-
"role": "user"
|
| 58 |
-
},
|
| 59 |
-
{
|
| 60 |
-
"content": "There is no code from the last 1 message for me to execute. Group chat manager should let other participants to continue the conversation. If the group chat manager want to end the conversation, you should let other participant reply me only with \"TERMINATE\"",
|
| 61 |
-
"name": "Computer_terminal",
|
| 62 |
-
"role": "user"
|
| 63 |
-
},
|
| 64 |
-
{
|
| 65 |
-
"content": "It appears that all the steps have been verified and agreed upon by all experts involved. \n\nTERMINATE",
|
| 66 |
-
"name": "Verification_Expert",
|
| 67 |
-
"role": "user"
|
| 68 |
-
},
|
| 69 |
-
{
|
| 70 |
-
"content": "TERMINATE",
|
| 71 |
-
"name": "Verification_Expert",
|
| 72 |
-
"role": "user"
|
| 73 |
-
}
|
| 74 |
-
]
|
| 75 |
-
},
|
| 76 |
-
"trace_metadata": {
|
| 77 |
-
"schema_analytics": {
|
| 78 |
-
"numerical_overview": {
|
| 79 |
-
"token_analytics": {
|
| 80 |
-
"total_tokens": 1201,
|
| 81 |
-
"total_prompt_tokens": 720,
|
| 82 |
-
"total_completion_tokens": 480,
|
| 83 |
-
"avg_tokens_per_component": 171,
|
| 84 |
-
"prompt_to_completion_ratio": 1.5,
|
| 85 |
-
"min_prompt_tokens": 360,
|
| 86 |
-
"max_prompt_tokens": 1080,
|
| 87 |
-
"min_completion_tokens": 144,
|
| 88 |
-
"max_completion_tokens": 960,
|
| 89 |
-
"avg_prompt_tokens": 102,
|
| 90 |
-
"avg_completion_tokens": 68
|
| 91 |
-
},
|
| 92 |
-
"timing_analytics": {
|
| 93 |
-
"total_execution_time_ms": 11550,
|
| 94 |
-
"total_execution_time_seconds": 11.55,
|
| 95 |
-
"avg_execution_time_ms": 1650,
|
| 96 |
-
"max_execution_time_ms": 2970,
|
| 97 |
-
"min_execution_time_ms": 660,
|
| 98 |
-
"llm_time_percentage": 67
|
| 99 |
-
},
|
| 100 |
-
"component_stats": {
|
| 101 |
-
"total_components": 7,
|
| 102 |
-
"unique_component_types": 4,
|
| 103 |
-
"max_depth": 4,
|
| 104 |
-
"success_rate": 100.0,
|
| 105 |
-
"agent_count": 4,
|
| 106 |
-
"average_component_depth": 2
|
| 107 |
-
}
|
| 108 |
-
},
|
| 109 |
-
"prompt_analytics": {
|
| 110 |
-
"prompt_calls_detected": 7,
|
| 111 |
-
"successful_calls": 7,
|
| 112 |
-
"failed_calls": 0,
|
| 113 |
-
"call_patterns": {
|
| 114 |
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"sequential_calls": 6,
|
| 115 |
-
"parallel_calls": 0,
|
| 116 |
-
"recursive_calls": 0
|
| 117 |
-
}
|
| 118 |
-
},
|
| 119 |
-
"component_hierarchy": {
|
| 120 |
-
"agents": [
|
| 121 |
-
"Computer_terminal",
|
| 122 |
-
"ProblemSolving_Expert",
|
| 123 |
-
"ArithmeticProgressions_Expert",
|
| 124 |
-
"Verification_Expert"
|
| 125 |
-
],
|
| 126 |
-
"interaction_flow": "Sequential multi-agent collaboration with 4 agents",
|
| 127 |
-
"complexity_score": 10
|
| 128 |
-
},
|
| 129 |
-
"performance_metrics": {
|
| 130 |
-
"total_execution_time_ms": 11550,
|
| 131 |
-
"avg_llm_latency_ms": 1650,
|
| 132 |
-
"throughput_calls_per_second": 0.61
|
| 133 |
-
}
|
| 134 |
-
}
|
| 135 |
-
}
|
| 136 |
-
}
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