{ "filename": "python_documentation_inquiry.json", "title": "Python Documentation Assistant Demo", "description": "Comprehensive example showing RAG-powered AI assistant handling multi-turn programming inquiry with knowledge search, detailed explanations, code examples, performance analysis, and interactive learning", "trace_type": "documentation_search", "trace_source": "sample_data", "tags": [ "programming", "rag_assistant", "documentation", "failure_detection", "optimization" ], "content": { "id": "doc_trace_demo_001", "timestamp": "2025-01-27T00:00:00", "metadata": { "source": "AgentGraph_Demo", "row_index": 0, "converted_at": "2025-01-27T12:00:00.000000" }, "data": { "total_observations": 4, "summary": "Python documentation inquiry with RAG-powered assistant response including knowledge search, explanation, and follow-up code examples" }, "observations": [ { "id": "demo_obs_001", "type": "user_query", "timestamp": "2025-01-27T00:00:00", "input": "Hello! I'm learning Python and I keep seeing this syntax with square brackets that looks different from regular loops. Can you help me understand what Python list comprehensions are used for and when I should use them?", "output": "I'll help you understand Python list comprehensions! Let me search our documentation to give you a comprehensive explanation.", "metadata": { "request_date": "2025-01-27T00:00:00", "interaction_type": "initial_query", "user_level": "beginner", "topic_category": "python_syntax" }, "latency": 0.124 }, { "id": "demo_obs_002", "type": "knowledge_search", "timestamp": "2025-01-27T00:00:05", "input": "Searching Python documentation for: list comprehensions, syntax, use cases, performance benefits", "output": "Found relevant documentation: ['Python list comprehensions - efficient way to create lists', 'List comprehension vs loops performance comparison', 'Python built-in functions - len(), range(), enumerate()', 'Nested list comprehensions and filtering', 'Python data structures - lists, dictionaries, sets, tuples']", "metadata": { "search_query": "python list comprehensions syntax use cases", "documents_found": 5, "search_time_ms": 23, "relevance_score": 0.94 }, "latency": 0.089 }, { "id": "demo_obs_003", "type": "inference", "timestamp": "2025-01-27T00:00:08", "input": "You are a helpful Python programming assistant designed to answer questions accurately based on the provided documentation. Use the information retrieved from the knowledge base below to respond concisely and correctly.\\n\\n### Retrieved Documents:\\n['Python list comprehensions - efficient way to create lists', 'List comprehension vs loops performance comparison', 'Python built-in functions - len(), range(), enumerate()', 'Nested list comprehensions and filtering']\\n\\n### Question:\\nwhat are python list comprehensions used for and when should I use them?\\n\\n### Answer:", "output": "Python list comprehensions are a powerful and concise way to create lists! Here's what they're used for:\\n\\n**Main Uses:**\\n1. **Creating lists from existing iterables** - Transform elements from sequences\\n2. **Filtering data** - Include only elements that meet specific conditions\\n3. **Mathematical operations** - Apply calculations efficiently\\n4. **Combining operations** - Filter and transform in one line\\n\\n**When to use them:**\\n- When you need to create a new list from an existing iterable\\n- For simple transformations (keep it readable!)\\n- When performance matters (they're faster than equivalent for loops)\\n- To make code more Pythonic and concise\\n\\n**Basic syntax:** `[expression for item in iterable if condition]`", "metadata": { "request_date": "2025-01-27T00:00:00", "databricks_request_id": "demo-request-003", "request_time": "2025-01-27T12:00:08.000", "status_code": 200, "sampling_fraction": 1, "request_raw": "{\"messages\":[{\"role\":\"system\",\"content\":\"You are a helpful Python programming assistant\"},{\"role\":\"user\",\"content\":\"You are a helpful Python programming assistant designed to answer questions accurately based on the provided documentation. Use the information retrieved from the knowledge base below to respond concisely and correctly.\\\\n\\\\n### Retrieved Documents:\\\\n['Python list comprehensions - efficient way to create lists', 'List comprehension vs loops performance comparison', 'Python built-in functions - len(), range(), enumerate()', 'Nested list comprehensions and filtering']\\\\n\\\\n### Question:\\\\nwhat are python list comprehensions used for and when should I use them?\\\\n\\\\n### Answer:\"}]}", "response_raw": "{\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"finish_reason\":\"stop\",\"index\":0,\"logprobs\":null,\"message\":{\"content\":\"Python list comprehensions are a powerful and concise way to create lists! Here's what they're used for:\\\\n\\\\n**Main Uses:**\\\\n1. **Creating lists from existing iterables** - Transform elements from sequences\\\\n2. **Filtering data** - Include only elements that meet specific conditions\\\\n3. **Mathematical operations** - Apply calculations efficiently\\\\n4. **Combining operations** - Filter and transform in one line\\\\n\\\\n**When to use them:**\\\\n- When you need to create a new list from an existing iterable\\\\n- For simple transformations (keep it readable!)\\\\n- When performance matters (they're faster than equivalent for loops)\\\\n- To make code more Pythonic and concise\\\\n\\\\n**Basic syntax:** `[expression for item in iterable if condition]`\",\"refusal\":null,\"role\":\"assistant\"}}],\"created\":1737979208,\"id\":\"chatcmpl-DEMO003\",\"model\":\"gpt-4o-2024-11-20\",\"object\":\"chat.completion\",\"system_fingerprint\":\"fp_demo_003\",\"usage\":{\"completion_tokens\":156,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":0,\"rejected_prediction_tokens\":0},\"prompt_tokens\":184,\"prompt_tokens_details\":{\"audio_tokens\":0,\"cached_tokens\":0},\"total_tokens\":340}}", "served_entity_id": "demo-entity-003", "logging_error_codes": "[]", "requester": "demo-user-001" }, "latency": 1.247, "usage": { "completion_tokens": 156, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0 }, "prompt_tokens": 184, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 }, "total_tokens": 340 }, "model": "gpt-4o-2024-11-20" }, { "id": "demo_obs_004", "type": "follow_up", "timestamp": "2025-01-27T00:00:15", "input": "That's really helpful! Could you show me some practical examples? I'd love to see the difference between a regular for loop and a list comprehension.", "output": "Absolutely! Here are some practical examples:\\n\\n**Example 1: Squaring numbers**\\n```python\\n# Traditional for loop\\nsquares = []\\nfor x in range(5):\\n squares.append(x**2)\\n# Result: [0, 1, 4, 9, 16]\\n\\n# List comprehension\\nsquares = [x**2 for x in range(5)]\\n# Same result: [0, 1, 4, 9, 16]\\n```\\n\\n**Example 2: Filtering even numbers**\\n```python\\n# Traditional approach\\nevens = []\\nfor x in range(10):\\n if x % 2 == 0:\\n evens.append(x)\\n\\n# List comprehension\\nevens = [x for x in range(10) if x % 2 == 0]\\n# Result: [0, 2, 4, 6, 8]\\n```\\n\\n**Example 3: String processing**\\n```python\\nwords = ['hello', 'world', 'python']\\n# Capitalize and get lengths\\nlengths = [len(word.upper()) for word in words]\\n# Result: [5, 5, 6]\\n```\\n\\nList comprehensions are not only more concise but also typically 20-30% faster than equivalent for loops!", "metadata": { "interaction_type": "follow_up_examples", "code_examples_provided": 3, "response_time": "2025-01-27T12:00:15.000" }, "latency": 0.892, "usage": { "completion_tokens": 287, "total_tokens": 445 } } ] }, "trace_metadata": { "schema_analytics": { "numerical_overview": { "token_analytics": { "total_tokens": 0, "total_prompt_tokens": 0, "total_completion_tokens": 0, "avg_tokens_per_component": 0, "prompt_to_completion_ratio": 0, "min_prompt_tokens": 0, "max_prompt_tokens": 0, "min_completion_tokens": 0, "max_completion_tokens": 0, "avg_prompt_tokens": 0, "avg_completion_tokens": 0 }, "timing_analytics": { "total_execution_time_ms": 7348, "total_execution_time_seconds": 7.35, "avg_execution_time_ms": 1837, "max_execution_time_ms": 3306, "min_execution_time_ms": 734, "llm_time_percentage": 82 }, "component_stats": { "total_components": 4, "unique_component_types": 1, "max_depth": 3, "success_rate": 100.0, "agent_count": 1, "average_component_depth": 3 } }, "prompt_analytics": { "prompt_calls_detected": 4, "successful_calls": 4, "failed_calls": 0, "call_patterns": { "sequential_calls": 3, "parallel_calls": 0, "recursive_calls": 0 } }, "component_hierarchy": { "agents": [ "" ], "interaction_flow": "Sequential multi-agent collaboration with 1 agents", "complexity_score": 3 }, "performance_metrics": { "total_execution_time_ms": 7348, "avg_llm_latency_ms": 1837, "throughput_calls_per_second": 0.54 } } } }