🔄 Replace Oxford Economics with Python Documentation Use Case

- ✨ Updated sample data to use Python programming documentation as example use case
- 🐍 Changed from economic inquiries to programming concept questions
- 📚 Replaced 'Oxford Economics' with 'Python Documentation Assistant' throughout
- 🔧 Maintained all entity IDs, schemas, and architectural structure
- 🚫 Removed aif_inference references to avoid customer data association
- 📖 Updated to documentation_search trace type for better clarity

Key changes:
- Query: 'what does oxford economics present?' → 'what are python list comprehensions used for?'
- Response: Economic services → Programming concept explanations
- Tool: Oxford Economics Knowledge Search → Python Documentation Search
- Domain: economics_consulting → programming_documentation
- More universally understandable use case for developers
- Maintains RAG architecture and all functionality examples

backend/database/README_sample_data.md

@@ -16,17 +16,17 @@ The enhanced sample data system automatically inserts curated examples showcasin
 
 The system includes 2 carefully selected examples showcasing AgentGraph's advanced capabilities:
 
-1. **Oxford Economics AI Assistant** (Enhanced)
+1. **Python Documentation Assistant** (Enhanced)
 
-   - Type: `aif_inference`
-   - Example: RAG-powered assistant processing economic inquiry with knowledge search and failure detection
+   - Type: `documentation_search`
+   - Example: RAG-powered assistant processing programming inquiry with knowledge search and failure detection
    - 6 entities, 5 relations, 1 failure, 2 optimizations
    - Features: Content references, quality scoring, system summary
 
 2. **Simple Q&A Demonstration** (Basic)
 
    - Type: `conversation`
-   - Example: Basic Oxford Economics service inquiry
+   - Example: Basic Python programming concept inquiry
    - 4 entities, 4 relations, 0 failures, 1 optimization
    - Features: Streamlined structure, clear interaction flow
 

backend/database/sample_data.py

@@ -13,14 +13,14 @@ logger = logging.getLogger(__name__)
 # Enhanced sample traces showcasing AgentGraph's full capabilities
 SAMPLE_TRACES = [
     {
-        "filename": "oxford_economics_inquiry.json",
-        "title": "Oxford Economics AI Assistant Demo",
-        "description": "Enhanced example showing RAG-powered AI assistant processing economic inquiry with knowledge search, failure detection and optimization suggestions",
-        "trace_type": "aif_inference",
+        "filename": "python_documentation_inquiry.json",
+        "title": "Python Documentation Assistant Demo",
+        "description": "Enhanced example showing RAG-powered AI assistant processing programming inquiry with knowledge search, failure detection and optimization suggestions",
+        "trace_type": "documentation_search",
         "trace_source": "sample_data",
-        "tags": ["economics", "rag_assistant", "knowledge_search", "failure_detection", "optimization"],
+        "tags": ["programming", "rag_assistant", "documentation", "failure_detection", "optimization"],
         "content": """{
-  "id": "aif_trace_demo_001",
+  "id": "doc_trace_demo_001",
   "timestamp": "2025-01-27T00:00:00",
   "metadata": {
     "source": "AgentGraph_Demo",
@@ -29,15 +29,15 @@ SAMPLE_TRACES = [
   },
   "data": {
     "total_observations": 1,
-    "summary": "Oxford Economics inquiry with RAG-powered assistant response"
+    "summary": "Python documentation inquiry with RAG-powered assistant response"
   },
   "observations": [
     {
       "id": "demo_obs_001",
       "type": "inference",
       "timestamp": "2025-01-27T00:00:00",
-      "input": "You are a knowledgeable assistant on Oxford Economics designed to answer questions accurately based on the provided context. Use the information retrieved from the knowledge base below to respond concisely and correctly.\\n\\n### Retrieved Documents:\\n['Oxford Economics contact: Mateusz Urban - murban@oxfordeconomics.com', 'Oxford Economics research and analysis services', 'Economic forecasting and consultancy expertise']\\n\\n### Question:\\nwhat does oxford economics present?\\n\\n### Answer:",
-      "output": "Oxford Economics provides economic analysis, forecasting, and consultancy services.",
+      "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', 'Python built-in functions - len(), range(), enumerate()', 'Python data structures - lists, dictionaries, sets, tuples']\\n\\n### Question:\\nwhat are python list comprehensions used for?\\n\\n### Answer:",
+      "output": "Python list comprehensions provide an efficient and readable way to create lists by applying expressions to elements in iterables.",
       "metadata": {
         "request_date": "2025-01-27T00:00:00",
         "databricks_request_id": "demo-request-001",
@@ -73,23 +73,23 @@ SAMPLE_TRACES = [
     },
     {
         "filename": "simple_qa_demonstration.txt",
-        "title": "Simple Q&A: Basic Interaction",
-        "description": "Streamlined example showing basic agent-human interaction with clear knowledge graph structure",
+        "title": "Simple Q&A: Basic Programming Help",
+        "description": "Streamlined example showing basic programming assistant interaction with clear knowledge graph structure",
         "trace_type": "conversation",
         "trace_source": "sample_data",
         "tags": ["simple", "qa", "basic", "demonstration"],
-        "content": """User: Hello, can you help me understand what services Oxford Economics provides?
+        "content": """User: Hello, can you help me understand what Python list comprehensions are used for?
 
-Assistant: Certainly! Oxford Economics is a leading economic consultancy that provides:
+Assistant: Certainly! Python list comprehensions are a powerful and concise way to create lists. Here are their main uses:
 
-1. **Economic Analysis & Forecasting**: Comprehensive economic research and predictive modeling
-2. **Consultancy Services**: Strategic advice for businesses, governments, and institutions
-3. **Industry Analysis**: Sector-specific insights and market intelligence
-4. **Policy Impact Assessment**: Evaluation of economic policies and their implications
+1. **Creating Lists from Iterables**: Transform elements from existing sequences
+2. **Filtering Data**: Include only elements that meet certain conditions
+3. **Mathematical Operations**: Apply calculations to each element efficiently
+4. **Nested Iterations**: Handle complex data transformations in readable syntax
 
-They specialize in data-driven insights that help organizations make informed decisions about their economic environment and strategic planning.
+For example: `[x**2 for x in range(10) if x % 2 == 0]` creates a list of squares of even numbers.
 
-Is there a specific aspect of their services you'd like to know more about?"""
+Would you like to see more specific examples or learn about related Python features?"""
     }
 ]
 
@@ -99,15 +99,15 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
         "filename": "kg_oxford_economics_enhanced.json",
         "trace_index": 0,  # Links to first trace
         "graph_data": {
-            "system_name": "Oxford Economics AI Assistant",
-            "system_summary": "This intelligent assistant processes user inquiries about Oxford Economics through a streamlined workflow. When users submit questions, the knowledgeable agent analyzes them using RAG-powered knowledge search and delivers accurate, contextual responses about economic analysis and forecasting services.",
+            "system_name": "Python Documentation Assistant",
+            "system_summary": "This intelligent assistant processes user inquiries about Python programming through a streamlined workflow. When users submit questions, the knowledgeable agent analyzes them using RAG-powered knowledge search and delivers accurate, contextual responses about Python concepts, syntax, and best practices.",
             "entities": [
                 {
                     "id": "agent_001",
                     "type": "Agent",
-                    "name": "Oxford Economics Knowledge Agent",
+                    "name": "Python Documentation Agent",
                     "importance": "HIGH",
-                    "raw_prompt": "You are a knowledgeable assistant on Oxford Economics designed to answer questions accurately based on retrieved knowledge base context. Use the search results to provide precise responses.",
+                    "raw_prompt": "You are a helpful Python programming assistant designed to answer questions accurately based on retrieved documentation context. Use the search results to provide precise responses.",
                     "raw_prompt_ref": [
                         {
                             "line_start": 31,
@@ -118,9 +118,9 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 {
                     "id": "task_001",
                     "type": "Task",
-                    "name": "Economic Inquiry Processing",
+                    "name": "Programming Question Processing",
                     "importance": "HIGH",
-                    "raw_prompt": "Process user inquiry about Oxford Economics and generate an accurate, contextual response based on available information and company expertise.",
+                    "raw_prompt": "Process user inquiry about Python programming and generate an accurate, contextual response based on available documentation and programming best practices.",
                     "raw_prompt_ref": [
                         {
                             "line_start": 26,
@@ -131,9 +131,9 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 {
                     "id": "input_001",
                     "type": "Input",
-                    "name": "User Economic Query",
+                    "name": "User Programming Query",
                     "importance": "HIGH",
-                    "raw_prompt": "what does oxford economics present?",
+                    "raw_prompt": "what are python list comprehensions used for?",
                     "raw_prompt_ref": [
                         {
                             "line_start": 19,
@@ -144,9 +144,9 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 {
                     "id": "output_001",
                     "type": "Output",
-                    "name": "Economic Services Overview",
+                    "name": "Programming Concept Explanation",
                     "importance": "HIGH",
-                    "raw_prompt": "Oxford Economics provides economic analysis, forecasting, and consultancy services.",
+                    "raw_prompt": "Python list comprehensions provide an efficient and readable way to create lists by applying expressions to elements in iterables.",
                     "raw_prompt_ref": [
                         {
                             "line_start": 20,
@@ -157,9 +157,9 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 {
                     "id": "human_001",
                     "type": "Human",
-                    "name": "Business User",
+                    "name": "Python Developer",
                     "importance": "MEDIUM",
-                    "raw_prompt": "Professional seeking economic insights and analysis",
+                    "raw_prompt": "Developer seeking Python programming guidance and documentation",
                     "raw_prompt_ref": [
                         {
                             "line_start": 31,
@@ -170,9 +170,9 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 {
                     "id": "tool_001",
                     "type": "Tool",
-                    "name": "Oxford Economics Knowledge Search",
+                    "name": "Python Documentation Search",
                     "importance": "HIGH",
-                    "raw_prompt": "Retrieval-Augmented Generation (RAG) system that searches Oxford Economics knowledge base for relevant documents, contact information, and service details to provide contextual information.",
+                    "raw_prompt": "Retrieval-Augmented Generation (RAG) system that searches Python documentation knowledge base for relevant concepts, syntax examples, and best practices to provide contextual information.",
                     "raw_prompt_ref": [
                         {
                             "line_start": 49,
@@ -188,7 +188,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "target": "agent_001",
                     "type": "CONSUMED_BY",
                     "importance": "HIGH",
-                    "interaction_prompt": "User query received and processed by the economic knowledge agent",
+                    "interaction_prompt": "User query received and processed by the Python documentation agent",
                     "interaction_prompt_ref": [
                         {
                             "line_start": 19,
@@ -202,7 +202,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "target": "task_001",
                     "type": "PERFORMS",
                     "importance": "HIGH",
-                    "interaction_prompt": "Agent executes economic inquiry processing task",
+                    "interaction_prompt": "Agent executes programming question processing task",
                     "interaction_prompt_ref": [
                         {
                             "line_start": 26,
@@ -216,7 +216,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "target": "output_001",
                     "type": "PRODUCES",
                     "importance": "HIGH",
-                    "interaction_prompt": "Processing task generates comprehensive economic services response",
+                    "interaction_prompt": "Processing task generates comprehensive programming concept explanation",
                     "interaction_prompt_ref": [
                         {
                             "line_start": 20,
@@ -230,7 +230,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "target": "human_001",
                     "type": "DELIVERS_TO",
                     "importance": "HIGH",
-                    "interaction_prompt": "Economic analysis delivered to requesting business user",
+                    "interaction_prompt": "Programming explanation delivered to requesting developer",
                     "interaction_prompt_ref": [
                         {
                             "line_start": 20,
@@ -244,7 +244,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "target": "tool_001",
                     "type": "USES",
                     "importance": "HIGH",
-                    "interaction_prompt": "Agent queries knowledge search system to retrieve relevant Oxford Economics documents and contextual information",
+                    "interaction_prompt": "Agent queries knowledge search system to retrieve relevant Python documentation and contextual information",
                     "interaction_prompt_ref": [
                         {
                             "line_start": 49,
@@ -257,8 +257,8 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 {
                     "id": "failure_001",
                     "risk_type": "HALLUCINATION",
-                    "description": "Minor spelling inconsistency in user query may affect search precision.",
-                    "raw_text": "what does oxford economics present?",
+                    "description": "Query lacks specificity - could ask about specific Python features rather than general usage.",
+                    "raw_text": "what are python list comprehensions used for?",
                     "raw_text_ref": [
                         {
                             "line_start": 19,
@@ -272,7 +272,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 {
                     "id": "opt_001",
                     "recommendation_type": "PROMPT_REFINEMENT",
-                    "description": "Enhance the agent prompt to include spell-checking and query normalization capabilities for improved accuracy and user experience.",
+                    "description": "Enhance the agent prompt to encourage more specific programming questions and provide structured code examples in responses.",
                     "affected_ids": ["agent_001"],
                     "raw_text_ref": [
                         {
@@ -284,7 +284,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 {
                     "id": "opt_002",
                     "recommendation_type": "TOOL_ENHANCEMENT",
-                    "description": "Expand knowledge search capabilities to include real-time market data, recent publications, and dynamic economic indicators alongside static company information.",
+                    "description": "Expand knowledge search capabilities to include code examples, interactive tutorials, and version-specific documentation alongside basic syntax references.",
                     "affected_ids": ["tool_001"],
                     "raw_text_ref": [
                         {
@@ -303,9 +303,9 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 "failure_count": 1,
                 "optimization_count": 2,
                 "processing_method": "production_enhanced",
-                "content_source": "aif_inference_trace",
+                "content_source": "documentation_trace",
                 "language": "en",
-                "domain": "economics_consulting"
+                "domain": "programming_documentation"
             }
         }
     },
@@ -313,15 +313,15 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
         "filename": "kg_simple_qa_demo.json",
         "trace_index": 1,  # Links to second trace
         "graph_data": {
-            "system_name": "Oxford Economics Q&A Assistant",
-            "system_summary": "A streamlined question-answering system designed to provide clear information about Oxford Economics services. The system processes user questions and delivers comprehensive responses about economic analysis and consulting capabilities.",
+            "system_name": "Python Q&A Assistant",
+            "system_summary": "A streamlined question-answering system designed to provide clear information about Python programming concepts. The system processes user questions and delivers comprehensive responses about Python syntax and best practices.",
             "entities": [
                 {
                     "id": "user_001",
                     "type": "Human",
                     "name": "User",
                     "importance": "HIGH",
-                    "raw_prompt": "Hello, can you help me understand what services Oxford Economics provides?",
+                    "raw_prompt": "Hello, can you help me understand what Python list comprehensions are used for?",
                     "raw_prompt_ref": [
                         {
                             "line_start": 1,
@@ -334,7 +334,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "type": "Agent",
                     "name": "Q&A Assistant",
                     "importance": "HIGH",
-                    "raw_prompt": "RAG-powered assistant specialized in searching knowledge base and providing accurate information about Oxford Economics services and capabilities.",
+                    "raw_prompt": "RAG-powered assistant specialized in searching knowledge base and providing accurate information about Python programming concepts and capabilities.",
                     "raw_prompt_ref": [
                         {
                             "line_start": 3,
@@ -347,7 +347,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "type": "Task",
                     "name": "Service Information Request",
                     "importance": "HIGH",
-                    "raw_prompt": "Process user inquiry about Oxford Economics services and provide comprehensive response.",
+                    "raw_prompt": "Process user inquiry about Python programming concepts and provide comprehensive response.",
                     "raw_prompt_ref": [
                         {
                             "line_start": 1,
@@ -360,7 +360,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "type": "Output",
                     "name": "Service Overview Response",
                     "importance": "HIGH",
-                    "raw_prompt": "Certainly! Oxford Economics is a leading economic consultancy that provides: 1. Economic Analysis & Forecasting 2. Consultancy Services 3. Industry Analysis 4. Policy Impact Assessment",
+                    "raw_prompt": "Certainly! Python list comprehensions are a powerful feature that provides: 1. Concise List Creation 2. Efficient Data Filtering 3. Readable Transformations 4. Performance Optimization",
                     "raw_prompt_ref": [
                         {
                             "line_start": 3,
@@ -376,7 +376,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "target": "assistant_001",
                     "type": "CONSUMED_BY",
                     "importance": "HIGH",
-                    "interaction_prompt": "User asks about Oxford Economics services",
+                    "interaction_prompt": "User asks about Python programming concepts",
                     "interaction_prompt_ref": [
                         {
                             "line_start": 1,
@@ -390,7 +390,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "target": "inquiry_task",
                     "type": "PERFORMS",
                     "importance": "HIGH",
-                    "interaction_prompt": "Assistant processes the service information request",
+                    "interaction_prompt": "Assistant processes the programming concept request",
                     "interaction_prompt_ref": [
                         {
                             "line_start": 3,
@@ -404,7 +404,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "target": "response_output",
                     "type": "PRODUCES",
                     "importance": "HIGH",
-                    "interaction_prompt": "Task generates comprehensive service overview",
+                    "interaction_prompt": "Task generates comprehensive programming concept overview",
                     "interaction_prompt_ref": [
                         {
                             "line_start": 3,
@@ -418,7 +418,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                     "target": "user_001",
                     "type": "DELIVERS_TO",
                     "importance": "HIGH",
-                    "interaction_prompt": "Comprehensive service information delivered to user",
+                    "interaction_prompt": "Comprehensive programming information delivered to user",
                     "interaction_prompt_ref": [
                         {
                             "line_start": 3,
@@ -432,7 +432,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 {
                     "id": "opt_simple_001",
                     "recommendation_type": "PROMPT_REFINEMENT",
-                    "description": "Consider adding interactive follow-up capabilities to allow users to ask for more specific information about any of the mentioned service areas.",
+                    "description": "Consider adding interactive code examples and hands-on tutorials to allow users to practice the programming concepts being discussed.",
                     "affected_ids": ["assistant_001"],
                     "raw_text_ref": [
                         {
@@ -453,7 +453,7 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 "processing_method": "simplified_demonstration",
                 "content_source": "conversation_trace",
                 "language": "en",
-                "domain": "economics_consulting"
+                "domain": "programming_documentation"
             }
         }
     }
@@ -560,5 +560,5 @@ def get_sample_data_info():
         "trace_types": list(set(t["trace_type"] for t in SAMPLE_TRACES)),
         "complexity_levels": ["enhanced", "simple"],
         "features": ["rag_search", "failure_detection", "optimization_recommendations", "content_references", "quality_scoring"],
-        "description": "Enhanced AgentGraph examples showcasing Oxford Economics RAG-powered assistant with knowledge search, failure detection, optimization suggestions, and advanced knowledge graph features"
+        "description": "Enhanced AgentGraph examples showcasing Python Documentation RAG-powered assistant with knowledge search, failure detection, optimization suggestions, and advanced knowledge graph features"
     }