🗑️ Remove Simple Q&A Demonstration - Keep Only Comprehensive Example

✅ **Removed Content:**
- simple_qa_demonstration.txt trace (second trace)
- kg_simple_qa_demo.json knowledge graph (second KG)
- References to 2 examples in documentation

✅ **Now Contains:**
- Single comprehensive Python Documentation Assistant example
- Multi-step RAG workflow (4 observations)
- Extended educational conversation with code examples
- Complete knowledge graph with failures and optimizations

✅ **Benefits:**
- Focused on single high-quality example
- Reduced complexity for users
- Maintained all advanced features in one comprehensive sample
- Cleaner sample data structure

**Result**: Sample data now contains one production-quality, feature-complete example that fully demonstrates AgentGraph capabilities without redundancy.

backend/database/README_sample_data.md

@@ -14,21 +14,14 @@ The enhanced sample data system automatically inserts curated examples showcasin
 
 ### 🎯 Enhanced Examples
 
-The system includes 2 carefully selected examples showcasing AgentGraph's advanced capabilities:
+The system includes a carefully selected example showcasing AgentGraph's advanced capabilities:
 
-1. **Python Documentation Assistant** (Enhanced)
+**Python Documentation Assistant** (Comprehensive)
 
    - Type: `documentation_search`
-   - Example: RAG-powered assistant processing programming inquiry with knowledge search and failure detection
+   - Example: RAG-powered assistant processing multi-turn 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 Python programming concept inquiry
-   - 4 entities, 4 relations, 0 failures, 1 optimization
-   - Features: Streamlined structure, clear interaction flow
+   - Features: Multi-step workflow, educational interactions, content references, quality scoring
 
 ### 🕸️ Enhanced Knowledge Graph Examples
 
@@ -123,7 +116,7 @@ To disable automatic sample data insertion, modify `init_db.py`:
 ## Benefits for Users
 
 1. **Immediate Value**: New users see AgentGraph's complete capabilities immediately
-2. **Learning**: Examples demonstrate RAG search, failure detection, optimization suggestions, and advanced features
+2. **Learning**: Example demonstrates RAG search, failure detection, optimization suggestions, and advanced features
 3. **Testing**: Users can test all AgentGraph features including quality assessment and content referencing
 4. **Reference**: Examples serve as high-quality templates showcasing best practices
 5. **Feature Discovery**: Users understand the full potential of knowledge graph enhancement
@@ -146,7 +139,7 @@ To disable automatic sample data insertion, modify `init_db.py`:
 To update sample data:
 
 1. Modify `SAMPLE_TRACES` and `SAMPLE_KNOWLEDGE_GRAPHS` in `sample_data.py`
-2. Ensure trace_index links are correct between traces and KGs
+2. Ensure trace_index links are correct between trace and KG
 3. Test with a fresh database initialization
 4. Update this documentation if needed
 

backend/database/sample_data.py

@@ -115,93 +115,6 @@ SAMPLE_TRACES = [
     }
   ]
 }"""
-    },
-    {
-        "filename": "simple_qa_demonstration.txt",
-        "title": "Simple Q&A: Basic Programming Help",
-        "description": "Extended example showing interactive programming assistant with multi-turn educational conversation, step-by-step explanations, and performance comparisons",
-        "trace_type": "conversation",
-        "trace_source": "sample_data",
-        "tags": ["detailed", "qa", "educational", "interactive", "performance"],
-        "content": """User: Hello, can you help me understand what Python list comprehensions are used for?
-
-Assistant: Certainly! Python list comprehensions are a powerful and concise way to create lists. Here are their main uses:
-
-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
-
-For example: `[x**2 for x in range(10) if x % 2 == 0]` creates a list of squares of even numbers.
-
-Would you like to see more specific examples or learn about related Python features?
-
-User: Yes, that example looks interesting! Can you break down how that specific example works step by step?
-
-Assistant: Absolutely! Let me break down `[x**2 for x in range(10) if x % 2 == 0]` step by step:
-
-**Step 1: The iterable**
-- `range(10)` generates numbers: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
-
-**Step 2: The condition (filter)**
-- `if x % 2 == 0` keeps only even numbers
-- This filters to: 0, 2, 4, 6, 8
-
-**Step 3: The expression (transformation)**
-- `x**2` squares each remaining number
-- Results in: 0², 2², 4², 6², 8² = 0, 4, 16, 36, 64
-
-**Final result**: `[0, 4, 16, 36, 64]`
-
-**Equivalent traditional loop:**
-```python
-result = []
-for x in range(10):
-    if x % 2 == 0:
-        result.append(x**2)
-```
-
-The list comprehension does the same thing in just one readable line!
-
-User: That makes so much sense now! Are there any performance differences between the two approaches?
-
-Assistant: Great question! Yes, there are notable performance differences:
-
-**Performance Benefits of List Comprehensions:**
-
-1. **Speed**: List comprehensions are typically 20-30% faster than equivalent for loops
-   - They're implemented in C under the hood
-   - Less Python bytecode overhead
-
-2. **Memory efficiency**: More memory-efficient during creation
-   - Pre-allocates the result list size when possible
-   - Fewer intermediate steps
-
-**Quick benchmark example:**
-```python
-import timeit
-
-# List comprehension
-time1 = timeit.timeit('[x**2 for x in range(1000)]', number=10000)
-
-# Traditional loop
-code = '''
-result = []
-for x in range(1000):
-    result.append(x**2)
-'''
-time2 = timeit.timeit(code, number=10000)
-
-print(f"List comprehension: {time1:.4f}s")
-print(f"Traditional loop: {time2:.4f}s")
-# Typical result: comprehension is ~25% faster
-```
-
-**When to use each:**
-- **List comprehensions**: Simple transformations, filtering, mathematical operations
-- **Traditional loops**: Complex logic, multiple statements, debugging needs
-
-The key is readability - if your logic fits nicely in one line, list comprehensions are usually the way to go!"""
     }
 ]
 
@@ -422,156 +335,6 @@ SAMPLE_KNOWLEDGE_GRAPHS = [
                 "domain": "programming_documentation"
             }
         }
-    },
-    {
-        "filename": "kg_simple_qa_demo.json",
-        "trace_index": 1,  # Links to second trace
-        "graph_data": {
-            "system_name": "Python Q&A Assistant",
-            "system_summary": "An interactive educational system designed to provide comprehensive information about Python programming concepts. The system processes user questions and delivers detailed multi-turn responses including step-by-step explanations, code examples, performance analysis, and practical guidance.",
-            "entities": [
-                {
-                    "id": "user_001",
-                    "type": "Human",
-                    "name": "User",
-                    "importance": "HIGH",
-                    "raw_prompt": "Hello, can you help me understand what Python list comprehensions are used for? [Extended multi-turn conversation with detailed examples and performance comparisons]",
-                    "raw_prompt_ref": [
-                        {
-                            "line_start": 1,
-                            "line_end": 1
-                        }
-                    ]
-                },
-                {
-                    "id": "assistant_001",
-                    "type": "Agent",
-                    "name": "Q&A Assistant",
-                    "importance": "HIGH",
-                    "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,
-                            "line_end": 3
-                        }
-                    ]
-                },
-                {
-                    "id": "inquiry_task",
-                    "type": "Task",
-                    "name": "Service Information Request",
-                    "importance": "HIGH",
-                    "raw_prompt": "Process user inquiry about Python programming concepts and provide comprehensive response.",
-                    "raw_prompt_ref": [
-                        {
-                            "line_start": 1,
-                            "line_end": 1
-                        }
-                    ]
-                },
-                {
-                    "id": "response_output",
-                    "type": "Output",
-                    "name": "Service Overview Response",
-                    "importance": "HIGH",
-                    "raw_prompt": "Comprehensive explanation covering list comprehension syntax, use cases, step-by-step breakdowns, performance comparisons with traditional loops, and practical coding examples with benchmarks.",
-                    "raw_prompt_ref": [
-                        {
-                            "line_start": 3,
-                            "line_end": 10
-                        }
-                    ]
-                }
-            ],
-            "relations": [
-                {
-                    "id": "user_inquiry",
-                    "source": "user_001",
-                    "target": "assistant_001",
-                    "type": "CONSUMED_BY",
-                    "importance": "HIGH",
-                    "interaction_prompt": "User asks about Python programming concepts",
-                    "interaction_prompt_ref": [
-                        {
-                            "line_start": 1,
-                            "line_end": 1
-                        }
-                    ]
-                },
-                {
-                    "id": "assistant_processes",
-                    "source": "assistant_001",
-                    "target": "inquiry_task",
-                    "type": "PERFORMS",
-                    "importance": "HIGH",
-                    "interaction_prompt": "Assistant processes the programming concept request",
-                    "interaction_prompt_ref": [
-                        {
-                            "line_start": 3,
-                            "line_end": 3
-                        }
-                    ]
-                },
-                {
-                    "id": "task_generates",
-                    "source": "inquiry_task",
-                    "target": "response_output",
-                    "type": "PRODUCES",
-                    "importance": "HIGH",
-                    "interaction_prompt": "Task generates comprehensive programming concept overview",
-                    "interaction_prompt_ref": [
-                        {
-                            "line_start": 3,
-                            "line_end": 10
-                        }
-                    ]
-                },
-                {
-                    "id": "response_delivered",
-                    "source": "response_output",
-                    "target": "user_001",
-                    "type": "DELIVERS_TO",
-                    "importance": "HIGH",
-                    "interaction_prompt": "Comprehensive programming information delivered to user",
-                    "interaction_prompt_ref": [
-                        {
-                            "line_start": 3,
-                            "line_end": 10
-                        }
-                    ]
-                }
-            ],
-            "failures": [],
-            "optimizations": [
-                {
-                    "id": "opt_simple_001",
-                    "recommendation_type": "PROMPT_REFINEMENT",
-                    "description": "Add interactive code playground integration and personalized learning paths based on user's demonstrated understanding level throughout the conversation.",
-                    "affected_ids": ["assistant_001"],
-                    "raw_text_ref": [
-                        {
-                            "line_start": 12,
-                            "line_end": 12
-                        }
-                    ]
-                }
-            ],
-            "metadata": {
-                "creation_timestamp": "2025-01-27T12:00:00Z",
-                "schema_version": "2.1.0",
-                "quality_score": 0.92,
-                "entity_count": 4,
-                "relation_count": 4,
-                "failure_count": 0,
-                "optimization_count": 1,
-                "interaction_depth": "extended",
-                "educational_value": "comprehensive",
-                "processing_method": "simplified_demonstration",
-                "content_source": "conversation_trace",
-                "language": "en",
-                "domain": "programming_documentation"
-            }
-        }
     }
 ]
 
@@ -676,5 +439,5 @@ 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": "Comprehensive AgentGraph examples showcasing Python Documentation RAG-powered assistant with multi-turn interactions, detailed knowledge search, educational content delivery, failure detection, optimization suggestions, and advanced knowledge graph features"
+        "description": "Comprehensive AgentGraph example showcasing Python Documentation RAG-powered assistant with multi-turn interactions, detailed knowledge search, educational content delivery, failure detection, optimization suggestions, and advanced knowledge graph features"
     }