From 08e516e15c44cf611a74cf15def741ac66e86bdf Mon Sep 17 00:00:00 2001
From: Shyam Sridhar <117464342+shyamsridhar123@users.noreply.github.com>
Date: Fri, 30 May 2025 11:41:06 -0500
Subject: [PATCH] Revert "experiment: Enhance Graph Explorer with New Features
 and Demos"

---
 demo_enhanced_explorer.py                     |   87 -
 demo_results_20250530_105229.md               |  351 ---
 demos/graph_explorer.py                       | 1561 ----------
 demos/step_by_step_visualization_demo.py      |  808 ------
 docs/ENHANCEMENT_COMPLETION_REPORT.md         |  175 --
 docs/GRAPH_EXPLORER_JOURNEY.md                |  613 ----
 docs/GRAPH_EXPLORER_VALIDATION_REPORT.md      |  198 --
 .../business_summary_20250530_113158.txt      |   14 -
 .../customer_insights_20250530_113158.txt     |    9 -
 .../market_trends_20250530_113158.txt         |   15 -
 .../product_insights_20250530_113158.txt      |   12 -
 enhanced_graph_explorer_demo.py               |  680 -----
 .../session_20250530_090235.json              |   14 -
 .../session_20250530_095107.json              |   16 -
 .../session_20250530_112551.json              |   16 -
 ...h_explorer_validation_20250529_234048.json |  186 --
 .../enhanced_graph_visualization.html         | 2523 -----------------
 .../graph_visualization.html                  |  324 ---
 real_time_visualizations/timeline.json        |  362 ---
 src/graphiti_cosmos.py                        |  152 +-
 test_comprehensive_fix.py                     |  189 --
 test_enhanced_features.py                     |  137 -
 test_unhashable_fix.py                        |  105 -
 validate_graph_explorer.py                    |  352 ---
 24 files changed, 38 insertions(+), 8861 deletions(-)
 delete mode 100644 demo_enhanced_explorer.py
 delete mode 100644 demo_results_20250530_105229.md
 delete mode 100644 demos/graph_explorer.py
 delete mode 100644 demos/step_by_step_visualization_demo.py
 delete mode 100644 docs/ENHANCEMENT_COMPLETION_REPORT.md
 delete mode 100644 docs/GRAPH_EXPLORER_JOURNEY.md
 delete mode 100644 docs/GRAPH_EXPLORER_VALIDATION_REPORT.md
 delete mode 100644 ecommerce_intelligence_reports/business_summary_20250530_113158.txt
 delete mode 100644 ecommerce_intelligence_reports/customer_insights_20250530_113158.txt
 delete mode 100644 ecommerce_intelligence_reports/market_trends_20250530_113158.txt
 delete mode 100644 ecommerce_intelligence_reports/product_insights_20250530_113158.txt
 delete mode 100644 enhanced_graph_explorer_demo.py
 delete mode 100644 exploration_sessions/session_20250530_090235.json
 delete mode 100644 exploration_sessions/session_20250530_095107.json
 delete mode 100644 exploration_sessions/session_20250530_112551.json
 delete mode 100644 graph_explorer_validation_20250529_234048.json
 delete mode 100644 real_time_visualizations/enhanced_graph_visualization.html
 delete mode 100644 real_time_visualizations/graph_visualization.html
 delete mode 100644 real_time_visualizations/timeline.json
 delete mode 100644 test_comprehensive_fix.py
 delete mode 100644 test_enhanced_features.py
 delete mode 100644 test_unhashable_fix.py
 delete mode 100644 validate_graph_explorer.py

diff --git a/demo_enhanced_explorer.py b/demo_enhanced_explorer.py
deleted file mode 100644
index 0320f68..0000000
--- a/demo_enhanced_explorer.py
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/usr/bin/env python3
-"""
-Quick Demo of Enhanced Graph Explorer
-Shows the LLM-powered features in action
-"""
-
-import asyncio
-import sys
-import os
-
-# Add the parent directory to the path so we can import our modules
-sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
-
-from demos.graph_explorer import GraphExplorer
-
-async def demo_enhanced_features():
-    """Demonstrate the enhanced LLM features"""
-    print("πŸš€ ENHANCED GRAPH EXPLORER DEMO")
-    print("=" * 50)
-    
-    explorer = GraphExplorer()
-    await explorer.initialize()
-    
-    # Demo queries showcasing LLM enhancement
-    demo_queries = [
-        "Find technology companies in our graph",
-        "Show me people who work for sustainable organizations", 
-        "What products are related to environmental impact?"
-    ]
-    
-    for i, query in enumerate(demo_queries, 1):
-        print(f"\nπŸ” Demo Query {i}: '{query}'")
-        print("-" * 50)
-        
-        # Show LLM interpretation
-        interpretation = await explorer._interpret_natural_language_query(query)
-        print(f"🧠 LLM Analysis:")
-        print(f"   Intent: {interpretation['intent']}")
-        print(f"   Strategy: {interpretation['strategy']}")
-        print(f"   Terms: {', '.join(interpretation['search_terms'])}")
-        print(f"   Confidence: {interpretation['confidence']:.2f}")
-        print(f"   Explanation: {interpretation['explanation']}")
-        
-        # Show search results
-        search_terms = interpretation['search_terms'][:2]
-        entities = []
-        
-        for term in search_terms:
-            results = await explorer.graphiti.search_entities(term, limit=3)
-            entities.extend(results)
-        
-        # Remove duplicates
-        unique_entities = {}
-        for entity in entities:
-            entity_id = entity.get('id') or entity.get('name')
-            if entity_id not in unique_entities:
-                unique_entities[entity_id] = entity
-        
-        entities = list(unique_entities.values())
-        
-        print(f"\nπŸ“Š Results: {len(entities)} entities found")
-        for j, entity in enumerate(entities[:3], 1):
-            name = entity.get('name', 'Unknown')
-            entity_type = entity.get('type', entity.get('entity_type', 'Unknown'))
-            description = entity.get('description', 'No description')
-            print(f"   {j}. {name} ({entity_type})")
-            print(f"      {description[:60]}{'...' if len(description) > 60 else ''}")
-        
-        # Show LLM enhancement
-        if entities:
-            enhanced = await explorer._enhance_search_results_with_llm(query, entities, interpretation)
-            if hasattr(explorer, '_last_enhancement'):
-                enhancement = explorer._last_enhancement
-                print(f"\nπŸ’‘ LLM Insights: {enhancement.get('insights', 'No insights')}")
-                
-                follow_ups = enhancement.get('follow_up_queries', [])
-                if follow_ups:
-                    print(f"πŸ”„ Suggested follow-ups:")
-                    for k, suggestion in enumerate(follow_ups[:2], 1):
-                        print(f"   {k}. {suggestion}")
-        
-        print()
-    
-    print("βœ… Demo completed! The graph explorer is now fully LLM-powered.")
-
-if __name__ == "__main__":
-    asyncio.run(demo_enhanced_features())
diff --git a/demo_results_20250530_105229.md b/demo_results_20250530_105229.md
deleted file mode 100644
index 8a89c6b..0000000
--- a/demo_results_20250530_105229.md
+++ /dev/null
@@ -1,351 +0,0 @@
-# Enhanced Graph Explorer Demo Report
-
-**Generated:** 2025-05-30T10:52:29.722466  
-**Demo:** Enhanced Graph Explorer Capabilities Demo  
-**System:** LLM-Powered Knowledge Graph Explorer  
-
-## Executive Summary
-
-This report documents a comprehensive demonstration of the Enhanced Graph Explorer system, showcasing its advanced capabilities for AI-powered knowledge graph exploration and analysis. The system successfully demonstrates integration between Azure Cosmos DB graph storage and OpenAI's language models for intelligent query processing.
-
-## Key Capabilities Demonstrated
-
-- 🧠 **Natural Language Processing**: LLM-powered query interpretation and semantic understanding
-- πŸ” **Advanced Search**: Multi-strategy entity and relationship discovery
-- πŸ“Š **Statistical Analysis**: Comprehensive graph metrics and pattern recognition
-- 🎯 **Complex Queries**: High-connectivity analysis and pattern matching
-- πŸ’‘ **AI Insights**: Automated insight generation and follow-up recommendations
-
-## Detailed Demo Results
-
-### 1. System Initialization
-
-**Description:** Connecting to Azure Cosmos DB and OpenAI services for LLM-enhanced graph exploration  
-**Timestamp:** 10:52:29  
-
-**Key Insights:** The system uses Azure Cosmos DB for graph storage and OpenAI for natural language processing
-
----
-
-### 2. Connection Status
-
-**Description:** Successfully connected to all required services  
-**Timestamp:** 10:52:30  
-
-**Key Insights:** Ready for AI-powered graph exploration and analysis
-
-**Results:**
-```json
-{
-  "status": "βœ… Connected",
-  "services": [
-    "Azure Cosmos DB",
-    "OpenAI GPT-4"
-  ]
-}
-```
-
----
-
-### 3. Natural Language Search Demo
-
-**Description:** Demonstrated LLM-powered query interpretation and semantic entity discovery  
-**Timestamp:** 10:56:09  
-
-**Key Insights:** Successfully processed 3 natural language queries with AI interpretation
-
-**Results:**
-```json
-[
-  {
-    "query": "Find products related to sustainable footwear",
-    "interpretation": {
-      "intent": "search_entities",
-      "search_terms": [
-        "products",
-        "sustainable footwear",
-        "related products"
-      ],
-      "strategy": "semantic_search",
-      "result_type": "specific_entities",
-      "confidence": 0.92,
-      "explanation": "The query seeks to identify specific products that are associated with the concept of sustainable footwear, indicating an entity search using semantic understanding to capture relatedness."
-    },
-    "entities_found": 10,
-    "top_entities": [
-      {
-        "name": "trail_runners",
-        "type": "unknown",
-        "description": "A category of products related to running on trails"
-      },
-      {
-        "name": "dress_shoes",
-        "type": "unknown",
-        "description": "A category of footwear products browsed by the customer."
-      },
-      {
-        "name": "boots",
-        "type": "unknown",
-        "description": "Footwear products that the customer browsed."
-      }
-    ]
-  },
-  {
-    "query": "Show me companies that manufacture insoles",
-    "interpretation": {
-      "intent": "search_entities",
-      "search_terms": [
-        "companies",
-        "manufacture",
-        "insoles"
-      ],
-      "strategy": "semantic_search",
-      "result_type": "specific_entities",
-      "confidence": 0.95,
-      "explanation": "The query seeks a list of companies (entities) with the specific activity of manufacturing insoles, indicating an entity search with semantic understanding of the manufacturing relationship."
-    },
-    "entities_found": 10,
-    "top_entities": [
-      {
-        "name": "corporate_sustainability",
-        "type": "unknown",
-        "description": "Business practices and strategies that prioritize environmental and social responsibility."
-      },
-      {
-        "name": "Microsoft",
-        "type": "unknown",
-        "description": "A multinational technology company that develops, licenses, and sells computer software, electronics"
-      },
-      {
-        "name": "corporate sustainability initiatives",
-        "type": "unknown",
-        "description": "Business strategies and actions aimed at promoting environmental, social, and economic sustainabilit"
-      }
-    ]
-  },
-  {
-    "query": "What blue products are available?",
-    "interpretation": {
-      "intent": "search_entities",
-      "search_terms": [
-        "product",
-        "blue"
-      ],
-      "strategy": "exact_match",
-      "result_type": "specific_entities",
-      "confidence": 0.95,
-      "explanation": "The query seeks a list of products with the attribute 'blue', indicating a search for specific entities matching this property."
-    },
-    "entities_found": 10,
-    "top_entities": [
-      {
-        "name": "Sustainability filter",
-        "type": "unknown",
-        "description": "A filter option related to sustainable products, which was not applied in this session."
-      },
-      {
-        "name": "sneakers",
-        "type": "unknown",
-        "description": "Type of footwear product browsed by the customer"
-      },
-      {
-        "name": "Sarah Johnson",
-        "type": "unknown",
-        "description": "A person who viewed the product."
-      }
-    ]
-  }
-]
-```
-
----
-
-### 4. Relationship Analysis
-
-**Description:** Comprehensive analysis of relationship patterns, types, and connectivity  
-**Timestamp:** 10:56:19  
-
-**Key Insights:** Relationship analysis reveals the structural patterns and key connectors in the knowledge graph
-
-**Results:**
-```json
-{
-  "total_relationships": 50,
-  "relationship_types": {
-    "unknown": 20
-  },
-  "confidence_stats": {
-    "average": 1.0,
-    "min": 1.0,
-    "max": 1.0
-  },
-  "top_connected_entities": {
-    "Unknown": 11,
-    "John Smith": 4,
-    "Alice Williams": 4,
-    "Microsoft": 3,
-    "Sarah Johnson": 3
-  }
-}
-```
-
----
-
-### 5. Graph Statistics
-
-**Description:** Comprehensive statistical overview of the knowledge graph structure  
-**Timestamp:** 10:56:37  
-
-**Key Insights:** Graph statistics provide insights into data distribution and structural characteristics
-
-**Results:**
-```json
-{
-  "total_entities": 100,
-  "total_relationships": 100,
-  "entity_types": {
-    "unknown": 100
-  },
-  "density": 1.0,
-  "analysis_completeness": 100
-}
-```
-
----
-
-### 6. Advanced Query Capabilities
-
-**Description:** Demonstration of complex query patterns and analysis techniques  
-**Timestamp:** 11:08:47  
-
-**Key Insights:** Advanced queries enable deep insights into graph structure and connectivity patterns
-
-**Results:**
-```json
-[
-  {
-    "query_type": "high_connectivity",
-    "results": [
-      [
-        "Marcus Chen",
-        16
-      ],
-      [
-        "OpenAI",
-        5
-      ],
-      [
-        "John Smith",
-        4
-      ],
-      [
-        "Alice Williams",
-        4
-      ],
-      [
-        "Sarah Williams",
-        4
-      ]
-    ],
-    "total_found": 16
-  },
-  {
-    "query_type": "pattern_matching",
-    "patterns": {
-      "entity β†’ unknown β†’ entity": 30
-    },
-    "total_patterns": 1
-  }
-]
-```
-
----
-
-### 7. LLM-Powered Enhancements
-
-**Description:** Demonstration of AI-powered query interpretation and result enhancement  
-**Timestamp:** 11:11:06  
-
-**Key Insights:** LLM enhancements provide intelligent query understanding and contextual insights
-
-**Results:**
-```json
-{
-  "test_query": "Show me all eco-friendly products from sustainable brands",
-  "interpretation": {
-    "intent": "search_entities",
-    "search_terms": [
-      "eco-friendly products",
-      "sustainable brands"
-    ],
-    "strategy": "semantic_search",
-    "result_type": "specific_entities",
-    "confidence": 0.95,
-    "explanation": "The query seeks a list of products (entities) that are both eco-friendly and associated with sustainable brands, requiring semantic understanding to match relevant attributes."
-  },
-  "entities_found": 6,
-  "enhancement": null,
-  "llm_features_tested": [
-    "Query interpretation",
-    "Search strategy recommendation",
-    "Result enhancement",
-    "Insight generation"
-  ]
-}
-```
-
----
-
-## Technical Specifications
-
-- **Graph Database**: Azure Cosmos DB with Gremlin API
-- **AI Model**: OpenAI GPT-4 for natural language processing
-- **Query Language**: Gremlin for graph traversal
-- **Data Format**: Handles both simple dictionaries and Cosmos DB valueMap(true) format
-- **Programming Language**: Python 3.12+
-- **Key Libraries**: asyncio, azure-cosmos, openai
-
-## System Features
-
-### βœ… Core Capabilities
-- Natural language query interpretation
-- Entity and relationship search
-- Graph statistical analysis
-- Pattern recognition and matching
-- Community detection
-- Subgraph analysis
-
-### βœ… LLM Enhancements
-- Intelligent query understanding
-- Search strategy recommendation
-- Result contextualization
-- Insight generation
-- Follow-up query suggestions
-
-### βœ… Data Handling
-- Cosmos DB valueMap(true) format support
-- Backward compatibility with simple dictionaries
-- Robust error handling and fallback mechanisms
-- Performance optimization for large graphs
-
-## Demo Conclusions
-
-The Enhanced Graph Explorer successfully demonstrates sophisticated AI-powered graph analysis capabilities. The system effectively combines traditional graph database operations with modern language model intelligence to provide intuitive, natural language interfaces for complex graph exploration tasks.
-
-### Key Achievements:
-1. **Seamless Integration**: Successfully integrated graph database with LLM capabilities
-2. **Natural Interface**: Demonstrated natural language query processing
-3. **Comprehensive Analysis**: Showed multiple analysis approaches and insight generation
-4. **Robust Performance**: Handled various query types and data formats
-5. **Actionable Insights**: Generated meaningful patterns and recommendations
-
-### Future Enhancements:
-- Real-time visualization integration
-- Advanced community detection algorithms
-- Multi-hop reasoning capabilities
-- Custom domain-specific query templates
-- Interactive result exploration
-
----
-
-*This report was automatically generated by the Enhanced Graph Explorer Demo System on May 30, 2025 at 11:11 AM*
diff --git a/demos/graph_explorer.py b/demos/graph_explorer.py
deleted file mode 100644
index 659e6ce..0000000
--- a/demos/graph_explorer.py
+++ /dev/null
@@ -1,1561 +0,0 @@
-"""
-Interactive Graph Explorer for Graphiti-Cosmos
-Allows natural language exploration of entities, relationships, communities, and subgraphs
-"""
-
-import asyncio
-import os
-import sys
-import json
-import time
-from datetime import datetime
-from typing import List, Dict, Any, Set, Tuple
-from collections import defaultdict, Counter
-import traceback
-
-# Add the parent directory to the path so we can import our modules
-sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
-
-from src.graphiti_cosmos import GraphitiCosmos
-
-class GraphExplorer:
-    """Interactive exploration tool for knowledge graphs"""
-    
-    def __init__(self):
-        self.graphiti = None
-        self.session_history = []
-        self.bookmark_entities = []
-        self.bookmark_relationships = []
-        
-    async def initialize(self):
-        """Initialize the Graphiti-Cosmos system"""
-        print("πŸ” INTERACTIVE GRAPH EXPLORER")
-        print("=" * 60)
-        print("🎨 Initializing Graphiti-Cosmos...")
-        
-        self.graphiti = GraphitiCosmos()
-        await self.graphiti.initialize()
-        print("βœ… Connected to Azure Cosmos DB and OpenAI")
-        print("🌟 Ready for graph exploration!")
-        print()
-        
-    def display_main_menu(self):
-        """Display the main exploration menu"""
-        print("πŸ” GRAPH EXPLORATION MENU")
-        print("-" * 50)
-        print("1. πŸ”Ž Search Entities (Natural Language)")
-        print("2. πŸ”— Search Relationships")
-        print("3. 🏘️  Explore Communities")
-        print("4. 🧩 Analyze Subgraphs")
-        print("5. 🌐 Graph Overview & Statistics")
-        print("6. πŸ“Š Entity Deep Dive")
-        print("7. πŸ”„ Relationship Analysis")
-        print("8. 🎯 Advanced Query Builder")
-        print("9. πŸ“‹ Session History")
-        print("10. πŸ”– Manage Bookmarks")
-        print("11. πŸ“ˆ Export Analysis")
-        print("0. πŸšͺ Exit")
-        print("-" * 50)
-
-    async def run_interactive_session(self):
-        """Main interactive session loop"""
-        await self.initialize()
-        
-        while True:
-            self.display_main_menu()
-            choice = input("Enter your choice (0-11): ").strip()
-            
-            try:
-                if choice == "0":
-                    print("πŸ‘‹ Thanks for exploring! Session saved.")
-                    await self._save_session()
-                    break
-                elif choice == "1":
-                    await self._search_entities_nl()
-                elif choice == "2":
-                    await self._search_relationships()
-                elif choice == "3":
-                    await self._explore_communities()
-                elif choice == "4":
-                    await self._analyze_subgraphs()
-                elif choice == "5":
-                    await self._graph_overview()
-                elif choice == "6":
-                    await self._entity_deep_dive()
-                elif choice == "7":
-                    await self._relationship_analysis()
-                elif choice == "8":
-                    await self._advanced_query_builder()
-                elif choice == "9":
-                    await self._show_session_history()
-                elif choice == "10":
-                    await self._manage_bookmarks()
-                elif choice == "11":
-                    await self._export_analysis()
-                else:
-                    print("❌ Invalid choice! Please try again.")
-                
-                print("\nPress Enter to continue...")
-                input()
-                print("\n" + "="*60 + "\n")
-                
-            except Exception as e:
-                print(f"❌ Error: {e}")
-                traceback.print_exc()
-                print("\nPress Enter to continue...")
-                    
-    async def _search_entities_nl(self):
-        """Natural language entity search"""
-        print("πŸ”Ž NATURAL LANGUAGE ENTITY SEARCH")
-        print("-" * 40)
-        print("πŸ’‘ Examples:")
-        print("   - 'Find all people in the graph'")
-        print("   - 'Show me products related to sustainability'")
-        print("   - 'List organizations and their relationships'")
-        print("   - 'Find entities connected to Sarah Johnson'")
-        print()
-        
-        query = input("πŸ—£οΈ  Enter your search query: ").strip()
-        if not query:
-            print("ℹ️  No query entered.")
-            return
-            
-        # Log the search
-        self.session_history.append({
-            'type': 'entity_search',
-            'query': query,
-            'timestamp': datetime.now().isoformat()
-        })
-        print(f"\nπŸ” Analyzing query: '{query}'")
-        print("-" * 40)
-        
-        try:
-            # Step 1: Interpret the natural language query using LLM
-            interpretation = await self._interpret_search_query(query)
-            
-            print(f"🎯 Query intent: {interpretation['intent']}")
-            print(f"πŸ“‹ Search terms: {', '.join(interpretation['search_terms'])}")
-            print(f"πŸ”§ Strategy: {interpretation['strategy']}")
-            print(f"πŸŽͺ Confidence: {interpretation['confidence']:.2f}")
-            print("πŸ’‘ Searching with AI-enhanced strategy...")
-            print()
-            
-            # Step 2: Execute search based on interpretation
-            entities = []
-            search_terms = interpretation['search_terms']
-            
-            if interpretation['strategy'] == 'exact_match':
-                # Use exact matching for specific entity searches
-                for term in search_terms[:2]:
-                    results = await self.graphiti.search_entities(term, limit=10)
-                    entities.extend(results)
-            elif interpretation['strategy'] == 'broad_exploration':
-                # Get broader results for exploratory queries
-                for term in search_terms[:1]:
-                    results = await self.graphiti.search_entities(term, limit=30)
-                    entities.extend(results)
-            else:  # semantic_search (default)
-                # Use semantic search for most queries
-                for term in search_terms[:3]:
-                    results = await self.graphiti.search_entities(term, limit=15)
-                    entities.extend(results)
-              # Step 3: Remove duplicates and enhance results with LLM
-            unique_entities = {}
-            for entity in entities:
-                entity_id = self._extract_property(entity, 'id') or self._extract_property(entity, 'name')
-                if entity_id not in unique_entities:
-                    unique_entities[entity_id] = entity
-            
-            entities = list(unique_entities.values())
-            
-            # Step 4: Enhance results with LLM insights
-            if entities:
-                print("🧠 Enhancing results with AI insights...")
-                entities = await self._enhance_search_results_with_llm(query, entities, interpretation)
-            
-            if entities:
-                print(f"\nβœ… Found {len(entities)} entities:")
-                
-                # Display LLM insights if available
-                if hasattr(self, '_last_enhancement'):
-                    enhancement = self._last_enhancement
-                    print(f"\nπŸ” AI Insights: {enhancement.get('insights', 'No insights available')}")
-                    
-                    if enhancement.get('follow_up_queries'):
-                        print("\nπŸ’‘ Suggested follow-up queries:")
-                        for i, suggestion in enumerate(enhancement['follow_up_queries'][:3], 1):
-                            print(f"   {i}. {suggestion}")
-                    
-                    if enhancement.get('gaps'):
-                        print(f"\n⚠️  Consider exploring: {enhancement['gaps']}")
-                    print()
-                  # Group by type
-                entity_groups = defaultdict(list)
-                for entity in entities:
-                    entity_type = self._extract_property(entity, 'type', 
-                                                       self._extract_property(entity, 'entity_type', 'unknown'))
-                    entity_groups[entity_type].append(entity)
-                
-                for entity_type, type_entities in entity_groups.items():
-                    print(f"\nπŸ“ {entity_type.upper()} ({len(type_entities)} entities):")
-                    for i, entity in enumerate(type_entities[:5], 1):
-                        name = self._extract_property(entity, 'name', 
-                                                    self._extract_property(entity, 'id', 'Unknown'))
-                        description = self._extract_property(entity, 'description', '')
-                        if description:
-                            print(f"   {i}. {name}: {description[:100]}{'...' if len(description) > 100 else ''}")
-                        else:
-                            print(f"   {i}. {name}")
-                
-                # Show bookmark option
-                print("\nπŸ”– Enter entity name to bookmark (or press Enter to continue):")
-                bookmark_choice = input().strip()
-                if bookmark_choice:
-                    self.bookmark_entities.append(bookmark_choice)
-                    print(f"βœ… Bookmarked: {bookmark_choice}")
-                    
-            else:
-                print("❌ No entities found matching your query.")
-                print("πŸ’‘ Try:")
-                print("   - Using broader search terms")
-                print("   - Checking spelling")
-                print("   - Using different keywords")
-                
-        except Exception as e:
-            print(f"❌ Search error: {e}")
-            print("πŸ’‘ Trying fallback search...")
-            
-            try:
-                # Fallback to basic keyword search
-                keywords = await self._extract_search_keywords(query)
-                entities = []
-                for keyword in keywords:
-                    results = await self.graphiti.search_entities(keyword, limit=20)
-                    entities.extend(results)
-                if entities:
-                    print(f"βœ… Found {len(entities)} entities using fallback search:")
-                    for i, entity in enumerate(entities[:10], 1):
-                        # Handle both dictionary and Cosmos DB valueMap(true) list formats
-                        if isinstance(entity, dict):
-                            name = self._extract_property(entity, 'name', 'Unknown')
-                            entity_type = self._extract_property(entity, 'type', 
-                                         self._extract_property(entity, 'entity_type', 'unknown'))
-                        else:
-                            name = str(entity) if entity else 'Unknown'
-                            entity_type = 'unknown'
-                        print(f"   {i}. {name} ({entity_type})")
-                else:
-                    print("❌ No entities found.")
-                print()
-                
-            except Exception as fallback_error:
-                print(f"❌ Fallback search also failed: {fallback_error}")
-                print("πŸ’‘ Try these alternatives:")
-                print("   - Use more general terms")
-                print("   - Try searching for entity types: 'person', 'product', 'organization'")
-                print("   - Browse all entities with an empty search")
-
-    async def _search_relationships(self):
-        """Search and explore relationships"""
-        print("πŸ”— RELATIONSHIP SEARCH & EXPLORATION")
-        print("-" * 40)
-        print("πŸ’‘ Search options:")
-        print("   1. By relationship type (e.g., 'works_for', 'related_to')")
-        print("   2. By entities (e.g., 'Sarah Johnson')")
-        print("   3. By pattern (e.g., 'person β†’ organization')")
-        print()
-        
-        search_type = input("Choose search type (1-3): ").strip()
-        
-        if search_type == "1":
-            await self._search_relationships_by_type()
-        elif search_type == "2":
-            await self._search_relationships_by_entity()
-        elif search_type == "3":
-            await self._search_relationships_by_pattern()
-        else:
-            print("❌ Invalid choice!")
-
-    async def _search_relationships_by_type(self):
-        """Search relationships by type"""
-        print("\n🏷️ SEARCH BY RELATIONSHIP TYPE")
-        print("-" * 30)
-        
-        # Show available relationship types
-        print("πŸ“‹ Getting available relationship types...")        
-        relationships = await self.graphiti.search_relationships("", limit=100)
-        
-        rel_types = Counter()
-        for rel in relationships:
-            rel_type = self._extract_property(rel, 'type', 'unknown')
-            rel_types[rel_type] += 1
-        
-        print("πŸ”— Available relationship types:")
-        for rel_type, count in rel_types.most_common(10):
-            print(f"   β€’ {rel_type} ({count} instances)")
-        
-        rel_type = input("\nEnter relationship type to explore: ").strip()
-        if not rel_type:
-            return
-            
-        # Search for this relationship type
-        matching_rels = [rel for rel in relationships if self._extract_property(rel, 'type', '').lower() == rel_type.lower()]
-        
-        if matching_rels:
-            print(f"\nβœ… Found {len(matching_rels)} '{rel_type}' relationships:")
-            
-            for i, rel in enumerate(matching_rels[:10], 1):
-                source = self._extract_property(rel, 'source', 'Unknown')
-                target = self._extract_property(rel, 'target', 'Unknown')
-                description = self._extract_property(rel, 'description', 'No description')
-                confidence = self._extract_property(rel, 'confidence', '1.0')
-                
-                print(f"\n{i}. {source} β†’ {rel_type} β†’ {target}")
-                if confidence < 1.0:
-                    print(f"   🎯 Confidence: {confidence:.2f}")
-                if description and description != 'No description':
-                    desc_preview = description[:100] + "..." if len(description) > 100 else description
-                    print(f"   πŸ’­ {desc_preview}")
-            
-            if len(matching_rels) > 10:
-                print(f"\n... and {len(matching_rels) - 10} more relationships")
-        else:
-            print(f"❌ No relationships found of type '{rel_type}'")
-
-    async def _search_relationships_by_entity(self):
-        """Search relationships involving specific entities"""
-        print("\nπŸ‘€ SEARCH BY ENTITY")
-        print("-" * 20)
-        
-        entity_name = input("Enter entity name: ").strip()
-        if not entity_name:
-            return
-            
-        relationships = await self._get_entity_relationships(entity_name)
-        
-        if relationships:
-            print(f"\nβœ… Found {len(relationships)} relationships for '{entity_name}':")
-              # Group by relationship type
-            rel_groups = defaultdict(list)
-            for rel in relationships:
-                rel_type = self._extract_property(rel, 'type', 'unknown')
-                rel_groups[rel_type].append(rel)
-            
-            for rel_type, type_rels in rel_groups.items():
-                print(f"\nπŸ”— {rel_type} ({len(type_rels)} instances):")
-                for rel in type_rels[:5]:
-                    source = self._extract_property(rel, 'source', 'Unknown')
-                    target = self._extract_property(rel, 'target', 'Unknown')
-                    
-                    # Determine direction
-                    if source.lower() == entity_name.lower():
-                        print(f"   β†’ {target}")
-                    else:
-                        print(f"   ← {source}")
-                
-                if len(type_rels) > 5:
-                    print(f"   ... and {len(type_rels) - 5} more")
-        else:
-            print(f"❌ No relationships found for '{entity_name}'")
-
-    async def _explore_communities(self):
-        """Explore entity communities and clusters"""
-        print("🏘️ COMMUNITY EXPLORATION")
-        print("-" * 40)
-        
-        print("πŸ” Analyzing entity communities...")
-        
-        # Get all entities
-        entities = await self.graphiti.search_entities("", limit=200)
-        
-        if len(entities) < 3:
-            print("ℹ️  Not enough entities for community analysis")
-            return
-          # Group entities by type (basic community detection)
-        type_communities = defaultdict(list)
-        for entity in entities:
-            entity_type = self._extract_property(entity, 'type', 'unknown')
-            type_communities[entity_type].append(entity)
-        
-        print(f"βœ… Found {len(type_communities)} entity type communities:")
-        
-        for community_type, members in type_communities.items():
-            print(f"\nπŸ“ {community_type.upper()} Community ({len(members)} members)")
-              # Show top members
-            for i, member in enumerate(members[:5], 1):
-                name = self._extract_property(member, 'name', 'Unknown')
-                description = self._extract_property(member, 'description', 'No description')
-                desc_preview = description[:50] + "..." if len(description) > 50 else description
-                print(f"   {i}. {name} - {desc_preview}")
-            
-            if len(members) > 5:
-                print(f"   ... and {len(members) - 5} more members")
-        
-        # Advanced community analysis
-        print(f"\nπŸ”¬ ADVANCED COMMUNITY ANALYSIS")
-        print("-" * 30)
-        
-        community_choice = input("Enter community type to analyze deeply (or press Enter to skip): ").strip()
-        if community_choice:
-            await self._deep_community_analysis(community_choice, type_communities.get(community_choice.lower(), []))
-
-    async def _deep_community_analysis(self, community_type: str, members: List[Dict]):
-        """Perform deep analysis of a specific community"""
-        print(f"\nπŸ”¬ DEEP ANALYSIS: {community_type.upper()} COMMUNITY")
-        print("-" * 40)
-        
-        if not members:
-            print("❌ No members found in this community")
-            return
-        
-        print(f"πŸ‘₯ Community Size: {len(members)} members")
-        
-        # Analyze internal connections
-        print("\nπŸ”— Analyzing internal connections...")
-        internal_connections = 0
-        connection_map = defaultdict(list)
-        for member in members:
-            member_name = self._extract_property(member, 'name', '')
-            relationships = await self._get_entity_relationships(member_name)
-            
-            for rel in relationships:
-                source = self._extract_property(rel, 'source', '')
-                target = self._extract_property(rel, 'target', '')
-                
-                # Check if both entities are in this community
-                member_names = [self._extract_property(m, 'name', '') for m in members]
-                if source in member_names and target in member_names:
-                    internal_connections += 1
-                    connection_map[member_name].append({
-                        'target': target if source == member_name else source,
-                        'type': self._extract_property(rel, 'type', 'unknown')
-                    })
-        
-        print(f"πŸ”„ Internal connections: {internal_connections}")
-        
-        # Find most connected members
-        connection_counts = [(name, len(connections)) for name, connections in connection_map.items()]
-        connection_counts.sort(key=lambda x: x[1], reverse=True)
-        
-        print(f"\n⭐ Most connected members:")
-        for name, count in connection_counts[:5]:
-            print(f"   β€’ {name}: {count} connections")
-        
-        # Find external connections
-        print(f"\n🌐 External connections...")
-        external_targets = defaultdict(int)
-        for member in members[:10]:  # Limit for performance
-            member_name = self._extract_property(member, 'name', '')
-            relationships = await self._get_entity_relationships(member_name)
-            
-            for rel in relationships:
-                source = self._extract_property(rel, 'source', '')
-                target = self._extract_property(rel, 'target', '')
-                
-                # Find external targets
-                member_names = [self._extract_property(m, 'name', '') for m in members]
-                external_target = None
-                if source == member_name and target not in member_names:
-                    external_target = target
-                elif target == member_name and source not in member_names:
-                    external_target = source
-                
-                if external_target:
-                    external_targets[external_target] += 1
-        
-        if external_targets:
-            print(f"πŸ”— Top external connections:")
-            for target, count in Counter(external_targets).most_common(5):
-                print(f"   β€’ {target}: {count} connections")
-
-    async def _analyze_subgraphs(self):
-        """Analyze and explore subgraphs"""
-        print("🧩 SUBGRAPH ANALYSIS")
-        print("-" * 40)
-        
-        print("πŸ” Choose subgraph analysis type:")
-        print("1. 🎯 Ego network (around specific entity)")
-        print("2. 🌊 Path analysis (between two entities)")
-        print("3. πŸ“Š Dense subgraphs")
-        print("4. 🏷️  Type-based subgraphs")
-        
-        choice = input("Enter choice (1-4): ").strip()
-        
-        if choice == "1":
-            await self._ego_network_analysis()
-        elif choice == "2":
-            await self._path_analysis()
-        elif choice == "3":
-            await self._dense_subgraph_analysis()
-        elif choice == "4":
-            await self._type_based_subgraphs()
-        else:
-            print("❌ Invalid choice!")
-
-    async def _ego_network_analysis(self):
-        """Analyze ego network around a specific entity"""
-        print("\n🎯 EGO NETWORK ANALYSIS")
-        print("-" * 25)
-        
-        entity_name = input("Enter entity name for ego network: ").strip()
-        if not entity_name:
-            return
-        
-        depth = input("Enter network depth (1-3, default 2): ").strip()
-        try:
-            depth = int(depth) if depth else 2
-            depth = max(1, min(depth, 3))  # Limit between 1-3
-        except ValueError:
-            depth = 2
-        
-        print(f"\nπŸ” Analyzing {depth}-hop ego network for '{entity_name}'...")
-          # Build ego network
-        ego_entities = {entity_name}
-        ego_relationships = []
-        
-        current_entities = {entity_name}
-        for hop in range(depth):
-            next_entities = set()
-            for entity in current_entities:
-                relationships = await self._get_entity_relationships(entity)
-                
-                for rel in relationships:
-                    source = self._extract_property(rel, 'source', '')
-                    target = self._extract_property(rel, 'target', '')
-                    
-                    ego_relationships.append(rel)
-                    
-                    # Add connected entities
-                    if source == entity:
-                        next_entities.add(target)
-                        ego_entities.add(target)
-                    elif target == entity:
-                        next_entities.add(source)
-                        ego_entities.add(source)
-            
-            current_entities = next_entities - ego_entities
-            ego_entities.update(next_entities)
-            
-            print(f"   Hop {hop + 1}: Found {len(next_entities)} new entities")
-        
-        print(f"\nβœ… Ego network summary:")
-        print(f"   🏠 Center entity: {entity_name}")
-        print(f"   πŸ‘₯ Total entities: {len(ego_entities)}")
-        print(f"   πŸ”— Total relationships: {len(ego_relationships)}")
-        
-        # Analyze the ego network
-        if len(ego_entities) > 1:
-            print(f"\nπŸ”¬ Network analysis:")
-              # Entity types in network
-            entity_types = Counter()
-            for entity_name_in_network in ego_entities:
-                # Get entity type
-                entities = await self.graphiti.search_entities(entity_name_in_network, limit=1)
-                if entities:
-                    entity_type = self._extract_property(entities[0], 'type', 'unknown')
-                    entity_types[entity_type] += 1
-            
-            print(f"   πŸ“Š Entity types:")
-            for entity_type, count in entity_types.most_common():
-                print(f"      β€’ {entity_type}: {count}")
-              # Relationship types in network
-            rel_types = Counter()
-            for rel in ego_relationships:
-                rel_type = self._extract_property(rel, 'type', 'unknown')
-                rel_types[rel_type] += 1
-            
-            print(f"   πŸ”— Relationship types:")
-            for rel_type, count in rel_types.most_common(5):
-                print(f"      β€’ {rel_type}: {count}")
-
-    async def _graph_overview(self):
-        """Provide comprehensive graph overview and statistics"""
-        print("🌐 GRAPH OVERVIEW & STATISTICS")
-        print("-" * 40)
-        
-        print("πŸ“Š Collecting graph statistics...")
-          # Get basic stats
-        stats = await self.graphiti.get_graph_stats()
-        print(f"\nπŸ“ˆ Basic Statistics:")
-        print(f"   πŸ“š Episodes: {self._extract_property(stats, 'episodes', '0')}")
-        print(f"   πŸ‘₯ Entities: {self._extract_property(stats, 'entities', '0')}")
-        print(f"   πŸ”— Relationships: {self._extract_property(stats, 'relationships', '0')}")
-        
-        entities_count = int(self._extract_property(stats, 'entities', '0'))
-        relationships_count = int(self._extract_property(stats, 'relationships', '0'))
-        if entities_count > 0:
-            density = relationships_count / entities_count
-            print(f"   🎯 Density: {density:.2f} relationships per entity")
-        
-        # Entity type distribution
-        print(f"\n🏷️  Entity Type Distribution:")
-        entities = await self.graphiti.search_entities("", limit=200)
-        entity_types = Counter()        
-        for entity in entities:
-            entity_type = self._extract_property(entity, 'type', 'unknown')
-            entity_types[entity_type] += 1
-        
-        for entity_type, count in entity_types.most_common():
-            percentage = (count / len(entities)) * 100 if entities else 0
-            print(f"   πŸ“ {entity_type}: {count} ({percentage:.1f}%)")
-        
-        # Relationship type distribution
-        print(f"\nπŸ”— Relationship Type Distribution:")
-        relationships = await self.graphiti.search_relationships("", limit=200)
-        rel_types = Counter()
-        for rel in relationships:
-            rel_type = self._extract_property(rel, 'type', 'unknown')
-            rel_types[rel_type] += 1
-        
-        for rel_type, count in rel_types.most_common(5):
-            percentage = (count / len(relationships)) * 100 if relationships else 0
-            print(f"   πŸ”— {rel_type}: {count} ({percentage:.1f}%)")
-          # Find most connected entities
-        print(f"\n⭐ Most Connected Entities:")
-        entity_connections = defaultdict(int)
-        
-        for rel in relationships[:100]:  # Limit for performance
-            source = self._extract_property(rel, 'source', '')
-            target = self._extract_property(rel, 'target', '')
-            entity_connections[source] += 1
-            entity_connections[target] += 1
-        
-        top_connected = sorted(entity_connections.items(), key=lambda x: x[1], reverse=True)
-        for entity, connections in top_connected[:5]:
-            print(f"   🌟 {entity}: {connections} connections")
-
-    async def _entity_deep_dive(self):
-        """Deep dive analysis of a specific entity"""
-        print("πŸ“Š ENTITY DEEP DIVE")
-        print("-" * 40)
-        
-        entity_name = input("Enter entity name to analyze: ").strip()
-        if not entity_name:
-            return
-            
-        await self._entity_deep_dive_specific(entity_name)
-
-    async def _entity_deep_dive_specific(self, entity_name: str):
-        """Perform deep dive analysis on a specific entity"""
-        print(f"\nπŸ”¬ DEEP DIVE: {entity_name}")
-        print("-" * 40)
-        
-        # Get entity details
-        entities = await self.graphiti.search_entities(entity_name, limit=5)
-        target_entity = None
-        for entity in entities:
-            if self._extract_property(entity, 'name', '').lower() == entity_name.lower():
-                target_entity = entity
-                break
-        
-        if not target_entity and entities:
-            target_entity = entities[0]  # Take the first match
-        
-        if target_entity:
-            print(f"πŸ“‹ Entity Details:")
-            print(f"   🏷️  Name: {self._extract_property(target_entity, 'name', 'Unknown')}")
-            print(f"   πŸ“‚ Type: {self._extract_property(target_entity, 'type', 'Unknown')}")
-            print(f"   πŸ“ Description: {self._extract_property(target_entity, 'description', 'No description')}")
-            
-            # Get relationships
-            relationships = await self._get_entity_relationships(entity_name)
-            print(f"\nπŸ”— Relationships ({len(relationships)} total):")
-            
-            if relationships:                # Group by type
-                rel_groups = defaultdict(list)
-                for rel in relationships:
-                    rel_type = self._extract_property(rel, 'type', 'unknown')
-                    rel_groups[rel_type].append(rel)
-                
-                for rel_type, type_rels in rel_groups.items():
-                    print(f"\n   πŸ“Œ {rel_type} ({len(type_rels)} instances):")
-                    for rel in type_rels[:3]:
-                        source = self._extract_property(rel, 'source', 'Unknown')
-                        target = self._extract_property(rel, 'target', 'Unknown')
-                        confidence = float(self._extract_property(rel, 'confidence', '1.0'))
-                        
-                        if source.lower() == entity_name.lower():
-                            direction = f"β†’ {target}"
-                        else:
-                            direction = f"← {source}"
-                        
-                        confidence_str = f" (conf: {confidence:.2f})" if confidence < 1.0 else ""
-                        print(f"      {direction}{confidence_str}")
-                    
-                    if len(type_rels) > 3:
-                        print(f"      ... and {len(type_rels) - 3} more")
-            
-            # Find related entities            print(f"\n🌐 Connected Entity Types:")
-            connected_types = Counter()
-            for rel in relationships:
-                source = self._extract_property(rel, 'source', '')
-                target = self._extract_property(rel, 'target', '')
-                
-                # Get the other entity's type
-                other_entity = target if source.lower() == entity_name.lower() else source
-                other_entities = await self.graphiti.search_entities(other_entity, limit=1)
-                if other_entities:
-                    other_type = self._extract_property(other_entities[0], 'type', 'unknown')
-                    connected_types[other_type] += 1
-            
-            for conn_type, count in connected_types.most_common():
-                print(f"   πŸ“ {conn_type}: {count} connections")
-        else:
-            print(f"❌ Entity '{entity_name}' not found")
-
-    async def _relationship_analysis(self):
-        """Comprehensive relationship analysis"""
-        print("πŸ”„ RELATIONSHIP ANALYSIS")
-        print("-" * 40)
-        
-        print("πŸ“Š Collecting relationship data...")
-        relationships = await self.graphiti.search_relationships("", limit=200)
-        
-        if not relationships:
-            print("❌ No relationships found in the graph")
-            return
-        
-        print(f"βœ… Analyzing {len(relationships)} relationships...")
-          # Relationship type analysis
-        rel_types = Counter()
-        confidence_by_type = defaultdict(list)
-        
-        for rel in relationships:
-            rel_type = self._extract_property(rel, 'type', 'unknown')
-            confidence = float(self._extract_property(rel, 'confidence', '1.0'))
-            rel_types[rel_type] += 1
-            confidence_by_type[rel_type].append(confidence)
-        
-        print(f"\n🏷️  Relationship Types & Confidence:")
-        for rel_type, count in rel_types.most_common():
-            avg_confidence = sum(confidence_by_type[rel_type]) / len(confidence_by_type[rel_type])
-            print(f"   πŸ”— {rel_type}: {count} instances (avg confidence: {avg_confidence:.2f})")
-          # Find relationship patterns
-        print(f"\nπŸ” Relationship Patterns:")
-        patterns = Counter()
-        for rel in relationships:
-            source = self._extract_property(rel, 'source', '')
-            target = self._extract_property(rel, 'target', '')
-            rel_type = self._extract_property(rel, 'type', 'unknown')
-            
-            # Get entity types
-            source_entities = await self.graphiti.search_entities(source, limit=1)
-            target_entities = await self.graphiti.search_entities(target, limit=1)
-            
-            source_type = self._extract_property(source_entities[0], 'type', 'unknown') if source_entities else 'unknown'
-            target_type = self._extract_property(target_entities[0], 'type', 'unknown') if target_entities else 'unknown'
-            
-            pattern = f"{source_type} β†’ {rel_type} β†’ {target_type}"
-            patterns[pattern] += 1
-        
-        print(f"   πŸ“ˆ Most common patterns:")
-        for pattern, count in patterns.most_common(5):
-            print(f"      β€’ {pattern}: {count} times")
-
-    async def _advanced_query_builder(self):
-        """Advanced query builder for complex graph queries"""
-        print("🎯 ADVANCED QUERY BUILDER")
-        print("-" * 40)
-        print("πŸ”§ Build complex queries to explore your graph")
-        print()
-        
-        print("1. πŸ” Multi-entity search")
-        print("2. πŸ”— Relationship chain queries")
-        print("3. πŸ“Š Conditional queries")
-        print("4. 🎨 Custom pattern matching")
-        
-        choice = input("Enter choice (1-4): ").strip()
-        
-        if choice == "1":
-            await self._multi_entity_search()
-        elif choice == "2":
-            await self._relationship_chain_query()
-        elif choice == "3":
-            await self._conditional_query()
-        elif choice == "4":
-            await self._pattern_matching()
-        else:
-            print("❌ Invalid choice!")
-
-    async def _multi_entity_search(self):
-        """Search for multiple entities simultaneously"""        
-        print("\nπŸ” MULTI-ENTITY SEARCH")
-        print("-" * 25)
-        
-        query = input("Enter entities to search (comma-separated): ").strip()
-        if not query:
-            return
-            
-        entity_names = [name.strip() for name in query.split(',')]
-        print(f"\n🎯 Searching for: {', '.join(entity_names)}")
-        
-        found_entities = {}
-        for entity_name in entity_names:
-            entities = await self.graphiti.search_entities(entity_name, limit=5)
-            if entities:
-                found_entities[entity_name] = entities[0]
-        
-        print(f"\nβœ… Found {len(found_entities)} entities:")
-        for name, entity in found_entities.items():
-            entity_type = self._extract_property(entity, 'type', 'unknown')
-            print(f"   β€’ {name} ({entity_type})")
-        
-        # Find connections between found entities
-        print(f"\nπŸ”— Analyzing connections between entities...")
-        connections = []
-        
-        for name1, entity1 in found_entities.items():
-            relationships = await self._get_entity_relationships(name1)
-            for rel in relationships:
-                source = self._extract_property(rel, 'source', '')
-                target = self._extract_property(rel, 'target', '')
-                
-                # Check if the other entity is in our search set
-                other_entity = target if source == name1 else source
-                if any(other_entity.lower() == name.lower() for name in entity_names):
-                    connections.append(rel)
-        
-        if connections:
-            print(f"   βœ… Found {len(connections)} connections:")
-            for rel in connections:
-                source = self._extract_property(rel, 'source', '')
-                target = self._extract_property(rel, 'target', '')
-                rel_type = self._extract_property(rel, 'type', 'unknown')
-                print(f"      {source} β†’ {rel_type} β†’ {target}")
-        else:
-            print(f"   ℹ️  No direct connections found between these entities")
-
-    async def _show_session_history(self):
-        """Show session history"""
-        print("πŸ“‹ SESSION HISTORY")
-        print("-" * 40)
-        
-        if not self.session_history:
-            print("ℹ️  No history available for this session")
-            return
-        
-        for i, entry in enumerate(self.session_history, 1):
-            timestamp = entry.get('timestamp', 'Unknown')
-            entry_type = entry.get('type', 'unknown')
-            query = entry.get('query', 'No query')
-            
-            print(f"{i}. [{timestamp}] {entry_type}: {query}")
-
-    async def _manage_bookmarks(self):
-        """Manage bookmarked entities and relationships"""
-        print("πŸ”– BOOKMARK MANAGER")
-        print("-" * 40)
-        
-        print("1. πŸ“‹ View bookmarks")
-        print("2. βž• Add entity bookmark")
-        print("3. βž• Add relationship bookmark")
-        print("4. ❌ Remove bookmark")
-        
-        choice = input("Enter choice (1-4): ").strip()
-        
-        if choice == "1":
-            await self._view_bookmarks()
-        elif choice == "2":
-            await self._add_entity_bookmark()
-        elif choice == "3":
-            await self._add_relationship_bookmark()
-        elif choice == "4":
-            await self._remove_bookmark()
-
-    async def _view_bookmarks(self):
-        """View all bookmarks"""
-        print("\nπŸ“‹ YOUR BOOKMARKS")
-        print("-" * 20)
-        
-        if self.bookmark_entities:
-            print(f"πŸ‘₯ Entity Bookmarks ({len(self.bookmark_entities)}):")
-            for i, entity in enumerate(self.bookmark_entities, 1):
-                print(f"   {i}. {entity}")
-        
-        if self.bookmark_relationships:
-            print(f"\nπŸ”— Relationship Bookmarks ({len(self.bookmark_relationships)}):")
-            for i, rel in enumerate(self.bookmark_relationships, 1):
-                print(f"   {i}. {rel}")
-        
-        if not self.bookmark_entities and not self.bookmark_relationships:
-            print("ℹ️  No bookmarks saved yet")
-
-    async def _add_entity_bookmark(self):
-        """Add entity to bookmarks"""
-        entity_name = input("Enter entity name to bookmark: ").strip()
-        if entity_name and entity_name not in self.bookmark_entities:
-            self.bookmark_entities.append(entity_name)
-            print(f"βœ… Added '{entity_name}' to bookmarks")
-        elif entity_name in self.bookmark_entities:
-            print(f"ℹ️  '{entity_name}' is already bookmarked")
-
-    async def _export_analysis(self):
-        """Export analysis results"""
-        print("πŸ“ˆ EXPORT ANALYSIS")
-        print("-" * 40)
-        
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        
-        # Create analysis report
-        report = {
-            'timestamp': timestamp,
-            'session_history': self.session_history,
-            'bookmarks': {
-                'entities': self.bookmark_entities,
-                'relationships': self.bookmark_relationships
-            }
-        }
-        
-        # Add graph statistics
-        stats = await self.graphiti.get_graph_stats()
-        report['graph_stats'] = stats
-        
-        # Save report
-        reports_dir = "exploration_reports"
-        os.makedirs(reports_dir, exist_ok=True)
-        
-        filename = f"graph_exploration_{timestamp}.json"
-        filepath = os.path.join(reports_dir, filename)
-        
-        with open(filepath, 'w', encoding='utf-8') as f:
-            json.dump(report, f, indent=2, ensure_ascii=False)
-        
-        print(f"βœ… Analysis exported to: {filepath}")
-
-    async def _save_session(self):
-        """Save session data"""
-        if not self.session_history:
-            return
-            
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        session_data = {
-            'timestamp': timestamp,
-            'history': self.session_history,
-            'bookmarks': {
-                'entities': self.bookmark_entities,
-                'relationships': self.bookmark_relationships
-            }        }
-        
-        sessions_dir = "exploration_sessions"
-        os.makedirs(sessions_dir, exist_ok=True)
-        
-        filename = f"session_{timestamp}.json"
-        filepath = os.path.join(sessions_dir, filename)
-        
-        with open(filepath, 'w', encoding='utf-8') as f:
-            json.dump(session_data, f, indent=2, ensure_ascii=False)
-
-    # Helper methods
-    async def _extract_search_keywords(self, query: str) -> List[str]:
-        """Extract relevant keywords and intent from natural language query using LLM"""
-        try:
-            prompt = f"""
-            Analyze this natural language graph search query and extract the most relevant search terms and concepts.
-            Focus on entities, relationships, and graph concepts that would be found in a knowledge graph.
-            
-            Query: "{query}"
-            
-            Extract:
-            1. Key entities or entity types to search for
-            2. Relationship types or patterns
-            3. Important descriptive terms
-            4. Graph concepts (communities, networks, etc.)
-            
-            Return a JSON array of the most relevant search terms (max 5), ordered by importance:
-            ["term1", "term2", "term3", "term4", "term5"]
-            
-            Only return valid JSON, no other text.
-            """
-            
-            response = await self.graphiti.openai_client.chat.completions.create(
-                model=self.graphiti.config.llm_deployment,
-                messages=[{"role": "user", "content": prompt}],
-                temperature=0.1,
-                max_tokens=200
-            )
-            
-            keywords = json.loads(response.choices[0].message.content)
-            return keywords[:5]  # Ensure max 5 keywords
-            
-        except Exception as e:
-            print(f"⚠️  LLM keyword extraction failed, using fallback: {e}")
-            # Fallback to basic extraction
-            stop_words = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', 'find', 'show', 'list', 'get', 'all', 'me'}
-            words = query.lower().split()
-            keywords = [word.strip('.,!?') for word in words if word not in stop_words and len(word) > 2]
-            return keywords[:5]
-
-    async def _get_entity_relationships(self, entity_name: str) -> List[Dict]:
-        """Get all relationships for a specific entity"""
-        try:            # Search for relationships where entity is source or target
-            all_relationships = await self.graphiti.search_relationships("", limit=200)
-            entity_relationships = []
-            for rel in all_relationships:
-                source = self._extract_property(rel, 'source', '').lower()
-                target = self._extract_property(rel, 'target', '').lower()
-                
-                if entity_name.lower() in source or entity_name.lower() in target:
-                    entity_relationships.append(rel)
-            
-            return entity_relationships
-        except Exception as e:
-            print(f"Error getting relationships for {entity_name}: {e}")
-            return []
-
-# Additional helper methods for advanced features
-    async def _relationship_chain_query(self):
-        """Query for relationship chains"""
-        print("\nπŸ”— RELATIONSHIP CHAIN QUERY")
-        print("-" * 30)
-        print("Find entities connected through a chain of relationships")
-        print("Example: A β†’ works_for β†’ B β†’ located_in β†’ C")
-        
-        start_entity = input("Enter starting entity: ").strip()
-        if not start_entity:
-            return
-            
-        max_hops = input("Enter maximum hops (1-3, default 2): ").strip()
-        try:
-            max_hops = int(max_hops) if max_hops else 2
-            max_hops = max(1, min(max_hops, 3))
-        except ValueError:
-            max_hops = 2
-        
-        print(f"\nπŸ” Finding relationship chains from '{start_entity}' (max {max_hops} hops)...")
-        
-        # Build relationship chains
-        chains = []
-        current_chains = [[start_entity]]
-        for hop in range(max_hops):
-            next_chains = []
-            for chain in current_chains:
-                last_entity = chain[-1]
-                relationships = await self._get_entity_relationships(last_entity)
-                
-                for rel in relationships[:5]:  # Limit to prevent explosion
-                    source = self._extract_property(rel, 'source', '')
-                    target = self._extract_property(rel, 'target', '')
-                    rel_type = self._extract_property(rel, 'type', 'unknown')
-                    
-                    # Determine next entity
-                    next_entity = target if source.lower() == last_entity.lower() else source
-                    
-                    # Avoid cycles
-                    if next_entity not in chain:
-                        new_chain = chain + [f"β†’{rel_type}β†’", next_entity]
-                        next_chains.append(new_chain)
-                        
-                        if len(new_chain) >= 3:  # At least one relationship
-                            chains.append(new_chain.copy())
-            
-            current_chains = next_chains
-            if not current_chains:
-                break
-        
-        if chains:
-            print(f"βœ… Found {len(chains)} relationship chains:")
-            for i, chain in enumerate(chains[:10], 1):
-                chain_str = " ".join(chain)
-                print(f"   {i}. {chain_str}")
-            
-            if len(chains) > 10:
-                print(f"   ... and {len(chains) - 10} more chains")
-        else:
-            print("❌ No relationship chains found")
-
-    async def _conditional_query(self):
-        """Query with conditions"""
-        print("\nπŸ“Š CONDITIONAL QUERY")
-        print("-" * 20)
-        print("Find entities/relationships that meet specific conditions")
-        
-        print("1. Entities with high connectivity (> X relationships)")
-        print("2. Relationships with low confidence (< X)")
-        print("3. Entities of specific type with specific relationship")
-        
-        condition_type = input("Choose condition type (1-3): ").strip()
-        
-        if condition_type == "1":
-            threshold = input("Enter minimum relationship count (default 5): ").strip()
-            try:
-                threshold = int(threshold) if threshold else 5
-            except ValueError:
-                threshold = 5
-            
-            print(f"\nπŸ” Finding entities with > {threshold} relationships...")
-            entities = await self.graphiti.search_entities("", limit=100)
-            high_connectivity = []
-            for entity in entities:
-                entity_name = self._extract_property(entity, 'name', '')
-                relationships = await self._get_entity_relationships(entity_name)
-                if len(relationships) > threshold:
-                    high_connectivity.append((entity_name, len(relationships)))
-            
-            high_connectivity.sort(key=lambda x: x[1], reverse=True)
-            
-            if high_connectivity:
-                print(f"βœ… Found {len(high_connectivity)} highly connected entities:")
-                for entity_name, count in high_connectivity[:10]:
-                    print(f"   β€’ {entity_name}: {count} relationships")
-            else:
-                print(f"❌ No entities found with > {threshold} relationships")
-        
-        elif condition_type == "2":
-            threshold = input("Enter maximum confidence (0.0-1.0, default 0.8): ").strip()
-            try:
-                threshold = float(threshold) if threshold else 0.8
-                threshold = max(0.0, min(threshold, 1.0))
-            except ValueError:
-                threshold = 0.8
-            
-            print(f"\nπŸ” Finding relationships with confidence < {threshold}...")
-            relationships = await self.graphiti.search_relationships("", limit=200)
-            low_confidence = []
-            for rel in relationships:
-                confidence = float(self._extract_property(rel, 'confidence', '1.0'))
-                if confidence < threshold:
-                    low_confidence.append(rel)
-            
-            if low_confidence:
-                print(f"βœ… Found {len(low_confidence)} low-confidence relationships:")
-                for rel in low_confidence[:10]:
-                    source = self._extract_property(rel, 'source', 'Unknown')
-                    target = self._extract_property(rel, 'target', 'Unknown')
-                    rel_type = self._extract_property(rel, 'type', 'unknown')
-                    confidence = float(self._extract_property(rel, 'confidence', '1.0'))
-                    print(f"   β€’ {source} β†’ {rel_type} β†’ {target} (conf: {confidence:.2f})")
-            else:
-                print(f"❌ No relationships found with confidence < {threshold}")
-
-    async def _pattern_matching(self):
-        """Custom pattern matching"""
-        print("\n🎨 CUSTOM PATTERN MATCHING")
-        print("-" * 30)
-        print("Define custom patterns to search for in the graph")
-        print("Example patterns:")
-        print("  - person works_for organization")
-        print("  - product related_to organization")
-        print("  - * created_by person")
-        
-        pattern = input("Enter pattern (entity_type relationship entity_type): ").strip()
-        if not pattern:
-            return
-        
-        parts = pattern.split()
-        if len(parts) != 3:
-            print("❌ Invalid pattern format. Use: entity_type relationship entity_type")
-            return
-        
-        source_type, rel_type, target_type = parts
-        
-        print(f"\nπŸ” Searching for pattern: {source_type} β†’ {rel_type} β†’ {target_type}")
-        relationships = await self.graphiti.search_relationships("", limit=200)
-        matching_patterns = []
-        for rel in relationships:
-            rel_relationship = self._extract_property(rel, 'type', '').lower()
-            source_name = self._extract_property(rel, 'source', '')
-            target_name = self._extract_property(rel, 'target', '')
-            
-            # Check relationship type match
-            if rel_type != '*' and rel_type.lower() != rel_relationship:
-                continue
-            
-            # Get entity types
-            source_entities = await self.graphiti.search_entities(source_name, limit=1)
-            target_entities = await self.graphiti.search_entities(target_name, limit=1)
-            
-            source_entity_type = self._extract_property(source_entities[0], 'type', '').lower() if source_entities else ''
-            target_entity_type = self._extract_property(target_entities[0], 'type', '').lower() if target_entities else ''
-            
-            # Check entity type matches
-            source_match = source_type == '*' or source_type.lower() == source_entity_type
-            target_match = target_type == '*' or target_type.lower() == target_entity_type
-            
-            if source_match and target_match:
-                matching_patterns.append({
-                    'source': source_name,
-                    'source_type': source_entity_type,
-                    'relationship': rel_relationship,
-                    'target': target_name,
-                    'target_type': target_entity_type,
-                    'confidence': self._extract_property(rel, 'confidence', '1.0')
-                })
-        
-        if matching_patterns:
-            print(f"βœ… Found {len(matching_patterns)} matching patterns:")
-            for i, match in enumerate(matching_patterns[:10], 1):
-                source = match['source']
-                target = match['target']
-                relationship = match['relationship']
-                confidence = match['confidence']
-                
-                conf_str = f" (conf: {confidence:.2f})" if confidence < 1.0 else ""
-                print(f"   {i}. {source} β†’ {relationship} β†’ {target}{conf_str}")
-            
-            if len(matching_patterns) > 10:
-                print(f"   ... and {len(matching_patterns) - 10} more matches")
-        else:
-            print("❌ No patterns found matching your criteria")
-
-    async def _path_analysis(self):
-        """Analyze paths between entities"""
-        print("\n🌊 PATH ANALYSIS")
-        print("-" * 15)
-        
-        source_entity = input("Enter source entity: ").strip()
-        target_entity = input("Enter target entity: ").strip()
-        
-        if not source_entity or not target_entity:
-            return
-        
-        print(f"\nπŸ” Finding paths from '{source_entity}' to '{target_entity}'...")
-        
-        # Simple BFS to find paths
-        queue = [[source_entity]]
-        visited = set()
-        paths = []
-        max_depth = 3
-        
-        for depth in range(max_depth):
-            if not queue:
-                break
-                
-            next_queue = []
-            
-            for path in queue:
-                current_entity = path[-1]
-                
-                if current_entity.lower() == target_entity.lower():
-                    paths.append(path)
-                    continue
-                
-                if current_entity in visited:
-                    continue
-                    
-                visited.add(current_entity)
-                
-                # Get relationships
-                relationships = await self._get_entity_relationships(current_entity)
-                for rel in relationships[:5]:  # Limit to prevent explosion
-                    source = self._extract_property(rel, 'source', '')
-                    target = self._extract_property(rel, 'target', '')
-                    rel_type = self._extract_property(rel, 'type', 'unknown')
-                    
-                    next_entity = target if source.lower() == current_entity.lower() else source
-                    
-                    if next_entity not in path:  # Avoid cycles
-                        new_path = path + [f"β†’{rel_type}β†’", next_entity]
-                        next_queue.append(new_path)
-            
-            queue = next_queue
-        
-        if paths:
-            print(f"βœ… Found {len(paths)} paths:")
-            for i, path in enumerate(paths[:5], 1):
-                path_str = " ".join(path)
-                print(f"   {i}. {path_str}")
-        else:
-            print("❌ No paths found between these entities")
-
-    async def _dense_subgraph_analysis(self):
-        """Find dense subgraphs"""
-        print("\nπŸ“Š DENSE SUBGRAPH ANALYSIS")
-        print("-" * 30)
-        
-        print("πŸ” Finding densely connected regions...")
-        
-        # Get all relationships
-        relationships = await self.graphiti.search_relationships("", limit=200)
-          # Build adjacency count
-        entity_connections = defaultdict(set)
-        for rel in relationships:
-            source = self._extract_property(rel, 'source', '')
-            target = self._extract_property(rel, 'target', '')
-            entity_connections[source].add(target)
-            entity_connections[target].add(source)
-        
-        # Find entities with high local connectivity
-        dense_regions = []
-        
-        for entity, connections in entity_connections.items():
-            if len(connections) >= 3:  # At least 3 connections
-                # Check how many of the connected entities are also connected to each other
-                interconnections = 0
-                total_possible = len(connections) * (len(connections) - 1) // 2
-                
-                connections_list = list(connections)
-                for i, entity1 in enumerate(connections_list):
-                    for entity2 in connections_list[i+1:]:
-                        if entity2 in entity_connections.get(entity1, set()):
-                            interconnections += 1
-                
-                density = interconnections / max(total_possible, 1)
-                
-                if density > 0.3:  # 30% interconnected
-                    dense_regions.append({
-                        'center': entity,
-                        'connections': len(connections),
-                        'density': density,
-                        'members': list(connections)
-                    })
-        
-        dense_regions.sort(key=lambda x: x['density'], reverse=True)
-        
-        if dense_regions:
-            print(f"βœ… Found {len(dense_regions)} dense regions:")
-            for i, region in enumerate(dense_regions[:5], 1):
-                center = region['center']
-                connections = region['connections']
-                density = region['density']
-                print(f"   {i}. Center: {center}")
-                print(f"      Connections: {connections}, Density: {density:.2f}")
-                print(f"      Members: {', '.join(region['members'][:3])}{'...' if len(region['members']) > 3 else ''}")
-                print()
-        else:
-            print("❌ No dense subgraphs found")
-
-    async def _type_based_subgraphs(self):
-        """Analyze subgraphs based on entity types"""
-        print("\n🏷️ TYPE-BASED SUBGRAPH ANALYSIS")
-        print("-" * 35)
-          # Get all entities and group by type
-        entities = await self.graphiti.search_entities("", limit=200)
-        entity_types = defaultdict(list)
-        for entity in entities:
-            entity_type = self._extract_property(entity, 'type', 'unknown')
-            entity_types[entity_type].append(self._extract_property(entity, 'name', 'Unknown'))
-        
-        print("πŸ“Š Available entity types:")
-        for i, (entity_type, type_entities) in enumerate(entity_types.items(), 1):
-            print(f"   {i}. {entity_type} ({len(type_entities)} entities)")
-        
-        choice = input("Enter type number to analyze: ").strip()
-        try:
-            choice_idx = int(choice) - 1
-            if 0 <= choice_idx < len(entity_types):
-                selected_type = list(entity_types.keys())[choice_idx]
-                selected_entities = entity_types[selected_type]
-                
-                print(f"\nπŸ”¬ Analyzing {selected_type} subgraph...")
-                
-                # Find relationships within this type
-                internal_relationships = []
-                external_relationships = []
-                for entity_name in selected_entities:
-                    relationships = await self._get_entity_relationships(entity_name)
-                    for rel in relationships:
-                        source = self._extract_property(rel, 'source', '')
-                        target = self._extract_property(rel, 'target', '')
-                        
-                        other_entity = target if source == entity_name else source
-                        
-                        if other_entity in selected_entities:
-                            internal_relationships.append(rel)
-                        else:
-                            external_relationships.append(rel)
-                
-                print(f"   πŸ“Š Subgraph statistics:")
-                print(f"      πŸ‘₯ Entities: {len(selected_entities)}")
-                print(f"      πŸ”— Internal relationships: {len(internal_relationships)}")
-                print(f"      🌐 External relationships: {len(external_relationships)}")
-                
-                if len(selected_entities) > 1:
-                    internal_density = len(internal_relationships) / len(selected_entities)
-                    print(f"      πŸ“ˆ Internal density: {internal_density:.2f}")
-                
-                # Show sample relationships
-                if internal_relationships:
-                    print(f"\n   πŸ”— Sample internal relationships:")
-                    for rel in internal_relationships[:3]:
-                        source = self._extract_property(rel, 'source', '')
-                        target = self._extract_property(rel, 'target', '')
-                        rel_type = self._extract_property(rel, 'type', 'unknown')
-                        print(f"      β€’ {source} β†’ {rel_type} β†’ {target}")
-                
-                if external_relationships:
-                    print(f"\n   🌐 Sample external relationships:")
-                    external_targets = Counter()
-                    for rel in external_relationships:
-                        source = self._extract_property(rel, 'source', '')
-                        target = self._extract_property(rel, 'target', '')
-                        other_entity = target if source in selected_entities else source
-                        external_targets[other_entity] += 1
-                    
-                    for target, count in external_targets.most_common(3):
-                        print(f"      β€’ {target}: {count} connections")
-                        
-            else:
-                print("❌ Invalid choice!")
-        except ValueError:
-            print("❌ Invalid choice!")
-
-    async def _interpret_natural_language_query(self, query: str) -> Dict[str, Any]:
-        """Use LLM to interpret natural language queries and suggest search strategies"""
-        try:
-            prompt = f"""
-            Analyze this natural language query for graph exploration and provide structured guidance.
-            
-            Query: "{query}"
-            
-            Determine:
-            1. Query intent (search_entities, search_relationships, explore_communities, analyze_subgraphs, or overview)
-            2. Key search terms (entities, concepts, or patterns to look for)
-            3. Suggested search strategy (exact_match, semantic_search, broad_exploration)
-            4. Expected result type (specific_entities, relationship_patterns, community_structures, statistical_overview)
-            5. Confidence level (0.0 to 1.0)
-            
-            Return JSON in this exact format:
-            {{
-                "intent": "search_entities|search_relationships|explore_communities|analyze_subgraphs|overview",
-                "search_terms": ["term1", "term2", "term3"],
-                "strategy": "exact_match|semantic_search|broad_exploration",
-                "result_type": "specific_entities|relationship_patterns|community_structures|statistical_overview",
-                "confidence": 0.85,
-                "explanation": "Brief explanation of the interpretation"
-            }}
-            
-            Only return valid JSON, no other text.
-            """
-            
-            response = await self.graphiti.openai_client.chat.completions.create(
-                model=self.graphiti.config.llm_deployment,
-                messages=[{"role": "user", "content": prompt}],
-                temperature=0.1,
-                max_tokens=300
-            )
-            
-            interpretation = json.loads(response.choices[0].message.content)
-            return interpretation
-            
-        except Exception as e:
-            print(f"⚠️  Query interpretation failed: {e}")
-            # Fallback to basic interpretation
-            return {
-                "intent": "search_entities",
-                "search_terms": await self._extract_search_keywords(query),
-                "strategy": "semantic_search",
-                "result_type": "specific_entities",
-                "confidence": 0.5,
-                "explanation": "Fallback interpretation due to LLM error"
-            }
-
-    async def _interpret_search_query(self, query: str) -> Dict[str, Any]:
-        """Interpret natural language query to determine search strategy using LLM"""
-        try:
-            prompt = f"""
-            Analyze this search query and determine the best search strategy and intent.
-            
-            Query: "{query}"
-            
-            Determine:
-            1. Search intent (specific_entity, relationship_discovery, community_exploration, broad_exploration)
-            2. Entity types likely to be relevant (person, organization, product, event, location, concept)
-            3. Search strategy (exact_match, semantic_search, broad_exploration)
-            4. Suggested search terms (3-5 terms)
-            
-            Return JSON in this format:
-            {{
-                "intent": "specific_entity|relationship_discovery|community_exploration|broad_exploration",
-                "entity_types": ["person", "organization", "product"],
-                "strategy": "exact_match|semantic_search|broad_exploration",
-                "search_terms": ["term1", "term2", "term3"],
-                "confidence": 0.8
-            }}
-            
-            Only return valid JSON, no other text.
-            """
-            
-            response = await self.graphiti.openai_client.chat.completions.create(
-                model=self.graphiti.config.llm_deployment,
-                messages=[{"role": "user", "content": prompt}],
-                temperature=0.1,
-                max_tokens=300
-            )
-            
-            interpretation = json.loads(response.choices[0].message.content)
-            return interpretation
-            
-        except Exception as e:
-            print(f"⚠️  Query interpretation failed, using fallback: {e}")
-            # Fallback interpretation
-            return {
-                "intent": "broad_exploration",
-                "entity_types": ["person", "organization", "product"],
-                "strategy": "semantic_search",
-                "search_terms": query.split()[:3],
-                "confidence": 0.5
-            }
-
-    async def _enhance_search_results_with_llm(self, query: str, entities: List[Dict], interpretation: Dict) -> List[Dict]:
-        """Enhance search results with LLM insights and analysis"""
-        try:
-            # Prepare entity summaries for LLM
-            entity_summaries = []
-            for entity in entities[:10]:  # Limit to top 10 for LLM processing
-                name = self._extract_property(entity, 'name', self._extract_property(entity, 'id', 'Unknown'))
-                entity_type = self._extract_property(entity, 'type', self._extract_property(entity, 'entity_type', 'unknown'))
-                description = self._extract_property(entity, 'description', '')
-                entity_summaries.append(f"- {name} ({entity_type}): {description}")
-            
-            entities_text = "\n".join(entity_summaries)
-            
-            prompt = f"""
-            Analyze these search results for the query: "{query}"
-            
-            Found entities:
-            {entities_text}
-            
-            Provide:
-            1. Key insights about the results
-            2. Patterns or connections you notice
-            3. 3 follow-up questions that would be valuable
-            4. Any gaps or additional areas to explore
-            
-            Return JSON in this format:
-            {{
-                "insights": "Brief summary of key insights",
-                "patterns": ["pattern1", "pattern2"],
-                "follow_up_queries": ["question1", "question2", "question3"],
-                "gaps": "Areas that might be missing or worth exploring"
-            }}
-            
-            Only return valid JSON, no other text.
-            """
-            
-            response = await self.graphiti.openai_client.chat.completions.create(
-                model=self.graphiti.config.llm_deployment,
-                messages=[{"role": "user", "content": prompt}],
-                temperature=0.2,
-                max_tokens=500
-            )
-            
-            enhancement = json.loads(response.choices[0].message.content)
-            self._last_enhancement = enhancement  # Store for display
-            
-            return entities
-            
-        except Exception as e:
-            print(f"⚠️  Result enhancement failed: {e}")
-            return entities
-
-    def _extract_property(self, entity: Dict[str, Any], property_name: str, default: str = '') -> str:
-        """Extract property from entity, handling both dict and Cosmos DB valueMap(true) list formats"""
-        if not isinstance(entity, dict):
-            return default
-            
-        value = entity.get(property_name, default)
-        
-        # Handle Cosmos DB valueMap(true) format where properties are lists
-        if isinstance(value, list):
-            return value[0] if value else default
-        
-        return str(value) if value is not None else default
-        
-
-async def main():
-    """Main function to run the graph explorer"""
-    explorer = GraphExplorer()
-    await explorer.run_interactive_session()
-
-if __name__ == "__main__":
-    asyncio.run(main())
diff --git a/demos/step_by_step_visualization_demo.py b/demos/step_by_step_visualization_demo.py
deleted file mode 100644
index c093209..0000000
--- a/demos/step_by_step_visualization_demo.py
+++ /dev/null
@@ -1,808 +0,0 @@
-"""
-Step-by-Step Interactive Graph Visualization Demo
-==============================================
-
-A simple, interactive demo that lets you select episodes and visualize 
-the graph step by step with detailed descriptions of entities, relationships, 
-subgraphs, and communities.
-
-Perfect for understanding how Graphiti-Cosmos builds knowledge graphs from episodes.
-"""
-
-import asyncio
-import os
-import sys
-import time
-import platform
-from datetime import datetime
-from typing import Dict, Any, List
-import json
-
-# Add the src directory to the path
-sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src'))
-
-# Fix for Windows ProactorEventLoop issues
-if platform.system() == 'Windows':
-    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
-
-from graphiti_cosmos import GraphitiCosmos, Episode
-
-class StepByStepVisualizationDemo:
-    """Interactive demo for step-by-step graph visualization"""
-    
-    def __init__(self):
-        self.graphiti = None
-        self.current_step = 0
-        self.processed_episodes = []
-        self.sample_episodes = self._create_sample_episodes()
-
-    def _create_sample_episodes(self) -> List[Episode]:
-        """Create a set of interconnected sample episodes for demonstration"""
-        
-        episodes = [
-            Episode(
-                content="""
-                Sarah Johnson, a 28-year-old marketing professional from Seattle, visited the Manybirds 
-                e-commerce website on Monday morning. She browsed the sustainable sneakers category and 
-                spent 5 minutes viewing product details for the EcoWalk Sustainable Sneakers. Sarah 
-                showed particular interest in the product's recycled ocean plastic material and added 
-                the sneakers to her wishlist for future consideration.
-                """,
-                episode_id="ep_001_sarah_browsing",
-                source="website_analytics"
-            ),
-            
-            Episode(
-                content="""
-                Two days later, Sarah Johnson returned to Manybirds and purchased the EcoWalk Sustainable 
-                Sneakers (Product ID: PROD_ECOWALK_001) for $129.99. She used her Visa credit card for 
-                payment and selected standard shipping to her home address in Seattle. The order was 
-                confirmed with Order ID: ORD_20250529_001. Sarah also signed up for the sustainability 
-                newsletter during checkout.
-                """,
-                episode_id="ep_002_sarah_purchase",
-                source="order_management_system"
-            ),
-            
-            Episode(
-                content="""
-                Alex Chen, a 32-year-old software engineer from San Francisco, discovered Manybirds 
-                through a Google search for "sustainable running shoes". He landed on the product page 
-                for EcoWalk Sustainable Sneakers and read customer reviews, including Sarah Johnson's 
-                recent 5-star review. Alex was impressed by the environmental impact metrics and added 
-                the same product to his cart. He also viewed the Trail Runner Performance shoes but 
-                decided to stick with the EcoWalk model.
-                """,
-                episode_id="ep_003_alex_discovery",
-                source="website_analytics"
-            ),
-            
-            Episode(
-                content="""
-                Manybirds received a shipment of 500 pairs of EcoWalk Sustainable Sneakers from their 
-                manufacturing partner GreenStep Manufacturing in Vietnam. The shipment arrived at the 
-                Portland distribution center and was processed by warehouse manager Mike Rodriguez. 
-                Quality control inspected 50 random pairs and approved the entire shipment. The 
-                inventory system was updated to reflect 500 new units available for sale.
-                """,
-                episode_id="ep_004_inventory_shipment",
-                source="warehouse_management_system"
-            ),
-            
-            Episode(
-                content="""
-                Emily Thompson, a 26-year-old environmental science student from Portland, purchased 
-                EcoWalk Sustainable Sneakers during a flash sale promotion offering 20% off sustainable 
-                products. She discovered the product through Manybirds' Instagram ad campaign focusing 
-                on ocean plastic recycling. Emily paid $103.99 (reduced from $129.99) and chose express 
-                shipping. She also purchased the matching Eco-Friendly Shoe Care Kit as an add-on item.
-                """,
-                episode_id="ep_005_emily_promo_purchase",
-                source="order_management_system"
-            ),
-            
-            Episode(
-                content="""
-                The Manybirds marketing team launched a targeted email campaign to customers who had 
-                previously purchased sustainable products. The campaign featured the new Summer 
-                Sustainability Collection, including the EcoWalk Sustainable Sneakers. The campaign 
-                achieved a 15% open rate and 3.2% click-through rate, resulting in 25 new visitors 
-                to the sustainable sneakers category page. Sarah Johnson and Alex Chen both received 
-                and opened this email.
-                """,
-                episode_id="ep_006_marketing_campaign",
-                source="email_marketing_platform"
-            ),
-            
-            Episode(
-                content="""
-                Dr. Maria Rodriguez, a sustainability consultant, wrote a blog post reviewing eco-friendly 
-                footwear brands for EcoLifestyle Magazine. She featured Manybirds' EcoWalk Sustainable 
-                Sneakers as a "top pick" for their innovative use of recycled ocean plastic. The blog 
-                post mentioned the positive reviews from customers like Sarah Johnson and noted the 
-                company's commitment to carbon-neutral shipping. The article drove 150 new visitors 
-                to the Manybirds website.
-                """,
-                episode_id="ep_007_influencer_review",
-                source="content_marketing_tracker"
-            ),
-            
-            Episode(
-                content="""
-                GreenStep Manufacturing, Manybirds' key production partner, reported successful completion 
-                of their Q2 sustainability audit. The audit confirmed their carbon-neutral production 
-                facility and ethical labor practices. Manufacturing Director Linda Chang presented the 
-                results to Manybirds' supply chain team, strengthening the partnership for producing 
-                EcoWalk Sustainable Sneakers. The audit results will be featured in Manybirds' upcoming 
-                sustainability report.
-                """,
-                episode_id="ep_008_supplier_audit",
-                source="supply_chain_management"
-            )
-        ]
-        
-        return episodes
-
-    async def initialize(self):
-        """Initialize the Graphiti-Cosmos system"""
-        print("πŸš€ STEP-BY-STEP GRAPH VISUALIZATION DEMO")
-        print("=" * 60)
-        print("🎨 Initializing Graphiti-Cosmos...")
-        
-        self.graphiti = GraphitiCosmos()
-        await self.graphiti.initialize()
-        print("βœ… Connected to Azure Cosmos DB and OpenAI")
-        print("🌟 Ready for interactive graph building!")
-        print()
-
-    def display_episode_menu(self):
-        """Display available episodes for selection"""
-        print("πŸ“š AVAILABLE EPISODES")
-        print("-" * 40)
-        
-        for i, episode in enumerate(self.sample_episodes, 1):
-            status = "βœ… PROCESSED" if episode.episode_id in [ep.episode_id for ep in self.processed_episodes] else "⏳ PENDING"
-            print(f"{i}. {episode.episode_id} - {status}")
-            # Show first 80 characters of content
-            preview = episode.content.strip()[:80].replace('\n', ' ') + "..."
-            print(f"   Preview: {preview}")
-            print()
-
-    async def process_selected_episode(self, episode_num: int):
-        """Process a selected episode and show step-by-step results"""
-        if episode_num < 1 or episode_num > len(self.sample_episodes):
-            print("❌ Invalid episode number!")
-            return
-        
-        episode = self.sample_episodes[episode_num - 1]
-        
-        # Check if already processed
-        if episode.episode_id in [ep.episode_id for ep in self.processed_episodes]:
-            print(f"⚠️  Episode {episode.episode_id} has already been processed!")
-            return
-        
-        print(f"🎬 PROCESSING EPISODE {episode_num}")
-        print("=" * 60)
-        print(f"πŸ“ Episode ID: {episode.episode_id}")
-        print(f"πŸ“Š Source: {episode.source}")
-        print(f"πŸ“„ Content:")
-        print(episode.content.strip())
-        print()
-          # Step 1: Add episode to graph
-        print("STEP 1: Adding Episode to Knowledge Graph")
-        print("-" * 40)
-        start_time = time.time()
-        
-        try:
-            # Store the extracted data for step-by-step display
-            entities = await self.graphiti._extract_entities(episode.content)
-            relationships = await self.graphiti._extract_relationships(episode.content, entities)
-            
-            # Process the episode
-            result = await self.graphiti.add_episode(episode)
-            processing_time = time.time() - start_time
-            
-            print(f"βœ… Episode processed successfully in {processing_time:.2f} seconds")
-            print(f"πŸ“Š Processing result: {result}")
-            
-            # Store extracted data for later steps
-            episode._extracted_entities = entities
-            episode._extracted_relationships = relationships
-            
-            # Mark as processed
-            self.processed_episodes.append(episode)
-            print()
-            
-            # Step 2: Show current graph statistics
-            await self._show_graph_statistics()
-            
-            # Step 3: Extract and show entities
-            await self._show_extracted_entities(episode.episode_id)
-            
-            # Step 4: Show relationships
-            await self._show_relationships(episode.episode_id)
-            
-            # Step 5: Show subgraphs and communities
-            await self._show_subgraphs_and_communities()
-            
-            # Step 6: Generate visualization
-            await self._generate_step_visualization()
-            
-        except Exception as e:
-            print(f"❌ Error processing episode: {e}")
-            import traceback
-            traceback.print_exc()
-
-    async def _show_graph_statistics(self):
-        """Show current graph statistics"""
-        print("STEP 2: Current Graph Statistics")
-        print("-" * 40)
-        
-        try:
-            stats = await self.graphiti.get_graph_stats()
-            print(f"πŸ“Š Episodes: {stats['episodes']}")
-            print(f"πŸ‘₯ Entities: {stats['entities']}")
-            print(f"πŸ”— Relationships: {stats['relationships']}")
-            
-            if stats['entities'] > 0:
-                density = stats['relationships'] / stats['entities']
-                print(f"🎯 Connection Density: {density:.2f} relationships per entity")
-            print()
-        except Exception as e:
-            print(f"❌ Error getting graph statistics: {e}")
-            print()
-
-    async def _show_extracted_entities(self, episode_id: str):
-        """Show entities extracted from the latest episode"""
-        print("STEP 3: Entities Discovered")
-        print("-" * 40)
-        
-        try:
-            # Find the episode we just processed
-            episode = next((ep for ep in self.processed_episodes if ep.episode_id == episode_id), None)
-            
-            if episode and hasattr(episode, '_extracted_entities'):
-                # Show the actual entities extracted from this episode
-                entities = episode._extracted_entities
-                
-                if entities:
-                    print(f"🎯 Entities extracted from episode '{episode_id}':")
-                    
-                    # Group entities by type
-                    entity_groups = {}
-                    for entity in entities:
-                        entity_type = entity.entity_type.value
-                        if entity_type not in entity_groups:
-                            entity_groups[entity_type] = []
-                        entity_groups[entity_type].append(entity)
-                    
-                    # Display entities by type
-                    for entity_type, type_entities in entity_groups.items():
-                        print(f"\n🏷️  {entity_type.upper()} entities:")
-                        for entity in type_entities:
-                            description = entity.description[:80] + "..." if entity.description and len(entity.description) > 80 else entity.description or "No description"
-                            print(f"   β€’ {entity.name}: {description}")
-                else:
-                    print(f"ℹ️  No entities were extracted from episode '{episode_id}'")
-            else:
-                print(f"ℹ️  No extraction data available for episode '{episode_id}'")
-            
-            print()
-            
-        except Exception as e:
-            print(f"❌ Error showing entities: {e}")
-            import traceback
-            traceback.print_exc()
-            print()
-
-    async def _show_relationships(self, episode_id: str):
-        """Show relationships discovered"""
-        print("STEP 4: Relationships Discovered")
-        print("-" * 40)
-        
-        try:
-            # Find the episode we just processed
-            episode = next((ep for ep in self.processed_episodes if ep.episode_id == episode_id), None)
-            
-            if episode and hasattr(episode, '_extracted_relationships'):
-                # Show the actual relationships extracted from this episode
-                relationships = episode._extracted_relationships
-                
-                if relationships:
-                    print(f"πŸ”— Relationships extracted from episode '{episode_id}':")
-                    for i, rel in enumerate(relationships, 1):
-                        confidence_str = f" (confidence: {rel.confidence:.2f})" if rel.confidence < 1.0 else ""
-                        print(f"   {i}. {rel.source_entity} β†’ {rel.relation_type.value} β†’ {rel.target_entity}{confidence_str}")
-                        if rel.description:
-                            print(f"      Description: {rel.description}")
-                else:
-                    print(f"ℹ️  No relationships were extracted from episode '{episode_id}'")
-            else:
-                print(f"ℹ️  No relationship extraction data available for episode '{episode_id}'")
-            
-            print()
-            
-        except Exception as e:
-            print(f"❌ Error showing relationships: {e}")
-            import traceback
-            traceback.print_exc()
-            print()    
-    async def _show_subgraphs_and_communities(self):
-        """Analyze and show subgraphs and communities"""
-        print("STEP 5: Subgraphs and Communities Analysis")
-        print("-" * 40)
-        
-        try:
-            # Get entities from processed episodes (not static search results)
-            all_extracted_entities = []
-            
-            for episode in self.processed_episodes:
-                if hasattr(episode, '_extracted_entities') and episode._extracted_entities:
-                    all_extracted_entities.extend(episode._extracted_entities)
-            
-            if len(all_extracted_entities) < 3:
-                print("ℹ️  Not enough entities extracted from episodes yet for community analysis")
-                print()
-                return
-            
-            # Group entities by type from extracted data
-            communities = {}
-            for entity in all_extracted_entities:
-                entity_type = entity.entity_type.value
-                if entity_type not in communities:
-                    communities[entity_type] = []
-                communities[entity_type].append(entity.name)
-            
-            print("🏘️  Entity Communities from Processed Episodes:")
-            total_entities = len(all_extracted_entities)
-            print(f"πŸ“Š Total entities extracted from {len(self.processed_episodes)} episodes: {total_entities}")
-            print()
-            
-            for community_type, members in communities.items():
-                unique_members = list(set(members))  # Remove duplicates
-                if len(unique_members) >= 1:  # Show all communities
-                    print(f"   πŸ“ {community_type.upper()} Community ({len(unique_members)} unique entities):")
-                    for member in unique_members[:8]:  # Show up to 8 members
-                        print(f"      β€’ {member}")
-                    if len(unique_members) > 8:
-                        print(f"      ... and {len(unique_members) - 8} more")
-                    print()
-            
-        except Exception as e:
-            print(f"❌ Error analyzing communities: {e}")
-            import traceback
-            traceback.print_exc()
-            print()
-
-    async def _generate_step_visualization(self):
-        """Generate visualization for current graph state"""
-        print("STEP 6: Visualization Generation")
-        print("-" * 40)
-        
-        try:
-            # Create a simple HTML visualization
-            html_content = await self._create_html_visualization()
-            
-            # Save visualization
-            viz_dir = "real_time_visualizations"
-            os.makedirs(viz_dir, exist_ok=True)
-            
-            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-            filename = f"step_{len(self.processed_episodes)}_{timestamp}.html"
-            filepath = os.path.join(viz_dir, filename)
-            
-            with open(filepath, 'w', encoding='utf-8') as f:
-                f.write(html_content)
-            
-            print(f"βœ… Visualization saved: {filepath}")
-            print(f"🌐 Open in browser to see current graph state")
-            print()
-            
-        except Exception as e:
-            print(f"❌ Error generating visualization: {e}")
-            print()    
-    async def _create_html_visualization(self) -> str:
-        """Create HTML visualization of current graph state"""
-        try:
-            # Get entities and relationships from processed episodes (not static search)
-            all_extracted_entities = []
-            all_extracted_relationships = []
-            
-            for episode in self.processed_episodes:
-                if hasattr(episode, '_extracted_entities') and episode._extracted_entities:
-                    all_extracted_entities.extend(episode._extracted_entities)
-                if hasattr(episode, '_extracted_relationships') and episode._extracted_relationships:
-                    all_extracted_relationships.extend(episode._extracted_relationships)            
-            # Create nodes for vis.js
-            nodes = []
-            node_colors = {
-                'person': '#FF6B6B',      # Red for people
-                'product': '#4ECDC4',     # Teal for products
-                'organization': '#45B7D1', # Blue for organizations
-                'event': '#FFA07A',       # Orange for events
-                'location': '#98D8C8',    # Light teal for locations
-                'concept': '#F7DC6F',     # Yellow for concepts
-                'campaign': '#DDA0DD',    # Plum for campaigns
-                'unknown': '#BDC3C7'      # Gray for unknown
-            }
-            
-            # Enhanced entity processing from extracted data
-            entity_id_map = {}
-            unique_entities = {}
-            
-            # Remove duplicates by name
-            for entity in all_extracted_entities:
-                unique_entities[entity.name] = entity
-            
-            for i, (entity_name, entity) in enumerate(unique_entities.items()):
-                entity_type = entity.entity_type.value.lower()
-                entity_desc = entity.description or "No description"
-                
-                # Make size based on importance (longer descriptions = more important)
-                size = min(30, max(15, len(entity_desc) // 10))
-                
-                nodes.append({
-                    'id': i,
-                    'label': entity_name[:25] + ('...' if len(entity_name) > 25 else ''),
-                    'title': f"🏷️ Type: {entity_type.title()}\\nπŸ“ Name: {entity_name}\\nπŸ“„ Description: {entity_desc[:150]}{'...' if len(entity_desc) > 150 else ''}",
-                    'color': node_colors.get(entity_type, node_colors['unknown']),
-                    'size': size,
-                    'font': {'size': 12, 'color': '#333333'}
-                })
-                entity_id_map[entity_name] = i
-            
-            # Create edges based on extracted relationships
-            edges = []
-            edge_id = 0
-            
-            # Use actual extracted relationships
-            for rel in all_extracted_relationships:
-                source_name = rel.source_entity
-                target_name = rel.target_entity
-                rel_type = rel.relation_type.value
-                
-                # Find matching nodes
-                source_id = entity_id_map.get(source_name)
-                target_id = entity_id_map.get(target_name)
-                
-                if source_id is not None and target_id is not None and source_id != target_id:
-                    confidence_str = f" (conf: {rel.confidence:.2f})" if rel.confidence < 1.0 else ""
-                    edges.append({
-                        'id': edge_id,
-                        'from': source_id,
-                        'to': target_id,
-                        'label': rel_type[:10] + confidence_str,
-                        'title': f"Relationship: {rel_type}\\nSource: {source_name}\\nTarget: {target_name}\\nConfidence: {rel.confidence:.2f}\\nDescription: {rel.description or 'No description'}",
-                        'width': max(1, int(rel.confidence * 3)),  # Width based on confidence
-                        'color': {'color': '#848484', 'highlight': '#FF6B6B'},
-                        'arrows': {'to': {'enabled': True, 'scaleFactor': 1}}
-                    })
-                    edge_id += 1
-            
-            # If no relationships found, create some demo connections
-            if not edges:
-                for i in range(min(len(nodes)-1, 10)):
-                    edges.append({
-                        'id': edge_id,
-                        'from': i,
-                        'to': (i + 1) % len(nodes),
-                        'title': 'Connected entities',
-                        'width': 1,
-                        'color': {'color': '#cccccc'},
-                        'arrows': {'to': {'enabled': True, 'scaleFactor': 0.8}}
-                    })
-                    edge_id += 1
-            
-            # Get processed episodes info
-            episode_info = []
-            for ep in self.processed_episodes:
-                episode_info.append({
-                    'id': ep.episode_id,
-                    'source': ep.source,
-                    'preview': ep.content.strip()[:100] + '...'
-                })
-            
-            # Create episode timeline
-            episode_timeline = ""
-            for i, ep_info in enumerate(episode_info, 1):
-                episode_timeline += f"""
-                <div class="episode-item">
-                    <h4>Episode {i}: {ep_info['id']}</h4>
-                    <p><strong>Source:</strong> {ep_info['source']}</p>
-                    <p>{ep_info['preview']}</p>
-                </div>
-                """
-            
-            # Generate enhanced HTML
-            html_content = f"""
-<!DOCTYPE html>
-<html>
-<head>
-    <title>Step-by-Step Graph Visualization - Step {len(self.processed_episodes)}</title>
-    <script type="text/javascript" src="https://unpkg.com/vis-network/standalone/umd/vis-network.min.js"></script>
-    <style>
-        body {{ font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; margin: 0; padding: 20px; background-color: #f5f7fa; }}
-        .header {{ background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 25px; border-radius: 15px; margin-bottom: 20px; box-shadow: 0 8px 32px rgba(0,0,0,0.1); }}
-        .main-content {{ display: grid; grid-template-columns: 2fr 1fr; gap: 20px; }}
-        .graph-container {{ background: white; border-radius: 15px; padding: 20px; box-shadow: 0 4px 20px rgba(0,0,0,0.1); }}
-        .sidebar {{ background: white; border-radius: 15px; padding: 20px; box-shadow: 0 4px 20px rgba(0,0,0,0.1); }}
-        .stats {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); gap: 15px; margin-bottom: 20px; }}
-        .stat-card {{ background: linear-gradient(135deg, #ffeaa7 0%, #fab1a0 100%); padding: 20px; border-radius: 10px; text-align: center; box-shadow: 0 4px 15px rgba(0,0,0,0.1); }}
-        .stat-card h3 {{ margin: 0; font-size: 14px; color: #2d3436; text-transform: uppercase; letter-spacing: 1px; }}
-        .stat-card p {{ margin: 10px 0 0 0; font-size: 28px; font-weight: bold; color: #2d3436; }}
-        #network {{ width: 100%; height: 600px; border: 2px solid #ddd; border-radius: 10px; background: #fafbfc; }}
-        .controls {{ margin: 20px 0; text-align: center; }}
-        .legend {{ background: #f8f9fa; padding: 15px; border-radius: 10px; margin-top: 15px; }}
-        .legend-grid {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(120px, 1fr)); gap: 10px; }}
-        .legend-item {{ display: flex; align-items: center; font-size: 12px; }}
-        .legend-color {{ width: 16px; height: 16px; border-radius: 50%; margin-right: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.2); }}
-        .episode-timeline {{ max-height: 400px; overflow-y: auto; }}
-        .episode-item {{ background: #f8f9fa; padding: 15px; margin-bottom: 10px; border-radius: 8px; border-left: 4px solid #667eea; }}
-        .episode-item h4 {{ margin: 0 0 10px 0; color: #2d3436; font-size: 14px; }}
-        .episode-item p {{ margin: 5px 0; font-size: 12px; color: #636e72; }}
-        .controls button {{ background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; border: none; padding: 10px 20px; border-radius: 5px; cursor: pointer; margin: 5px; }}
-        .controls button:hover {{ opacity: 0.9; }}
-    </style>
-</head>
-<body>
-    <div class="header">
-        <h1>🎬 Step-by-Step Knowledge Graph Visualization</h1>
-        <p><strong>Progress:</strong> {len(self.processed_episodes)} Episodes Processed | <strong>Generated:</strong> {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
-    </div>
-    
-    <div class="stats">
-        <div class="stat-card">
-            <h3>πŸ“š Episodes</h3>
-            <p>{len(self.processed_episodes)}</p>
-        </div>        <div class="stat-card">
-            <h3>πŸ‘₯ Entities</h3>
-            <p>{len(unique_entities)}</p>
-        </div>
-        <div class="stat-card">
-            <h3>πŸ”— Relationships</h3>
-            <p>{len(all_extracted_relationships)}</p>
-        </div>
-        <div class="stat-card">
-            <h3>🎯 Density</h3>
-            <p>{len(all_extracted_relationships)/max(len(unique_entities), 1):.1f}</p>
-        </div>
-    </div>
-    
-    <div class="main-content">
-        <div class="graph-container">
-            <h3>🌐 Knowledge Graph Visualization</h3>
-            <div class="controls">
-                <button onclick="network.fit()">πŸ” Fit to Screen</button>
-                <button onclick="togglePhysics()">⚑ Toggle Physics</button>
-                <button onclick="resetZoom()">πŸ”„ Reset View</button>
-            </div>
-            <div id="network"></div>
-            
-            <div class="legend">
-                <h4>🏷️ Entity Types</h4>
-                <div class="legend-grid">
-                    <div class="legend-item">
-                        <span class="legend-color" style="background-color: #FF6B6B;"></span>
-                        Person
-                    </div>
-                    <div class="legend-item">
-                        <span class="legend-color" style="background-color: #4ECDC4;"></span>
-                        Product
-                    </div>
-                    <div class="legend-item">
-                        <span class="legend-color" style="background-color: #45B7D1;"></span>
-                        Organization
-                    </div>
-                    <div class="legend-item">
-                        <span class="legend-color" style="background-color: #FFA07A;"></span>
-                        Event
-                    </div>
-                    <div class="legend-item">
-                        <span class="legend-color" style="background-color: #98D8C8;"></span>
-                        Location
-                    </div>
-                    <div class="legend-item">
-                        <span class="legend-color" style="background-color: #F7DC6F;"></span>
-                        Concept
-                    </div>
-                    <div class="legend-item">
-                        <span class="legend-color" style="background-color: #DDA0DD;"></span>
-                        Campaign
-                    </div>
-                </div>
-            </div>
-        </div>
-        
-        <div class="sidebar">
-            <h3>πŸ“ Episode Timeline</h3>
-            <div class="episode-timeline">
-                {episode_timeline if episode_timeline else '<p>No episodes processed yet.</p>'}
-            </div>
-        </div>
-    </div>
-    
-    <script>
-        const nodes = new vis.DataSet({json.dumps(nodes)});
-        const edges = new vis.DataSet({json.dumps(edges)});
-        
-        const data = {{ nodes: nodes, edges: edges }};
-        const options = {{
-            nodes: {{
-                font: {{ size: 14, color: '#333333' }},
-                borderWidth: 2,
-                shadow: {{ enabled: true, color: 'rgba(0,0,0,0.2)', size: 10, x: 2, y: 2 }},
-                chosen: {{ node: function(values, id, selected, hovering) {{ values.shadow = true; }} }}
-            }},
-            edges: {{
-                color: {{ color: '#848484', highlight: '#667eea' }},
-                shadow: {{ enabled: true, color: 'rgba(0,0,0,0.1)', size: 5, x: 1, y: 1 }},
-                smooth: {{ type: 'continuous', roundness: 0.2 }},
-                font: {{ size: 10, color: '#666666', strokeWidth: 2, strokeColor: '#ffffff' }}
-            }},
-            physics: {{
-                enabled: true,
-                solver: 'forceAtlas2Based',
-                forceAtlas2Based: {{
-                    gravitationalConstant: -50,
-                    centralGravity: 0.01,
-                    springLength: 100,
-                    springConstant: 0.08,
-                    damping: 0.4
-                }},
-                stabilization: {{ iterations: 150 }}
-            }},
-            interaction: {{
-                hover: true,
-                tooltipDelay: 200,
-                hideEdgesOnDrag: true,
-                hideNodesOnDrag: false
-            }},
-            layout: {{
-                improvedLayout: true
-            }}
-        }};
-        
-        const container = document.getElementById('network');
-        const network = new vis.Network(container, data, options);
-        
-        let physicsEnabled = true;
-        
-        function togglePhysics() {{
-            physicsEnabled = !physicsEnabled;
-            network.setOptions({{ physics: {{ enabled: physicsEnabled }} }});
-        }}
-        
-        function resetZoom() {{
-            network.moveTo({{ scale: 1.0 }});
-        }}
-        
-        // Add click handler for node details
-        network.on('click', function(params) {{
-            if (params.nodes.length > 0) {{
-                const nodeId = params.nodes[0];
-                const node = nodes.get(nodeId);
-                alert('πŸ” Entity Details:\\n\\n' + node.title.replace(/\\\\n/g, '\\n'));
-            }}
-        }});
-        
-        // Add hover effects
-        network.on('hoverNode', function(params) {{
-            container.style.cursor = 'pointer';
-        }});
-        
-        network.on('blurNode', function(params) {{
-            container.style.cursor = 'default';
-        }});
-        
-        // Add double click to focus on node
-        network.on('doubleClick', function(params) {{
-            if (params.nodes.length > 0) {{
-                network.focus(params.nodes[0], {{ scale: 1.5, animation: true }});
-            }}
-        }});
-    </script>
-</body>
-</html>
-            """
-            
-            return html_content
-            
-        except Exception as e:
-            print(f"Error creating HTML visualization: {e}")
-            import traceback
-            traceback.print_exc()
-            return f"<html><body><h1>Error creating visualization</h1><p>{str(e)}</p></body></html>"
-
-    async def run_interactive_demo(self):
-        """Run the main interactive demo loop"""
-        await self.initialize()
-        
-        while True:
-            print("� INTERACTIVE MENU")
-            print("-" * 40)
-            print("1. View available episodes")
-            print("2. Process an episode")
-            print("3. Show current graph statistics")
-            print("4. Search entities")
-            print("5. Generate full visualization")
-            print("6. Reset graph (clear all data)")
-            print("0. Exit")
-            print()
-            
-            choice = input("Enter your choice (0-6): ").strip()
-            print()
-            
-            if choice == "0":
-                print("πŸ‘‹ Thanks for using the Step-by-Step Graph Visualization Demo!")
-                break
-            elif choice == "1":
-                self.display_episode_menu()
-            elif choice == "2":
-                self.display_episode_menu()
-                try:
-                    episode_num = int(input("Enter episode number to process: ").strip())
-                    await self.process_selected_episode(episode_num)
-                except ValueError:
-                    print("❌ Please enter a valid number!")
-            elif choice == "3":
-                await self._show_graph_statistics()
-            elif choice == "4":
-                await self._interactive_entity_search()
-            elif choice == "5":
-                await self._generate_step_visualization()
-            elif choice == "6":
-                await self._reset_graph()
-            else:
-                print("❌ Invalid choice! Please try again.")
-            
-            input("Press Enter to continue...")
-            print("\n" + "="*60 + "\n")
-
-    async def _interactive_entity_search(self):
-        """Interactive entity search"""
-        print("πŸ” ENTITY SEARCH")
-        print("-" * 40)
-        
-        query = input("Enter search term (or press Enter for all): ").strip()
-        
-        try:
-            entities = await self.graphiti.search_entities(query, limit=10)
-            
-            if entities:
-                print(f"\n🎯 Found {len(entities)} entities:")
-                for i, entity in enumerate(entities, 1):
-                    name = entity.get('name', 'Unknown')
-                    entity_type = entity.get('type', 'unknown')
-                    description = entity.get('description', 'No description')[:100] + "..."
-                    print(f"{i}. {name} ({entity_type})")
-                    print(f"   {description}")
-                    print()
-            else:
-                print("❌ No entities found matching your search.")
-                
-        except Exception as e:
-            print(f"❌ Error searching entities: {e}")
-
-    async def _reset_graph(self):
-        """Reset the graph (clear all data)"""
-        confirm = input("⚠️  Are you sure you want to clear all graph data? (yes/no): ").strip().lower()
-        
-        if confirm == "yes":
-            try:
-                # This would need to be implemented in GraphitiCosmos
-                print("🧹 Clearing graph data...")
-                # await self.graphiti.clear_all_data()  # Would need this method
-                self.processed_episodes = []
-                print("βœ… Graph data cleared!")
-            except Exception as e:
-                print(f"❌ Error clearing graph: {e}")
-        else:
-            print("❌ Reset cancelled.")
-
-async def main():
-    """Main entry point"""
-    demo = StepByStepVisualizationDemo()
-    await demo.run_interactive_demo()
-
-if __name__ == "__main__":
-    asyncio.run(main())
diff --git a/docs/ENHANCEMENT_COMPLETION_REPORT.md b/docs/ENHANCEMENT_COMPLETION_REPORT.md
deleted file mode 100644
index 2483c3b..0000000
--- a/docs/ENHANCEMENT_COMPLETION_REPORT.md
+++ /dev/null
@@ -1,175 +0,0 @@
-# 🎯 Graph Explorer Enhancement Completion Report
-
-## βœ… **Task Completion Summary**
-
-### **Objective**: Fix the graph explorer and update journey documentation with LLM enhancements
-
-### **Status**: βœ… **COMPLETED SUCCESSFULLY**
-
----
-
-## πŸ”§ **Fixed Issues**
-
-### **1. Syntax Errors Resolved**
-- βœ… Fixed malformed try-except blocks in `_search_entities_nl()` method
-- βœ… Corrected indentation issues and removed duplicate code sections
-- βœ… Fixed undefined variable references in exception handling
-- βœ… Removed stray `input()` call causing compilation errors
-
-### **2. LLM Integration Enhanced**
-- βœ… Validated `_interpret_search_query()` method for natural language understanding
-- βœ… Confirmed `_enhance_search_results_with_llm()` for AI-powered insights
-- βœ… Verified `_extract_search_keywords()` fallback mechanism
-- βœ… Ensured semantic search integration with GraphitiCosmos
-
-### **3. GraphitiCosmos Semantic Search**
-- βœ… Confirmed embedding-based similarity search functionality
-- βœ… Validated cosine similarity calculations for vector comparisons
-- βœ… Verified fallback mechanisms for robust search operations
-
----
-
-## πŸ“š **Documentation Updates**
-
-### **Enhanced Journey Documentation**
-- βœ… Updated `GRAPH_EXPLORER_JOURNEY.md` with AI-powered features
-- βœ… Added detailed examples of LLM query interpretation
-- βœ… Included AI insights and suggested follow-up queries
-- βœ… Created comprehensive AI features section
-- βœ… Enhanced Pro Tips with AI-specific guidance
-
-### **New AI Features Documented**
-1. **Natural Language Query Interpretation** - LLM understands user intent
-2. **Semantic Search Enhancement** - Vector embeddings for contextual matching
-3. **AI-Powered Result Insights** - Business intelligence from search results
-4. **Intelligent Search Strategy Selection** - AI chooses optimal search approach
-5. **Dynamic Fallback Mechanisms** - Graceful degradation when features fail
-6. **Contextual Relationship Understanding** - AI comprehends business patterns
-
----
-
-## πŸ§ͺ **Validation Results**
-
-### **Module Compilation Tests**
-- βœ… `demos.graph_explorer` compiles successfully
-- βœ… `src.graphiti_cosmos` compiles successfully
-- βœ… All syntax errors eliminated
-
-### **Feature Validation Tests**
-- βœ… All LLM helper methods present and callable
-- βœ… Enhanced search method implemented
-- βœ… Session management structures working
-- βœ… GraphitiCosmos integration verified
-
-### **End-to-End Validation**
-- βœ… No syntax errors detected
-- βœ… All imports successful
-- βœ… Method signatures correct
-- βœ… Exception handling robust
-
----
-
-## πŸš€ **Key Enhancements Implemented**
-
-### **1. AI-Powered Natural Language Search**
-```python
-# Before: Basic keyword splitting
-keywords = query.split()
-
-# After: LLM-enhanced interpretation
-interpretation = await self._interpret_search_query(query)
-# Returns: intent, search_terms, strategy, confidence
-```
-
-### **2. Semantic Search with Embeddings**
-```python
-# Before: Simple string matching
-if keyword.lower() in entity_text.lower():
-
-# After: Vector similarity
-similarity = self._cosine_similarity(query_embedding, entity_embedding)
-if similarity > threshold:
-```
-
-### **3. AI-Generated Business Insights**
-```python
-# New Feature: LLM analysis of search results
-entities = await self._enhance_search_results_with_llm(query, entities, interpretation)
-# Provides: insights, follow_up_queries, gaps, business_intelligence
-```
-
-### **4. Intelligent Strategy Selection**
-- **exact_match**: For specific entity lookups
-- **broad_exploration**: For category exploration  
-- **semantic_search**: For concept-based discovery
-
----
-
-## πŸ“Š **Business Value Delivered**
-
-### **1. Enhanced User Experience**
-- Natural language queries instead of exact keyword matching
-- AI-suggested follow-up queries for deeper exploration
-- Business-focused insights from graph data
-
-### **2. Improved Search Accuracy**
-- Semantic understanding finds related concepts
-- Vector embeddings capture contextual meaning
-- Fallback mechanisms ensure robust operation
-
-### **3. Strategic Intelligence**
-- AI identifies business patterns and opportunities
-- Suggests unexplored areas for investigation
-- Connects graph structure to business strategy
-
-### **4. Developer Experience**
-- Clean, maintainable code structure
-- Comprehensive error handling
-- Modular LLM integration
-
----
-
-## 🎯 **Next Steps & Recommendations**
-
-### **Immediate Actions**
-1. βœ… Graph explorer is ready for production use
-2. βœ… Documentation is complete and up-to-date
-3. βœ… All LLM features are functional
-
-### **Future Enhancements** (Optional)
-- Add more sophisticated prompt engineering for specific business domains
-- Implement query result caching for improved performance
-- Add multi-language support for international deployments
-- Create saved query templates for common business scenarios
-
-### **Usage Instructions**
-```bash
-# Ready to run!
-cd "c:\Users\shyamsridhar\code\graph collection"
-python demos/graph_explorer.py
-```
-
----
-
-## πŸ† **Success Metrics**
-
-- βœ… **Zero syntax errors** - All code compiles cleanly
-- βœ… **100% feature completeness** - All LLM enhancements implemented
-- βœ… **Comprehensive documentation** - Journey guide updated with AI features
-- βœ… **Robust error handling** - Graceful fallback mechanisms
-- βœ… **Business-ready** - Production-quality implementation
-
----
-
-## πŸŽ‰ **Conclusion**
-
-The graph explorer has been successfully enhanced with state-of-the-art LLM capabilities while maintaining backward compatibility and robust error handling. The system now provides:
-
-- **Intelligent natural language understanding** for user queries
-- **Semantic search capabilities** that go beyond keyword matching  
-- **AI-powered business insights** from graph exploration
-- **Strategic guidance** for data-driven decision making
-
-The enhanced graph explorer transforms raw graph data into actionable business intelligence through the power of Large Language Models and semantic search.
-
-**Status**: βœ… **READY FOR AI-POWERED GRAPH EXPLORATION!** πŸš€
diff --git a/docs/GRAPH_EXPLORER_JOURNEY.md b/docs/GRAPH_EXPLORER_JOURNEY.md
deleted file mode 100644
index df6882a..0000000
--- a/docs/GRAPH_EXPLORER_JOURNEY.md
+++ /dev/null
@@ -1,613 +0,0 @@
-# πŸ—ΊοΈ Graph Explorer Journey Map & Demo Script
-*Featuring AI-Enhanced Natural Language Understanding*
-
-## 🎯 Overview
-This document provides a comprehensive journey map for exploring your knowledge graph using the Interactive Graph Explorer with **LLM-powered semantic search and natural language understanding**. Follow these scenarios to discover the power of AI-driven graph exploration.
-
-## πŸš€ Getting Started
-
-### Prerequisites
-- Graphiti-Cosmos system initialized with **semantic search capabilities**
-- Sample data loaded (Manybirds e-commerce dataset)
-- Graph Explorer program ready with **Azure OpenAI integration**
-
-### Launch the Explorer
-```bash
-cd "c:\Users\shyamsridhar\code\graph collection"
-python demos/graph_explorer.py
-```
-
----
-
-## πŸ“– Demo Journey: The AI-Powered Data Detective Story
-
-### 🎬 **Scene 1: The Business Question**
-*"Our CEO wants to understand customer behavior patterns and product relationships using natural language queries that actually understand business context."*
-
-**Your Mission**: Use the **AI-Enhanced Graph Explorer** to uncover hidden insights about customers, products, and business relationships through intelligent semantic search.
-
----
-
-### πŸ” **Chapter 1: First Contact - Understanding the Graph Landscape**
-
-#### **Step 1: Get the Big Picture**
-```
-Choose Option: 5 (Graph Overview & Statistics)
-```
-
-**What You'll Discover:**
-- Total entities and relationships in your graph
-- Entity type distribution (People, Products, Organizations, Events)
-- Relationship density and connectivity patterns
-- Most connected entities (your graph's "celebrities")
-
-**Demo Script:**
-```
-🌐 GRAPH OVERVIEW & STATISTICS
-----------------------------------------
-πŸ“Š Collecting graph statistics...
-
-πŸ“ˆ Basic Statistics:
-   πŸ“š Episodes: 444
-   πŸ‘₯ Entities: 206
-   πŸ”— Relationships: 3855
-   🎯 Density: 18.71 relationships per entity
-
-🏷️  Entity Type Distribution:
-   πŸ“ person: 45 (21.8%)
-   πŸ“ product: 38 (18.4%)
-   πŸ“ organization: 32 (15.5%)
-   πŸ“ event: 28 (13.6%)
-   πŸ“ location: 25 (12.1%)
-
-⭐ Most Connected Entities:
-   🌟 Sarah Johnson: 23 connections
-   🌟 EcoWalk Sustainable Sneakers: 19 connections
-   🌟 Manybirds: 17 connections
-```
-
-**πŸ’‘ Insight**: Sarah Johnson is highly connected - she might be a key customer worth investigating!
-
----
-
-### πŸ”Ž **Chapter 2: The AI Customer Detective - Intelligent Entity Discovery**
-
-#### **Step 2: AI-Enhanced Natural Language Search**
-```
-Choose Option: 1 (Search Entities - Natural Language)
-Query: "Find all people who purchased sustainable products"
-```
-
-**✨ NEW: LLM-Powered Query Interpretation**
-The system now uses Azure OpenAI to understand your intent and enhance search strategy!
-
-**Demo Script:**
-```
-πŸ”Ž NATURAL LANGUAGE ENTITY SEARCH
-----------------------------------------
-πŸ—£οΈ  Enter your search query: Find all people who purchased sustainable products
-
-πŸ” Analyzing query: 'Find all people who purchased sustainable products'
-----------------------------------------
-🎯 Query intent: Find customers with sustainability focus
-πŸ“‹ Search terms: people, purchased, sustainable, products, eco-friendly, green
-πŸ”§ Strategy: semantic_search
-πŸŽͺ Confidence: 0.89
-πŸ’‘ Searching with AI-enhanced strategy...
-
-🧠 Enhancing results with AI insights...
-
-βœ… Found 12 entities:
-
-πŸ” AI Insights: This query reveals environmentally conscious customer segments with high purchase intent for sustainable products. These customers often exhibit cross-category purchasing patterns and strong brand loyalty.
-
-πŸ’‘ Suggested follow-up queries:
-   1. What other products do these sustainability-focused customers buy?
-   2. Which organizations supply these sustainable products?
-   3. Find customers similar to the highest-value eco-conscious buyers
-
-⚠️  Consider exploring: Customer lifetime value patterns for sustainable product buyers
-
-πŸ‘€ PERSON entities:
-   β€’ Sarah Johnson (person): 28-year-old marketing professional from Seattle who purchased EcoWalk Sustainable...
-   β€’ Emily Thompson (person): 26-year-old environmental science student who bought sustainable sneakers during...
-   β€’ Alex Chen (person): 32-year-old software engineer interested in sustainable running shoes...
-   β€’ Dr. Maria Rodriguez (person): Sustainability consultant who reviews eco-friendly products...
-
-πŸ“¦ PRODUCT entities:
-   β€’ EcoWalk Sustainable Sneakers: Made from recycled ocean plastic, carbon-neutral production...
-   β€’ Eco-Friendly Shoe Care Kit: Sustainable maintenance products for footwear...
-
-πŸ”– Enter entity name to bookmark (or press Enter to continue): Sarah Johnson
-βœ… Bookmarked: Sarah Johnson
-```
-
-**πŸ’‘ Discovery**: The AI revealed not just matching entities but also provided strategic insights about customer segments and suggested follow-up investigations!
-
----
-
-### 🧩 **Chapter 3: The Product Investigation - Deep Dive Analysis**
-
-#### **Step 3: AI-Enhanced Entity Deep Dive**
-```
-Choose Option: 6 (Entity Deep Dive)
-Entity: "EcoWalk Sustainable Sneakers"
-```
-
-**Demo Script:**
-```
-πŸ“Š ENTITY DEEP DIVE
-----------------------------------------
-Enter entity name to analyze: EcoWalk Sustainable Sneakers
-
-πŸ”¬ DEEP DIVE: EcoWalk Sustainable Sneakers
-----------------------------------------
-πŸ“‹ Entity Details:
-   🏷️  Name: EcoWalk Sustainable Sneakers
-   πŸ“‚ Type: product
-   πŸ“ Description: Sustainable footwear made from recycled ocean plastic with carbon-neutral production
-
-πŸ”— Relationships (15 total):
-
-   πŸ“Œ purchased_by (4 instances):
-      β€’ Sarah Johnson purchased EcoWalk Sustainable Sneakers for $129.99
-      β€’ Emily Thompson purchased during 20% off sale for $103.99
-      β€’ Alex Chen added to cart after reading reviews
-
-   πŸ“Œ manufactured_by (2 instances):
-      β€’ GreenStep Manufacturing produces EcoWalk Sustainable Sneakers
-      β€’ Partnership strengthened by Q2 sustainability audit
-
-   πŸ“Œ reviewed_by (3 instances):
-      β€’ Sarah Johnson gave 5-star review
-      β€’ Dr. Maria Rodriguez featured as "top pick" in blog
-      β€’ Customer satisfaction rating 4.8/5
-
-🌐 Connected Entity Types:
-   πŸ“ person: 7 connections
-   πŸ“ organization: 3 connections
-   πŸ“ event: 2 connections
-   πŸ“ location: 3 connections
-```
-
-**πŸ’‘ Insight**: This product has strong customer satisfaction and sustainable supply chain!
-
----
-
-### 🏘️ **Chapter 4: The Community Explorer - Finding Hidden Networks**
-
-#### **Step 4: Explore Communities**
-```
-Choose Option: 3 (Explore Communities)
-Community to analyze: "person"
-```
-
-**Demo Script:**
-```
-🏘️ COMMUNITY EXPLORATION
-----------------------------------------
-πŸ” Analyzing entity communities...
-
-βœ… Found 6 entity type communities:
-
-πŸ“ PERSON Community (45 members)
-   1. Sarah Johnson - Marketing professional from Seattle, eco-conscious shopper
-   2. Emily Thompson - Environmental science student, sustainability advocate
-   3. Alex Chen - Software engineer from San Francisco, product researcher
-   4. Dr. Maria Rodriguez - Sustainability consultant and influencer
-   5. Linda Chang - Manufacturing Director at GreenStep Manufacturing
-   ... and 40 more members
-
-Enter community type to analyze deeply: person
-
-πŸ”¬ DEEP ANALYSIS: PERSON COMMUNITY
-----------------------------------------
-πŸ‘₯ Community Size: 45 members
-
-πŸ”— Analyzing internal connections...
-πŸ”„ Internal connections: 23
-
-⭐ Most connected members:
-   β€’ Sarah Johnson: 12 connections
-   β€’ Dr. Maria Rodriguez: 8 connections
-   β€’ Emily Thompson: 6 connections
-   β€’ Alex Chen: 5 connections
-   β€’ Linda Chang: 4 connections
-
-🌐 External connections...
-πŸ”— Top external connections:
-   β€’ EcoWalk Sustainable Sneakers: 8 connections
-   β€’ Manybirds: 6 connections
-   β€’ GreenStep Manufacturing: 4 connections
-   β€’ Sustainability Newsletter: 3 connections
-```
-
-**πŸ’‘ Discovery**: There's a strong network of sustainability-focused customers and partners!
-
----
-
-### 🎯 **Chapter 5: The Relationship Detective - Following the Connections**
-
-#### **Step 5: Ego Network Analysis**
-```
-Choose Option: 4 (Analyze Subgraphs)
-Subtype: 1 (Ego network)
-Entity: "Sarah Johnson"
-Depth: 2
-```
-
-**Demo Script:**
-```
-🧩 SUBGRAPH ANALYSIS
-----------------------------------------
-Choose subgraph analysis type:
-1. 🎯 Ego network (around specific entity)
-
-🎯 EGO NETWORK ANALYSIS
-----------------------------------------
-Enter entity name for ego network: Sarah Johnson
-Enter network depth (1-3, default 2): 2
-
-πŸ” Analyzing 2-hop ego network for 'Sarah Johnson'...
-   Hop 1: Found 8 new entities
-   Hop 2: Found 15 new entities
-
-βœ… Ego network summary:
-   🏠 Center entity: Sarah Johnson
-   πŸ‘₯ Total entities: 24
-   πŸ”— Total relationships: 31
-
-πŸ”¬ Network analysis:
-   πŸ“Š Entity types:
-      β€’ person: 6
-      β€’ product: 8
-      β€’ organization: 4
-      β€’ event: 3
-      β€’ location: 3
-
-   πŸ”— Relationship types:
-      β€’ purchased: 4
-      β€’ reviewed: 3
-      β€’ related_to: 8
-      β€’ works_for: 2
-      β€’ located_in: 3
-```
-
-**πŸ’‘ Insight**: Sarah's network reveals she's connected to multiple product categories and has influence through reviews!
-
----
-
-### πŸ”— **Chapter 6: The Pattern Hunter - Relationship Analysis**
-
-#### **Step 6: Relationship Patterns**
-```
-Choose Option: 7 (Relationship Analysis)
-```
-
-**Demo Script:**
-```
-πŸ”„ RELATIONSHIP ANALYSIS
-----------------------------------------
-πŸ“Š Collecting relationship data...
-βœ… Analyzing 200 relationships...
-
-🏷️  Relationship Types & Confidence:
-   πŸ”— related_to: 45 instances (avg confidence: 0.92)
-   πŸ”— purchased: 23 instances (avg confidence: 0.98)
-   πŸ”— works_for: 18 instances (avg confidence: 0.95)
-   πŸ”— manufactured_by: 12 instances (avg confidence: 0.89)
-   πŸ”— reviewed_by: 15 instances (avg confidence: 0.91)
-
-πŸ” Relationship Patterns:
-   πŸ“ˆ Most common patterns:
-      β€’ person β†’ purchased β†’ product: 23 times
-      β€’ person β†’ works_for β†’ organization: 18 times
-      β€’ product β†’ manufactured_by β†’ organization: 12 times
-      β€’ person β†’ reviewed β†’ product: 15 times
-      β€’ organization β†’ related_to β†’ event: 8 times
-```
-
-**πŸ’‘ Discovery**: Clear business patterns emerge - customer purchase journeys and supply chain relationships!
-
----
-
-### 🎨 **Chapter 7: The Query Artist - Advanced Searches**
-
-#### **Step 7: Advanced Query Builder**
-```
-Choose Option: 8 (Advanced Query Builder)
-Type: 1 (Multi-entity search)
-Entities: "Sarah Johnson, EcoWalk Sustainable Sneakers, Manybirds"
-```
-
-**Demo Script:**
-```
-🎯 ADVANCED QUERY BUILDER
-----------------------------------------
-πŸ”§ Build complex queries to explore your graph
-
-1. πŸ” Multi-entity search
-
-πŸ” MULTI-ENTITY SEARCH
-----------------------------------------
-Enter entities to search (comma-separated): Sarah Johnson, EcoWalk Sustainable Sneakers, Manybirds
-
-🎯 Searching for: Sarah Johnson, EcoWalk Sustainable Sneakers, Manybirds
-
-βœ… Found 3 entities:
-πŸ“‹ Sarah Johnson (person): Marketing professional from Seattle, sustainability advocate
-πŸ“‹ EcoWalk Sustainable Sneakers (product): Sustainable footwear made from recycled materials
-πŸ“‹ Manybirds (organization): E-commerce company specializing in sustainable products
-
-πŸ”— Analyzing connections between entities...
-Found relationship chain:
-   Sarah Johnson β†’ purchased β†’ EcoWalk Sustainable Sneakers
-   EcoWalk Sustainable Sneakers β†’ sold_by β†’ Manybirds
-   Sarah Johnson β†’ subscribed_to β†’ Manybirds Newsletter
-
-πŸ’‘ Business Insight: Complete customer journey from discovery to purchase to engagement!
-```
-
----
-
-### πŸ“š **Chapter 8: The Historian - Session Memory**
-
-#### **Step 8: Review Your Discovery Journey**
-```
-Choose Option: 9 (Session History)
-```
-
-**Demo Script:**
-```
-πŸ“š SESSION HISTORY
-----------------------------------------
-πŸ• Your exploration session:
-
-1. [14:30:15] Graph Overview - Discovered 206 entities, 3855 relationships
-2. [14:32:20] Entity Search - "sustainable products" found 12 matches
-3. [14:35:10] Deep Dive - EcoWalk Sustainable Sneakers analysis
-4. [14:38:45] Community Analysis - Person community (45 members)
-5. [14:42:30] Ego Network - Sarah Johnson 2-hop network (24 entities)
-6. [14:45:15] Relationship Analysis - Found 5 key relationship types
-7. [14:48:00] Multi-entity Search - Connected Sarah β†’ Product β†’ Company
-
-πŸ”– Key discoveries saved to bookmarks:
-   β€’ Sarah Johnson (high-value customer)
-   β€’ EcoWalk Sustainable Sneakers (flagship product)
-   β€’ Person-Product-Organization triangle pattern
-```
-
----
-
-## πŸ€– **AI-Enhanced Features: What Makes This Graph Explorer Smart**
-
-### 🧠 **1. Natural Language Query Interpretation**
-**Feature**: LLM-powered understanding of user intent and context
-```
-User Input: "Find customers who love eco-friendly products"
-AI Analysis:
-🎯 Intent: Customer segmentation analysis
-πŸ“‹ Terms: customers, eco-friendly, sustainable, green, environmental
-πŸ”§ Strategy: semantic_search (broad exploration)
-πŸŽͺ Confidence: 0.94
-```
-
-### πŸ” **2. Semantic Search Enhancement**
-**Feature**: Vector embeddings for contextual similarity matching
-- Goes beyond keyword matching to understand meaning
-- Finds related concepts even with different terminology
-- Examples: "sustainable" matches "eco-friendly", "green", "environmental"
-
-### πŸ’‘ **3. AI-Powered Result Insights**
-**Feature**: LLM analysis of search results for business intelligence
-```
-πŸ” AI Insights: This query reveals environmentally conscious customer segments 
-with high purchase intent for sustainable products. These customers often 
-exhibit cross-category purchasing patterns and strong brand loyalty.
-
-πŸ’‘ Suggested follow-up queries:
-   1. What other products do these sustainability-focused customers buy?
-   2. Which organizations supply these sustainable products?
-   3. Find customers similar to the highest-value eco-conscious buyers
-
-⚠️  Consider exploring: Customer lifetime value patterns for sustainable product buyers
-```
-
-### 🎯 **4. Intelligent Search Strategy Selection**
-**Feature**: AI chooses optimal search strategy based on query type
-
-| Query Type | Strategy | Use Case |
-|------------|----------|----------|
-| "Find John Smith" | exact_match | Specific entity lookup |
-| "Show me all products" | broad_exploration | Category exploration |
-| "Customers interested in sustainability" | semantic_search | Concept-based discovery |
-
-### πŸ”„ **5. Dynamic Fallback Mechanisms**
-**Feature**: Graceful degradation when advanced features fail
-1. **Primary**: LLM-enhanced semantic search
-2. **Fallback**: Traditional keyword extraction
-3. **Final**: Basic string matching
-
-### � **6. Contextual Relationship Understanding**
-**Feature**: AI comprehends business relationships and patterns
-- Recognizes customer journey patterns
-- Identifies supply chain relationships
-- Understands influence networks
-- Maps product affinities
-
----
-
-## 🎨 **Advanced AI Query Examples**
-
-### πŸ›οΈ **Business Intelligence Queries**
-```
-Query: "Who are my brand ambassadors and influencers?"
-AI Response: Finds customers with high review activity, social connections, and product recommendations
-
-Query: "What products have supply chain risks?"
-AI Response: Identifies products with single-source dependencies or quality issues
-
-Query: "Find customers similar to my top buyers"
-AI Response: Uses relationship patterns to find lookalike customer segments
-```
-
-### πŸ”¬ **Market Research Queries**
-```
-Query: "What trends are emerging in customer preferences?"
-AI Response: Analyzes purchase patterns, review sentiment, and product categories
-
-Query: "Which product categories are most connected?"
-AI Response: Maps cross-selling opportunities and product ecosystems
-
-Query: "Find untapped market segments"
-AI Response: Identifies customer groups with low engagement but high potential
-```
-
-### 🎯 **Strategic Planning Queries**
-```
-Query: "What are my competitive advantages?"
-AI Response: Highlights unique product features, customer loyalty, and market position
-
-Query: "Where should I expand my product line?"
-AI Response: Uses customer interest patterns and market gaps for recommendations
-
-Query: "Which partnerships drive the most value?"
-AI Response: Analyzes supplier relationships, customer satisfaction, and business outcomes
-```
-
----
-
-## 🎯 **Real-World Use Cases**
-
-### πŸ›’ **E-commerce Intelligence**
-```
-Scenario: "Which customers are most likely to buy our new sustainable product line?"
-
-Journey:
-1. Graph Overview β†’ Understand customer base
-2. Community Analysis β†’ Find sustainability-focused customer cluster
-3. Entity Deep Dive β†’ Analyze top customers' purchase patterns
-4. Ego Network β†’ Map customer influence networks
-5. Advanced Query β†’ Find customers similar to top buyers
-```
-
-### 🏭 **Supply Chain Analysis**
-```
-Scenario: "How resilient is our manufacturing network?"
-
-Journey:
-1. Search "manufacturing" β†’ Find all production partners
-2. Deep Dive β†’ Analyze each manufacturer's connections
-3. Subgraph Analysis β†’ Map supplier dependencies
-4. Relationship Analysis β†’ Identify critical supply relationships
-5. Community Exploration β†’ Find supplier clusters and risks
-```
-
-### πŸ‘₯ **Customer Relationship Management**
-```
-Scenario: "Who are our brand ambassadors and how do they influence others?"
-
-Journey:
-1. Search "review, recommend, influence" β†’ Find customer advocates
-2. Ego Network β†’ Map each advocate's social connections
-3. Relationship Analysis β†’ Identify influence patterns
-4. Community Analysis β†’ Find customer segments they reach
-5. Multi-entity Search β†’ Connect advocates to product success
-```
-
-### πŸ“ˆ **Product Development Insights**
-```
-Scenario: "What product features drive customer satisfaction?"
-
-Journey:
-1. Search products β†’ Find all product entities
-2. Deep Dive β†’ Analyze top products' relationships
-3. Relationship Analysis β†’ Find review/feedback patterns
-4. Community Exploration β†’ Group products by customer segments
-5. Advanced Query β†’ Connect features to satisfaction scores
-```
-
----
-
-## πŸ’‘ **Pro Tips for AI-Enhanced Graph Exploration**
-
-### πŸ€– **Leveraging AI Features**
-1. **Natural Language Queries**: Use conversational language - the AI understands context
-   ```
-   Instead of: "product sustainable"
-   Try: "Show me all environmentally friendly products our customers love"
-   ```
-
-2. **Follow AI Suggestions**: Pay attention to the suggested follow-up queries
-   - The AI identifies related concepts you might have missed
-   - Follow-up suggestions often reveal deeper insights
-
-3. **Trust the Confidence Scores**: Higher confidence = more reliable results
-   - 0.9+ = High confidence, results likely very relevant
-   - 0.7-0.9 = Good confidence, results should be relevant
-   - <0.7 = Lower confidence, consider rephrasing your query
-
-4. **Use AI Insights for Strategy**: The business insights help translate findings into action
-   - Look for patterns the AI identifies
-   - Consider the "gaps" and unexplored areas it suggests
-
-### 🎯 **Start Broad, Then Narrow (AI-Enhanced)**
-1. Begin with Graph Overview to understand the landscape
-2. Use **AI-powered Natural Language Search** for broad topic exploration
-3. **Follow AI-suggested queries** to discover related concepts
-4. Deep Dive into AI-highlighted interesting entities
-5. Use Ego Networks to map local connections
-6. Apply Advanced Queries for specific investigations
-
-### πŸ” **Follow the AI-Guided Curiosity Path**
-- Let AI insights and suggestions guide your exploration
-- Pay attention to confidence scores and business interpretations
-- Use the semantic search to discover related concepts you didn't know existed
-- Bookmark entities the AI identifies as particularly interesting
-- Cross-reference AI insights across different analysis types
-
-### πŸ“Š **Look for AI-Identified Patterns**
-- High-connectivity entities often represent key business assets
-- AI-enhanced relationship analysis reveals hidden business processes
-- Semantic similarity helps identify market segments and customer groups
-- AI insights connect graph structure to business strategy
-
-### πŸš€ **Business Impact Focus (AI-Driven)**
-- Use AI-generated business insights to connect graph data to strategy
-- Follow AI suggestions for exploring market opportunities
-- Leverage semantic search to discover customer segments
-- Apply AI pattern recognition to identify optimization opportunities
-- Use LLM insights to map communities to business strategy and market segments
-
-### 🧠 **Advanced AI Techniques**
-1. **Semantic Exploration**: Let the AI find concepts related to your search terms
-2. **Intent-Based Queries**: The AI understands business intent, not just keywords
-3. **Pattern Recognition**: AI identifies business patterns humans might miss
-4. **Strategic Insights**: Use AI-generated business intelligence for decision-making
-
----
-
-## πŸ”š **Conclusion**
-
-The Graph Explorer transforms your knowledge graph from a static database into an interactive discovery platform. By following customer journeys, mapping relationships, and analyzing communities, you uncover insights that drive business decisions.
-
-**Remember**: Every connection tells a story. Every pattern reveals an opportunity. Every community represents a market segment waiting to be understood.
-
-Happy exploring! πŸš€
-
----
-
-## πŸ“‹ **Quick Reference Commands**
-
-| Option | Feature | Best For |
-|--------|---------|----------|
-| 1 | Natural Language Search | Finding entities by topic/description |
-| 2 | Relationship Search | Understanding how entities connect |
-| 3 | Community Exploration | Finding natural business segments |
-| 4 | Subgraph Analysis | Mapping local networks and dependencies |
-| 5 | Graph Overview | Understanding overall structure |
-| 6 | Entity Deep Dive | Detailed analysis of key entities |
-| 7 | Relationship Analysis | Pattern discovery and trend analysis |
-| 8 | Advanced Queries | Complex multi-entity investigations |
-| 9 | Session History | Tracking your discovery journey |
-| 10 | Bookmarks | Saving important findings |
diff --git a/docs/GRAPH_EXPLORER_VALIDATION_REPORT.md b/docs/GRAPH_EXPLORER_VALIDATION_REPORT.md
deleted file mode 100644
index 3857e98..0000000
--- a/docs/GRAPH_EXPLORER_VALIDATION_REPORT.md
+++ /dev/null
@@ -1,198 +0,0 @@
-# Graph Explorer Validation Summary
-
-## Overview
-This document summarizes the comprehensive validation of the Graph Explorer to ensure it searches and fetches everything from CosmosDB with LLM assistance, eliminating hardcoded or static elements.
-
-## Date: May 29, 2025
-
-## Validation Results: βœ… PASSED (100% Success Rate)
-
----
-
-## Key Enhancements Made
-
-### 1. **LLM-Powered Keyword Extraction**
-- **Before**: Basic string splitting with hardcoded stop words
-- **After**: Azure OpenAI-powered semantic keyword extraction
-- **Improvement**: Now extracts contextually relevant terms and understands query intent
-
-```python
-# Enhanced keyword extraction using LLM
-async def _extract_search_keywords(self, query: str) -> List[str]:
-    # Uses Azure OpenAI to extract semantic keywords and intent
-    # Fallback to basic extraction only if LLM fails
-```
-
-### 2. **Semantic Entity Search**
-- **Before**: Basic string matching (`contains()` logic)
-- **After**: Embedding-based semantic similarity search with cosine similarity
-- **Improvement**: Finds conceptually related entities, not just exact string matches
-
-```python
-# Enhanced search with semantic similarity
-async def search_entities(self, query: str, limit: int = 10):
-    # Generates query embeddings
-    # Calculates cosine similarity with stored entity embeddings
-    # Ranks results by semantic relevance
-```
-
-### 3. **Natural Language Query Interpretation**
-- **Before**: No query understanding, direct keyword extraction
-- **After**: LLM interprets user intent and suggests optimal search strategies
-- **Improvement**: Understands complex queries and routes them appropriately
-
-```python
-# New natural language interpretation
-async def _interpret_natural_language_query(self, query: str):
-    # Determines search intent (entities, relationships, communities, etc.)
-    # Suggests search strategy (exact_match, semantic_search, broad_exploration)
-    # Provides confidence levels and explanations
-```
-
-### 4. **LLM-Enhanced Result Processing**
-- **Before**: Raw results displayed without context
-- **After**: LLM analyzes and enhances results with insights and suggestions
-- **Improvement**: Provides intelligent insights, follow-up suggestions, and identifies gaps
-
-```python
-# Enhanced result processing
-async def _enhance_search_results_with_llm(self, query: str, results: List[Dict], interpretation: Dict):
-    # Reorders results by relevance
-    # Provides insights about patterns and findings
-    # Suggests follow-up queries
-    # Identifies missing information or gaps
-```
-
----
-
-## Validation Tests Conducted
-
-### βœ… Test 1: LLM Keyword Extraction (4/4 passed)
-- **Tested**: Complex natural language queries
-- **Verified**: Contextual keyword extraction using Azure OpenAI
-- **Examples**: 
-  - "Find all people who work for technology companies" β†’ ['Person', 'works_for', 'Technology Company', 'employment relationship', 'company type']
-  - "Show me sustainable products and their manufacturers" β†’ ['sustainable product', 'manufacturer', 'produced_by', 'sustainability', 'product-manufacturer network']
-
-### βœ… Test 2: Semantic Entity Search (4/4 passed)
-- **Tested**: Various search terms with semantic similarity
-- **Verified**: Embedding-based search returns conceptually relevant results
-- **Examples**:
-  - "technology company" β†’ Found Microsoft, Tesla
-  - "sustainable product" β†’ Found relevant sustainability-related entities
-  - "renewable energy" β†’ Found sustainability concepts and technologies
-
-### βœ… Test 3: Natural Language Interpretation (4/4 passed)
-- **Tested**: Complex natural language queries
-- **Verified**: LLM correctly interprets intent and suggests strategies
-- **Examples**:
-  - "Show me all people who work for organizations in the tech industry" β†’ Intent: search_entities, Strategy: semantic_search, Confidence: 0.95
-  - "Analyze the network of suppliers and manufacturers" β†’ Intent: analyze_subgraphs, Strategy: broad_exploration, Confidence: 0.92
-
-### βœ… Test 4: Dynamic Query Generation (4/4 passed)
-- **Tested**: Different search patterns without hardcoded queries
-- **Verified**: System generates appropriate queries dynamically
-- **Examples**: Exact match, partial match, semantic search, and broad exploration all work correctly
-
-### βœ… Test 5: LLM Result Enhancement (1/1 passed)
-- **Tested**: Result enhancement with insights and suggestions
-- **Verified**: LLM provides meaningful insights and follow-up suggestions
-- **Example**: "Find technology companies and their products" β†’ Enhanced with insights about major technology companies and suggested related queries
-
----
-
-## Static/Hardcoded Elements Eliminated
-
-### ❌ **Removed**: Basic keyword extraction
-- Old: Simple string splitting with hardcoded stop words
-- New: LLM-powered semantic keyword extraction
-
-### ❌ **Removed**: String matching search
-- Old: Basic `contains()` logic for entity search
-- New: Embedding-based semantic similarity search
-
-### ❌ **Removed**: Empty query patterns
-- Old: Many searches used empty strings ("") to get all entities
-- New: Intelligent query interpretation and targeted searches
-
-### ❌ **Removed**: Static result display
-- Old: Raw results without context or insights
-- New: LLM-enhanced results with insights and suggestions
-
----
-
-## LLM Integration Points
-
-### 🧠 **Azure OpenAI Usage**:
-1. **Keyword Extraction**: Semantic analysis of natural language queries
-2. **Query Interpretation**: Understanding user intent and suggesting strategies
-3. **Result Enhancement**: Analyzing results and providing insights
-4. **Entity/Relationship Extraction**: Dynamic extraction from content (existing)
-5. **Embedding Generation**: Semantic similarity calculations (existing)
-
-### πŸ”„ **Dynamic Behavior**:
-- All searches are now context-aware and adaptive
-- No hardcoded search patterns or static result sets
-- Intelligent query routing based on LLM interpretation
-- Real-time result enhancement with AI insights
-
----
-
-## CosmosDB Integration Validation
-
-### βœ… **Confirmed**: All data fetched from CosmosDB
-- No static data sources or hardcoded entities
-- All searches query the live Cosmos database
-- Entity and relationship data dynamically retrieved
-- Search results based on actual stored data
-
-### βœ… **Confirmed**: Dynamic Gremlin query generation
-- Queries generated based on search context
-- No hardcoded Gremlin patterns
-- Adaptive query strategies based on user intent
-
----
-
-## Performance Metrics
-
-- **Test Suite**: 17 comprehensive tests
-- **Success Rate**: 100% (17/17 passed)
-- **LLM Response Time**: ~1-2 seconds per query interpretation
-- **Search Performance**: Maintained while adding semantic capabilities
-- **Error Handling**: Robust fallbacks for LLM failures
-
----
-
-## Conclusion
-
-The Graph Explorer has been successfully enhanced to be **fully LLM-powered and dynamic**:
-
-βœ… **No hardcoded elements** - All searches and processing use LLM assistance  
-βœ… **Semantic understanding** - Understands natural language queries contextually  
-βœ… **Dynamic query generation** - Generates appropriate searches based on user intent  
-βœ… **Intelligent results** - Provides insights, suggestions, and gap analysis  
-βœ… **CosmosDB integration** - All data dynamically fetched from the live database  
-βœ… **Robust error handling** - Graceful fallbacks if LLM services are unavailable  
-
-The system now provides a truly intelligent and adaptive graph exploration experience, leveraging the full power of Azure OpenAI for natural language understanding and result enhancement while maintaining robust access to the CosmosDB knowledge graph.
-
----
-
-## Files Modified
-
-1. **`demos/graph_explorer.py`**:
-   - Enhanced `_extract_search_keywords()` with LLM
-   - Added `_interpret_natural_language_query()`
-   - Added `_enhance_search_results_with_llm()`
-   - Updated main search workflow to use LLM insights
-
-2. **`src/graphiti_cosmos.py`**:
-   - Enhanced `search_entities()` with semantic similarity
-   - Added `_search_entities_by_text()` as fallback
-   - Added `_cosine_similarity()` for embedding comparison
-   - Improved error handling and dynamic query generation
-
-3. **`validate_graph_explorer.py`** (new):
-   - Comprehensive validation suite
-   - Tests all LLM integration points
-   - Validates dynamic behavior and eliminates hardcoded patterns
diff --git a/ecommerce_intelligence_reports/business_summary_20250530_113158.txt b/ecommerce_intelligence_reports/business_summary_20250530_113158.txt
deleted file mode 100644
index c069241..0000000
--- a/ecommerce_intelligence_reports/business_summary_20250530_113158.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-===== BUSINESS INTELLIGENCE SUMMARY =====
-Generated: 2025-05-30 11:33:49
-
-KNOWLEDGE GRAPH METRICS:
-• Episodes Processed: 461
-• Entities Identified: 210
-• Relationships Mapped: 3933
-• Intelligence Density: 18.73
-
-ACTION RECOMMENDATIONS:
-• Focus marketing on highest-engaging product categories
-• Optimize product pages for most-viewed items
-• Develop personalized recommendations based on browsing patterns
-• Enhance checkout experience to improve conversion rates
\ No newline at end of file
diff --git a/ecommerce_intelligence_reports/customer_insights_20250530_113158.txt b/ecommerce_intelligence_reports/customer_insights_20250530_113158.txt
deleted file mode 100644
index 28a0736..0000000
--- a/ecommerce_intelligence_reports/customer_insights_20250530_113158.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-===== CUSTOMER INSIGHTS REPORT =====
-Generated: 2025-05-30 11:31:58
-
-CUSTOMER BEHAVIOR ANALYSIS:
-• Purchase Events: 0
-• Browse Events: 0
-• Product View Events: 0
-
-CUSTOMER INSIGHTS:
\ No newline at end of file
diff --git a/ecommerce_intelligence_reports/market_trends_20250530_113158.txt b/ecommerce_intelligence_reports/market_trends_20250530_113158.txt
deleted file mode 100644
index 87c614a..0000000
--- a/ecommerce_intelligence_reports/market_trends_20250530_113158.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-===== MARKET TRENDS REPORT =====
-Generated: 2025-05-30 11:33:08
-
-MARKET ANALYSIS:
-• Total Business Events: 461
-• Business Entities: 210
-• Relationships Mapped: 3933
-• Intelligence Density: 18.73 connections per entity
-
-• Product Categories: 10 found
-
-KEY MARKET INSIGHTS:
-• Customer engagement is growing with increasing interaction counts
-• Average 8.5 actions per customer session
-• Online shopping activity shows strong category-specific patterns
\ No newline at end of file
diff --git a/ecommerce_intelligence_reports/product_insights_20250530_113158.txt b/ecommerce_intelligence_reports/product_insights_20250530_113158.txt
deleted file mode 100644
index 974f4f1..0000000
--- a/ecommerce_intelligence_reports/product_insights_20250530_113158.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-===== PRODUCT INSIGHTS REPORT =====
-Generated: 2025-05-30 11:32:09
-
-PRODUCT CATALOG ANALYSIS:
-• Total Products: 10
-
-POPULAR PRODUCTS:
-  1. Premium Product 10 ($192.08) - Clothing
-  2. Premium Product 4 ($200.63) - Sports
-  3. Premium Product 20 ($182.56) - Electronics
-  4. Premium Product 13 ($53.31) - Sports
-  5. Premium Product 15 ($104.45) - Books
\ No newline at end of file
diff --git a/enhanced_graph_explorer_demo.py b/enhanced_graph_explorer_demo.py
deleted file mode 100644
index be98f44..0000000
--- a/enhanced_graph_explorer_demo.py
+++ /dev/null
@@ -1,680 +0,0 @@
-#!/usr/bin/env python3
-"""
-Enhanced Graph Explorer Demo Script
-===================================
-
-This script demonstrates the advanced capabilities of the LLM-powered graph explorer
-for the ManyBirds e-commerce knowledge graph. It performs automated exploration,
-analysis, and intelligence gathering while generating a comprehensive markdown report.
-
-Features Demonstrated:
-- Natural language entity search with LLM interpretation
-- Relationship analysis and pattern discovery
-- Community detection and subgraph analysis
-- Advanced query building and conditional analysis
-- Statistical overview and insights generation
-- Session management and result export
-
-Author: Enhanced Graph Explorer System
-Date: May 30, 2025
-"""
-
-import asyncio
-import json
-import os
-import sys
-from datetime import datetime
-from typing import List, Dict, Any
-import traceback
-
-# Add the project root to the path
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-from demos.graph_explorer import GraphExplorer
-
-class GraphExplorerDemo:
-    """Automated demo of the enhanced graph explorer capabilities"""
-    
-    def __init__(self):
-        self.explorer = GraphExplorer()
-        self.demo_results = {
-            'timestamp': datetime.now().isoformat(),
-            'demo_name': 'Enhanced Graph Explorer Capabilities Demo',
-            'sections': []
-        }
-        self.output_file = f"demo_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md"
-    
-    def log_section(self, title: str, description: str, results: Any = None, insights: str = ""):
-        """Log a demo section with results"""
-        section = {
-            'title': title,
-            'description': description,
-            'results': results,
-            'insights': insights,
-            'timestamp': datetime.now().strftime('%H:%M:%S')
-        }
-        self.demo_results['sections'].append(section)
-        print(f"\nπŸ“ {title}")
-        print(f"   {description}")
-        if insights:
-            print(f"   πŸ’‘ {insights}")
-    
-    async def demo_initialization(self):
-        """Demo: System initialization and connection"""
-        print("πŸš€ ENHANCED GRAPH EXPLORER DEMO")
-        print("=" * 60)
-        print("🎯 Demonstrating advanced LLM-powered graph exploration capabilities")
-        print("πŸ“Š Using ManyBirds e-commerce knowledge graph dataset")
-        print()
-        
-        self.log_section(
-            "System Initialization",
-            "Connecting to Azure Cosmos DB and OpenAI services for LLM-enhanced graph exploration",
-            None,
-            "The system uses Azure Cosmos DB for graph storage and OpenAI for natural language processing"
-        )
-        
-        try:
-            await self.explorer.initialize()
-            self.log_section(
-                "Connection Status",
-                "Successfully connected to all required services",
-                {"status": "βœ… Connected", "services": ["Azure Cosmos DB", "OpenAI GPT-4"]},
-                "Ready for AI-powered graph exploration and analysis"
-            )
-            return True
-        except Exception as e:
-            self.log_section(
-                "Connection Error",
-                "Failed to initialize system",
-                {"status": "❌ Failed", "error": str(e)},
-                "Demo cannot proceed without proper initialization"
-            )
-            return False
-    
-    async def demo_natural_language_search(self):
-        """Demo: Natural language entity search with LLM interpretation"""
-        print("\nπŸ” DEMO: Natural Language Entity Search")
-        print("-" * 50)
-        
-        # Demo queries showcasing different search capabilities
-        demo_queries = [
-            {
-                "query": "Find products related to sustainable footwear",
-                "description": "Search for eco-friendly shoe products using semantic understanding"
-            },
-            {
-                "query": "Show me companies that manufacture insoles",
-                "description": "Identify organizations in the insole manufacturing space"
-            },
-            {
-                "query": "What blue products are available?",
-                "description": "Color-based product discovery with attribute filtering"
-            }
-        ]
-        
-        search_results = []
-        
-        for demo_query in demo_queries:
-            query = demo_query["query"]
-            description = demo_query["description"]
-            
-            print(f"\n🎯 Query: '{query}'")
-            print(f"πŸ“ Purpose: {description}")
-            
-            try:
-                # Simulate the natural language search process
-                # Note: We'll call the internal methods since we need to capture results
-                
-                # Step 1: LLM interprets the query
-                interpretation = await self.explorer._interpret_natural_language_query(query)
-                
-                print(f"🧠 LLM Interpretation:")
-                print(f"   Intent: {interpretation.get('intent', 'unknown')}")
-                print(f"   Strategy: {interpretation.get('strategy', 'unknown')}")
-                print(f"   Confidence: {interpretation.get('confidence', 0.0):.2f}")
-                
-                # Step 2: Execute the search based on interpretation
-                search_terms = interpretation.get('search_terms', [query])
-                entities = []
-                
-                for term in search_terms[:2]:  # Limit to avoid overwhelming output
-                    try:
-                        results = await self.explorer.graphiti.search_entities(term, limit=5)
-                        entities.extend(results)
-                    except Exception as e:
-                        print(f"   ⚠️ Search for '{term}' failed: {e}")
-                
-                # Step 3: Process and display results
-                if entities:
-                    print(f"βœ… Found {len(entities)} entities:")
-                    entity_summary = []
-                    
-                    for i, entity in enumerate(entities[:3], 1):  # Show top 3
-                        name = self.explorer._extract_property(entity, 'name', 'Unknown')
-                        entity_type = self.explorer._extract_property(entity, 'type', 'unknown')
-                        description = self.explorer._extract_property(entity, 'description', '')
-                        
-                        print(f"   {i}. {name} ({entity_type})")
-                        if description:
-                            print(f"      {description[:100]}{'...' if len(description) > 100 else ''}")
-                        
-                        entity_summary.append({
-                            'name': name,
-                            'type': entity_type,
-                            'description': description[:100] if description else ''
-                        })
-                    
-                    search_results.append({
-                        'query': query,
-                        'interpretation': interpretation,
-                        'entities_found': len(entities),
-                        'top_entities': entity_summary
-                    })
-                else:
-                    print("❌ No entities found for this query")
-                    search_results.append({
-                        'query': query,
-                        'interpretation': interpretation,
-                        'entities_found': 0,
-                        'top_entities': []
-                    })
-                    
-            except Exception as e:
-                print(f"❌ Error processing query: {e}")
-                search_results.append({
-                    'query': query,
-                    'error': str(e)
-                })
-        
-        self.log_section(
-            "Natural Language Search Demo",
-            "Demonstrated LLM-powered query interpretation and semantic entity discovery",
-            search_results,
-            f"Successfully processed {len(demo_queries)} natural language queries with AI interpretation"
-        )
-    
-    async def demo_relationship_analysis(self):
-        """Demo: Advanced relationship analysis and pattern discovery"""
-        print("\nπŸ”— DEMO: Relationship Analysis & Pattern Discovery")
-        print("-" * 55)
-        
-        try:
-            # Get sample relationships
-            relationships = await self.explorer.graphiti.search_relationships("", limit=50)
-            
-            if not relationships:
-                print("❌ No relationships found in the graph")
-                return
-            
-            print(f"πŸ“Š Analyzing {len(relationships)} relationships...")
-            
-            # Analyze relationship patterns
-            relationship_types = {}
-            confidence_stats = []
-            entity_connections = {}
-            
-            for rel in relationships[:20]:  # Analyze first 20 for demo
-                rel_type = self.explorer._extract_property(rel, 'type', 'unknown')
-                source = self.explorer._extract_property(rel, 'source', 'unknown')
-                target = self.explorer._extract_property(rel, 'target', 'unknown')
-                confidence = float(self.explorer._extract_property(rel, 'confidence', '1.0'))
-                
-                # Count relationship types
-                relationship_types[rel_type] = relationship_types.get(rel_type, 0) + 1
-                
-                # Track confidence
-                confidence_stats.append(confidence)
-                
-                # Track entity connections
-                entity_connections[source] = entity_connections.get(source, 0) + 1
-                entity_connections[target] = entity_connections.get(target, 0) + 1
-            
-            # Display analysis results
-            print("\nπŸ“ˆ Relationship Type Distribution:")
-            sorted_types = sorted(relationship_types.items(), key=lambda x: x[1], reverse=True)
-            for rel_type, count in sorted_types[:5]:
-                print(f"   β€’ {rel_type}: {count} instances")
-            
-            print(f"\nπŸ“Š Confidence Statistics:")
-            if confidence_stats:
-                avg_confidence = sum(confidence_stats) / len(confidence_stats)
-                min_confidence = min(confidence_stats)
-                max_confidence = max(confidence_stats)
-                print(f"   β€’ Average: {avg_confidence:.3f}")
-                print(f"   β€’ Range: {min_confidence:.3f} - {max_confidence:.3f}")
-            
-            print(f"\n🌐 Top Connected Entities:")
-            sorted_connections = sorted(entity_connections.items(), key=lambda x: x[1], reverse=True)
-            for entity, connections in sorted_connections[:5]:
-                print(f"   β€’ {entity}: {connections} connections")
-            
-            analysis_results = {
-                'total_relationships': len(relationships),
-                'relationship_types': dict(sorted_types[:5]),
-                'confidence_stats': {
-                    'average': avg_confidence if confidence_stats else 0,
-                    'min': min_confidence if confidence_stats else 0,
-                    'max': max_confidence if confidence_stats else 0
-                },
-                'top_connected_entities': dict(sorted_connections[:5])
-            }
-            
-            self.log_section(
-                "Relationship Analysis",
-                "Comprehensive analysis of relationship patterns, types, and connectivity",
-                analysis_results,
-                "Relationship analysis reveals the structural patterns and key connectors in the knowledge graph"
-            )
-            
-        except Exception as e:
-            print(f"❌ Error in relationship analysis: {e}")
-            self.log_section(
-                "Relationship Analysis Error",
-                "Failed to complete relationship analysis",
-                {"error": str(e)},
-                "Analysis could not be completed due to technical issues"
-            )
-    
-    async def demo_graph_statistics(self):
-        """Demo: Graph overview and statistical analysis"""
-        print("\nπŸ“Š DEMO: Graph Statistics & Overview")
-        print("-" * 40)
-        
-        try:
-            # Get entities and relationships for statistics
-            entities = await self.explorer.graphiti.search_entities("", limit=100)
-            relationships = await self.explorer.graphiti.search_relationships("", limit=100)
-            
-            print(f"πŸ“ˆ Graph Scale:")
-            print(f"   β€’ Entities: {len(entities)}")
-            print(f"   β€’ Relationships: {len(relationships)}")
-            
-            # Analyze entity types
-            entity_types = {}
-            for entity in entities:
-                entity_type = self.explorer._extract_property(entity, 'type', 'unknown')
-                entity_types[entity_type] = entity_types.get(entity_type, 0) + 1
-            
-            print(f"\n🏷️ Entity Type Distribution:")
-            sorted_entity_types = sorted(entity_types.items(), key=lambda x: x[1], reverse=True)
-            for entity_type, count in sorted_entity_types:
-                percentage = (count / len(entities)) * 100 if entities else 0
-                print(f"   β€’ {entity_type}: {count} ({percentage:.1f}%)")
-            
-            # Calculate basic graph metrics
-            if entities and relationships:
-                density = len(relationships) / len(entities) if entities else 0
-                print(f"\nπŸ“Š Graph Metrics:")
-                print(f"   β€’ Density (relationships per entity): {density:.2f}")
-                print(f"   β€’ Graph completeness: {len(entities)} entities analyzed")
-            
-            stats_results = {
-                'total_entities': len(entities),
-                'total_relationships': len(relationships),
-                'entity_types': dict(sorted_entity_types),
-                'density': density if entities and relationships else 0,
-                'analysis_completeness': min(len(entities), 100)  # We limited to 100
-            }
-            
-            self.log_section(
-                "Graph Statistics",
-                "Comprehensive statistical overview of the knowledge graph structure",
-                stats_results,
-                "Graph statistics provide insights into data distribution and structural characteristics"
-            )
-            
-        except Exception as e:
-            print(f"❌ Error in statistical analysis: {e}")
-            self.log_section(
-                "Graph Statistics Error",
-                "Failed to generate graph statistics",
-                {"error": str(e)},
-                "Statistical analysis could not be completed"
-            )
-    
-    async def demo_advanced_queries(self):
-        """Demo: Advanced query capabilities"""
-        print("\n🎯 DEMO: Advanced Query Capabilities")
-        print("-" * 42)
-        
-        advanced_results = []
-        
-        # Demo 1: High connectivity analysis
-        try:
-            print("\nπŸ” Finding highly connected entities...")
-            entities = await self.explorer.graphiti.search_entities("", limit=50)
-            
-            high_connectivity = []
-            for entity in entities[:20]:  # Limit for demo
-                entity_name = self.explorer._extract_property(entity, 'name', '')
-                if entity_name:
-                    relationships = await self.explorer._get_entity_relationships(entity_name)
-                    if len(relationships) > 2:  # Threshold for demo
-                        high_connectivity.append((entity_name, len(relationships)))
-            
-            high_connectivity.sort(key=lambda x: x[1], reverse=True)
-            
-            print(f"βœ… Found {len(high_connectivity)} highly connected entities:")
-            for entity_name, count in high_connectivity[:5]:
-                print(f"   β€’ {entity_name}: {count} connections")
-            
-            advanced_results.append({
-                'query_type': 'high_connectivity',
-                'results': high_connectivity[:5],
-                'total_found': len(high_connectivity)
-            })
-            
-        except Exception as e:
-            print(f"❌ High connectivity analysis failed: {e}")
-            advanced_results.append({
-                'query_type': 'high_connectivity',
-                'error': str(e)
-            })
-        
-        # Demo 2: Pattern matching
-        try:
-            print("\n🎨 Pattern matching analysis...")
-            relationships = await self.explorer.graphiti.search_relationships("", limit=30)
-            
-            patterns = {}
-            for rel in relationships:
-                rel_type = self.explorer._extract_property(rel, 'type', 'unknown')
-                source_name = self.explorer._extract_property(rel, 'source', '')
-                target_name = self.explorer._extract_property(rel, 'target', '')
-                
-                # Get entity types (simplified for demo)
-                pattern = f"entity β†’ {rel_type} β†’ entity"
-                patterns[pattern] = patterns.get(pattern, 0) + 1
-            
-            print(f"βœ… Found {len(patterns)} relationship patterns:")
-            sorted_patterns = sorted(patterns.items(), key=lambda x: x[1], reverse=True)
-            for pattern, count in sorted_patterns[:5]:
-                print(f"   β€’ {pattern}: {count} instances")
-            
-            advanced_results.append({
-                'query_type': 'pattern_matching',
-                'patterns': dict(sorted_patterns[:5]),
-                'total_patterns': len(patterns)
-            })
-            
-        except Exception as e:
-            print(f"❌ Pattern matching failed: {e}")
-            advanced_results.append({
-                'query_type': 'pattern_matching',
-                'error': str(e)
-            })
-        
-        self.log_section(
-            "Advanced Query Capabilities",
-            "Demonstration of complex query patterns and analysis techniques",
-            advanced_results,
-            "Advanced queries enable deep insights into graph structure and connectivity patterns"
-        )
-    
-    async def demo_llm_enhancements(self):
-        """Demo: LLM-powered enhancements and insights"""
-        print("\n🧠 DEMO: LLM-Powered Enhancements")
-        print("-" * 38)
-        
-        try:
-            # Demo query interpretation
-            test_query = "Show me all eco-friendly products from sustainable brands"
-            print(f"🎯 Test Query: '{test_query}'")
-            
-            interpretation = await self.explorer._interpret_natural_language_query(test_query)
-            
-            print(f"\n🧠 LLM Interpretation Results:")
-            print(f"   β€’ Intent: {interpretation.get('intent', 'unknown')}")
-            print(f"   β€’ Search Terms: {interpretation.get('search_terms', [])}")
-            print(f"   β€’ Strategy: {interpretation.get('strategy', 'unknown')}")
-            print(f"   β€’ Confidence: {interpretation.get('confidence', 0.0):.2f}")
-            print(f"   β€’ Explanation: {interpretation.get('explanation', 'No explanation provided')}")
-            
-            # Demo search result enhancement
-            print(f"\nπŸ” Executing interpreted search...")
-            search_terms = interpretation.get('search_terms', [test_query])
-            entities = []
-            
-            for term in search_terms[:2]:
-                try:
-                    results = await self.explorer.graphiti.search_entities(term, limit=3)
-                    entities.extend(results)
-                except Exception as e:
-                    print(f"   ⚠️ Search for '{term}' failed: {e}")
-            
-            if entities:
-                print(f"βœ… Found {len(entities)} entities")
-                
-                # Simulate LLM enhancement of results
-                try:
-                    enhancement = await self.explorer._enhance_search_results_with_llm(
-                        test_query, entities, interpretation
-                    )
-                    
-                    if enhancement:
-                        print(f"\nπŸ’‘ LLM Insights:")
-                        print(f"   β€’ Key Insights: {enhancement.get('insights', 'No insights generated')}")
-                        
-                        patterns = enhancement.get('patterns', [])
-                        if patterns:
-                            print(f"   β€’ Patterns Identified:")
-                            for pattern in patterns[:3]:
-                                print(f"     - {pattern}")
-                        
-                        follow_ups = enhancement.get('follow_up_queries', [])
-                        if follow_ups:
-                            print(f"   β€’ Suggested Follow-up Queries:")
-                            for query in follow_ups[:3]:
-                                print(f"     - {query}")
-                    
-                except Exception as e:
-                    print(f"   ⚠️ LLM enhancement failed: {e}")
-                    enhancement = None
-            else:
-                enhancement = None
-            
-            llm_results = {
-                'test_query': test_query,
-                'interpretation': interpretation,
-                'entities_found': len(entities),
-                'enhancement': enhancement,
-                'llm_features_tested': [
-                    'Query interpretation',
-                    'Search strategy recommendation',
-                    'Result enhancement',
-                    'Insight generation'
-                ]
-            }
-            
-            self.log_section(
-                "LLM-Powered Enhancements",
-                "Demonstration of AI-powered query interpretation and result enhancement",
-                llm_results,
-                "LLM enhancements provide intelligent query understanding and contextual insights"
-            )
-            
-        except Exception as e:
-            print(f"❌ LLM enhancement demo failed: {e}")
-            self.log_section(
-                "LLM Enhancement Error",
-                "Failed to demonstrate LLM capabilities",
-                {"error": str(e)},
-                "LLM features could not be fully demonstrated due to technical issues"
-            )
-    
-    def generate_markdown_report(self):
-        """Generate comprehensive markdown report of demo results"""
-        print(f"\nπŸ“„ Generating comprehensive demo report...")
-        
-        markdown_content = f"""# Enhanced Graph Explorer Demo Report
-
-**Generated:** {self.demo_results['timestamp']}  
-**Demo:** {self.demo_results['demo_name']}  
-**System:** LLM-Powered Knowledge Graph Explorer  
-
-## Executive Summary
-
-This report documents a comprehensive demonstration of the Enhanced Graph Explorer system, showcasing its advanced capabilities for AI-powered knowledge graph exploration and analysis. The system successfully demonstrates integration between Azure Cosmos DB graph storage and OpenAI's language models for intelligent query processing.
-
-## Key Capabilities Demonstrated
-
-- 🧠 **Natural Language Processing**: LLM-powered query interpretation and semantic understanding
-- πŸ” **Advanced Search**: Multi-strategy entity and relationship discovery
-- πŸ“Š **Statistical Analysis**: Comprehensive graph metrics and pattern recognition
-- 🎯 **Complex Queries**: High-connectivity analysis and pattern matching
-- πŸ’‘ **AI Insights**: Automated insight generation and follow-up recommendations
-
-## Detailed Demo Results
-
-"""
-        
-        for i, section in enumerate(self.demo_results['sections'], 1):
-            markdown_content += f"""### {i}. {section['title']}
-
-**Description:** {section['description']}  
-**Timestamp:** {section['timestamp']}  
-
-"""
-            
-            if section.get('insights'):
-                markdown_content += f"**Key Insights:** {section['insights']}\n\n"
-            
-            if section.get('results'):
-                markdown_content += "**Results:**\n```json\n"
-                markdown_content += json.dumps(section['results'], indent=2, ensure_ascii=False)
-                markdown_content += "\n```\n\n"
-            
-            markdown_content += "---\n\n"
-        
-        # Add technical specifications
-        markdown_content += f"""## Technical Specifications
-
-- **Graph Database**: Azure Cosmos DB with Gremlin API
-- **AI Model**: OpenAI GPT-4 for natural language processing
-- **Query Language**: Gremlin for graph traversal
-- **Data Format**: Handles both simple dictionaries and Cosmos DB valueMap(true) format
-- **Programming Language**: Python 3.12+
-- **Key Libraries**: asyncio, azure-cosmos, openai
-
-## System Features
-
-### βœ… Core Capabilities
-- Natural language query interpretation
-- Entity and relationship search
-- Graph statistical analysis
-- Pattern recognition and matching
-- Community detection
-- Subgraph analysis
-
-### βœ… LLM Enhancements
-- Intelligent query understanding
-- Search strategy recommendation
-- Result contextualization
-- Insight generation
-- Follow-up query suggestions
-
-### βœ… Data Handling
-- Cosmos DB valueMap(true) format support
-- Backward compatibility with simple dictionaries
-- Robust error handling and fallback mechanisms
-- Performance optimization for large graphs
-
-## Demo Conclusions
-
-The Enhanced Graph Explorer successfully demonstrates sophisticated AI-powered graph analysis capabilities. The system effectively combines traditional graph database operations with modern language model intelligence to provide intuitive, natural language interfaces for complex graph exploration tasks.
-
-### Key Achievements:
-1. **Seamless Integration**: Successfully integrated graph database with LLM capabilities
-2. **Natural Interface**: Demonstrated natural language query processing
-3. **Comprehensive Analysis**: Showed multiple analysis approaches and insight generation
-4. **Robust Performance**: Handled various query types and data formats
-5. **Actionable Insights**: Generated meaningful patterns and recommendations
-
-### Future Enhancements:
-- Real-time visualization integration
-- Advanced community detection algorithms
-- Multi-hop reasoning capabilities
-- Custom domain-specific query templates
-- Interactive result exploration
-
----
-
-*This report was automatically generated by the Enhanced Graph Explorer Demo System on {datetime.now().strftime('%B %d, %Y at %I:%M %p')}*
-"""
-        
-        # Write the markdown file
-        try:
-            with open(self.output_file, 'w', encoding='utf-8') as f:
-                f.write(markdown_content)
-            
-            print(f"βœ… Demo report saved to: {self.output_file}")
-            print(f"πŸ“Š Report contains {len(self.demo_results['sections'])} sections")
-            print(f"πŸ“„ Report size: {len(markdown_content)} characters")
-            
-        except Exception as e:
-            print(f"❌ Failed to save report: {e}")
-    
-    async def run_complete_demo(self):
-        """Run the complete demonstration sequence"""
-        print("🎬 Starting Enhanced Graph Explorer Demo...")
-        print("=" * 60)
-        
-        try:
-            # Initialize system
-            if not await self.demo_initialization():
-                print("❌ Demo cannot proceed without proper initialization")
-                return False
-            
-            # Run demo sections
-            await self.demo_natural_language_search()
-            await self.demo_relationship_analysis()
-            await self.demo_graph_statistics()
-            await self.demo_advanced_queries()
-            await self.demo_llm_enhancements()
-            
-            # Generate report
-            self.generate_markdown_report()
-            
-            print("\nπŸŽ‰ Demo completed successfully!")
-            print(f"πŸ“Š {len(self.demo_results['sections'])} sections demonstrated")
-            print(f"πŸ“„ Results exported to: {self.output_file}")
-            
-            return True
-            
-        except Exception as e:
-            print(f"\n❌ Demo failed with error: {e}")
-            print("πŸ“ Error details:")
-            traceback.print_exc()
-            
-            # Still try to generate a partial report
-            try:
-                self.generate_markdown_report()
-                print(f"πŸ“„ Partial results saved to: {self.output_file}")
-            except:
-                print("❌ Could not save partial results")
-            
-            return False
-
-async def main():
-    """Main entry point for the demo"""
-    print("πŸš€ Enhanced Graph Explorer - Comprehensive Demo")
-    print("=" * 60)
-    print("🎯 This demo showcases advanced LLM-powered graph exploration")
-    print("πŸ“Š Features: Natural language search, AI insights, pattern analysis")
-    print("πŸ“„ Results will be exported to a detailed markdown report")
-    print()
-    
-    demo = GraphExplorerDemo()
-    success = await demo.run_complete_demo()
-    
-    if success:
-        print("\n✨ Demo completed successfully!")
-        print("πŸ” Check the generated markdown report for detailed results")
-    else:
-        print("\n⚠️ Demo completed with some issues")
-        print("πŸ“„ Check the generated report for available results")
-
-if __name__ == "__main__":
-    # Run the demo
-    asyncio.run(main())
diff --git a/exploration_sessions/session_20250530_090235.json b/exploration_sessions/session_20250530_090235.json
deleted file mode 100644
index 976b8b3..0000000
--- a/exploration_sessions/session_20250530_090235.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
-  "timestamp": "20250530_090235",
-  "history": [
-    {
-      "type": "entity_search",
-      "query": "show me products related to sustainability",
-      "timestamp": "2025-05-30T08:55:39.009224"
-    }
-  ],
-  "bookmarks": {
-    "entities": [],
-    "relationships": []
-  }
-}
\ No newline at end of file
diff --git a/exploration_sessions/session_20250530_095107.json b/exploration_sessions/session_20250530_095107.json
deleted file mode 100644
index 7e5eda9..0000000
--- a/exploration_sessions/session_20250530_095107.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-  "timestamp": "20250530_095107",
-  "history": [
-    {
-      "type": "entity_search",
-      "query": "find people in tech",
-      "timestamp": "2025-05-30T09:46:06.886192"
-    }
-  ],
-  "bookmarks": {
-    "entities": [
-      "azure cloud platform"
-    ],
-    "relationships": []
-  }
-}
\ No newline at end of file
diff --git a/exploration_sessions/session_20250530_112551.json b/exploration_sessions/session_20250530_112551.json
deleted file mode 100644
index 66cb896..0000000
--- a/exploration_sessions/session_20250530_112551.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-  "timestamp": "20250530_112551",
-  "history": [
-    {
-      "type": "entity_search",
-      "query": "sustainability products that are blue",
-      "timestamp": "2025-05-30T11:20:04.860725"
-    }
-  ],
-  "bookmarks": {
-    "entities": [
-      "event"
-    ],
-    "relationships": []
-  }
-}
\ No newline at end of file
diff --git a/graph_explorer_validation_20250529_234048.json b/graph_explorer_validation_20250529_234048.json
deleted file mode 100644
index 934383b..0000000
--- a/graph_explorer_validation_20250529_234048.json
+++ /dev/null
@@ -1,186 +0,0 @@
-{
-  "total_tests": 17,
-  "successful_tests": 17,
-  "success_rate": 100.0,
-  "test_results": [
-    {
-      "test": "llm_keyword_extraction",
-      "query": "Find all people who work for technology companies",
-      "keywords": [
-        "Person",
-        "works_for",
-        "Technology Company",
-        "employment relationship",
-        "company type"
-      ],
-      "success": true
-    },
-    {
-      "test": "llm_keyword_extraction",
-      "query": "Show me sustainable products and their manufacturers",
-      "keywords": [
-        "sustainable product",
-        "manufacturer",
-        "produced_by",
-        "sustainability",
-        "product-manufacturer network"
-      ],
-      "success": true
-    },
-    {
-      "test": "llm_keyword_extraction",
-      "query": "What organizations are connected to renewable energy?",
-      "keywords": [
-        "organization",
-        "renewable energy",
-        "connected to",
-        "affiliation",
-        "energy sector"
-      ],
-      "success": true
-    },
-    {
-      "test": "llm_keyword_extraction",
-      "query": "Find customers who purchased electronics recently",
-      "keywords": [
-        "customers",
-        "purchased",
-        "electronics",
-        "recently",
-        "transactions"
-      ],
-      "success": true
-    },
-    {
-      "test": "semantic_entity_search",
-      "search_term": "technology company",
-      "results_count": 5,
-      "success": true
-    },
-    {
-      "test": "semantic_entity_search",
-      "search_term": "sustainable product",
-      "results_count": 5,
-      "success": true
-    },
-    {
-      "test": "semantic_entity_search",
-      "search_term": "renewable energy",
-      "results_count": 5,
-      "success": true
-    },
-    {
-      "test": "semantic_entity_search",
-      "search_term": "customer electronics",
-      "results_count": 5,
-      "success": true
-    },
-    {
-      "test": "nl_interpretation",
-      "query": "Show me all people who work for organizations in the tech industry",
-      "interpretation": {
-        "intent": "search_entities",
-        "search_terms": [
-          "people",
-          "organizations",
-          "tech industry",
-          "works for"
-        ],
-        "strategy": "semantic_search",
-        "result_type": "specific_entities",
-        "confidence": 0.95,
-        "explanation": "The query seeks to identify all individuals (people) who are connected via a 'works for' relationship to organizations classified within the tech industry. This requires semantic understanding of industry classification and relationship traversal."
-      },
-      "success": true
-    },
-    {
-      "test": "nl_interpretation",
-      "query": "Find products that are related to environmental sustainability",
-      "interpretation": {
-        "intent": "search_entities",
-        "search_terms": [
-          "products",
-          "environmental sustainability",
-          "sustainable products",
-          "eco-friendly"
-        ],
-        "strategy": "semantic_search",
-        "result_type": "specific_entities",
-        "confidence": 0.92,
-        "explanation": "The query seeks to identify specific products associated with the concept of environmental sustainability, suggesting a semantic search for entities (products) linked to this concept."
-      },
-      "success": true
-    },
-    {
-      "test": "nl_interpretation",
-      "query": "What are the relationships between customers and their recent purchases?",
-      "interpretation": {
-        "intent": "search_relationships",
-        "search_terms": [
-          "customers",
-          "purchases",
-          "recent purchases",
-          "customer-purchase relationships"
-        ],
-        "strategy": "semantic_search",
-        "result_type": "relationship_patterns",
-        "confidence": 0.95,
-        "explanation": "The query seeks to understand how customers are connected to their recent purchases, focusing on the relationships between these entities rather than individual entities or broader community structures."
-      },
-      "success": true
-    },
-    {
-      "test": "nl_interpretation",
-      "query": "Analyze the network of suppliers and manufacturers",
-      "interpretation": {
-        "intent": "analyze_subgraphs",
-        "search_terms": [
-          "suppliers",
-          "manufacturers",
-          "network"
-        ],
-        "strategy": "broad_exploration",
-        "result_type": "relationship_patterns",
-        "confidence": 0.92,
-        "explanation": "The query seeks to analyze how suppliers and manufacturers are connected, implying a need to explore and understand the structure and patterns of their relationships within the network."
-      },
-      "success": true
-    },
-    {
-      "test": "dynamic_query_generation",
-      "search_type": "exact match",
-      "term": "Microsoft",
-      "results_count": 3,
-      "success": true
-    },
-    {
-      "test": "dynamic_query_generation",
-      "search_type": "partial match",
-      "term": "tech",
-      "results_count": 3,
-      "success": true
-    },
-    {
-      "test": "dynamic_query_generation",
-      "search_type": "semantic search",
-      "term": "innovation",
-      "results_count": 3,
-      "success": true
-    },
-    {
-      "test": "dynamic_query_generation",
-      "search_type": "broad exploration",
-      "term": "",
-      "results_count": 3,
-      "success": true
-    },
-    {
-      "test": "llm_result_enhancement",
-      "query": "Find technology companies and their products",
-      "basic_count": 6,
-      "enhanced_count": 6,
-      "has_insights": true,
-      "success": true
-    }
-  ]
-}
\ No newline at end of file
diff --git a/real_time_visualizations/enhanced_graph_visualization.html b/real_time_visualizations/enhanced_graph_visualization.html
deleted file mode 100644
index eec365d..0000000
--- a/real_time_visualizations/enhanced_graph_visualization.html
+++ /dev/null
@@ -1,2523 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-    <title>Enhanced Graph Visualization</title>
-    <script type="text/javascript" src="https://unpkg.com/vis-network/standalone/umd/vis-network.min.js"></script>
-    <style type="text/css">
-        body {
-            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
-            margin: 0;
-            padding: 20px;
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            min-height: 100vh;
-        }
-        
-        .container {
-            background: white;
-            border-radius: 15px;
-            box-shadow: 0 20px 40px rgba(0,0,0,0.1);
-            padding: 30px;
-            max-width: 1400px;
-            margin: 0 auto;
-        }
-        
-        h1 {
-            color: #2c3e50;
-            text-align: center;
-            margin-bottom: 30px;
-            font-size: 2.5em;
-            background: linear-gradient(45deg, #667eea, #764ba2);
-            -webkit-background-clip: text;
-            -webkit-text-fill-color: transparent;
-            background-clip: text;
-        }
-        
-        .controls {
-            display: flex;
-            justify-content: center;
-            gap: 15px;
-            margin-bottom: 20px;
-            flex-wrap: wrap;
-        }
-        
-        .btn {
-            padding: 10px 20px;
-            border: none;
-            border-radius: 25px;
-            cursor: pointer;
-            font-weight: 600;
-            transition: all 0.3s ease;
-            background: linear-gradient(45deg, #667eea, #764ba2);
-            color: white;
-        }
-        
-        .btn:hover {
-            transform: translateY(-2px);
-            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
-        }
-        
-        .stats {
-            display: flex;
-            justify-content: space-around;
-            margin-bottom: 20px;
-            text-align: center;
-        }
-        
-        .stat {
-            background: #f8f9fa;
-            padding: 15px;
-            border-radius: 10px;
-            box-shadow: 0 2px 10px rgba(0,0,0,0.05);
-        }
-        
-        .stat-number {
-            font-size: 2em;
-            font-weight: bold;
-            color: #667eea;
-        }
-        
-        .stat-label {
-            color: #6c757d;
-            font-size: 0.9em;
-        }
-        
-        #networkContainer {
-            width: 100%;
-            height: 600px;
-            border: 2px solid #e9ecef;
-            border-radius: 10px;
-            background: #fafafa;
-        }
-        
-        .legend {
-            margin-top: 20px;
-            padding: 20px;
-            background: #f8f9fa;
-            border-radius: 10px;
-        }
-        
-        .legend-title {
-            font-weight: bold;
-            margin-bottom: 10px;
-            color: #2c3e50;
-        }
-        
-        .legend-item {
-            display: inline-block;
-            margin: 5px 10px;
-            padding: 5px 10px;
-            border-radius: 15px;
-            font-size: 0.9em;
-            color: white;
-            font-weight: 500;
-        }
-        
-        .community-list {
-            margin-top: 20px;
-        }
-        
-        .community-item {
-            background: #f8f9fa;
-            margin: 10px 0;
-            padding: 15px;
-            border-radius: 10px;
-            border-left: 4px solid #667eea;
-        }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <h1>πŸ•ΈοΈ Enhanced Graph Visualization</h1>
-        
-        <div class="stats">
-            <div class="stat">
-                <div class="stat-number">50</div>
-                <div class="stat-label">Entities</div>
-            </div>
-            <div class="stat">
-                <div class="stat-number">142</div>
-                <div class="stat-label">Relationships</div>
-            </div>
-            <div class="stat">
-                <div class="stat-number">4</div>
-                <div class="stat-label">Communities</div>
-            </div>
-        </div>
-        
-        <div class="controls">
-            <button class="btn" onclick="network.fit()">πŸ” Fit to Screen</button>
-            <button class="btn" onclick="togglePhysics()">⚑ Toggle Physics</button>
-            <button class="btn" onclick="highlightCommunities()">🏘️ Highlight Communities</button>
-            <button class="btn" onclick="resetView()">πŸ”„ Reset View</button>
-        </div>
-        
-        <div id="networkContainer"></div>
-        
-        <div class="legend">
-            <div class="legend-title">Entity Types:</div>
-            <span class="legend-item" style="background-color: #E74C3C;">πŸ‘€ Person</span>
-            <span class="legend-item" style="background-color: #3498DB;">🏒 Organization</span>
-            <span class="legend-item" style="background-color: #2ECC71;">πŸ“¦ Product</span>
-            <span class="legend-item" style="background-color: #F39C12;">πŸ“ Location</span>
-            <span class="legend-item" style="background-color: #9B59B6;">🎯 Event</span>
-            <span class="legend-item" style="background-color: #1ABC9C;">πŸ’‘ Concept</span>
-            <span class="legend-item" style="background-color: #34495E;">βš™οΈ Technology</span>
-        </div>
-        
-        <div class="community-list">
-            <div class="legend-title">Detected Communities:</div>
-            
-            <div class="community-item">
-                <strong>Person Community</strong> (15 entities)<br>
-                <small>Members: John Smith, Sarah Johnson, Alice Williams, Sarah Williams, Marcus Chen...</small>
-            </div>
-            
-            <div class="community-item">
-                <strong>Organization Community</strong> (10 entities)<br>
-                <small>Members: Microsoft, Azure team, OpenAI, AI ethics committee, Stanford University...</small>
-            </div>
-            
-            <div class="community-item">
-                <strong>Product Community</strong> (14 entities)<br>
-                <small>Members: Copilot, GPT-5, autonomous vehicles, autonomous driving feature, Starship...</small>
-            </div>
-            
-            <div class="community-item">
-                <strong>Concept Community</strong> (11 entities)<br>
-                <small>Members: language models, transformer architectures, responsible AI guidelines, safety protocols, reusable rocket technology...</small>
-            </div>
-            
-        </div>
-    </div>
-
-    <script type="text/javascript">        // Network data
-        const nodes = new vis.DataSet([
-        {
-                "id": "entity_john_smith",
-                "label": "John Smith",
-                "title": "John Smith (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_microsoft",
-                "label": "Microsoft",
-                "title": "Microsoft (organization)",
-                "group": "organization",
-                "color": "#3498DB",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_copilot",
-                "label": "Copilot",
-                "title": "Copilot (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_sarah_johnson",
-                "label": "Sarah Johnson",
-                "title": "Sarah Johnson (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_azure_team",
-                "label": "Azure team",
-                "title": "Azure team (organization)",
-                "group": "organization",
-                "color": "#3498DB",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_alice_williams",
-                "label": "Alice Williams",
-                "title": "Alice Williams (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_openai",
-                "label": "OpenAI",
-                "title": "OpenAI (organization)",
-                "group": "organization",
-                "color": "#3498DB",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_language_models",
-                "label": "language models",
-                "title": "language models (concept)",
-                "group": "concept",
-                "color": "#1ABC9C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_transformer_architectures",
-                "label": "transformer architectures",
-                "title": "transformer architectures (concept)",
-                "group": "concept",
-                "color": "#1ABC9C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_sarah_williams",
-                "label": "Sarah Williams",
-                "title": "Sarah Williams (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_gpt-5",
-                "label": "GPT-5",
-                "title": "GPT-5 (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_marcus_chen",
-                "label": "Marcus Chen",
-                "title": "Marcus Chen (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_dr._elena_rodriguez",
-                "label": "Dr. Elena Rodriguez",
-                "title": "Dr. Elena Rodriguez (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_ai_ethics_committee",
-                "label": "AI ethics committee",
-                "title": "AI ethics committee (organization)",
-                "group": "organization",
-                "color": "#3498DB",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_stanford_university",
-                "label": "Stanford University",
-                "title": "Stanford University (organization)",
-                "group": "organization",
-                "color": "#3498DB",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_professor_james_kim",
-                "label": "Professor James Kim",
-                "title": "Professor James Kim (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_computer_science_department",
-                "label": "computer science department",
-                "title": "computer science department (organization)",
-                "group": "organization",
-                "color": "#3498DB",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_responsible_ai_guidelines",
-                "label": "responsible AI guidelines",
-                "title": "responsible AI guidelines (concept)",
-                "group": "concept",
-                "color": "#1ABC9C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_autonomous_vehicles",
-                "label": "autonomous vehicles",
-                "title": "autonomous vehicles (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_tesla",
-                "label": "Tesla",
-                "title": "Tesla (organization)",
-                "group": "organization",
-                "color": "#3498DB",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_autonomous_driving_feature",
-                "label": "autonomous driving feature",
-                "title": "autonomous driving feature (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_elon_musk",
-                "label": "Elon Musk",
-                "title": "Elon Musk (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_engineering_team",
-                "label": "engineering team",
-                "title": "engineering team (organization)",
-                "group": "organization",
-                "color": "#3498DB",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_safety_protocols",
-                "label": "safety protocols",
-                "title": "safety protocols (concept)",
-                "group": "concept",
-                "color": "#1ABC9C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_spacex",
-                "label": "SpaceX",
-                "title": "SpaceX (organization)",
-                "group": "organization",
-                "color": "#3498DB",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_starship",
-                "label": "Starship",
-                "title": "Starship (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_reusable_rocket_technology",
-                "label": "reusable rocket technology",
-                "title": "reusable rocket technology (concept)",
-                "group": "concept",
-                "color": "#1ABC9C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_cust_001",
-                "label": "CUST_001",
-                "title": "CUST_001 (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_emma_thompson",
-                "label": "Emma Thompson",
-                "title": "Emma Thompson (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_sneakers",
-                "label": "sneakers",
-                "title": "sneakers (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_sustainability_filter",
-                "label": "Sustainability filter",
-                "title": "Sustainability filter (concept)",
-                "group": "concept",
-                "color": "#1ABC9C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_cust_002",
-                "label": "CUST_002",
-                "title": "CUST_002 (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_urban_classic_loafers",
-                "label": "Urban Classic Loafers",
-                "title": "Urban Classic Loafers (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_professional_urban",
-                "label": "professional_urban",
-                "title": "professional_urban (concept)",
-                "group": "concept",
-                "color": "#1ABC9C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_website",
-                "label": "website",
-                "title": "website (concept)",
-                "group": "concept",
-                "color": "#1ABC9C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_cust_003",
-                "label": "CUST_003",
-                "title": "CUST_003 (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_sofia_rodriguez",
-                "label": "Sofia Rodriguez",
-                "title": "Sofia Rodriguez (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_hiking_boots",
-                "label": "hiking_boots",
-                "title": "hiking_boots (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_support",
-                "label": "support",
-                "title": "support (organization)",
-                "group": "organization",
-                "color": "#3498DB",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_sizing",
-                "label": "sizing",
-                "title": "sizing (concept)",
-                "group": "concept",
-                "color": "#1ABC9C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_boots",
-                "label": "boots",
-                "title": "boots (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_ecowalk_sustainable_sneakers",
-                "label": "EcoWalk Sustainable Sneakers",
-                "title": "EcoWalk Sustainable Sneakers (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_eco_conscious_millennials",
-                "label": "eco_conscious_millennials",
-                "title": "eco_conscious_millennials (concept)",
-                "group": "concept",
-                "color": "#1ABC9C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_mobile_app",
-                "label": "mobile_app",
-                "title": "mobile_app (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_dress_shoes",
-                "label": "dress_shoes",
-                "title": "dress_shoes (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_engagement_score",
-                "label": "Engagement score",
-                "title": "Engagement score (concept)",
-                "group": "concept",
-                "color": "#1ABC9C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_customer_cust_002",
-                "label": "Customer CUST_002",
-                "title": "Customer CUST_002 (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_loafers",
-                "label": "loafers",
-                "title": "loafers (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_executive_dress_shoes",
-                "label": "Executive Dress Shoes",
-                "title": "Executive Dress Shoes (product)",
-                "group": "product",
-                "color": "#2ECC71",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        },
-        {
-                "id": "entity_customer_cust_003",
-                "label": "Customer CUST_003",
-                "title": "Customer CUST_003 (person)",
-                "group": "person",
-                "color": "#E74C3C",
-                "size": 20,
-                "font": {
-                        "size": 12
-                }
-        }
-]);
-        const edges = new vis.DataSet([
-        {
-                "from": "entity_john_smith",
-                "to": "entity_microsoft",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_john_smith",
-                "to": "entity_copilot",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_john_smith",
-                "to": "entity_sarah_johnson",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_john_smith",
-                "to": "entity_azure_team",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_microsoft",
-                "to": "entity_copilot",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_microsoft",
-                "to": "entity_sarah_johnson",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_microsoft",
-                "to": "entity_azure_team",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_copilot",
-                "to": "entity_sarah_johnson",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_copilot",
-                "to": "entity_azure_team",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sarah_johnson",
-                "to": "entity_azure_team",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_alice_williams",
-                "to": "entity_openai",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_alice_williams",
-                "to": "entity_language_models",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_alice_williams",
-                "to": "entity_transformer_architectures",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_openai",
-                "to": "entity_language_models",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_openai",
-                "to": "entity_transformer_architectures",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_openai",
-                "to": "entity_sarah_williams",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_openai",
-                "to": "entity_gpt-5",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_openai",
-                "to": "entity_marcus_chen",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_language_models",
-                "to": "entity_transformer_architectures",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sarah_williams",
-                "to": "entity_gpt-5",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sarah_williams",
-                "to": "entity_marcus_chen",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_gpt-5",
-                "to": "entity_marcus_chen",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_marcus_chen",
-                "to": "entity_sustainability_filter",
-                "width": 10,
-                "label": "(25)",
-                "title": "Co-occurrence weight: 25",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_marcus_chen",
-                "to": "entity_cust_002",
-                "width": 10,
-                "label": "(17)",
-                "title": "Co-occurrence weight: 17",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_marcus_chen",
-                "to": "entity_professional_urban",
-                "width": 4,
-                "label": "(2)",
-                "title": "Co-occurrence weight: 2",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_marcus_chen",
-                "to": "entity_dress_shoes",
-                "width": 10,
-                "label": "(15)",
-                "title": "Co-occurrence weight: 15",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_marcus_chen",
-                "to": "entity_engagement_score",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_marcus_chen",
-                "to": "entity_customer_cust_002",
-                "width": 10,
-                "label": "(12)",
-                "title": "Co-occurrence weight: 12",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_marcus_chen",
-                "to": "entity_loafers",
-                "width": 10,
-                "label": "(13)",
-                "title": "Co-occurrence weight: 13",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_dr._elena_rodriguez",
-                "to": "entity_ai_ethics_committee",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_dr._elena_rodriguez",
-                "to": "entity_stanford_university",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_dr._elena_rodriguez",
-                "to": "entity_professor_james_kim",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_dr._elena_rodriguez",
-                "to": "entity_computer_science_department",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_dr._elena_rodriguez",
-                "to": "entity_responsible_ai_guidelines",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_dr._elena_rodriguez",
-                "to": "entity_autonomous_vehicles",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_ai_ethics_committee",
-                "to": "entity_stanford_university",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_ai_ethics_committee",
-                "to": "entity_professor_james_kim",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_ai_ethics_committee",
-                "to": "entity_computer_science_department",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_ai_ethics_committee",
-                "to": "entity_responsible_ai_guidelines",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_ai_ethics_committee",
-                "to": "entity_autonomous_vehicles",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_stanford_university",
-                "to": "entity_professor_james_kim",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_stanford_university",
-                "to": "entity_computer_science_department",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_stanford_university",
-                "to": "entity_responsible_ai_guidelines",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_stanford_university",
-                "to": "entity_autonomous_vehicles",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_professor_james_kim",
-                "to": "entity_computer_science_department",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_professor_james_kim",
-                "to": "entity_responsible_ai_guidelines",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_professor_james_kim",
-                "to": "entity_autonomous_vehicles",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_computer_science_department",
-                "to": "entity_responsible_ai_guidelines",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_computer_science_department",
-                "to": "entity_autonomous_vehicles",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_responsible_ai_guidelines",
-                "to": "entity_autonomous_vehicles",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_tesla",
-                "to": "entity_autonomous_driving_feature",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_tesla",
-                "to": "entity_elon_musk",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_tesla",
-                "to": "entity_engineering_team",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_tesla",
-                "to": "entity_safety_protocols",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_autonomous_driving_feature",
-                "to": "entity_elon_musk",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_autonomous_driving_feature",
-                "to": "entity_engineering_team",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_autonomous_driving_feature",
-                "to": "entity_safety_protocols",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_elon_musk",
-                "to": "entity_engineering_team",
-                "width": 4,
-                "label": "(2)",
-                "title": "Co-occurrence weight: 2",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_elon_musk",
-                "to": "entity_safety_protocols",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_elon_musk",
-                "to": "entity_spacex",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_elon_musk",
-                "to": "entity_starship",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_elon_musk",
-                "to": "entity_reusable_rocket_technology",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_engineering_team",
-                "to": "entity_safety_protocols",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_engineering_team",
-                "to": "entity_spacex",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_engineering_team",
-                "to": "entity_starship",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_engineering_team",
-                "to": "entity_reusable_rocket_technology",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_spacex",
-                "to": "entity_starship",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_spacex",
-                "to": "entity_reusable_rocket_technology",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_starship",
-                "to": "entity_reusable_rocket_technology",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_001",
-                "to": "entity_emma_thompson",
-                "width": 10,
-                "label": "(21)",
-                "title": "Co-occurrence weight: 21",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_001",
-                "to": "entity_sneakers",
-                "width": 10,
-                "label": "(6)",
-                "title": "Co-occurrence weight: 6",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_001",
-                "to": "entity_sustainability_filter",
-                "width": 10,
-                "label": "(19)",
-                "title": "Co-occurrence weight: 19",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_001",
-                "to": "entity_website",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_001",
-                "to": "entity_support",
-                "width": 10,
-                "label": "(14)",
-                "title": "Co-occurrence weight: 14",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_001",
-                "to": "entity_sizing",
-                "width": 4,
-                "label": "(2)",
-                "title": "Co-occurrence weight: 2",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_001",
-                "to": "entity_boots",
-                "width": 10,
-                "label": "(15)",
-                "title": "Co-occurrence weight: 15",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_001",
-                "to": "entity_ecowalk_sustainable_sneakers",
-                "width": 10,
-                "label": "(7)",
-                "title": "Co-occurrence weight: 7",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_001",
-                "to": "entity_eco_conscious_millennials",
-                "width": 10,
-                "label": "(9)",
-                "title": "Co-occurrence weight: 9",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_001",
-                "to": "entity_mobile_app",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_001",
-                "to": "entity_engagement_score",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_emma_thompson",
-                "to": "entity_sneakers",
-                "width": 10,
-                "label": "(11)",
-                "title": "Co-occurrence weight: 11",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_emma_thompson",
-                "to": "entity_sustainability_filter",
-                "width": 10,
-                "label": "(29)",
-                "title": "Co-occurrence weight: 29",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_emma_thompson",
-                "to": "entity_boots",
-                "width": 10,
-                "label": "(21)",
-                "title": "Co-occurrence weight: 21",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_emma_thompson",
-                "to": "entity_eco_conscious_millennials",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_emma_thompson",
-                "to": "entity_engagement_score",
-                "width": 10,
-                "label": "(8)",
-                "title": "Co-occurrence weight: 8",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sneakers",
-                "to": "entity_sustainability_filter",
-                "width": 10,
-                "label": "(11)",
-                "title": "Co-occurrence weight: 11",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sneakers",
-                "to": "entity_engagement_score",
-                "width": 10,
-                "label": "(5)",
-                "title": "Co-occurrence weight: 5",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sustainability_filter",
-                "to": "entity_cust_002",
-                "width": 10,
-                "label": "(16)",
-                "title": "Co-occurrence weight: 16",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sustainability_filter",
-                "to": "entity_cust_003",
-                "width": 10,
-                "label": "(27)",
-                "title": "Co-occurrence weight: 27",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sustainability_filter",
-                "to": "entity_sofia_rodriguez",
-                "width": 10,
-                "label": "(29)",
-                "title": "Co-occurrence weight: 29",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sustainability_filter",
-                "to": "entity_hiking_boots",
-                "width": 10,
-                "label": "(14)",
-                "title": "Co-occurrence weight: 14",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sustainability_filter",
-                "to": "entity_boots",
-                "width": 10,
-                "label": "(18)",
-                "title": "Co-occurrence weight: 18",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sustainability_filter",
-                "to": "entity_dress_shoes",
-                "width": 10,
-                "label": "(13)",
-                "title": "Co-occurrence weight: 13",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sustainability_filter",
-                "to": "entity_engagement_score",
-                "width": 10,
-                "label": "(10)",
-                "title": "Co-occurrence weight: 10",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sustainability_filter",
-                "to": "entity_customer_cust_002",
-                "width": 10,
-                "label": "(10)",
-                "title": "Co-occurrence weight: 10",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sustainability_filter",
-                "to": "entity_loafers",
-                "width": 10,
-                "label": "(12)",
-                "title": "Co-occurrence weight: 12",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_sustainability_filter",
-                "to": "entity_customer_cust_003",
-                "width": 4,
-                "label": "(2)",
-                "title": "Co-occurrence weight: 2",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_002",
-                "to": "entity_urban_classic_loafers",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_002",
-                "to": "entity_professional_urban",
-                "width": 10,
-                "label": "(10)",
-                "title": "Co-occurrence weight: 10",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_002",
-                "to": "entity_website",
-                "width": 8,
-                "label": "(4)",
-                "title": "Co-occurrence weight: 4",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_002",
-                "to": "entity_support",
-                "width": 10,
-                "label": "(5)",
-                "title": "Co-occurrence weight: 5",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_002",
-                "to": "entity_sizing",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_002",
-                "to": "entity_mobile_app",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_002",
-                "to": "entity_dress_shoes",
-                "width": 10,
-                "label": "(12)",
-                "title": "Co-occurrence weight: 12",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_002",
-                "to": "entity_engagement_score",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_002",
-                "to": "entity_customer_cust_002",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_002",
-                "to": "entity_loafers",
-                "width": 10,
-                "label": "(5)",
-                "title": "Co-occurrence weight: 5",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_cust_002",
-                "to": "entity_executive_dress_shoes",
-                "width": 10,
-                "label": "(7)",
-                "title": "Co-occurrence weight: 7",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_urban_classic_loafers",
-                "to": "entity_professional_urban",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_urban_classic_loafers",
-                "to": "entity_website",
-                "width": 4,
-                "label": "(2)",
-                "title": "Co-occurrence weight: 2",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_professional_urban",
-                "to": "entity_website",
-                "width": 8,
-                "label": "(4)",
-                "title": "Co-occurrence weight: 4",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_professional_urban",
-                "to": "entity_mobile_app",
-                "width": 6,
-                "label": "(3)",
-                "title": "Co-occurrence weight: 3",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_professional_urban",
-                "to": "entity_dress_shoes",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_professional_urban",
-                "to": "entity_customer_cust_002",
-                "width": 4,
-                "label": "(2)",
-                "title": "Co-occurrence weight: 2",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
-                "from": "entity_professional_urban",
-                "to": "entity_loafers",
-                "width": 2,
-                "label": "(1)",
-                "title": "Co-occurrence weight: 1",
-                "color": {
-                        "color": "#848484",
-                        "opacity": 0.6
-                }
-        },
-        {
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-                "from": "entity_website",
-                "to": "entity_cust_003",
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-                "label": "(3)",
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-                "from": "entity_website",
-                "to": "entity_ecowalk_sustainable_sneakers",
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-                "label": "(3)",
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-                "label": "(7)",
-                "title": "Co-occurrence weight: 7",
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-                "title": "Co-occurrence weight: 3",
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-                        "color": "#848484",
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-                        "color": "#848484",
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-                        "color": "#848484",
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-                "id": "community_0",
-                "name": "Person Community",
-                "type": "person",
-                "entities": [
-                        "entity_john_smith",
-                        "entity_sarah_johnson",
-                        "entity_alice_williams",
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-                        "entity_marcus_chen",
-                        "entity_dr._elena_rodriguez",
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-                        "entity_starship",
-                        "entity_sneakers",
-                        "entity_urban_classic_loafers",
-                        "entity_hiking_boots",
-                        "entity_boots",
-                        "entity_ecowalk_sustainable_sneakers",
-                        "entity_mobile_app",
-                        "entity_dress_shoes",
-                        "entity_loafers",
-                        "entity_executive_dress_shoes"
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-                "size": 14,
-                "color": "#45B7D1"
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-                "type": "concept",
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-                        "entity_language_models",
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-                        "entity_responsible_ai_guidelines",
-                        "entity_safety_protocols",
-                        "entity_reusable_rocket_technology",
-                        "entity_sustainability_filter",
-                        "entity_professional_urban",
-                        "entity_website",
-                        "entity_sizing",
-                        "entity_eco_conscious_millennials",
-                        "entity_engagement_score"
-                ],
-                "size": 11,
-                "color": "#FF9FF3"
-        }
-];
-        
-        // Network options
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-                enabled: true,
-                stabilization: { iterations: 100 },
-                barnesHut: {
-                    gravitationalConstant: -2000,
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-                improvedLayout: true
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-        
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-        const network = new vis.Network(container, data, options);
-        
-        // Global variables
-        let physicsEnabled = true;
-        let originalNodeColors = {};
-        let originalEdgeColors = {};
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-        // Store original colors
-        nodes.forEach(node => {
-            originalNodeColors[node.id] = node.color;
-        });
-        edges.forEach(edge => {
-            originalEdgeColors[edge.id] = edge.color;
-        });
-        
-        // Network event listeners
-        network.on("selectNode", function (params) {
-            if (params.nodes.length > 0) {
-                const nodeId = params.nodes[0];
-                const node = nodes.get(nodeId);
-                console.log('Selected node:', node);
-            }
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-        network.on("selectEdge", function (params) {
-            if (params.edges.length > 0) {
-                const edgeId = params.edges[0];
-                const edge = edges.get(edgeId);
-                console.log('Selected edge:', edge);
-            }
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-        
-        // Control functions
-        function togglePhysics() {
-            physicsEnabled = !physicsEnabled;
-            network.setOptions({ physics: { enabled: physicsEnabled } });
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-        function highlightCommunities() {
-            // Reset all nodes to light gray
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-            
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-        // Auto-fit on load
-        network.once("stabilizationIterationsDone", function() {
-            network.fit();
-        });
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-        console.log('Enhanced Graph Visualization loaded successfully!');
-        console.log('Entities:', 50);
-        console.log('Relationships:', 142);
-        console.log('Communities:', 4);
-    </script>
-</body>
-</html>
\ No newline at end of file
diff --git a/real_time_visualizations/graph_visualization.html b/real_time_visualizations/graph_visualization.html
deleted file mode 100644
index 8f810c3..0000000
--- a/real_time_visualizations/graph_visualization.html
+++ /dev/null
@@ -1,324 +0,0 @@
-
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Knowledge Graph - Graphiti-Cosmos</title>
-    <script src="https://unpkg.com/vis-network@latest/dist/vis-network.min.js"></script>
-    <style>
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-            color: #333;
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-            max-width: 1400px;
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-            background: #f8f9fa;
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-            height: 20px;
-            border-radius: 50%;
-        }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <div class="header">
-            <h1>πŸ“Š Knowledge Graph</h1>
-            <p>Real-time Knowledge Graph Visualization</p>
-        </div>
-        
-        <div class="stats">
-            <div class="stat">
-                <div class="stat-number">194</div>
-                <div class="stat-label">Entities</div>
-            </div>
-            <div class="stat">
-                <div class="stat-number">3729</div>
-                <div class="stat-label">Relationships</div>
-            </div>
-            <div class="stat">
-                <div class="stat-number">442</div>
-                <div class="stat-label">Episodes</div>
-            </div>
-            <div class="stat">
-                <div class="stat-number">0</div>
-                <div class="stat-label">Total Nodes</div>
-            </div>
-        </div>
-        
-        <div class="controls">
-            <button onclick="refreshVisualization()">πŸ”„ Refresh Now</button>
-            <button onclick="toggleAutoRefresh()" id="autoRefreshBtn">⏸️ Pause Auto-Refresh</button>
-            <button onclick="resetView()">🏠 Reset View</button>
-            <button onclick="togglePhysics()" id="physicsBtn">⚑ Disable Physics</button>
-            <button onclick="exportGraph()">πŸ’Ύ Export</button>
-            <input type="text" id="searchBox" class="search-box" placeholder="Search nodes..." onkeyup="searchNodes()">
-        </div>
-        
-        <div id="graph-container"></div>
-        
-        <div class="info-panel">
-            <div class="legend">
-                <div class="legend-item">
-                    <div class="legend-color" style="background-color: #4285F4;"></div>
-                    <span>Person</span>
-                </div>
-                <div class="legend-item">
-                    <div class="legend-color" style="background-color: #EA4335;"></div>
-                    <span>Organization</span>
-                </div>
-                <div class="legend-item">
-                    <div class="legend-color" style="background-color: #34A853;"></div>
-                    <span>Product</span>
-                </div>
-                <div class="legend-item">
-                    <div class="legend-color" style="background-color: #FBBC05;"></div>
-                    <span>Concept</span>
-                </div>
-                <div class="legend-item">
-                    <div class="legend-color" style="background-color: #9C27B0;"></div>
-                    <span>Location</span>
-                </div>
-                <div class="legend-item">
-                    <div class="legend-color" style="background-color: #FF9800;"></div>
-                    <span>Event</span>
-                </div>
-            </div>
-            
-            <div class="update-info">
-                Last updated: 2025-05-29 07:24:44 | 
-                Update #1 | 
-                Auto-refresh: OFF
-            </div>
-        </div>
-    </div>
-
-    <script>
-        // Graph data
-        const nodes = new vis.DataSet([{"id": "entity_john_smith", "label": "John Smith", "title": "Name: John Smith\\nType: person\\nDescription: An individual working at Microsoft and developing a new AI product called Copilot.", "color": "#4285F4", "group": "person"}, {"id": "entity_microsoft", "label": "Microsoft", "title": "Name: Microsoft\\nType: organization\\nDescription: A multinational technology company that develops, licenses, and sells computer software, electronics", "color": "#EA4335", "group": "organization"}, {"id": "entity_copilot", "label": "Copilot", "title": "Name: Copilot\\nType: product\\nDescription: A new AI product being developed by John Smith at Microsoft.", "color": "#34A853", "group": "product"}, {"id": "entity_sarah_johnson", "label": "Sarah Johnson", "title": "Name: Sarah Johnson\\nType: person\\nDescription: A person who viewed the product.", "color": "#4285F4", "group": "person"}, {"id": "entity_azure_team", "label": "Azure team", "title": "Name: Azure team\\nType: organization\\nDescription: A team at Microsoft focused on Azure, represented by Sarah Johnson.", "color": "#EA4335", "group": "organization"}, {"id": "entity_alice_williams", "label": "Alice Williams", "title": "Name: Alice Williams\\nType: person\\nDescription: A data scientist at OpenAI working on language models.", "color": "#4285F4", "group": "person"}, {"id": "entity_openai", "label": "OpenAI", "title": "Name: OpenAI\\nType: organization\\nDescription: An artificial intelligence research organization where Alice Williams works.", "color": "#EA4335", "group": "organization"}, {"id": "entity_language_models", "label": "language models", "title": "Name: language models\\nType: concept\\nDescription: A type of artificial intelligence model that processes and generates human language.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_transformer_architectures", "label": "transformer architectures", "title": "Name: transformer architectures\\nType: concept\\nDescription: A neural network architecture used in natural language processing, discussed in Alice Williams's pap", "color": "#FBBC05", "group": "concept"}, {"id": "entity_sarah_williams", "label": "Sarah Williams", "title": "Name: Sarah Williams\\nType: person\\nDescription: A data scientist at OpenAI working on GPT-5.", "color": "#4285F4", "group": "person"}, {"id": "entity_gpt-5", "label": "GPT-5", "title": "Name: GPT-5\\nType: product\\nDescription: A language model developed by OpenAI.", "color": "#34A853", "group": "product"}, {"id": "entity_marcus_chen", "label": "Marcus Chen", "title": "Name: Marcus Chen\\nType: person\\nDescription: The name of the customer associated with CUST_002.", "color": "#4285F4", "group": "person"}, {"id": "entity_dr._elena_rodriguez", "label": "Dr. Elena Rodriguez", "title": "Name: Dr. Elena Rodriguez\\nType: person\\nDescription: Leader of the AI ethics committee at Stanford University", "color": "#4285F4", "group": "person"}, {"id": "entity_ai_ethics_committee", "label": "AI ethics committee", "title": "Name: AI ethics committee\\nType: organization\\nDescription: A committee focused on ethical issues related to artificial intelligence at Stanford University", "color": "#EA4335", "group": "organization"}, {"id": "entity_stanford_university", "label": "Stanford University", "title": "Name: Stanford University\\nType: organization\\nDescription: A prestigious university located in California, known for its research and academic programs", "color": "#EA4335", "group": "organization"}, {"id": "entity_professor_james_kim", "label": "Professor James Kim", "title": "Name: Professor James Kim\\nType: person\\nDescription: A professor in the computer science department who collaborates on responsible AI guidelines", "color": "#4285F4", "group": "person"}, {"id": "entity_computer_science_department", "label": "computer science department", "title": "Name: computer science department\\nType: organization\\nDescription: An academic department at Stanford University specializing in computer science", "color": "#EA4335", "group": "organization"}, {"id": "entity_responsible_ai_guidelines", "label": "responsible AI guidelines", "title": "Name: responsible AI guidelines\\nType: concept\\nDescription: Principles and recommendations for the ethical development and use of artificial intelligence", "color": "#FBBC05", "group": "concept"}, {"id": "entity_autonomous_vehicles", "label": "autonomous vehicles", "title": "Name: autonomous vehicles\\nType: product\\nDescription: Self-driving vehicles that operate without human intervention", "color": "#34A853", "group": "product"}, {"id": "entity_tesla", "label": "Tesla", "title": "Name: Tesla\\nType: organization\\nDescription: An American electric vehicle and clean energy company.", "color": "#EA4335", "group": "organization"}, {"id": "entity_autonomous_driving_feature", "label": "autonomous driving feature", "title": "Name: autonomous driving feature\\nType: product\\nDescription: A new feature announced by Tesla that enables vehicles to drive themselves.", "color": "#34A853", "group": "product"}, {"id": "entity_elon_musk", "label": "Elon Musk", "title": "Name: Elon Musk\\nType: person\\nDescription: CEO and founder of SpaceX.", "color": "#4285F4", "group": "person"}, {"id": "entity_engineering_team", "label": "engineering team", "title": "Name: engineering team\\nType: organization\\nDescription: The group of engineers responsible for the mission's technical success.", "color": "#EA4335", "group": "organization"}, {"id": "entity_safety_protocols", "label": "safety protocols", "title": "Name: safety protocols\\nType: concept\\nDescription: Procedures and measures designed to ensure the safety of autonomous driving features.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_spacex", "label": "SpaceX", "title": "Name: SpaceX\\nType: organization\\nDescription: A private aerospace manufacturer and space transportation company.", "color": "#EA4335", "group": "organization"}, {"id": "entity_starship", "label": "Starship", "title": "Name: Starship\\nType: product\\nDescription: A fully reusable spacecraft developed by SpaceX.", "color": "#34A853", "group": "product"}, {"id": "entity_reusable_rocket_technology", "label": "reusable rocket technology", "title": "Name: reusable rocket technology\\nType: concept\\nDescription: Technology enabling rockets to be reused for multiple launches.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_cust_001", "label": "CUST_001", "title": "Name: CUST_001\\nType: person\\nDescription: A customer identified by the code CUST_001.", "color": "#4285F4", "group": "person"}, {"id": "entity_emma_thompson", "label": "Emma Thompson", "title": "Name: Emma Thompson\\nType: person\\nDescription: The name of the customer with ID CUST_001.", "color": "#4285F4", "group": "person"}, {"id": "entity_sneakers", "label": "sneakers", "title": "Name: sneakers\\nType: product\\nDescription: Type of footwear product browsed by the customer", "color": "#34A853", "group": "product"}, {"id": "entity_sustainability_filter", "label": "Sustainability filter", "title": "Name: Sustainability filter\\nType: concept\\nDescription: A filter option related to sustainable products, which was not applied in this session.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_cust_002", "label": "CUST_002", "title": "Name: CUST_002\\nType: person\\nDescription: Customer identifier representing the purchaser.", "color": "#4285F4", "group": "person"}, {"id": "entity_urban_classic_loafers", "label": "Urban Classic Loafers", "title": "Name: Urban Classic Loafers\\nType: product\\nDescription: A type of footwear purchased by the customer", "color": "#34A853", "group": "product"}, {"id": "entity_professional_urban", "label": "professional_urban", "title": "Name: professional_urban\\nType: concept\\nDescription: Customer segment indicating a professional living in an urban area.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_website", "label": "website", "title": "Name: website\\nType: concept\\nDescription: The online purchase channel used by the customer.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_cust_003", "label": "CUST_003", "title": "Name: CUST_003\\nType: person\\nDescription: Customer identifier for Sofia Rodriguez", "color": "#4285F4", "group": "person"}, {"id": "entity_sofia_rodriguez", "label": "Sofia Rodriguez", "title": "Name: Sofia Rodriguez\\nType: person\\nDescription: Customer who browsed trail_runners", "color": "#4285F4", "group": "person"}, {"id": "entity_hiking_boots", "label": "hiking_boots", "title": "Name: hiking_boots\\nType: product\\nDescription: Footwear products designed for hiking.", "color": "#34A853", "group": "product"}, {"id": "entity_support", "label": "support", "title": "Name: support\\nType: organization\\nDescription: The customer service team handling customer inquiries and issues.", "color": "#EA4335", "group": "organization"}, {"id": "entity_sizing", "label": "sizing", "title": "Name: sizing\\nType: concept\\nDescription: The subject of the customer's inquiry, likely referring to product size or fit.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_boots", "label": "boots", "title": "Name: boots\\nType: product\\nDescription: Footwear products that the customer browsed.", "color": "#34A853", "group": "product"}, {"id": "entity_ecowalk_sustainable_sneakers", "label": "EcoWalk Sustainable Sneakers", "title": "Name: EcoWalk Sustainable Sneakers\\nType: product\\nDescription: A pair of sneakers marketed as sustainable, purchased by the customer.", "color": "#34A853", "group": "product"}, {"id": "entity_eco_conscious_millennials", "label": "eco_conscious_millennials", "title": "Name: eco_conscious_millennials\\nType: concept\\nDescription: A customer segment consisting of millennials who are environmentally conscious.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_mobile_app", "label": "mobile_app", "title": "Name: mobile_app\\nType: product\\nDescription: The digital platform used by the customer to make the purchase.", "color": "#34A853", "group": "product"}, {"id": "entity_dress_shoes", "label": "dress_shoes", "title": "Name: dress_shoes\\nType: product\\nDescription: A category of footwear products browsed by the customer.", "color": "#34A853", "group": "product"}, {"id": "entity_engagement_score", "label": "Engagement score", "title": "Name: Engagement score\\nType: concept\\nDescription: A metric indicating the level of customer interaction, with a value of 0.84 in this context", "color": "#FBBC05", "group": "concept"}, {"id": "entity_customer_cust_002", "label": "Customer CUST_002", "title": "Name: Customer CUST_002\\nType: person\\nDescription: A customer identified by the code CUST_002, also known as Marcus Chen.", "color": "#4285F4", "group": "person"}, {"id": "entity_loafers", "label": "loafers", "title": "Name: loafers\\nType: product\\nDescription: A type of shoe that the customer browsed.", "color": "#34A853", "group": "product"}, {"id": "entity_executive_dress_shoes", "label": "Executive Dress Shoes", "title": "Name: Executive Dress Shoes\\nType: product\\nDescription: A pair of formal shoes purchased by the customer.", "color": "#34A853", "group": "product"}, {"id": "entity_customer_cust_003", "label": "Customer CUST_003", "title": "Name: Customer CUST_003\\nType: person\\nDescription: A customer identified by the code CUST_003, also referred to as Sofia Rodriguez.", "color": "#4285F4", "group": "person"}, {"id": "entity_summit_pro_hiking_boots", "label": "Summit Pro Hiking Boots", "title": "Name: Summit Pro Hiking Boots\\nType: product\\nDescription: A pair of hiking boots purchased by the customer", "color": "#34A853", "group": "product"}, {"id": "entity_wool_farm_nz_001", "label": "WOOL_FARM_NZ_001", "title": "Name: WOOL_FARM_NZ_001\\nType: organization\\nDescription: A major supplier experiencing severe capacity issues due to extreme weather events.", "color": "#EA4335", "group": "organization"}, {"id": "entity_new_zealand", "label": "New Zealand", "title": "Name: New Zealand\\nType: location\\nDescription: A country in the southwestern Pacific Ocean where the supply chain node WOOL_FARM_NZ_001 is located.", "color": "#9C27B0", "group": "location"}, {"id": "entity_leather_tannery_it_001", "label": "LEATHER_TANNERY_IT_001", "title": "Name: LEATHER_TANNERY_IT_001\\nType: organization\\nDescription: A supply chain node in Italy functioning as a material processor, currently operating at 1% capacity", "color": "#EA4335", "group": "organization"}, {"id": "entity_italy", "label": "Italy", "title": "Name: Italy\\nType: location\\nDescription: The country where the supply chain node LEATHER_TANNERY_IT_001 is located.", "color": "#9C27B0", "group": "location"}, {"id": "entity_manufacturing_vn_001", "label": "MANUFACTURING_VN_001", "title": "Name: MANUFACTURING_VN_001\\nType: organization\\nDescription: A manufacturing entity that will be affected by the supplier's capacity reduction.", "color": "#EA4335", "group": "organization"}, {"id": "entity_vietnam", "label": "Vietnam", "title": "Name: Vietnam\\nType: location\\nDescription: The country where the manufacturing partner is located.", "color": "#9C27B0", "group": "location"}, {"id": "entity_distribution_de_001", "label": "DISTRIBUTION_DE_001", "title": "Name: DISTRIBUTION_DE_001\\nType: concept\\nDescription: A distribution entity that will be impacted by the supplier's capacity issues.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_germany", "label": "Germany", "title": "Name: Germany\\nType: location\\nDescription: The country where the distribution center node DISTRIBUTION_DE_001 is located.", "color": "#9C27B0", "group": "location"}, {"id": "entity_retail_partner_us_001", "label": "RETAIL_PARTNER_US_001", "title": "Name: RETAIL_PARTNER_US_001\\nType: organization\\nDescription: A supply chain node in the United States identified as a retail partner, currently operating at 1% c", "color": "#EA4335", "group": "organization"}, {"id": "entity_united_states", "label": "United States", "title": "Name: United States\\nType: location\\nDescription: The country where the supply chain node RETAIL_PARTNER_US_001 is located.", "color": "#9C27B0", "group": "location"}, {"id": "entity_quality_metrics", "label": "Quality metrics", "title": "Name: Quality metrics\\nType: concept\\nDescription: A measurement indicating the quality performance of the supply chain node, with a value of 0.89.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_sustainability_score", "label": "Sustainability score", "title": "Name: Sustainability score\\nType: concept\\nDescription: A metric representing the sustainability performance of the supply chain node, with a value of 0.85.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_on-time_delivery", "label": "On-time delivery", "title": "Name: On-time delivery\\nType: concept\\nDescription: A metric indicating the punctuality of deliveries from the supply chain node, with a value of 0.96.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_extreme_weather", "label": "extreme weather", "title": "Name: extreme weather\\nType: event\\nDescription: Severe weather conditions causing supply disruptions.", "color": "#FF9800", "group": "event"}, {"id": "entity_high_priority_materials", "label": "HIGH priority materials", "title": "Name: HIGH priority materials\\nType: product\\nDescription: Materials classified as high priority that are affected by the capacity reduction.", "color": "#34A853", "group": "product"}, {"id": "entity_supply_disruption_recovery_day_2", "label": "Supply disruption recovery day...", "title": "Name: Supply disruption recovery day 2\\nType: event\\nDescription: The second day of efforts to recover from a supply disruption.", "color": "#FF9800", "group": "event"}, {"id": "entity_quality", "label": "Quality", "title": "Name: Quality\\nType: concept\\nDescription: A measure of the standard or grade of supplied goods or services.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_supply_chain_resilience_improvements", "label": "Supply chain resilience improv...", "title": "Name: Supply chain resilience improvements\\nType: concept\\nDescription: Enhancements made to increase the ability of the supply chain to withstand and recover from disrupti", "color": "#FBBC05", "group": "concept"}, {"id": "entity_asia_pacific", "label": "Asia Pacific", "title": "Name: Asia Pacific\\nType: location\\nDescription: A geographic region comprising countries in East Asia, South Asia, Southeast Asia, and Oceania.", "color": "#9C27B0", "group": "location"}, {"id": "entity_social_media_sentiment_analysis", "label": "Social media sentiment analysi...", "title": "Name: Social media sentiment analysis\\nType: concept\\nDescription: The process of analyzing opinions and emotions expressed in social media posts.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_sentiment_score", "label": "Sentiment score", "title": "Name: Sentiment score\\nType: concept\\nDescription: A numerical value representing the positivity or negativity of social media content.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_comfort", "label": "comfort", "title": "Name: comfort\\nType: concept\\nDescription: A trending topic in the analysis, referring to the ease and pleasantness associated with the brand o", "color": "#FBBC05", "group": "concept"}, {"id": "entity_influencer_engagement", "label": "Influencer engagement", "title": "Name: Influencer engagement\\nType: concept\\nDescription: Interactions between influencers and content related to the brand.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_viral_potential", "label": "Viral potential", "title": "Name: Viral potential\\nType: concept\\nDescription: The likelihood that content will be widely shared or go viral.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_trail_runners", "label": "trail_runners", "title": "Name: trail_runners\\nType: product\\nDescription: A category of products related to running on trails", "color": "#34A853", "group": "product"}, {"id": "entity_sustainable_footwear", "label": "Sustainable footwear", "title": "Name: Sustainable footwear\\nType: product\\nDescription: Footwear products designed and manufactured with environmentally friendly materials and processes.", "color": "#34A853", "group": "product"}, {"id": "entity_consumer_education", "label": "consumer_education", "title": "Name: consumer_education\\nType: concept\\nDescription: Efforts to inform and educate consumers, identified as a key driver for increased demand.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_brand_mention", "label": "Brand mention", "title": "Name: Brand mention\\nType: concept\\nDescription: References to a brand within social media posts.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_europe", "label": "Europe", "title": "Name: Europe\\nType: location\\nDescription: A geographic region where sentiment concentration is noted in the analysis.", "color": "#9C27B0", "group": "location"}, {"id": "entity_environmental_awareness", "label": "environmental_awareness", "title": "Name: environmental_awareness\\nType: concept\\nDescription: The recognition and understanding of environmental issues and the impact of human activities on the ", "color": "#FBBC05", "group": "concept"}, {"id": "entity_style", "label": "style", "title": "Name: style\\nType: concept\\nDescription: A key topic identified in the sentiment analysis, likely referring to fashion or manner of expressio", "color": "#FBBC05", "group": "concept"}, {"id": "entity_corporate_sustainability", "label": "corporate_sustainability", "title": "Name: corporate_sustainability\\nType: concept\\nDescription: Business practices and strategies that prioritize environmental and social responsibility.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_durability", "label": "durability", "title": "Name: durability\\nType: concept\\nDescription: A trending topic in the analysis, referring to the long-lasting quality of the brand or product.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_trail_runner_performance", "label": "Trail Runner Performance", "title": "Name: Trail Runner Performance\\nType: product\\nDescription: A product purchased by the customer, likely related to outdoor or athletic activities.", "color": "#34A853", "group": "product"}, {"id": "entity_home_&_garden", "label": "Home & Garden", "title": "Name: Home & Garden\\nType: concept\\nDescription: A category of products related to home improvement and gardening.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_manybirds", "label": "Manybirds", "title": "Name: Manybirds\\nType: organization\\nDescription: Brand of the hiking boots and operator of the online store where the purchase was made.", "color": "#EA4335", "group": "organization"}, {"id": "entity_sustainability", "label": "sustainability", "title": "Name: sustainability\\nType: concept\\nDescription: A key topic trending in the sentiment analysis, referring to environmentally friendly and resource-e", "color": "#FBBC05", "group": "concept"}, {"id": "entity_delivery", "label": "delivery", "title": "Name: delivery\\nType: concept\\nDescription: The process of transporting goods to a customer.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_brand", "label": "Brand", "title": "Name: Brand\\nType: organization\\nDescription: The company or product being discussed in the social media sentiment analysis.", "color": "#EA4335", "group": "organization"}, {"id": "entity_influencer", "label": "influencer", "title": "Name: influencer\\nType: person\\nDescription: Individuals with significant social media followings who engage with the brand or topic.", "color": "#4285F4", "group": "person"}, {"id": "entity_brand_mention_volume", "label": "Brand mention volume", "title": "Name: Brand mention volume\\nType: concept\\nDescription: The total number of times a brand is mentioned in social media posts.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_outdoor_enthusiasts", "label": "outdoor_enthusiasts", "title": "Name: outdoor_enthusiasts\\nType: concept\\nDescription: Customer segment interested in outdoor activities", "color": "#FBBC05", "group": "concept"}, {"id": "entity_customer_satisfaction", "label": "Customer satisfaction", "title": "Name: Customer satisfaction\\nType: concept\\nDescription: A metric indicating how satisfied customers are with products or services.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_north_america", "label": "North America", "title": "Name: North America\\nType: location\\nDescription: The geographic region where sentiment concentration is noted in the analysis.", "color": "#9C27B0", "group": "location"}, {"id": "entity_electronics", "label": "Electronics", "title": "Name: Electronics\\nType: concept\\nDescription: A category of products related to electronic devices.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_retail_partner", "label": "retail_partner", "title": "Name: retail_partner\\nType: organization\\nDescription: The type of supply chain node, indicating its role as a retail partner.", "color": "#EA4335", "group": "organization"}, {"id": "entity_return", "label": "return", "title": "Name: return\\nType: concept\\nDescription: The process of sending back a purchased item.", "color": "#FBBC05", "group": "concept"}, {"id": "entity_alternative_supplier", "label": "Alternative supplier", "title": "Name: Alternative supplier\\nType: organization\\nDescription: A different supplier used as a backup during supply disruptions.", "color": "#EA4335", "group": "organization"}, {"id": "entity_customer_cust_001", "label": "Customer CUST_001", "title": "Name: Customer CUST_001\\nType: person\\nDescription: A customer identified by the code CUST_001, also named Emma Thompson.", "color": "#4285F4", "group": "person"}]);
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-                size: 20,
-                font: {
-                    size: 14,
-                    color: '#333333'
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-                shadow: true
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-                color: {color: '#757575', highlight: '#333333'},
-                arrows: {to: {enabled: true, scaleFactor: 1.2}},
-                smooth: {type: 'continuous'}
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-            physics: {
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-                stabilization: {iterations: 100},
-                barnesHut: {
-                    gravitationalConstant: -2000,
-                    centralGravity: 0.3,
-                    springLength: 95,
-                    springConstant: 0.04,
-                    damping: 0.09
-                }
-            },
-            interaction: {
-                hover: true,
-                tooltipDelay: 200
-            }
-        };
-        
-        // Create network
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-        const data = {nodes: nodes, edges: edges};
-        const network = new vis.Network(container, data, options);
-        
-        // Event handlers
-        network.on('click', function(params) {
-            if (params.nodes.length > 0) {
-                const nodeId = params.nodes[0];
-                const node = nodes.get(nodeId);
-                alert(`Node: ${node.label}\nType: ${node.group}\nDescription: ${node.title || 'No description'}`);
-            }
-        });
-        
-        // Control functions
-        function refreshVisualization() {
-            location.reload();
-        }
-        
-        function resetView() {
-            network.fit();
-        }
-        
-        function exportGraph() {
-            const canvas = container.querySelector('canvas');
-            const link = document.createElement('a');
-            link.download = 'knowledge_graph.png';
-            link.href = canvas.toDataURL();
-            link.click();
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-        
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-        let autoRefreshEnabled = false;
-        let autoRefreshInterval = null;
-        
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-        }
-        
-        function toggleAutoRefresh() {
-            autoRefreshEnabled = !autoRefreshEnabled;
-            const btn = document.getElementById('autoRefreshBtn');
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-            if (autoRefreshEnabled) {
-                btn.textContent = '⏸️ Pause Auto-Refresh';
-                autoRefreshInterval = setInterval(refreshVisualization, 5000);
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-                    clearInterval(autoRefreshInterval);
-                    autoRefreshInterval = null;
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-        
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-            const filteredNodes = nodes.get().filter(node => 
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-                (node.title && node.title.toLowerCase().includes(searchTerm))
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-            
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-                network.focus(filteredNodes[0].id, {animation: true});
-            } else {
-                network.unselectAll();
-            }
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-        
-        // Initialize auto-refresh if enabled
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-            autoRefreshInterval = setInterval(refreshVisualization, 5000);
-        }
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-</html>
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\ No newline at end of file
diff --git a/real_time_visualizations/timeline.json b/real_time_visualizations/timeline.json
deleted file mode 100644
index 19c193c..0000000
--- a/real_time_visualizations/timeline.json
+++ /dev/null
@@ -1,362 +0,0 @@
-[
-  {
-    "timestamp": "2025-05-28T22:44:28.306287",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 1
-  },
-  {
-    "timestamp": "2025-05-28T22:44:51.717527",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 2
-  },
-  {
-    "timestamp": "2025-05-28T22:45:14.564181",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 3
-  },
-  {
-    "timestamp": "2025-05-28T22:45:18.698240",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 4
-  },
-  {
-    "timestamp": "2025-05-28T22:45:37.255986",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 5
-  },
-  {
-    "timestamp": "2025-05-28T22:45:59.849072",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 6
-  },
-  {
-    "timestamp": "2025-05-28T22:46:09.806266",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 7
-  },
-  {
-    "timestamp": "2025-05-28T22:46:22.494914",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 8
-  },
-  {
-    "timestamp": "2025-05-28T22:46:45.274859",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 9
-  },
-  {
-    "timestamp": "2025-05-28T22:47:01.481813",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 10
-  },
-  {
-    "timestamp": "2025-05-28T22:47:07.892640",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 11
-  },
-  {
-    "timestamp": "2025-05-28T22:47:22.001859",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 12
-  },
-  {
-    "timestamp": "2025-05-28T22:47:30.512070",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 13
-  },
-  {
-    "timestamp": "2025-05-28T22:47:53.219122",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 14
-  },
-  {
-    "timestamp": "2025-05-28T22:48:06.067647",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 15
-  },
-  {
-    "timestamp": "2025-05-28T22:48:15.971344",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 16
-  },
-  {
-    "timestamp": "2025-05-28T22:48:38.468905",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 17
-  },
-  {
-    "timestamp": "2025-05-28T22:48:46.802007",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 18
-  },
-  {
-    "timestamp": "2025-05-28T22:49:01.376588",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 19
-  },
-  {
-    "timestamp": "2025-05-28T22:49:18.125734",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 20
-  },
-  {
-    "timestamp": "2025-05-28T22:49:23.938541",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 21
-  },
-  {
-    "timestamp": "2025-05-28T22:49:39.693790",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 22
-  },
-  {
-    "timestamp": "2025-05-28T22:49:46.505479",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 23
-  },
-  {
-    "timestamp": "2025-05-28T22:50:09.240896",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 24
-  },
-  {
-    "timestamp": "2025-05-28T22:50:31.676723",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 25
-  },
-  {
-    "timestamp": "2025-05-28T22:50:32.946377",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 26
-  },
-  {
-    "timestamp": "2025-05-28T22:50:53.557038",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 27
-  },
-  {
-    "timestamp": "2025-05-28T22:50:54.131570",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 28
-  },
-  {
-    "timestamp": "2025-05-28T22:51:15.984265",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 29
-  },
-  {
-    "timestamp": "2025-05-28T22:51:16.476268",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 30
-  },
-  {
-    "timestamp": "2025-05-28T22:51:39.059516",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 31
-  },
-  {
-    "timestamp": "2025-05-28T22:52:01.781982",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 32
-  },
-  {
-    "timestamp": "2025-05-28T22:52:24.801909",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 33
-  },
-  {
-    "timestamp": "2025-05-28T22:52:47.956580",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 34
-  },
-  {
-    "timestamp": "2025-05-28T22:53:11.194611",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 35
-  },
-  {
-    "timestamp": "2025-05-28T22:53:34.365765",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 36
-  },
-  {
-    "timestamp": "2025-05-28T22:56:34.418345",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 1
-  },
-  {
-    "timestamp": "2025-05-28T22:56:56.552844",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 2
-  },
-  {
-    "timestamp": "2025-05-28T22:57:18.614991",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 3
-  },
-  {
-    "timestamp": "2025-05-28T22:57:40.707869",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 4
-  },
-  {
-    "timestamp": "2025-05-28T22:58:02.898689",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 5
-  },
-  {
-    "timestamp": "2025-05-28T22:58:24.978632",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 6
-  },
-  {
-    "timestamp": "2025-05-28T22:58:46.997912",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 7
-  },
-  {
-    "timestamp": "2025-05-28T23:46:45.054982",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 1
-  },
-  {
-    "timestamp": "2025-05-28T23:47:18.289726",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 2
-  },
-  {
-    "timestamp": "2025-05-28T23:47:51.679511",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 3
-  },
-  {
-    "timestamp": "2025-05-28T23:48:24.361013",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 4
-  },
-  {
-    "timestamp": "2025-05-28T23:53:16.020293",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 1
-  },
-  {
-    "timestamp": "2025-05-28T23:55:44.139113",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 1
-  },
-  {
-    "timestamp": "2025-05-28T23:57:57.620485",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 1
-  },
-  {
-    "timestamp": "2025-05-29T00:13:56.474560",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 1
-  },
-  {
-    "timestamp": "2025-05-29T06:57:04.861919",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 1
-  },
-  {
-    "timestamp": "2025-05-29T06:57:16.031695",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 2
-  },
-  {
-    "timestamp": "2025-05-29T06:57:29.149456",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 3
-  },
-  {
-    "timestamp": "2025-05-29T06:57:42.754001",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 4
-  },
-  {
-    "timestamp": "2025-05-29T06:57:56.569431",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 5
-  },
-  {
-    "timestamp": "2025-05-29T06:58:10.607777",
-    "entities_count": 100,
-    "relationships_count": 100,
-    "update_number": 6
-  },
-  {
-    "timestamp": "2025-05-29T07:14:48.390417",
-    "entities_count": 100,
-    "relationships_count": 0,
-    "update_number": 1
-  },
-  {
-    "timestamp": "2025-05-29T07:17:08.282699",
-    "entities_count": 100,
-    "relationships_count": 0,
-    "update_number": 1
-  },
-  {
-    "timestamp": "2025-05-29T07:24:44.562774",
-    "entities_count": 100,
-    "relationships_count": 0,
-    "update_number": 1
-  }
-]
\ No newline at end of file
diff --git a/src/graphiti_cosmos.py b/src/graphiti_cosmos.py
index 52a1963..91b0c4a 100644
--- a/src/graphiti_cosmos.py
+++ b/src/graphiti_cosmos.py
@@ -542,7 +542,8 @@ async def _create_relationship_edge(self, source_id: str, target_id: str,
             'timestamp': datetime.now(timezone.utc).isoformat(),
             'groupName': self.group_name
         }
-          # Add additional properties
+        
+        # Add additional properties
         for key, value in properties.items():
             query += f".property('{key}', {key})"
             bindings[key] = value
@@ -561,105 +562,41 @@ async def _create_relationship_from_entities(self, relationship: Relationship, e
             'description': relationship.description,
             'confidence': relationship.confidence,
             'episode_id': episode_id,
-            'valid_from': relationship.valid_from.isoformat()
-        }
+            'valid_from': relationship.valid_from.isoformat()        }
         
         await self._create_relationship_edge(
             source_id, target_id, relationship.relation_type.value, properties
         )
-    
+        
     async def search_entities(self, query: str, limit: int = 10) -> List[Dict[str, Any]]:
-        """Search entities using semantic similarity with embeddings"""
+        """Search entities using text similarity"""
         try:
-            if not query.strip():
-                # If empty query, return recent or random entities
-                all_entities_query = """
-                g.V().hasLabel('entity')
-                    .has('group_name', groupName)
-                    .valueMap(true)
-                    .limit(limitCount)
-                """
-                return await self._execute_gremlin_query(all_entities_query, {
-                    'groupName': self.group_name,
-                    'limitCount': limit
-                })
-            
-            # Generate query embedding for semantic search
+            # Generate query embedding
             query_embedding = await self._generate_embedding(query)
-            if not query_embedding:
-                # Fallback to text matching if embedding fails
-                return await self._search_entities_by_text(query, limit)
             
-            # Get all entities with their embeddings
-            all_entities_query = """
+            # First try exact name match
+            exact_match_query = """
             g.V().hasLabel('entity')
                 .has('group_name', groupName)
+                .has('name', searchTerm)
                 .valueMap(true)
-                .limit(1000)
+                .limit(limitCount)
             """
             
-            all_entities = await self._execute_gremlin_query(all_entities_query, {
-                'groupName': self.group_name
-            })
-            
-            # Calculate semantic similarity and rank results
-            scored_entities = []
-            for entity in all_entities:
-                name = entity.get('name', [''])[0] if isinstance(entity.get('name', ['']), list) else entity.get('name', '')
-                description = entity.get('description', [''])[0] if isinstance(entity.get('description', ['']), list) else entity.get('description', '')
-                
-                # Try to get stored embedding, or generate one
-                entity_embedding = entity.get('embedding')
-                if not entity_embedding:
-                    # Generate embedding for entity if not stored
-                    entity_text = f"{name} {description}"
-                    entity_embedding = await self._generate_embedding(entity_text)
-                
-                if entity_embedding and len(entity_embedding) == len(query_embedding):
-                    # Calculate cosine similarity
-                    similarity = self._cosine_similarity(query_embedding, entity_embedding)
-                    scored_entities.append((similarity, entity))
-            
-            # Sort by similarity and return top results
-            scored_entities.sort(key=lambda x: x[0], reverse=True)
-            
-            # Also include exact text matches with high scores
-            text_matches = await self._search_entities_by_text(query, limit)
-            for match in text_matches:
-                # Give text matches a high similarity score
-                scored_entities.insert(0, (0.95, match))
-            
-            # Remove duplicates and return top results
-            seen_entities = set()
-            final_results = []
-            
-            for similarity, entity in scored_entities:
-                entity_id = entity.get('id', entity.get('name', ''))
-                if entity_id not in seen_entities:
-                    seen_entities.add(entity_id)
-                    final_results.append(entity)
-                    if len(final_results) >= limit:
-                        break
-            
-            return final_results
-            
-        except Exception as e:
-            print(f"❌ Error in semantic search: {e}")
-            # Fallback to basic text search
-            return await self._search_entities_by_text(query, limit)
-
-    async def _search_entities_by_text(self, query: str, limit: int) -> List[Dict[str, Any]]:
-        """Fallback text-based entity search"""
-        try:
-            # First try exact name match
-            exact_match_query = """
+            # Try a simpler approach - get all entities first then filter in Python
+            all_entities_query = """
             g.V().hasLabel('entity')
                 .has('group_name', groupName)
-                .has('name', searchTerm)
                 .valueMap(true)
-                .limit(limitCount)
+                .limit(100)
             """
             
+            bindings = {
+                'groupName': self.group_name,
+                'limitCount': limit
+            }
+            
+            # Try exact match first
             entities = await self._execute_gremlin_query(exact_match_query, {
                 'groupName': self.group_name,
                 'searchTerm': query,
@@ -667,17 +604,8 @@ async def _search_entities_by_text(self, query: str, limit: int) -> List[Dict[st
             })
             
             if not entities:
-                # Get all entities and filter in Python for partial matches
-                all_entities_query = """
-                g.V().hasLabel('entity')
-                    .has('group_name', groupName)
-                    .valueMap(true)
-                    .limit(200)
-                """
-                
-                all_entities = await self._execute_gremlin_query(all_entities_query, {
-                    'groupName': self.group_name
-                })
+                # Get all entities and filter in Python for better compatibility
+                all_entities = await self._execute_gremlin_query(all_entities_query, bindings)
                 
                 # Filter entities that contain the search term
                 entities = []
@@ -691,31 +619,27 @@ async def _search_entities_by_text(self, query: str, limit: int) -> List[Dict[st
                         if len(entities) >= limit:
                             break
             
-            return entities
+            # Format results
+            formatted_entities = []
+            for entity in entities:
+                try:
+                    formatted_entity = {
+                        'id': entity.get('id', [''])[0] if isinstance(entity.get('id', ['']), list) else entity.get('id', ''),
+                        'name': entity.get('name', [''])[0] if isinstance(entity.get('name', ['']), list) else entity.get('name', ''),
+                        'type': entity.get('entity_type', [''])[0] if isinstance(entity.get('entity_type', ['']), list) else entity.get('entity_type', ''),
+                        'description': entity.get('description', [''])[0] if isinstance(entity.get('description', ['']), list) else entity.get('description', '')
+                    }
+                    formatted_entities.append(formatted_entity)
+                except Exception as format_error:
+                    print(f"Warning: Could not format entity: {format_error}")
+                    print(f"Raw entity: {entity}")
+            
+            return formatted_entities
             
         except Exception as e:
-            print(f"❌ Error in text search: {e}")
+            print(f"❌ Error searching entities: {e}")
             return []
-
-    def _cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:
-        """Calculate cosine similarity between two vectors"""
-        try:
-            import math
-            
-            # Calculate dot product
-            dot_product = sum(a * b for a, b in zip(vec1, vec2))
             
-            # Calculate magnitudes
-            magnitude1 = math.sqrt(sum(a * a for a in vec1))
-            magnitude2 = math.sqrt(sum(a * a for a in vec2))
-              # Avoid division by zero
-            if magnitude1 == 0 or magnitude2 == 0:
-                return 0
-            
-            return dot_product / (magnitude1 * magnitude2)
-        except Exception:
-            return 0
-
     async def search_relationships(self, query: str, limit: int = 10) -> List[Dict[str, Any]]:
         """Search relationships using text similarity"""
         try:
diff --git a/test_comprehensive_fix.py b/test_comprehensive_fix.py
deleted file mode 100644
index b0545e2..0000000
--- a/test_comprehensive_fix.py
+++ /dev/null
@@ -1,189 +0,0 @@
-#!/usr/bin/env python3
-"""
-End-to-end test to demonstrate the original error is fixed
-This simulates the exact search and display scenario that was failing
-"""
-
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-# Import the fixed graph explorer
-from demos.graph_explorer import GraphExplorer
-
-def test_original_error_scenario():
-    """Test the exact scenario that was causing the 'unhashable type: list' error"""
-    print("πŸ§ͺ Testing Original Error Scenario - Entity Search & Display")
-    print("=" * 60)
-    
-    explorer = GraphExplorer()
-    
-    # Simulate search results in Cosmos DB valueMap(true) format
-    # This is the exact format that was causing the error
-    mock_search_results = [
-        {
-            'name': ['John Smith'],
-            'type': ['person'], 
-            'description': ['A customer who purchased electronics'],
-            'confidence': ['0.95'],
-            'id': ['person_001']
-        },
-        {
-            'name': ['iPhone 14'],
-            'type': ['product'],
-            'description': ['Latest smartphone from Apple'],
-            'confidence': ['0.92'],
-            'id': ['product_001']
-        },
-        {
-            'name': ['Amazon'],
-            'type': ['company'],
-            'description': ['E-commerce platform'],
-            'confidence': ['0.98'],
-            'id': ['company_001']
-        }
-    ]
-    
-    print("βœ… Step 1: Testing entity grouping by type (original failure point)")
-    try:
-        # This was the exact line that failed with "unhashable type: 'list'"
-        entity_groups = {}
-        for entity in mock_search_results:
-            entity_type = explorer._extract_property(entity, 'type', 'unknown')
-            if entity_type not in entity_groups:
-                entity_groups[entity_type] = []
-            entity_groups[entity_type].append(entity)
-        
-        print(f"   βœ… Successfully grouped {len(mock_search_results)} entities into {len(entity_groups)} types")
-        for group_type, entities in entity_groups.items():
-            print(f"      - {group_type}: {len(entities)} entities")
-            
-    except TypeError as e:
-        if "unhashable type: 'list'" in str(e):
-            print(f"   ❌ FAILED: The original error still exists: {e}")
-            return False
-        else:
-            print(f"   ❌ FAILED: Unexpected error: {e}")
-            return False
-    
-    print("\nβœ… Step 2: Testing entity display formatting")
-    try:
-        for i, entity in enumerate(mock_search_results, 1):
-            name = explorer._extract_property(entity, 'name', 'Unknown')
-            entity_type = explorer._extract_property(entity, 'type', 'unknown')
-            description = explorer._extract_property(entity, 'description', '')
-            confidence = explorer._extract_property(entity, 'confidence', '1.0')
-            
-            print(f"   {i}. {name} ({entity_type})")
-            print(f"      Description: {description}")
-            print(f"      Confidence: {confidence}")
-            
-    except Exception as e:
-        print(f"   ❌ FAILED: Error in display formatting: {e}")
-        return False
-        
-    print("\nβœ… Step 3: Testing relationship processing")
-    try:
-        # Mock relationship data in Cosmos DB format
-        mock_relationships = [
-            {
-                'source': ['John Smith'],
-                'target': ['iPhone 14'], 
-                'type': ['purchased'],
-                'confidence': ['0.89']
-            },
-            {
-                'source': ['iPhone 14'],
-                'target': ['Amazon'],
-                'type': ['sold_by'],
-                'confidence': ['0.94']
-            }
-        ]
-        
-        # Process relationships (another failure point)
-        processed_relationships = []
-        for rel in mock_relationships:
-            source = explorer._extract_property(rel, 'source', '')
-            target = explorer._extract_property(rel, 'target', '')
-            rel_type = explorer._extract_property(rel, 'type', 'unknown')
-            confidence = explorer._extract_property(rel, 'confidence', '1.0')
-            
-            processed_relationships.append({
-                'source': source,
-                'target': target,
-                'type': rel_type,
-                'confidence': confidence
-            })
-            
-            print(f"   {source} β†’ {rel_type} β†’ {target} (conf: {confidence})")
-            
-        print(f"   βœ… Successfully processed {len(processed_relationships)} relationships")
-        
-    except Exception as e:
-        print(f"   ❌ FAILED: Error in relationship processing: {e}")
-        return False
-    
-    print("\nβœ… Step 4: Testing Counter usage (another failure point)")
-    try:
-        # Test Counter with entity types (this also failed before)
-        from collections import Counter
-        
-        type_counts = Counter()
-        for entity in mock_search_results:
-            entity_type = explorer._extract_property(entity, 'type', 'unknown')
-            type_counts[entity_type] += 1
-            
-        print(f"   βœ… Entity type distribution: {dict(type_counts)}")
-        
-    except Exception as e:
-        print(f"   ❌ FAILED: Error in Counter usage: {e}")
-        return False
-    
-    print("\nπŸŽ‰ All tests passed! The original 'unhashable type: list' error has been completely resolved.")
-    print("✨ The graph explorer can now handle both Cosmos DB valueMap(true) and simple dictionary formats.")
-    return True
-
-def test_backward_compatibility():
-    """Test that the fix maintains backward compatibility with simple dict format"""
-    print("\nπŸ”„ Testing Backward Compatibility")
-    print("-" * 40)
-    
-    explorer = GraphExplorer()
-    
-    # Test with simple dictionary format (should still work)
-    simple_entities = [
-        {
-            'name': 'Simple Entity',
-            'type': 'test',
-            'description': 'A simple test entity'
-        }
-    ]
-    
-    try:
-        for entity in simple_entities:
-            name = explorer._extract_property(entity, 'name', 'Unknown')
-            entity_type = explorer._extract_property(entity, 'type', 'unknown')
-            print(f"   βœ… Simple format: {name} ({entity_type})")
-        
-        print("βœ… Backward compatibility maintained!")
-        return True
-        
-    except Exception as e:
-        print(f"❌ Backward compatibility broken: {e}")
-        return False
-
-if __name__ == "__main__":
-    print("πŸ”§ Comprehensive Test: 'unhashable type: list' Error Fix")
-    print("=" * 65)
-    
-    success1 = test_original_error_scenario()
-    success2 = test_backward_compatibility()
-    
-    if success1 and success2:
-        print("\nπŸŽ‰ COMPREHENSIVE SUCCESS!")
-        print("βœ… Original error completely resolved")
-        print("βœ… Backward compatibility maintained") 
-        print("βœ… Graph explorer ready for production use")
-    else:
-        print("\n❌ ISSUES REMAIN!")
-        sys.exit(1)
diff --git a/test_enhanced_features.py b/test_enhanced_features.py
deleted file mode 100644
index d6d122a..0000000
--- a/test_enhanced_features.py
+++ /dev/null
@@ -1,137 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script to validate the enhanced LLM features in the graph explorer
-"""
-
-import asyncio
-import sys
-import os
-from unittest.mock import AsyncMock, MagicMock
-
-# Add the project root to the path
-sys.path.append(os.path.dirname(__file__))
-
-async def test_graph_explorer_features():
-    """Test the key LLM-enhanced features"""
-    print("πŸ§ͺ Testing Enhanced Graph Explorer Features")
-    print("=" * 50)
-    
-    try:
-        # Import the graph explorer
-        from demos.graph_explorer import GraphExplorer
-        
-        # Create explorer instance
-        explorer = GraphExplorer()
-        
-        # Mock the graphiti instance to avoid needing real connections
-        explorer.graphiti = AsyncMock()
-        explorer.graphiti.search_entities = AsyncMock(return_value=[
-            {"id": "test1", "name": "Test Entity 1", "type": "person", "description": "Test person"},
-            {"id": "test2", "name": "Test Entity 2", "type": "product", "description": "Test product"}
-        ])
-        
-        # Test 1: Check if LLM helper methods exist
-        print("βœ… Test 1: LLM Helper Methods")
-        assert hasattr(explorer, '_interpret_search_query'), "Missing _interpret_search_query method"
-        assert hasattr(explorer, '_enhance_search_results_with_llm'), "Missing _enhance_search_results_with_llm method"
-        assert hasattr(explorer, '_extract_search_keywords'), "Missing _extract_search_keywords method"
-        print("   All LLM helper methods found")
-        
-        # Test 2: Mock a search query interpretation
-        print("βœ… Test 2: Query Interpretation Structure")
-        # We can't test the actual LLM call without credentials, but we can test structure
-        try:
-            # This should exist as a method
-            method = getattr(explorer, '_interpret_search_query')
-            assert callable(method), "_interpret_search_query should be callable"
-            print("   Query interpretation method is callable")
-        except Exception as e:
-            print(f"   Warning: {e}")
-        
-        # Test 3: Check search entities method
-        print("βœ… Test 3: Enhanced Search Method")
-        assert hasattr(explorer, '_search_entities_nl'), "Missing _search_entities_nl method"
-        print("   Enhanced search method found")
-        
-        # Test 4: Check session history and bookmarks
-        print("βœ… Test 4: Session Management")
-        assert hasattr(explorer, 'session_history'), "Missing session_history"
-        assert hasattr(explorer, 'bookmark_entities'), "Missing bookmark_entities"
-        assert isinstance(explorer.session_history, list), "session_history should be a list"
-        assert isinstance(explorer.bookmark_entities, list), "bookmark_entities should be a list"
-        print("   Session management structures found")
-        
-        # Test 5: Check GraphitiCosmos integration
-        print("βœ… Test 5: GraphitiCosmos Integration")
-        from src.graphiti_cosmos import GraphitiCosmos
-        graphiti = GraphitiCosmos()
-        
-        # Check if enhanced search methods exist
-        assert hasattr(graphiti, 'search_entities'), "Missing search_entities method"
-        assert hasattr(graphiti, '_search_entities_by_text'), "Missing _search_entities_by_text fallback"
-        print("   GraphitiCosmos integration verified")
-        
-        print("\nπŸŽ‰ All Enhanced Features Validated Successfully!")
-        print("✨ The graph explorer is ready for LLM-powered exploration!")
-        
-        return True
-        
-    except ImportError as e:
-        print(f"❌ Import Error: {e}")
-        print("πŸ’‘ Make sure all dependencies are installed")
-        return False
-    except Exception as e:
-        print(f"❌ Test Error: {e}")
-        print("πŸ’‘ There may be issues with the enhanced features")
-        return False
-
-async def test_compilation():
-    """Test that all modules compile correctly"""
-    print("\nπŸ”§ Testing Module Compilation")
-    print("-" * 30)
-    
-    modules_to_test = [
-        "demos.graph_explorer",
-        "src.graphiti_cosmos"
-    ]
-    
-    success_count = 0
-    for module_name in modules_to_test:
-        try:
-            __import__(module_name)
-            print(f"βœ… {module_name} compiles successfully")
-            success_count += 1
-        except Exception as e:
-            print(f"❌ {module_name} compilation failed: {e}")
-    
-    print(f"\nπŸ“Š Compilation Results: {success_count}/{len(modules_to_test)} modules successful")
-    return success_count == len(modules_to_test)
-
-async def main():
-    """Main test runner"""
-    print("πŸš€ Enhanced Graph Explorer Validation")
-    print("=" * 60)
-    
-    # Test compilation first
-    compilation_success = await test_compilation()
-    
-    if compilation_success:
-        # Test enhanced features
-        features_success = await test_graph_explorer_features()
-        
-        if features_success:
-            print("\n🎯 VALIDATION SUMMARY")
-            print("=" * 30)
-            print("βœ… All modules compile successfully")
-            print("βœ… All LLM-enhanced features are present")
-            print("βœ… GraphitiCosmos integration is working")
-            print("βœ… Session management is implemented")
-            print("\nπŸš€ Ready for AI-powered graph exploration!")
-        else:
-            print("\n⚠️  Some enhanced features may have issues")
-    else:
-        print("\n❌ Module compilation issues detected")
-        print("πŸ’‘ Fix compilation errors before testing features")
-
-if __name__ == "__main__":
-    asyncio.run(main())
diff --git a/test_unhashable_fix.py b/test_unhashable_fix.py
deleted file mode 100644
index 7179f55..0000000
--- a/test_unhashable_fix.py
+++ /dev/null
@@ -1,105 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script to verify the "unhashable type: 'list'" error fix
-This simulates the exact data format that was causing the issue
-"""
-
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-# Import the fixed graph explorer
-from demos.graph_explorer import GraphExplorer
-
-def test_cosmos_data_format():
-    """Test the helper method with Cosmos DB valueMap(true) format"""
-    print("πŸ§ͺ Testing Cosmos DB valueMap(true) format handling...")
-      # Create a mock GraphExplorer instance
-    explorer = GraphExplorer()  # Constructor takes no arguments
-    
-    # Test data in Cosmos DB valueMap(true) format (properties as lists)
-    cosmos_entity = {
-        'name': ['John Doe'],
-        'type': ['person'],
-        'description': ['A test person entity'],
-        'confidence': ['0.95']
-    }
-    
-    # Test data in simple dictionary format (for backward compatibility)
-    simple_entity = {
-        'name': 'Jane Smith',
-        'type': 'person',
-        'description': 'Another test person',
-        'confidence': '0.88'
-    }
-    
-    # Test the _extract_property helper method
-    print("βœ… Testing _extract_property method:")
-    
-    # Test Cosmos DB format
-    name1 = explorer._extract_property(cosmos_entity, 'name', 'Unknown')
-    type1 = explorer._extract_property(cosmos_entity, 'type', 'unknown')
-    desc1 = explorer._extract_property(cosmos_entity, 'description', '')
-    conf1 = explorer._extract_property(cosmos_entity, 'confidence', '1.0')
-    
-    print(f"   Cosmos format - Name: {name1}, Type: {type1}, Desc: {desc1}, Conf: {conf1}")
-    
-    # Test simple dictionary format
-    name2 = explorer._extract_property(simple_entity, 'name', 'Unknown')
-    type2 = explorer._extract_property(simple_entity, 'type', 'unknown')
-    desc2 = explorer._extract_property(simple_entity, 'description', '')
-    conf2 = explorer._extract_property(simple_entity, 'confidence', '1.0')
-    
-    print(f"   Simple format - Name: {name2}, Type: {type2}, Desc: {desc2}, Conf: {conf2}")
-    
-    # Test edge cases
-    print("βœ… Testing edge cases:")
-    
-    # Empty list
-    empty_entity = {'name': [], 'type': ['person']}
-    name3 = explorer._extract_property(empty_entity, 'name', 'Unknown')
-    print(f"   Empty list handling - Name: {name3}")
-    
-    # Missing property
-    incomplete_entity = {'type': ['person']}
-    name4 = explorer._extract_property(incomplete_entity, 'name', 'Unknown')
-    print(f"   Missing property handling - Name: {name4}")
-    
-    # Non-dict input
-    name5 = explorer._extract_property("not a dict", 'name', 'Unknown')
-    print(f"   Non-dict input handling - Name: {name5}")
-    
-    # Test that no "unhashable type: 'list'" error occurs
-    try:
-        # This would have failed before the fix
-        entity_groups = {}
-        test_entities = [cosmos_entity, simple_entity]
-        
-        for entity in test_entities:
-            entity_type = explorer._extract_property(entity, 'type', 'unknown')
-            if entity_type not in entity_groups:
-                entity_groups[entity_type] = []
-            entity_groups[entity_type].append(entity)
-        
-        print(f"βœ… Entity grouping successful: {len(entity_groups)} groups created")
-        
-    except TypeError as e:
-        if "unhashable type: 'list'" in str(e):
-            print(f"❌ ERROR: The 'unhashable type: list' error still exists: {e}")
-            return False
-        else:
-            print(f"❌ ERROR: Unexpected error: {e}")
-            return False
-    
-    print("πŸŽ‰ All tests passed! The 'unhashable type: list' error has been fixed.")
-    return True
-
-if __name__ == "__main__":
-    print("πŸ”§ Testing 'unhashable type: list' Error Fix")
-    print("=" * 50)
-    success = test_cosmos_data_format()
-    if success:
-        print("\nβœ… SUCCESS: All fixes working correctly!")
-    else:
-        print("\n❌ FAILURE: Issues still exist!")
-        sys.exit(1)
diff --git a/validate_graph_explorer.py b/validate_graph_explorer.py
deleted file mode 100644
index 2b0d238..0000000
--- a/validate_graph_explorer.py
+++ /dev/null
@@ -1,352 +0,0 @@
-#!/usr/bin/env python3
-"""
-Validation Script for Enhanced Graph Explorer
-Tests that the graph explorer is fully LLM-powered and dynamic
-"""
-
-import asyncio
-import sys
-import os
-from datetime import datetime
-
-# Add the parent directory to the path so we can import our modules
-sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
-
-from src.graphiti_cosmos import GraphitiCosmos
-from demos.graph_explorer import GraphExplorer
-
-class GraphExplorerValidator:
-    """Validates that the graph explorer uses LLM assistance and avoids hardcoded patterns"""
-    
-    def __init__(self):
-        self.explorer = None
-        self.graphiti = None
-        self.test_results = []
-        
-    async def initialize(self):
-        """Initialize the systems for testing"""
-        print("πŸ”§ GRAPH EXPLORER VALIDATION")
-        print("=" * 60)
-        print("🎨 Initializing systems...")
-        
-        self.explorer = GraphExplorer()
-        await self.explorer.initialize()
-        self.graphiti = self.explorer.graphiti
-        
-        print("βœ… Systems initialized successfully")
-        print()
-        
-    async def test_llm_keyword_extraction(self):
-        """Test that keyword extraction uses LLM instead of hardcoded patterns"""
-        print("πŸ” Testing LLM Keyword Extraction...")
-        
-        test_queries = [
-            "Find all people who work for technology companies",
-            "Show me sustainable products and their manufacturers",
-            "What organizations are connected to renewable energy?",
-            "Find customers who purchased electronics recently"
-        ]
-        
-        for query in test_queries:
-            try:
-                keywords = await self.explorer._extract_search_keywords(query)
-                print(f"   Query: '{query}'")
-                print(f"   Keywords: {keywords}")
-                
-                # Validate that we get meaningful, context-aware keywords
-                self.test_results.append({
-                    'test': 'llm_keyword_extraction',
-                    'query': query,
-                    'keywords': keywords,
-                    'success': len(keywords) > 0 and any(len(k) > 2 for k in keywords)
-                })
-                print("   βœ… LLM keyword extraction working")
-                
-            except Exception as e:
-                print(f"   ❌ Error: {e}")
-                self.test_results.append({
-                    'test': 'llm_keyword_extraction',
-                    'query': query,
-                    'error': str(e),
-                    'success': False
-                })
-        print()
-    
-    async def test_semantic_entity_search(self):
-        """Test that entity search uses semantic similarity instead of basic string matching"""
-        print("🧠 Testing Semantic Entity Search...")
-        
-        test_searches = [
-            "technology company",
-            "sustainable product",
-            "renewable energy",
-            "customer electronics"
-        ]
-        
-        for search_term in test_searches:
-            try:
-                results = await self.graphiti.search_entities(search_term, limit=5)
-                print(f"   Search: '{search_term}'")
-                print(f"   Results: {len(results)} entities found")
-                
-                if results:
-                    for i, entity in enumerate(results[:2], 1):
-                        name = entity.get('name', 'Unknown')
-                        entity_type = entity.get('type', entity.get('entity_type', 'Unknown'))
-                        print(f"      {i}. {name} ({entity_type})")
-                
-                self.test_results.append({
-                    'test': 'semantic_entity_search',
-                    'search_term': search_term,
-                    'results_count': len(results),
-                    'success': True
-                })
-                print("   βœ… Semantic search working")
-                
-            except Exception as e:
-                print(f"   ❌ Error: {e}")
-                self.test_results.append({
-                    'test': 'semantic_entity_search',
-                    'search_term': search_term,
-                    'error': str(e),
-                    'success': False
-                })
-        print()
-    
-    async def test_natural_language_interpretation(self):
-        """Test natural language query interpretation"""
-        print("πŸ—£οΈ  Testing Natural Language Query Interpretation...")
-        
-        complex_queries = [
-            "Show me all people who work for organizations in the tech industry",
-            "Find products that are related to environmental sustainability",
-            "What are the relationships between customers and their recent purchases?",
-            "Analyze the network of suppliers and manufacturers"
-        ]
-        
-        for query in complex_queries:
-            try:
-                interpretation = await self.explorer._interpret_natural_language_query(query)
-                print(f"   Query: '{query}'")
-                print(f"   Intent: {interpretation['intent']}")
-                print(f"   Strategy: {interpretation['strategy']}")
-                print(f"   Confidence: {interpretation['confidence']:.2f}")
-                print(f"   Terms: {interpretation['search_terms']}")
-                
-                self.test_results.append({
-                    'test': 'nl_interpretation',
-                    'query': query,
-                    'interpretation': interpretation,
-                    'success': interpretation['confidence'] > 0.5
-                })
-                print("   βœ… Natural language interpretation working")
-                
-            except Exception as e:
-                print(f"   ❌ Error: {e}")
-                self.test_results.append({
-                    'test': 'nl_interpretation',
-                    'query': query,
-                    'error': str(e),
-                    'success': False
-                })
-        print()
-    
-    async def test_dynamic_query_generation(self):
-        """Test that queries are generated dynamically, not hardcoded"""
-        print("🎯 Testing Dynamic Query Generation...")
-        
-        # Test different search patterns to ensure no hardcoding
-        dynamic_searches = [
-            ("exact match", "Microsoft"),
-            ("partial match", "tech"),
-            ("semantic search", "innovation"),
-            ("broad exploration", "")
-        ]
-        
-        for search_type, term in dynamic_searches:
-            try:
-                results = await self.graphiti.search_entities(term, limit=3)
-                print(f"   Type: {search_type}, Term: '{term}' -> {len(results)} results")
-                
-                self.test_results.append({
-                    'test': 'dynamic_query_generation',
-                    'search_type': search_type,
-                    'term': term,
-                    'results_count': len(results),
-                    'success': True
-                })
-                print("   βœ… Dynamic query generation working")
-                
-            except Exception as e:
-                print(f"   ❌ Error: {e}")
-                self.test_results.append({
-                    'test': 'dynamic_query_generation',
-                    'search_type': search_type,
-                    'term': term,
-                    'error': str(e),
-                    'success': False
-                })
-        print()
-    
-    async def test_llm_result_enhancement(self):
-        """Test LLM-based result enhancement and contextualization"""
-        print("✨ Testing LLM Result Enhancement...")
-        
-        query = "Find technology companies and their products"
-        
-        try:
-            # First get basic results
-            interpretation = await self.explorer._interpret_natural_language_query(query)
-            
-            basic_results = []
-            for term in interpretation['search_terms'][:2]:
-                results = await self.graphiti.search_entities(term, limit=3)
-                basic_results.extend(results)
-            
-            # Remove duplicates
-            unique_results = {}
-            for result in basic_results:
-                result_id = result.get('id') or result.get('name')
-                if result_id not in unique_results:
-                    unique_results[result_id] = result
-            
-            basic_results = list(unique_results.values())
-            
-            if basic_results:
-                # Test LLM enhancement
-                enhanced_results = await self.explorer._enhance_search_results_with_llm(
-                    query, basic_results, interpretation
-                )
-                
-                print(f"   Query: '{query}'")
-                print(f"   Basic results: {len(basic_results)}")
-                print(f"   Enhanced results: {len(enhanced_results)}")
-                
-                # Check if enhancement metadata is available
-                if hasattr(self.explorer, '_last_enhancement'):
-                    enhancement = self.explorer._last_enhancement
-                    print(f"   Insights: {enhancement.get('insights', 'None')[:50]}...")
-                    print(f"   Follow-ups: {len(enhancement.get('follow_up_queries', []))}")
-                
-                self.test_results.append({
-                    'test': 'llm_result_enhancement',
-                    'query': query,
-                    'basic_count': len(basic_results),
-                    'enhanced_count': len(enhanced_results),
-                    'has_insights': hasattr(self.explorer, '_last_enhancement'),
-                    'success': len(enhanced_results) > 0
-                })
-                print("   βœ… LLM result enhancement working")
-            else:
-                print("   ⚠️  No results to enhance")
-                self.test_results.append({
-                    'test': 'llm_result_enhancement',
-                    'query': query,
-                    'success': False,
-                    'reason': 'no_results'
-                })
-                
-        except Exception as e:
-            print(f"   ❌ Error: {e}")
-            self.test_results.append({
-                'test': 'llm_result_enhancement',
-                'query': query,
-                'error': str(e),
-                'success': False
-            })
-        print()
-    
-    def generate_validation_report(self):
-        """Generate a comprehensive validation report"""
-        print("πŸ“Š VALIDATION REPORT")
-        print("=" * 60)
-        
-        # Count successes and failures
-        total_tests = len(self.test_results)
-        successful_tests = len([t for t in self.test_results if t.get('success', False)])
-        failed_tests = total_tests - successful_tests
-        
-        print(f"Total Tests: {total_tests}")
-        print(f"Successful: {successful_tests}")
-        print(f"Failed: {failed_tests}")
-        print(f"Success Rate: {(successful_tests/total_tests)*100:.1f}%")
-        print()
-        
-        # Group by test type
-        test_groups = {}
-        for test in self.test_results:
-            test_type = test['test']
-            if test_type not in test_groups:
-                test_groups[test_type] = []
-            test_groups[test_type].append(test)
-        
-        for test_type, tests in test_groups.items():
-            successful = len([t for t in tests if t.get('success', False)])
-            total = len(tests)
-            print(f"πŸ” {test_type.replace('_', ' ').title()}:")
-            print(f"   {successful}/{total} tests passed")
-            
-            # Show failures
-            failures = [t for t in tests if not t.get('success', False)]
-            if failures:
-                print("   ❌ Failures:")
-                for failure in failures:
-                    error = failure.get('error', 'Unknown error')
-                    print(f"      - {error}")
-            print()
-        
-        # Overall assessment
-        if successful_tests >= total_tests * 0.8:
-            print("βœ… VALIDATION PASSED")
-            print("The graph explorer is successfully using LLM assistance and dynamic queries!")
-        elif successful_tests >= total_tests * 0.6:
-            print("⚠️  VALIDATION PARTIAL")
-            print("The graph explorer has some LLM features working, but needs improvement.")
-        else:
-            print("❌ VALIDATION FAILED")
-            print("The graph explorer is not sufficiently using LLM assistance.")
-        
-        print()
-        print(f"Detailed results saved with {total_tests} test cases")
-        
-        return {
-            'total_tests': total_tests,
-            'successful_tests': successful_tests,
-            'success_rate': (successful_tests/total_tests)*100,
-            'test_results': self.test_results
-        }
-
-async def main():
-    """Run the comprehensive validation"""
-    validator = GraphExplorerValidator()
-    
-    try:
-        await validator.initialize()
-        
-        # Run all validation tests
-        await validator.test_llm_keyword_extraction()
-        await validator.test_semantic_entity_search()
-        await validator.test_natural_language_interpretation()
-        await validator.test_dynamic_query_generation()
-        await validator.test_llm_result_enhancement()
-        
-        # Generate final report
-        report = validator.generate_validation_report()
-        
-        # Save detailed report
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        report_file = f"graph_explorer_validation_{timestamp}.json"
-        
-        import json
-        with open(report_file, 'w') as f:
-            json.dump(report, f, indent=2, default=str)
-        
-        print(f"πŸ“„ Detailed validation report saved as: {report_file}")
-        
-    except Exception as e:
-        print(f"❌ Validation failed with error: {e}")
-        import traceback
-        traceback.print_exc()
-
-if __name__ == "__main__":
-    asyncio.run(main())