kg-engine-v2 2.3.1

Advanced Knowledge Graph Engine with semantic search and temporal tracking

Knowledge Graph Engine v2

Modern Neo4j-based knowledge graph engine with semantic search capabilities, intelligent relationship management, and performance optimizations.

🎯 Overview

A production-ready knowledge graph system built entirely on Neo4j for persistent graph storage and vector search. Combines graph database operations with semantic vector search to provide intelligent information storage, retrieval, and reasoning.

✨ Key Features

• 🏗️ Neo4j-Native Architecture: Complete Neo4j integration for both graph and vector operations
• 🔍 Enhanced Semantic Search: Improved vector search with dynamic thresholds and contextual boosting
• 🤖 LLM Integration: OpenAI/Ollama support for entity extraction and query processing
• ⚔️ Conflict Resolution: Intelligent handling of contradicting information with temporal tracking
• ⏰ Temporal Tracking: Complete relationship history with date ranges and conflict resolution
• 🎯 Smart Query Understanding: Context-aware search with semantic category matching
• 📊 Optimized Performance: 50-74% faster queries with smart caching and lazy loading
• 🚀 Production Ready: ACID compliance, comprehensive error handling, modern architecture
• 🏷️ Edge Classification: Intelligent edge categorization with vector similarity (85% threshold)
• 🔄 Complete CRUD API: Full create, read, update, delete operations for edges and nodes
• 📦 External Package Support: Clean API exports for use as external dependency

🆕 New in v2.1.0

• ⚡ Performance Optimizations: GraphQueryOptimizer and Neo4jOptimizer for 50-74% faster queries
• 💾 Smart Caching: Query result caching with 5-minute TTL for near-instant repeated queries
• 🔧 Refactored GraphEdge: Lazy loading with safe accessors, 18% smaller codebase
• 🛠️ Dynamic Relationships: WORKS_AT, LIVES_IN instead of generic RELATES_TO
• 🐛 Bug Fixes: Fixed "Relationship not populated" errors, enhanced source filtering
• 🏷️ Edge Classifier System: Vector similarity-based edge classification (replaced LLM approach)
• 🔄 CRUD Operations: Complete API for edge and node management including merge operations
• 📦 API Exports: All types exported for external package usage
• 🌐 Separate API Server: Production-ready FastAPI server as external project

📁 Project Structure

src/                                  # Main source directory
├── kg_engine/                        # Knowledge Graph Engine
│   ├── core/                         # Core engine
│   │   └── engine.py                 # Main KG Engine
│   ├── models/                       # Data models
│   │   ├── models.py                 # Graph data structures
│   │   └── classifier_map.py         # Edge classifier management
│   ├── storage/                      # Storage components
│   │   ├── graph_db.py               # Neo4j graph operations
│   │   ├── neo4j_vector_store.py     # Vector storage
│   │   ├── vector_store.py           # Vector store interface
│   │   └── ...                       # Other storage components
│   ├── llm/                          # LLM integration
│   │   └── llm_interface.py          # OpenAI/Ollama interface
│   ├── config/                       # Configuration
│   │   ├── neo4j_config.py           # Neo4j settings
│   │   └── neo4j_schema.py           # Schema management
│   ├── utils/                        # Utilities
│   │   ├── date_parser.py            # Date parsing utilities
│   │   ├── graph_query_optimizer.py  # Query optimization
│   │   ├── neo4j_optimizer.py        # Neo4j optimizations
│   │   └── classifier_detector.py    # Edge classification
│   └── __init__.py                   # Package exports
├── api/                              # API endpoints
│   └── main.py                       # FastAPI CRUD operations
├── examples/                         # Usage examples
│   ├── examples.py                   # Basic examples
│   ├── bio_example.py                # Biographical demo
│   └── simple_bio_demo.py            # Simple demo
└── tests/                            # Test suite

kg_api_server/                        # Separate API server project
├── app/                              # FastAPI application
│   ├── __init__.py                   # Package init
│   └── main.py                       # API server implementation
├── tests/                            # API tests
├── requirements.txt                  # Dependencies
├── Dockerfile                        # Container configuration
├── docker-compose.yml                # Full stack deployment
└── README.md                         # API documentation

docs/                                 # Comprehensive documentation
├── architecture/                     # System design
├── user-guide/                       # Getting started
├── api/                              # API reference
└── development/                      # Development guides

🚀 Quick Start

Prerequisites

# Install Neo4j (required)
docker run --name neo4j -p7474:7474 -p7687:7687 -d \
    -e NEO4J_AUTH=neo4j/password \
    neo4j:latest

Installation

pip install -e .

Basic Usage

As a Library

from kg_engine import KnowledgeGraphEngineV2, InputItem, Neo4jConfig

# Initialize with Neo4j
engine = KnowledgeGraphEngineV2(
    api_key="your-openai-key",  # or "ollama" for local LLM
    neo4j_config=Neo4jConfig()
)

# Add knowledge
result = engine.process_input([
    InputItem(description="Alice works as a software engineer at Google"),
    InputItem(description="Bob lives in San Francisco")
])

# Search with natural language
response = engine.search("Who works at Google?")
print(response.answer)  # "Alice works as a software engineer at Google."

Using the API Server

# Start the API server
cd kg_api_server
python app/main.py

# Process text via API
curl -X POST "http://localhost:8080/process" \
     -H "Content-Type: application/json" \
     -d '{
       "texts": ["Alice works at Google", "Bob lives in San Francisco"]
     }'

# Search via API
curl -X POST "http://localhost:8080/search" \
     -H "Content-Type: application/json" \
     -d '{"query": "Who works at Google?"}'

🤖 LLM Setup Options

Option 1: OpenAI (Recommended for Production)

export OPENAI_API_KEY="your-api-key"

engine = KnowledgeGraphEngineV2(
    api_key="your-openai-key",
    model="gpt-4.1-nano"  # Fast and cost-effective
)

Option 2: Local Ollama (Privacy & Cost-Free)

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Start server
ollama serve

# Pull a model
ollama pull llama3.2:3b  # Recommended: good balance of size/performance

engine = KnowledgeGraphEngineV2(
    api_key="ollama",
    base_url="http://localhost:11434/v1",
    model="llama3.2:3b"
)

🏗️ Optimized Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   LLM Interface │    │   Graph Database │    │  Vector Store   │
│                 │    │                  │    │                 │
│ • Entity Extract│    │ • Neo4j Native   │    │ • Neo4j Vectors │
│ • Query Parse   │    │ • Query Cache    │    │ • Semantic      │
│ • Answer Gen.   │    │ • Optimizations  │    │ • Search        │
└─────────────────┘    └──────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 │
                    ┌─────────────────────┐
                    │ KG Engine v2        │
                    │  (Optimized)        │
                    │                     │
                    │ • Process Input     │
                    │ • Smart Updates     │
                    │ • Hybrid Search     │
                    │ • Query Caching     │
                    │ • Safe Accessors    │
                    └─────────────────────┘

📊 Advanced Features

Edge Classification System

# Automatic edge classification with vector similarity (85% threshold)
engine.process_input([
    InputItem(description="Alice works at Google"),      # → category: "business"
    InputItem(description="Bob lives in Paris"),         # → category: "location"
    InputItem(description="Charlie loves photography")   # → category: "interests"
])

# Similar edges are grouped intelligently
# "works_at", "employed_by", "works_for" → all map to WORKS_AT relationship

Complete CRUD Operations

# Create edges manually
from kg_engine import EdgeData, EdgeMetadata, RelationshipStatus

metadata = EdgeMetadata(
    summary="John is the CTO of TechCorp",
    confidence=0.95,
    category="business",
    status=RelationshipStatus.ACTIVE
)

edge_data = EdgeData(
    subject="John",
    relationship="WORKS_AS",
    object="CTO at TechCorp",
    metadata=metadata
)

# Node operations
engine.graph_db.merge_nodes_auto("John Smith", "J. Smith")  # Auto merge
engine.graph_db.merge_nodes_manual("John", "Jonathan", "John Smith")  # Manual

Intelligent Conflict Resolution

# Initial information
engine.process_input([InputItem(description="Alice lives in Boston")])

# Update with conflicting information (automatically resolves)
engine.process_input([InputItem(description="Alice moved to Seattle in 2024")])

# System automatically:
# 1. Marks old relationship as obsolete
# 2. Adds new relationship as active
# 3. Maintains complete history

Optimized Search Performance

# Fast cached queries (< 1ms for repeated searches)
response = engine.search("Who works in technology?")  # First call: ~100ms
response = engine.search("Who works in technology?")  # Cached: < 1ms

# Enhanced semantic understanding with contextual boosting
response = engine.search("Who was born in Europe?")
# ✅ Returns all European births: Berlin, Lyon, Barcelona, Paris

# Safe relationship access (no more "Relationship not populated" errors)
for result in response.results:
    edge = result.triplet.edge
    subject = edge.get_subject_safe()  # Safe accessor
    relationship = edge.get_relationship_safe()  # Safe accessor
    obj = edge.get_object_safe()  # Safe accessor

Temporal Relationship Tracking

# Natural language dates with simple parse_date utility
from kg_engine import parse_date

engine.process_input([
    InputItem(description="Project started", from_date=parse_date("2 months ago")),
    InputItem(description="Alice joined", from_date=parse_date("last week"))
])

📦 Using as External Package

The KG Engine is designed to be used as an external dependency in your projects:

# Import all needed components
from kg_engine import (
    KnowledgeGraphEngineV2, InputItem, Neo4jConfig,
    EdgeData, EdgeMetadata, RelationshipStatus,
    SearchType, parse_date, __version__
)

print(f"Using KG Engine v{__version__}")

# Full API available for external applications
# See kg_api_server/ for a complete FastAPI example

API Server Example

A complete FastAPI server is provided as a separate project in kg_api_server/:

cd kg_api_server
pip install -r requirements.txt
python app/main.py  # Starts at http://localhost:8080

Features:

• Complete REST API with all CRUD operations
• Interactive documentation at /docs
• Docker support for production deployment
• Comprehensive test suite

📚 Documentation

• 📖 Quick Start: Get running in 5 minutes
• 🏗️ Architecture: System design and components
• 📊 Workflows: Process flows with diagrams
• 🔧 API Reference: Complete API documentation
• 👩‍💻 Development: Development setup and guidelines
• 🌐 API Server: External API server documentation

🚦 Running Examples

# Run basic examples
python src/examples/examples.py

# Run biographical knowledge graph demo  
python src/examples/simple_bio_demo.py

# Verify project structure
python verify_structure.py

Expected output:

✅ Neo4j connection verified
🚀 Knowledge Graph Engine v2 initialized
   - Vector store: kg_v2 (neo4j)
   - Graph database: Neo4j (persistent)
   
=== Example: Semantic Relationship Handling ===
1. Adding: John Smith teaches at MIT
   Result: 1 new edge(s) created
...

🔍 Search Capabilities

The Knowledge Graph Engine v2 features advanced semantic search with:

• Performance Optimizations: Query caching, lazy loading, and optimized Cypher queries
• Dynamic Similarity Thresholds: Base threshold of 0.3 with context-specific adjustments
• Semantic Category Matching: Understands relationships between concepts (e.g., "technology" → "software engineer")
• Query-Specific Boosting: Different query types get tailored relevance scoring
• Geographic Intelligence: Recognizes European cities and other geographic relationships
• Safe Data Access: Robust error handling with safe accessor methods

Example Queries

# Technology and profession queries
"Who works in technology?" → Finds software engineers, developers, tech professionals
"Tell me about engineers" → Returns all engineering-related professions

# Geographic queries  
"Who was born in Europe?" → Finds Berlin, Lyon, Barcelona, Paris births
"Who lives in Paris?" → Returns all Paris residents

# Activity and interest queries
"What do people do for hobbies?" → Returns all "enjoys" relationships
"Tell me about photographers" → Finds people who enjoy or specialize in photography

# Entity-specific queries
"Tell me about Emma Johnson" → Returns all relationships for Emma

🧪 Testing

Run the comprehensive test suite:

# Core integration tests
python test_neo4j_integration.py

# Performance optimization tests
python test_optimizations.py

# Relationship fix validation
python test_relationship_fix.py

# Edge classifier tests
python test_classifier_system.py

# API export tests
python test_api_exports.py

# Quick validation
python test_quick_relationship_fix.py

# API server tests (from kg_api_server directory)
cd kg_api_server && pytest tests/

📈 Performance Benchmarks

Operation	Before Optimization	After Optimization	Improvement
Entity Exploration	20-50ms	8-15ms	~60% faster
Vector Search	100-200ms	40-80ms	~50% faster
Conflict Detection	150-300ms	50-100ms	~67% faster
Path Finding	80-160ms	25-50ms	~70% faster
Cached Queries	N/A	< 1ms	Near-instant

🔧 Development

For development setup and contributing guidelines, see docs/development/README.md.

Key Implementation Details

# Safe edge property access
edge = result.triplet.edge
if edge.has_graph_data():
    subject, relationship, obj = edge.get_graph_data()
else:
    subject = edge.get_subject_safe() or "Unknown"
    relationship = edge.get_relationship_safe() or "Unknown"
    obj = edge.get_object_safe() or "Unknown"

# Optimized queries with caching
cache_key = f"entity_exploration_{entity_name}"
if cached_result := self.graph_db._get_cache(cache_key):
    return cached_result
    
result = self.graph_db.get_entity_relationships_optimized(entity_name)
self.graph_db._set_cache(cache_key, result)

License

MIT License