ComputerAssignments/CA10_knowledge_graphs/docs/README_ADVANCED.md

🚀 CA10 Knowledge Graphs - Advanced Features

✨ Added Advanced Features

The CA10 Knowledge Graphs project has been enhanced with the following advanced capabilities:

🧠 LangGraph Integration

• Workflow-based Reasoning: Workflow-based reasoning with LangGraph
• Multi-Agent Systems: Multi-agent systems for complex reasoning
• State Management: Advanced state management for reasoning processes
• Async Processing: Asynchronous processing for better performance

🔍 RAG (Retrieval-Augmented Generation)

• Vector Search: Vector search with ChromaDB and FAISS
• Semantic Retrieval: Semantic retrieval from knowledge graphs
• Context-Aware Answers: Context-based answers
• Multi-Modal Search: Search across different data types

🌐 Advanced API

• FastAPI Integration: Complete API with FastAPI
• Interactive Web Interface: Interactive web interface
• RESTful Endpoints: Complete RESTful endpoints
• Real-time Visualization: Real-time visualization
• Auto-generated Documentation: Auto-generated documentation

📊 Enhanced Visualizations

• Interactive Networks: Interactive networks with PyVis
• Real-time Updates: Real-time updates
• Custom Layouts: Custom layouts
• Export Capabilities: Export capabilities in different formats

🛠️ Installation and Setup

1. Install Dependencies

pip install -r requirements.txt

2. Configure API Keys

# Copy sample file
cp env_example.txt .env

# Edit .env file and add API keys
# OPENAI_API_KEY=your_key_here
# ANTHROPIC_API_KEY=your_key_here

3. Run Project

# Complete execution with advanced features
./run.sh

# Or direct execution
python main_execution_advanced.py

🚀 Using the API

Starting API Server

# Start server with advanced scientific graph
python start_api.py --kg-type scientific --port 8000

# Start with auto-reload
python start_api.py --reload

Accessing API

• Web Interface: http://localhost:8000
• API Documentation: http://localhost:8000/docs
• Health Check: http://localhost:8000/health

Sample API Usage

import requests

# Query with advanced reasoning
response = requests.post("http://localhost:8000/query", json={
    "query": "What did Einstein discover?",
    "use_multi_agent": True,
    "include_reasoning": True
})

# Query with RAG
response = requests.post("http://localhost:8000/rag/query", json={
    "query": "Who are the most influential scientists?",
    "max_results": 10
})

🧠 Advanced Reasoning Features

Multi-Agent Reasoning

The system includes multiple specialized agents:

• Entity Agent: Handles entity recognition and extraction
• Relation Agent: Manages relationship discovery
• Inference Agent: Performs logical reasoning
• Coordinator Agent: Orchestrates multi-agent workflows

LangGraph Workflows

# Example workflow configuration
workflow_config = {
    "agents": ["entity", "relation", "inference"],
    "max_iterations": 5,
    "reasoning_depth": "deep"
}

🔍 RAG System Features

Vector Search Capabilities

• ChromaDB Integration: Efficient vector storage and retrieval
• FAISS Support: High-performance similarity search
• Semantic Embeddings: Context-aware entity embeddings
• Multi-Modal Search: Text, image, and structured data search

Retrieval Strategies

• Dense Retrieval: Semantic similarity-based retrieval
• Sparse Retrieval: Keyword-based retrieval
• Hybrid Retrieval: Combination of dense and sparse methods
• Contextual Retrieval: Context-aware retrieval strategies

📊 Advanced Visualizations

Interactive Network Visualization

• PyVis Integration: Interactive network graphs
• Real-time Updates: Dynamic graph updates
• Custom Styling: Customizable node and edge styles
• Export Options: PNG, SVG, HTML export

Embedding Visualizations

• PCA Projections: 2D projections of high-dimensional embeddings
• t-SNE Visualizations: Non-linear dimensionality reduction
• UMAP Projections: Uniform manifold approximation
• Interactive Plots: Zoomable and explorable visualizations

🎯 Performance Optimization

Scalability Features

• Batch Processing: Efficient batch processing of large graphs
• Memory Optimization: Optimized memory usage for large datasets
• Parallel Processing: Multi-threaded processing capabilities
• Caching: Intelligent caching for frequently accessed data

Monitoring and Logging

• Weights & Biases Integration: Experiment tracking and monitoring
• MLflow Support: Model versioning and tracking
• Comprehensive Logging: Detailed execution logs
• Performance Metrics: Real-time performance monitoring

🔧 Configuration Options

Environment Variables

# API Configuration
API_HOST=0.0.0.0
API_PORT=8000
RAG_API_PORT=8001

# Database Configuration
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your_password

# ChromaDB Configuration
CHROMA_PERSIST_DIRECTORY=./chroma_db

# Logging Configuration
LOG_LEVEL=INFO
LOG_FILE=logs/kg_execution.log

Advanced Configuration

# Advanced system configuration
config = {
    "reasoning": {
        "max_depth": 5,
        "timeout": 30,
        "parallel_agents": True
    },
    "rag": {
        "chunk_size": 512,
        "overlap": 50,
        "top_k": 10
    },
    "visualization": {
        "max_nodes": 1000,
        "layout": "force_directed",
        "interactive": True
    }
}

🧪 Testing and Evaluation

Automated Testing

# Run all tests
python test_setup.py
python test_api_keys.py

# Run specific tests
python -m pytest tests/

Performance Benchmarks

• Link Prediction: Evaluation on standard benchmarks
• Entity Resolution: Accuracy metrics for entity matching
• Reasoning Tasks: Performance on logical reasoning tasks
• Scalability Tests: Performance under different load conditions

📚 Additional Resources

Documentation

• API Documentation: Complete API reference
• Code Examples: Comprehensive code examples
• Tutorials: Step-by-step tutorials
• Best Practices: Recommended usage patterns

Community and Support

• GitHub Repository: Source code and issues
• Documentation Site: Online documentation
• Community Forum: User discussions and support
• Contributing Guide: How to contribute to the project

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Ready to explore advanced Knowledge Graph capabilities! 🚀