Welcome to the most advanced hybrid RAG system tutorial - where traditional vector similarity search meets the sophisticated intelligence of graph databases to create AI that truly understands complex relationships between concepts, entities, and ideas.
This isn't just another RAG tutorial. This repository contains the complete implementation of a hybrid retrieval system that:
- 🧠 Thinks Beyond Surface-Level Similarity: Discovers hidden connections across multiple degrees of separation
- 🔗 Navigates Complex Knowledge Networks: Follows chains of reasoning and traces causal relationships
- ⚡ Adapts Intelligence Dynamically: Uses LLM-powered query generation and intelligent filtering
- Knowledge Graph Engineering with Neo4j
- Multi-Vector Embedding Strategy for nuanced retrieval
- 11+ Advanced Graph Traversal Algorithms (BFS, DFS, A*, Beam Search, Context-to-Cypher)
- LLM-Powered Intelligence for entity extraction and dynamic queries
- Production-Ready Architecture with comprehensive error handling
- Initialize Knowledge Graph:
python ingestion.py - Start Querying:
python query.py - Explore Visually: Open Neo4j Browser at
http://localhost:7474
👉 Open the Complete Notebook Tutorial
The notebook contains:
- Comprehensive Setup Instructions
- Detailed Architecture Explanations
- Step-by-Step Implementation Guide
- Advanced Algorithm Deep-Dives
- Production Deployment Tips
- Python 3.8+
- Neo4j Database
- OpenAI API Key
- Docker (recommended for Neo4j)
Ready to master the future of intelligent information retrieval?
Start with the notebook tutorial and transform from a RAG novice into a hybrid AI architect! 🎓