Project Overview
A lightweight implementation of Graph RAG combining Neo4j knowledge graphs with vector search for enhanced context retrieval.
Problem Definition & Goals
- Problem: Traditional RAG struggles when connected information is spread across document chunks.
- Goal 1: Build a hybrid retrieval system leveraging structured relationships and unstructured text.
- Goal 2: Implement automatic knowledge graph construction from documents.
- Goal 3: Improve retrieval quality for multi-hop reasoning queries.
Key Features & Contributions
- Automatic Graph Construction: LLM-based entity and relation extraction.
- Neo4j Integration: Efficient graph queries with Cypher support.
- Hybrid Retrieval: Combines graph traversal with vector similarity search.
- Multi-hop Reasoning: Supports complex queries traversing multiple relationships.
Technical Challenges & Solutions
- Entity Resolution: Implemented normalization and clustering-based resolution.
- Relation Extraction Quality: Added filtering and validation steps.
- Query Complexity: Implemented adaptive routing based on query type.
- Scalability: Optimized with indexing and caching strategies.
Results & Learnings
- Retrieval Improvement: Graph-based retrieval significantly improved answer quality.
- Knowledge Discovery: Graph visualization enables discovering hidden connections.
- Educational Value: Implementation serves as learning reference.
- Key Learning: Gained expertise in knowledge graphs and hybrid architectures.