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LIGHTRAG
Simple and fast retrieval-augmented generation with graph-based document retrieval
MIT
ABOUT
Traditional RAG systems struggle with multi-hop reasoning, context diversity, and incremental updates — retrieving isolated chunks rather than understanding document relationships. LightRAG solves this by constructing a knowledge graph from documents so retrieval follows entity relationships, supports incremental graph updates without full re-indexing, and combines dense vector search with graph traversal for more accurate answers on complex queries.
INSTALL
pip install lightrag-hkuINTEGRATION GUIDE
1. Build knowledge-graph-powered RAG systems for enterprise document question answering
2. Perform multi-hop reasoning across complex, interconnected document collections
3. Incrementally add new documents with automatic graph regeneration and deduplication
4. Enable hybrid search combining dense embeddings and graph traversal for higher recall
5. Support multimodal RAG pipelines with text, image, and table extraction integration
TAGS
ragknowledge-graphgraphragretrievalllmgenaihybrid-searchemnlppython