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RAGFreeOpen Source
KAG
Knowledge-augmented generation with logical reasoning
Apache-2.0
ABOUT
Traditional RAG performs shallow vector similarity search that misses cross-document logical relationships and struggles with multi-hop reasoning. GraphRAG approaches using OpenIE introduce noise from automatic triple extraction. KAG solves this with a logical form-guided retrieval framework that models document knowledge as structured SPG (Semantic Property Graph) and performs bidirectional enhancement between LLMs and KGs. It supports logical reasoning, multi-hop Q&A, and factual querying over professional domain knowledge bases — achieving significantly higher precision on complex knowledge tasks than both vector RAG and naive GraphRAG.
INSTALL
pip install openspg-kagINTEGRATION GUIDE
1. Build a domain-specific Q&A system over legal, medical, or financial knowledge bases with logical reasoning
2. Create a knowledge graph-powered RAG pipeline that handles multi-hop questions requiring cross-document inference
3. Power an enterprise knowledge assistant that answers factual queries with traceable evidence from structured knowledge
4. Implement a hybrid retrieval system combining vector search with knowledge graph traversal for maximum accuracy
5. Replace naive vector RAG in scenarios where answer precision and logical consistency are critical
TAGS
ragknowledge-graphgraphragreasoningknowledge-basellmopen-source