Graph RAG: Knowledge-Graph-Based Retrieval
~12 min read
Graph RAG converts retrieved content into a knowledge graph capturing entities and their relationships, giving the LLM structured relational context alongside raw text — well suited to questions that span multiple connected entities.
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Key points
- •Vector similarity is good at finding semantically-similar text, but weak at answering relationship-between-entities questions
- •Graph RAG converts retrieved content into a knowledge graph — entities as nodes, relationships as edges
- •Retrieval can then traverse graph structure (following relevant edges), not just match on semantic similarity
- •Well suited to naturally graph-shaped domains: org structures, supply chains, citation networks, biomedical relationships
- •Trade-off: requires an entity/relationship extraction step and graph infrastructure that plain vector RAG doesn't need — substantial overhead for simple fact-lookup use cases