# Graph RAG

**Source:** https://promtable.com/glossary/graph-rag

> Graph RAG builds a knowledge graph from the corpus during ingestion — entities, relationships, facts — and retrieves via graph traversal alongside vector search, improving recall on relational queries.

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Graph RAG builds a knowledge graph from the corpus during ingestion — entities, relationships, facts — and retrieves via graph traversal alongside vector search, improving recall on relational queries.

Standard vector RAG retrieves chunks by similarity. Graph RAG (popularised by Microsoft GraphRAG and Anthropic's contextual retrieval evolutions in 2024-2026) adds a knowledge graph built during ingestion: entities (people, products, concepts), relationships (X works at Y, X subsumes Y), and propositions. Retrieval traverses the graph for relational context plus standard vector search. Empirically beats vector-only RAG on questions that require multi-hop reasoning ("Which products use the same supplier as X?"). Cost trade-off: graph construction is expensive (LLM-driven), and graph storage adds ops. Worth it for high-value relational corpora.

## When to use

- Corpora with rich relational structure (org charts, supply chains, scientific literature).
- Multi-hop questions where standard RAG fails.

## Common mistakes

- Building a graph for corpora where flat chunks would suffice — cost without benefit.
- Skipping the standard vector retrieval — graph alone misses semantic similarity.

## Related terms

- [rag](https://promtable.com/glossary/rag)
- [embeddings](https://promtable.com/glossary/embeddings)
- [contextual-retrieval](https://promtable.com/glossary/contextual-retrieval)
- [multimodal-rag](https://promtable.com/glossary/multimodal-rag)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/graph-rag
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