What is GraphRAG?
GraphRAG is a retrieval-augmented generation approach that leverages knowledge graphs instead of or in addition to vector-based retrieval. Rather than searching for semantically similar text chunks, GraphRAG retrieves information by traversing entity-relationship structures in a knowledge graph, enabling more precise retrieval of connected facts and supporting multi-hop reasoning that requires following relationships between entities.
The approach typically involves extracting entities and relationships from documents to build a knowledge graph, then using graph traversal or graph queries to find relevant information when answering questions. For example, when asked "Who are Alice's colleagues?", a GraphRAG system might find the Alice entity, traverse "works_at" relationships to find her company, then traverse back to find other employees. This structured retrieval can be more accurate than semantic search for questions requiring precise relational reasoning.
GraphRAG is particularly powerful for domains with rich entity relationships like scientific literature, business intelligence, legal documents, or technical documentation. It can be combined with traditional vector RAG in hybrid systems that use graph-based retrieval for structured queries and vector search for semantic similarity. Microsoft Research has developed a notable GraphRAG implementation that builds knowledge graphs from documents and uses them to improve retrieval quality, demonstrating significant advantages for complex analytical questions.