What is GraphRAG?
Ask a normal RAG system how three contracts relate and it hands back three separate passages. It never reads them as one story.
GraphRAG
graph-based RAG
GraphRAG is a retrieval method that builds a knowledge graph of entities and relationships from a document set. It retrieves connected nodes at query time instead of matching text chunks by similarity, so it answers questions about how things relate.
A pipeline builds the graph before any question arrives. A model reads the corpus and pulls out entities: people, companies, clauses. It links them by the relationships stated in the text. Who signed what. Which policy supersedes which.
At query time, retrieval walks the graph outward from the entities in the question, rather than ranking passages by how similar their wording is. That is the whole difference from standard RAG, and it is expensive to build well.
Where the graph earns its cost
Standard RAG is strong at "what does this document say". Ask "how are these things connected" and it struggles, because similarity search treats every passage on its own. Two clauses that reference each other by name can sit far apart in embedding space and never get retrieved together.
A graph stores the relationship explicitly instead. Trace a chain of ownership, an approval trail or a network of related cases by walking edges, not by hoping the right passages rank near each other. That helps on a narrow class of question. On the rest, it adds nothing.
- ExtractPull entities from the corpus
- LinkRelationships stated in the text
- ClusterGroup related entities
- QueryWalk the graph from the question
- UpdateRebuild as source documents change
The graph has to be built and rebuilt as the source documents change. That upkeep is the real cost.
01Is GraphRAG better than regular RAG?
Not generally. GraphRAG wins on questions that span multiple connected documents, such as tracing an approval chain or a network of related entities. On single-document lookups, plain RAG with chunking and re-ranking is cheaper to build and just as accurate.
02How do you know if you need GraphRAG?
Look at the actual questions users ask, not the shape of the data. If most questions can be answered from one passage, you do not need a graph. If users regularly ask how several documents or entities relate to each other, a graph is worth the extra build and maintenance cost.
03Does GraphRAG replace vector search?
No. Most production GraphRAG systems still use vector search to find the starting entities, then walk the graph from there. The graph adds a relationship layer on top of retrieval rather than replacing it.
