GraphRAG beat naive RAG with 72-83% comprehensiveness win rates on 1M-token datasets. See when vector or graph wins in production. Read now.
Two Different Ways to Retrieve Knowledge
Retrieval-augmented generation lives or dies on what you feed the model before it answers. Vector databases and graph databases represent two philosophies for that retrieval step. A vector database stores text as embeddings and finds passages by semantic similarity: you ask a question, it returns the chunks that sit closest to your query in embedding space. A graph database, by contrast, stores entities and the relationships between them, so retrieval traverses connections rather than measuring distance.
Naive RAG is the vector approach in its simplest form — embed everything, pull the top matches, stuff them into the prompt. It is fast to build and works well when answers live inside a handful of passages. GraphRAG structures the same source material into an entity-and-relationship graph first, then retrieves by walking that structure, which lets it assemble context that no single passage contains.
Where Graph Retrieval Pulls Ahead
The advantage of graphs shows up on questions that span an entire corpus rather than sitting in one place. In evaluations on 1M-token datasets, GraphRAG posted comprehensiveness win rates of 72–83% against naive RAG — meaning that for broad, "what are the main themes across all of this" style questions, the graph-structured answers covered more of the relevant ground. That gap comes from structure: when the answer requires connecting facts scattered across many documents, similarity search alone tends to return several passages about the same idea while missing the links between ideas.
Graph retrieval is strongest when your questions are global and relational. Consider it when you need answers like these:
- Summaries that must synthesize themes across a whole document set, not quote one section.
- Multi-hop questions where the answer depends on how entities relate to each other.
- Domains with rich, explicit connections — org charts, dependencies, citations, supply chains.
Where Vector Search Still Wins
Comprehensiveness is not free. Building a knowledge graph means an extraction and indexing pass over your data, which adds cost, latency, and moving parts before a single query runs. For many production workloads that overhead is not justified. If your users ask specific, local questions — "what does this clause say," "how do I configure this feature" — the answer usually lives in one or two passages, and a vector index finds them quickly and cheaply.
Vector databases also update more simply: adding a document means embedding it and inserting it, with no graph to reconstruct. When your content changes constantly or your queries are narrow and fact-based, the simpler pipeline is often the better engineering choice, and the comprehensiveness edge of a graph never gets exercised.
Choosing for Production
The practical decision comes down to the shape of your questions and the structure of your data. Reach for vector search when queries are specific, latency matters, and content churns; reach for graph retrieval when queries are broad, relationships carry meaning, and completeness of coverage is what users judge you on. Many teams end up running both — a vector index for pinpoint lookups and a graph layer for synthesis — and routing each query to whichever retrieval method fits it. Start by categorizing a representative sample of your real questions as local or global, and let that split, not the benchmark headline, drive the architecture.