Knowledge Graph vs. Vector Database for RAG: When Each One Wins
- Pick by question shape. Similarity questions want vectors; path questions want a graph; most real systems ask both kinds.
- Similarity always returns its closest match and never signals that the match was weak. Count what that costs before you count setup time.
- If someone will ask why an answer came back, you need a traversal. A path is evidence; a passage is an assertion.
- Neither one settles a contested definition, so fix that first or both will serve whichever version they happened to reach.
What are vector search and GraphRAG?
Both are answers to the same problem: an AI system cannot hold your business in its head, so something has to hand it the relevant material at question time. That is retrieval, and it is the layer that sets the ceiling on everything above it.
Vector search converts your content into numerical representations and finds the passages that sit closest to the question. Chunk, embed, index, query. It is looking for text that reads like the answer.
GraphRAG models entities and the relationships between them, then walks that structure at question time and hands the model a connected subgraph. It is looking for a path.
Neither is an upgrade on the other. They fail differently and they succeed on different questions.
What actually differs between them?
Vector search is probabilistic and graph traversal is not. That is the whole difference and everything else follows from it.
A similarity search always returns its closest match, whether or not the match is any good, and it has no way to tell you the match was weak. A traversal either connects or it does not. That makes vector search forgiving of messy input and unreliable at the edges, and graph traversal unforgiving of messy input and precise once it holds.
The second difference is what each one costs to stand up. An index is a week. A useful graph is a modelling exercise, and the modelling is the project.
| Approach | What it is | Where it earns its place |
|---|---|---|
| Vector search | Content embedded as vectors, retrieved by similarity to the question | Support deflection across a decade of tickets, where the same problem is described a hundred ways. Policy and contract search, where the phrasing in the question never matches the phrasing in the document. |
| GraphRAG | Entities and relationships modelled as a graph, retrieved by traversal | Supplier exposure across tiers after a recall, where the answer is a path nobody has written down. Entitlement questions that run from a parent agreement to a subsidiary to a specific contract. |
When should you choose GraphRAG?
When the answer is not in any single document and has to be assembled.
Use it when questions cross several entities, when the same real-world thing appears in several systems under different identifiers, or when someone will eventually ask why an answer came back. That last one is underrated. The traversal is the explanation, so the system can show its reasoning rather than assert that a passage was relevant.
Do not use it when your corpus is unstructured prose with no stable entities in it. You will spend the project extracting a graph that your questions never traverse.
When should you choose vector search?
When the answer exists in text and the hard part is finding it.
It tolerates paraphrase, handles synonyms nobody enumerated, and degrades gracefully on questions phrased unlike anything in the corpus. Over a large, messy, human-written body of material it extracts value a graph will not, without modelling work you may never recoup.
Do not use it alone when precision matters more than recall, or when a wrong answer is expensive. Similarity will always hand you something.
| Vector search | Knowledge graph | |
|---|---|---|
| Strength | Breadth and recall over messy text | Precision and multi-hop reasoning |
| Strength | Fast to stand up, cheap to run | Explainable by construction |
| Weakness | Always returns something, never flags a weak match | Modelling cost is real and front-loaded |
| Weakness | No notion of identity across systems | Brittle if entities were never resolved |
| Fails by | Returning a plausible passage that is out of date or contradicted elsewhere | Returning a confident path built on relationships nobody ratified |
What are both of them missing?
Ground truth. Neither one decides what is true.
This is easy to miss because both feel like they are answering the question. Vector search returns the closest passage. Traversal returns the connected path. Both are doing exactly what they were built to do, and neither has any view on which of two definitions of an active customer the business actually stands behind.
So when finance and sales hold different ones, both architectures will serve whichever version they happened to reach, with the same confidence they use when they are right. If the relationships in your graph were estimated rather than agreed, the path reflects what a model worked out about your business rather than how your business works. If the passages in your index contradict each other, similarity picks the closest one and never mentions the other.
The retrieval decision is real and worth making carefully. It is also downstream of a decision most teams have not made: which terms, rules and identifiers the organization has actually settled, recorded somewhere a system can reach at query time, with an owner attached. Ontologies are how those relationships get expressed so they stay stable as the business changes.
Get that right and the retrieval question becomes what it should always have been, an implementation choice rather than the thing your answers depend on. Context management is the discipline that gets you there, a context graph is what the structure looks like when rules and processes travel with the entities, and GraphRAG goes deeper on the retrieval pattern itself.
