Graph RAG vs vector RAG

GraphRAG · 6 min read

Vector RAG and graph RAG answer two different retrieval questions. Vector search finds text that's semantically similar to your query; graph traversal finds facts that are connected to a starting point. Neither is a strict upgrade of the other — and the strongest systems in 2026 use both.

Two different questions

Vector RAG asks "what in my corpus reads like this question?" It embeds everything into a vector space and returns nearest neighbours. That's exactly right for fuzzy, semantic recall over unstructured text.

Graph RAG asks "starting from this entity, what is it connected to?" It follows typed relationships. That's exactly right for multi-hop, structural questions where the answer is defined by connections rather than by wording.

Where vector RAG wins

For a large class of applications, vector RAG is the right and sufficient choice: unstructured documents, semantic search, "find me passages about X." It's simple to stand up, it degrades gracefully, and it's often cheaper — you don't have to model a schema first. If your questions are mostly "what does the corpus say about this topic," you may never need a graph.

Where graph RAG wins

Graph retrieval pulls ahead when questions span relationships: "what depends on the service that failed," "how is this account connected to a flagged one," "which decisions superseded this one and who owns the downstream systems." It's deterministic — the same query returns the same path — and that path is an inspectable trace, so answers are auditable. It also stays compact: retrieval is bounded by the neighbourhood you walk, not the size of the corpus.

The 2026 answer is hybrid

You rarely have to choose. The pattern that's winning is to use vectors for breadth and the graph for depth: embed to find good entry-point entities by semantic similarity, then traverse the graph to expand context and attach provenance. Vectors get you into the right neighbourhood; the graph tells you what's actually there and why it matters.

Building the hybrid with CognoDB

CognoDB is the graph half. Keep your vector store for embeddings and similarity — CognoDB doesn't do vector search — and use CognoDB to model the entities and relationships, then traverse them in Cypher. Full-text BM25 search inside CognoDB covers keyword entry points, and the MCP server lets an agent orchestrate the whole retrieval itself. The two systems compose: a vector hit becomes a node id, and that id is the entry point for the traversal.

At a glance

How the two retrieval styles compare

Vector RAGGraph RAG
RetrievesSimilar text chunksConnected entities & facts
Matches byEmbedding similarityTyped relationships (traversal)
Multi-hop questionsWeak — chunks are independentNative — that's the point
ResultProbabilistic, rankedDeterministic, a path
ExplainabilityMetadata on a chunkThe traversed path is the trace
Cost as corpus growsGrows with what you retrieveBounded by neighbourhood size
Best forSemantic search over textStructure, provenance, reasoning

Questions

Common questions.

Is graph RAG better than vector RAG?

Neither is universally better — they solve different problems. Vector RAG is best for semantic recall over unstructured text and is often cheaper and enough. Graph RAG is best for multi-hop, structural, auditable questions. Most production systems combine them.

Can I use both together?

Yes, and that's the recommended approach. Use vector search to find relevant entry-point entities, then traverse the graph from those entry points to expand context and attach provenance. CognoDB provides the graph half; your vector store provides the similarity half.

Does CognoDB include a vector index?

No. CognoDB is the graph/traversal engine and ships full-text BM25 search, not vector similarity. Pair it with a dedicated vector store for embeddings — the two integrate cleanly because a vector hit is just an entry-point node id for the traversal.

Which should I start with?

If your questions are 'what does the corpus say about X,' start with vector RAG. Add a graph when you find yourself needing multi-hop answers, deterministic results, or provenance you can audit. The free tier is a cheap way to prototype the graph half against your own data.

Keep reading

GraphRAG

What is GraphRAG?

GraphRAG grounds an LLM by traversing a knowledge graph instead of retrieving text chunks by similarity alone. Here's how it works, why it improves multi-hop answers, and how to build it.

Read

Concepts

What is a graph database?

A graph database stores data as nodes and relationships instead of tables and joins. Here's how the model works, why traversals beat joins for connected data, and when to use one.

Read

Guidance

When not to use a graph

Graph databases are powerful for connected data, but they're the wrong tool for several common workloads. Here's when to reach for Postgres, a columnar store, a queue or a vector index instead.

Read

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