GraphRAG vs Vector RAG: What's Different

When adopting RAG (retrieval-augmented generation) in the enterprise, the first fork you hit is vector search versus knowledge-graph retrieval (GraphRAG). They solve slightly different problems.

Vector RAG — semantic similarity

Documents are embedded into a vector space, and the chunks semantically closest to the question are retrieved.

• Good at: finding "similar content," fast to build
• Weak at: questions that require connecting scattered facts — multi-hop relationships like "Among the systems that use part A, which are subject to regulation B?"

Vectors know what is "close," but not "how things connect."

GraphRAG — the structure of relationships

You model entities (people, parts, organizations, regulations…) and the relationships between them as a graph. When a question arrives, semantic search finds an entry point, then you traverse the graph to gather connected facts.

Aspect	Vector RAG	GraphRAG
Finds	similar text	connected facts
Strength	simple queries, speed	multi-hop reasoning
Hallucination	provides evidence chunks	traceable by path

Why hallucination drops

GraphRAG can present the basis of an answer as a path. When "this conclusion came from the X→Y→Z relationship" is visible, a human can verify it and the LLM is less likely to wander off. You can even confirm that path directly with a query like Cypher.

So which do you use

It isn't either/or. Enter with semantic search → expand with the graph is the strongest combination in practice. That is the direction ONTOFLOW takes too — because in deep industries, "relationships" are the core of value.