Knowledge graphs vs vector search for RAG
When people talk about RAG, they usually mean vector search. But vector search has a blind spot, and for a whole class of questions a knowledge graph is the better tool. The strongest systems use both.
What each one is good at
Vector search embeds text into numbers and finds passages that are semantically similar to a question. It is excellent at “find me content about X,” even when the wording differs. It is the right default for most document Q&A.
A knowledge graph stores entities and the relationships between them: this company owns that subsidiary, this patient had that visit, this claim belongs to that policy. It is excellent at questions where the answer lives in the connections, not in a single passage.
Where vector search falls short
Ask a vector store “which entities are in-network for this plan,” and it will happily return the most similar-sounding passages, which may be completely wrong. The information is relational, and a pile of similar text chunks cannot express it. This is where teams see confident, wrong answers.
Where the graph wins
On a FinTech information-retrieval platform, we combined Neo4j knowledge graphs for entity relationships with vector embeddings for unstructured content. The dual-module approach lifted query accuracy by 40% over a vector-only baseline, because each half answered the questions the other could not.
How to combine them
- Use vector search for open-ended, content-based questions.
- Use the graph for questions about relationships, hierarchy and structure.
- Add a routing or hybrid layer that sends each query to the right retriever, or fuses both.
You do not have to choose. The practical answer is a hybrid retrieval system where vector search and a knowledge graph cover each other’s weaknesses.
EpochC builds hybrid retrieval systems that combine vector search and knowledge graphs. Start a project or hire on Upwork.