Vector RAG vs. GraphRAG: Which retrieval do you need?

A vector-only pipeline can be a great fit for your agentic AI project — right up until an accurate, explainable response depends on how disparate facts connect.

Take a support example, where a manager asks which vendor’s outage caused three tickets last week. The tickets, the vendor record, and the outage report are all in the index, but none mention each other by name, so a similarity search has no way to string them together. The result: an incomplete or inaccurate answer, or none at all.

That’s the limit of similarity search, which is what vector RAG uses to provide an LLM with relevant information at query time. It ranks text by how closely it reads like the question, not by how the underlying facts relate. GraphRAG takes a different approach, which is why it has been shown to more than double factual accuracy, offer better token efficiency, and provide a traceable path back to each answer’s sources.

So what’s the difference between GraphRAG and vector RAG? And will adding GraphRAG to your pipeline be worth the investment? In this blog post, you’ll get the answers. We share how each approach retrieves context, look at where GraphRAG’s complexity earns its keep, and walk through sample code to show you how it works.

Vector RAG vs. GraphRAG

Before getting into the mechanics of each one, here’s how the two approaches compare in how they retrieve context, handle connected questions, offer explainability, and fit different use cases.

Where vector RAG’s similarity search breaks down

Vector RAG uses semantic similarity to find the text most relevant to a question. It chunks and embeds the source content and query, then ranks the best matches by vector similarity, a common retrieval pattern in standard RAG. Someone searching a support knowledge base for the phrase “can I send this back after a month” can get a return policy that never uses those exact words, because embeddings capture meaning rather than phrasing. That’s why this kind of matching works well in documentation search, product discovery, and anywhere people describe the same idea a dozen different ways.

However, this model doesn’t always work. For a question like “which vendor’s outage caused three tickets last week?”, a similarity search can return the ticket, the service record, and the outage record if the semantic phrasing lines up, but it has no notion of “connects to.” The meaning is lost.

Retrieval size isn’t the issue, either. In fact, it can create problems in both directions. Set top_k (the number of chunks returned) too low, and useful evidence gets left out of the prompt. Set it too high, and you pull in repetitive or tangential chunks. Regardless, it can’t understand relationships that the vectors never stored in the first place. The returned chunks, metadata, and similarity scores don’t tell you how those passages are contextually relevant.

Why GraphRAG works better for complex questions

Instead of ranking by similarity, GraphRAG follows relationships in a graph. The graph can be a knowledge graph that captures facts and relationships between them, or a context graph that also includes situational information such as current state, conversation history, prior decisions, and reasoning traces.

Model our example’s support data (tickets, services, vendors, outages) as a graph, and your agent can traverse those relationships to find relevant data. Tickets connect to the services they reference, services connect to the vendors that run them, and vendors connect to the outage records logged against them. The data itself captures the path between those records through their relationships.

GraphRAG retrieval still often starts with a search. A vector lookup finds the ticket that matches the question most closely, or a full-text search locates an entity by name. From that initial point, a graph traversal follows the relationships, pulling in relevant records that a similarity ranking would never return. 

Neo4j’s VectorCypherRetriever, for example, runs a vector search first and hands the matching node to a Cypher query that expands from it; HybridCypherRetriever does the same thing but starts from a combined vector and full-text search instead.

For the outage question, this means you can get the right response in a single retrieval pass. A vector pipeline would require three separate lookups to retrieve those same records, but it would have no way to confirm how they relate to one another.

GraphRAG outperforms vector-only RAG

A 2026 study run independently by the U.K.’s National Innovation Centre for Data (NICD) tested three setups — no database, vector-only, and vector plus graph against 510 complex questions. GraphRAG scored roughly 80% higher on truthfulness than vector-only RAG, and successfully answered more than 65.3% of the complex questions, whereas vector-only RAG only answered 28.9%.  The study also improved token efficiency because GraphRAG could target the relevant information they needed rather than reading entire articles. 

A separate IDC study sponsored by Neo4j, published in May 2026, examined enterprises already running Neo4j in production and found GenAI hallucination rates dropped by an average of 44%. (Note: NICD tested a specific question-answering setup, while IDC examined organizations already running Neo4j in production, so the findings are not directly comparable.)

GraphRAG also leaves a trail, since the nodes and relationships used to assemble an answer stay visible after the fact. That matters whether you’re debugging a wrong result or explaining a correct decision. Because those relationships are explicit and traceable, teams can apply governance controls to the GraphRAG and trace the supporting context back to its source.

Does GraphRAG replace vector search?

GraphRAG doesn’t replace vector search. Vector search is still the right tool when a question can be answered from one relevant passage with semantic matching. GraphRAG is a better fit for questions that require multi-hop reasoning, where the relationships between facts matter. 

The line between them is fluid in practice. In GraphRAG, a graph can store vector embeddings inside its entities and relationships, and vector search often serves as  the starting point for graph traversal. 

The query below shows the retrieval pattern, taking the support-ticket question and tracing the outage back to a vendor. It uses the neo4j-graphrag package (pip install neo4j-graphrag).

from neo4j_graphrag.retrievers import VectorCypherRetriever

graph_retriever = VectorCypherRetriever(
    driver,
    index_name="ticket_embeddings",
    embedder=embedder,
    retrieval_query="""
    WITH node AS ticket
    MATCH (ticket)-[:AFFECTS]->(service:Service)
          <-[:RUNS]-(vendor:Vendor)
          -[:HAD]->(outage:Outage)
    RETURN ticket.text AS ticket_text,
           service.name AS service,
           vendor.name AS vendor,
           outage.summary AS outage_summary
    """
)

First, the vector index finds the closest matching ticket. Then the graph query continues from that match, traversing the graph to return connected context, such as the affected services, the vendors that run them, and their outages. 

If you want to put hybrid retrieval (vector and graph) into practice, follow this step-by-step guide to combining full-text and vector search with graph traversal using HybridCypherRetriever.

How to choose the right retrieval for your pipeline

Look at the retrieval failure before changing the architecture. Your results will tell you which retrieval architecture is right for your situation.

Poor semantic matches point to a tuning problem — chunking, embeddings, or filtering. Consistently retrieving the right facts without the relationships between them points to something a tuning pass won’t fix.

To tell which one you have, build a test set that mirrors your production traffic: clean and messy phrasing, single-hop lookups, multi-hop questions, and cases where the answer needs evidence from more than one source. Run it through your pipeline and compare context precision (the share of retrieved results that were relevant), context recall (the share of needed evidence you retrieved), answer faithfulness, latency, and token cost. If GraphRAG doesn’t move those numbers, the added complexity of modeling and maintaining a graph isn’t worth it. If it does, that’s your evidence to build it.

Get started with GraphRAG

Vectors retrieve, graphs reason. For complex questions that depend on connected facts, GraphRAG performs better than vector-only retrieval. Preserving the relationships among those facts can give an LLM the context needed to answer questions that similarity alone struggles to resolve, and also offers a traceable path back to the evidence behind the answers.

You don’t need to choose one architecture over the other:    

Neo4j supports vector search and graph traversal in the same database, so you can add GraphRAG when needed, and build from there with the GraphRAG Python package. 

FAQs: Vector RAG vs. GraphRAG