Choose vector retrieval when your LLM needs to find a relevant passage or semantically similar item; choose graph retrieval when answers depend on explicit links among entities and records. Use both when a semantic search can find a starting point but the answer needs connected context. The right choice follows the questions users ask and the structure and quality of your data—not a universal winner.
What each approach retrieves
Vector retrieval finds semantic matches
A vector database stores embeddings—numerical representations of content—and searches for items close to a query in that vector space. A text query must first be embedded. In a common RAG pattern, the system retrieves text chunks and supplies them, with the question, to an LLM. Results depend on the embedding model, chunking, filters, ranking, and source quality; similarity is not a guarantee that a passage answers the question. Neo4j’s RAG documentation describes vector retrieval and related retriever patterns.
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A knowledge graph makes relationships explicit
A knowledge graph represents entities—such as services, vendors, and incidents—and typed relationships between them. A query can retrieve an entity or passage, then traverse links to gather related records. This is useful when an answer depends on how facts connect, rather than on finding one passage that resembles the wording of the question. Neo4j’s GraphRAG overview describes combining entity and relationship information with vector and graph retrieval.
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Which questions fit each store?
| Question or data condition | Good starting point | Reason |
|---|---|---|
| “Find the policy passage about reimbursing home-office equipment.” | Vector retrieval | The answer is likely in one relevant passage, even if the user phrases the request differently from the source. |
| “Which vendor, service, and outage are connected to this support issue?” | Graph retrieval, or hybrid | The answer requires joining facts across records through explicit relationships. |
| “Find discussions like this one, then show the affected products and owners.” | Hybrid retrieval | Semantic search can locate likely starting records; graph traversal can expand to connected entities and evidence. |
| Data already contains reliable identifiers and relationships | Graph or hybrid | Existing structured links can support traversal without reconstructing every relationship from raw text. |
| Mostly unstructured documents with no dependable entity model | Vector first; assess graph extraction effort | Vector retrieval can index chunks directly. A graph requires a useful schema and reliable entity and relationship data. |
These are design heuristics, not performance guarantees. Test representative questions against the actual data: a single-fact query can still fail with poor chunking, while a graph cannot answer a relationship that was never modeled or extracted.
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When a hybrid design earns its complexity
A hybrid retriever can use vector or full-text search to identify a starting entity or passage, then run a graph query to fetch connected context. Neo4j documents VectorCypherRetriever, which searches a vector index and follows with a Cypher traversal, alongside vector, hybrid, and hybrid-plus-graph retrievers. The vector index and graph need not be the same storage system; its documentation also covers external vector databases. See the retriever guide.
Hybrid retrieval is justified when connected context materially improves answers, citations, or traceability and the organization can maintain the graph. It is unnecessary complexity if users mainly need a relevant passage and retrieval quality is already adequate. Evaluate it with real questions and inspect whether the added edges contribute evidence rather than merely more context.
What graph retrieval costs beyond vector search
- Modeling: Define entity types, relationship types, identifiers, and the scope of traversals that support the questions users ask.
- Extraction and resolution: Raw text may need entity and relationship extraction, plus entity resolution so different mentions of the same thing connect correctly. Errors or inconsistent naming can undermine downstream retrieval.
- Refresh and quality control: Keep relationships current as source records change, and check extraction quality before relying on graph paths as evidence.
- Query and operations: Design and maintain graph queries, indexes, permissions, and monitoring alongside the rest of the retrieval stack.
- Compute and latency: Graph construction can add indexing work, and traversal adds retrieval steps. Measure end-to-end latency and operating cost for the workload rather than assuming either architecture is faster.
Microsoft’s GraphRAG indexing pipeline can extract entities, relationships, and summaries, optionally extract claims, detect communities, generate reports, and embed text. Each stage introduces compute requirements and quality-control decisions. Its methods documentation outlines the indexing approach, while the overview describes outputs including Parquet tables and embeddings in a configured vector store.
How to make the decision
- Write down representative questions. Include simple passage lookups and questions that require combining facts. Identify which answers must be traceable to source records.
- Map each answer to its evidence. If one chunk can support the answer, evaluate vector retrieval. If the answer must follow links among entities or records, identify those links and whether they already exist.
- Build the simplest viable baseline. Tune chunking, embeddings, filters, and ranking for vector retrieval before adding a graph layer.
- Add graph retrieval only for a demonstrated gap. Define the needed schema and assess extraction, entity resolution, refresh, and query maintenance. If a vector match can seed a useful traversal, test a hybrid design.
- Evaluate on the same questions. Compare answer correctness, evidence coverage, traceability, latency, indexing and refresh cost, and operational burden. Keep the extra layer only if it improves outcomes that matter to the application.
Microsoft Azure AI Search’s RAG overview likewise emphasizes that retrieval and index design depend on the content and query types, and distinguishes simpler classic RAG from more complex agentic retrieval.
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How to interpret published GraphRAG results
Neo4j’s vendor-authored comparison reports that a 2026 National Innovation Centre for Data (NICD) evaluation across 510 complex questions found GraphRAG answered 65.3% of questions versus 28.9% for vector-only RAG, and reported roughly 80% higher truthfulness for GraphRAG. Those figures describe the reported evaluation, not a general guarantee for other datasets or systems. The same article reports a separate May 2026 IDC study, sponsored by Neo4j, finding an average 44% reduction in hallucination rates among enterprises already using Neo4j in production. The two results come from different studies and populations and should not be treated as directly comparable. See Neo4j’s comparison for its account of both studies.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is Microsoft GraphRAG a supported product?
Microsoft describes GraphRAG as research into RAG indexing methods and warns that indexing can be expensive. Its repository says it is largely in maintenance mode, accepts no new feature work, and is not an officially supported Microsoft offering. Treat it as a research and implementation reference, and verify its current status before adopting it. See the Microsoft GraphRAG repository and its methods documentation.
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