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Use vector retrieval when your LLM needs to find relevant passages by meaning; consider a knowledge graph when an answer depends on connecting entities and relationships across records. Combine them when you need semantic matching to find a starting point and graph traversal to gather connected evidence. The right choice depends on the questions your system must answer and the shape and quality of your data—not on a universal winner.
What is the difference between vector retrieval and a knowledge graph?
A vector database indexes embeddings: numeric representations of text or other content. At query time, the system embeds the user’s text and searches for nearby indexed vectors, often returning passages or records likely to be relevant. Similarity is a way to find candidates, not proof that a passage answers the question; results depend on the embedding model, chunking, filters, ranking, and source quality. Neo4j’s RAG documentation describes this basic pattern as retrieving text chunks through vector embeddings and passing them, with the question, to an LLM.
A knowledge graph represents entities—such as services, vendors, products, or people—and typed relationships between them. Retrieval can follow those connections to gather related records or passages. Instead of treating each text chunk as isolated, a graph can make an explicit path such as “support issue affects service; service is supplied by vendor” available for a query. The graph is only as useful as its schema, extracted facts, and entity resolution. Neo4j Labs’ GraphRAG overview describes combining entity and relationship extraction with graph-based retrieval and other techniques.
Which questions point to each approach?
Start with vector retrieval for semantic passage lookup
Vector-first retrieval is a sensible baseline when the user wants a relevant passage, a product description, or one fact that may be phrased in many different ways. It is especially practical when useful evidence already lives in documents and no dependable relationship model is available. Evaluate the whole retrieval pipeline: relevant results can be lost through poor chunk boundaries, weak embeddings, missing metadata filters, or unsuitable ranking.
#1 Best Overall
- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Consider graph traversal when the answer joins facts
Graph retrieval is a candidate when answering requires connecting facts spread across entities or records—for example, identifying which vendor is tied to a service affected by a particular support issue. A graph query can retrieve a connected subgraph or follow defined paths, rather than relying on a set of independently similar passages. That is a fit rationale, not a guarantee of better accuracy or speed.
Use a hybrid when semantic discovery and relationships both matter
A hybrid design can search vectors or full text to find likely starting nodes, then traverse the graph to collect related context. Neo4j’s Python documentation lists retrievers including VectorRetriever, VectorCypherRetriever, HybridRetriever, and HybridCypherRetriever; its VectorCypherRetriever searches a vector index before running a Cypher query from matching nodes. It also documents retrievers that work with external vector databases, including Weaviate, Pinecone, and Qdrant. See the retriever documentation for the available patterns.
Rank #2
Compare the design against your data and operating constraints
| Decision factor | Vector retrieval | Knowledge graph retrieval |
|---|---|---|
| Question shape | Find passages or records semantically similar to a query. | Connect entities and facts through defined relationships. |
| Source-data fit | Useful when evidence is primarily text and relationships need not be modeled explicitly. | Useful when entities and their relationships can be represented reliably. |
| Ingestion and refresh | Requires chunking, embedding, indexing, and refresh processes. | May require entity and relationship extraction, entity resolution, graph construction, and ongoing maintenance. |
| Retrieval design | Requires choices about embeddings, chunking, filters, and ranking. | Requires a graph model and queries or traversal patterns suited to the questions. |
| Evidence relationships | Returns similar items; connections between them are not inherently explicit. | Can expose paths and connected context when those relationships are represented. |
| Operating considerations | Embedding and index costs, relevance evaluation, and index maintenance. | Graph storage and query operations, plus potential extraction, resolution, and indexing costs. |
Microsoft’s Azure AI Search guidance emphasizes that retrieval and index design depend on content and query types, and distinguishes simpler classic RAG from more complex agentic retrieval. Use representative questions and real source data to test the alternatives rather than choosing by architecture label alone. Microsoft Learn: RAG and Generative AI.
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What extra work does graph-based retrieval add?
Graph retrieval depends on having useful entities and relationships, which may require more than loading documents into a store. Microsoft GraphRAG’s documented indexing pipeline can extract entities, relationships, and claims from raw text, perform community detection, generate reports at multiple granularities, and embed text. Its standard indexing method uses an LLM for entity and relationship extraction and summarization, optional claim extraction, and community report generation. Those steps add compute cost and quality-control needs: extraction mistakes or unstable entity resolution can distort the graph and subsequent retrieval.
Microsoft says indexing can be expensive. Its repository describes GraphRAG as a research project, says it is largely in maintenance mode and accepts no new feature work, and states that it is not an officially supported Microsoft offering. Treat it as a research and implementation reference, and verify its current status before adopting it. The repository’s own description calls GraphRAG “a platform for our research into RAG indexing methods that produce optimal context window content for language models.” See the GraphRAG methods, overview, and repository.
Quick Recap
Best Value
Rank #4
How should you evaluate a vector, graph, or hybrid design?
- Write representative questions. Include direct passage lookups and questions that require joining facts across entities. Separate answerable questions from those your source data cannot support.
- Inspect the source relationships. Determine whether entities and links are already reliable, or whether extraction, normalization, and entity resolution would have to create them.
- Build the least complex useful baseline. Try vector retrieval for semantic lookup. Add a graph only where explicit relationships enable a needed retrieval path; test hybrid retrieval when semantic matching can identify starting points for traversal.
- Check evidence and answer quality. Judge whether retrieved passages or graph paths actually support the answer, including cases with ambiguous names, missing links, and misleadingly similar text.
- Account for the full operating cost. Include indexing and refresh work, extraction compute, latency, query maintenance, monitoring, and the effort needed to correct bad entities or relationships.
Neo4j reports figures from two different studies in its vendor-authored comparison, which should not be treated as universal performance guarantees. It says a National Innovation Centre for Data (NICD) benchmark in 2026 tested three setups across 510 complex questions, with GraphRAG answering 65.3% versus 28.9% for vector-only RAG and roughly 80% higher truthfulness. Separately, Neo4j reports that a May 2026 IDC study sponsored by Neo4j found an average 44% reduction in hallucination rates among enterprises already using Neo4j in production. The studies have different setups and populations; the figures do not establish what your system will achieve. See Neo4j’s comparison for its reporting.
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