Knowledge Graph vs Vector Database (and When to Use Both)

A vector database stores numerical embeddings and answers “what is similar to this” by nearest-neighbor search, while a knowledge graph stores explicit, typed relationships between named entities and answers “how exactly are these things connected” by traversal. The vector database is good at fuzzy recall over unstructured text; the knowledge graph is good at precise, explainable facts and multi-hop reasoning. In retrieval-augmented generation (RAG) the two are increasingly combined, with the graph supplying structure and provenance and the vector index supplying semantic recall, a pattern usually called GraphRAG.

Two ways of representing what is known

A vector database holds embeddings: fixed-length arrays of floating-point numbers produced by a model such as OpenAI’s text-embedding-3 or an open-source sentence transformer. Each embedding represents a chunk of text, an image, or another object, arranged so that semantically similar inputs land near each other. The database indexes those vectors (commonly with HNSW or IVF structures) so that, given a query vector, it can return the k closest stored vectors along with attached metadata. Relationships between items are implicit: two chunks are related to the degree that their vectors are close, and nothing in the store says why.

A knowledge graph holds entities and typed edges. Marie Curie is a node with an identifier; the Nobel Prize in Physics is another; between them is an edge labeled “awarded”, possibly with a year attached. Every relationship is explicit, named, and inspectable. Queries follow edges: from Curie to her prizes, from each prize to its other recipients, from those recipients to their institutions. The graph can say exactly why two entities are connected. See what is a knowledge graph for the full definition.

The phrase “vector database vs graph database” is often used for the same comparison. Strictly, a graph database is the storage engine (Neo4j, GraphDB, Neptune) and a knowledge graph is the data it holds; see knowledge graph vs graph database. Here the two can be read together, since the point is the data model rather than the product.

Comparison table

Aspect	Vector database	Knowledge graph
Data model	High-dimensional vectors with attached metadata; one vector per chunk or object	Nodes (entities) and typed, directed edges, often with properties; RDF triples or labeled property graph (LPG)
Relationships	Implicit, expressed as distance in embedding space	Explicit, named, and typed (awarded, locatedIn, subclassOf)
Query type	Approximate nearest-neighbor similarity search, optionally filtered by metadata	Traversal, pattern matching, and path queries in SPARQL, Cypher, or GQL; exact match on identifiers
Typical question	“Which passages are about radioactivity research in the early 1900s?”	“Which Nobel laureates were doctoral students of other Nobel laureates?”
Strengths	Handles unstructured text, tolerant of wording variation, fast to build from documents, no schema needed	Precise answers, multi-hop reasoning, explainability (every hop is a stored fact), deduplication through identifiers, constraint checking
Weaknesses	Cannot explain why results match, struggles with aggregation and multi-step logic, no notion of entity identity, retrieval quality depends on chunking and embedding model	Requires extraction or curation to build, schema decisions up front, brittle when the question uses words not in the graph, sparse for long-tail facts
Schema	None beyond metadata fields	Ontology or labels (RDFS, OWL, SHACL, schema.org, or LPG conventions)
Update cost	Re-embed changed chunks	Add or retract triples or edges; identifiers stay stable
Typical tools	Pinecone, Weaviate, Qdrant, Milvus, Chroma, pgvector (PostgreSQL extension), Elasticsearch and OpenSearch vector fields	Neo4j, GraphDB, Stardog, Amazon Neptune, Apache Jena Fuseki, Memgraph, Wikidata (as a public source)

Graph search vs vector search in practice

Vector search excels when the question is phrased in natural language and the answer lives somewhere in a body of documents. It does not care whether the query says “cancer” and the passage says “malignant tumor”; the embeddings are close. It fails in characteristic ways. It cannot count. It cannot reliably answer “all X that satisfy Y” because it returns the k most similar items rather than the complete set. It retrieves passages about Pierre Curie when asked about Marie because the two co-occur in text. And when it returns a wrong result, there is no chain of evidence to inspect, only a similarity score.

Graph traversal excels when the question is structural. “Which universities employed two or more Nobel laureates in physics before 1930” is a pattern-matching query over typed edges, and a graph answers it completely, with each hop available as justification. The graph fails when the question uses a concept that was never modeled. If there is no edge type for “influenced by”, no traversal will find intellectual influence, however well documented in the source texts. Building the graph also requires manual modeling or an extraction pipeline (entity linking, NER, and extraction) that introduces its own errors.

The two failure modes are close to complementary, which is why the combined architecture has become the usual recommendation for retrieval systems that need both recall and correctness.

Worked example: two questions, two systems

Take a corpus of biographies of twentieth-century scientists, indexed both as text chunks in a vector database and as an extracted knowledge graph.

Question one: “What did early researchers believe about the health effects of handling radioactive materials?”

This is a vector search question. There is no entity called “beliefs about health effects” in any graph. The answer is scattered across passages that use different wording, and the best response is a handful of relevant paragraphs handed to a language model for synthesis. A query embedding compared against the chunk index returns passages about Curie carrying test tubes of radium in her pockets and about early radiologists’ skin lesions. A graph would have nothing to traverse.

Question two: “Which scientists in this collection won a Nobel Prize in the same field as their doctoral advisor?”

This is a graph question. It joins relationship types (doctoral advisor, award received) across pairs of entities and returns the complete set of matches. Vector search would return passages mentioning advisors and prizes, but no passage states the answer, and the model would be left to assemble it from partial evidence with no guarantee of completeness. In SPARQL against a Wikidata-style graph the query is direct:

PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?student ?advisor ?prize WHERE {
  ?student wdt:P184 ?advisor .      # doctoral advisor
  ?student wdt:P166 ?prize .        # award received
  ?advisor wdt:P166 ?prize .        # advisor received the same prize
  ?prize   wdt:P31  <http://www.wikidata.org/entity/Q7191> .  # instance of: Nobel Prize
}

Every row returned is backed by three stored facts that can be shown to the user. That is the property vector search cannot offer.

When to use which

Use a vector database alone when the corpus is unstructured text, questions are open-ended, exact completeness is not required, and there is no budget or need to model the domain. Customer support search over help articles, semantic search over a document archive, and first-pass retrieval for a chatbot fit this description.

Use a knowledge graph alone when the data is already structured or can be reliably extracted, questions involve exact relationships, aggregation, or multi-hop logic, and answers must be explainable or auditable. Product catalogs with compatibility rules, regulatory and compliance data, master data about customers and organizations, and any domain where “show me why” is a requirement fit here.

Use both, in a GraphRAG arrangement, when a language model must answer questions over a domain with both a structured core and a long tail of text. The common patterns are vector search to find candidate entities or passages followed by graph traversal to pull their verified neighborhood into the prompt; graph traversal to find exact entities followed by vector search over text attached to them; or embeddings stored as node properties inside the graph database itself (Neo4j, Neptune Analytics, and several triple stores support vector indexes) so one query mixes similarity and traversal. Implementation options are covered in GraphRAG.

For developers

The decision that matters most is entity identity. A vector store has no concept of “the same thing”; two chunks about Marie Curie are just two nearby vectors. If the application needs to say “this passage is about entity Q7186”, something has to resolve mentions to identifiers, and that something is a knowledge graph or at least an entity index. A graph of well-identified entities with a vector index over the text attached to each is a smaller, more debuggable system than a vector index over a million anonymous chunks. For a Python starting point see knowledge graph Python; for knowledge graph embeddings, a different technique despite the shared word, see that page.

For SEOs

AI search systems use both mechanisms. Retrieval over web text is largely embedding-based, which favors content that states facts plainly in the words users search with. Entity grounding is graph-based: Google reconciles mentions against the Google Knowledge Graph, and an organization or person that exists there as a well-connected entity is easier to cite and harder to confuse with a namesake. Structured data with sameAs links to Wikidata and other authorities is the site-side contribution to the graph half. See knowledge graphs, AI search, and LLMs and entity SEO.

Common misconceptions

One misconception is that vector databases have made knowledge graphs unnecessary for AI applications. Vector retrieval solved recall over unstructured text; it did not solve entity identity, aggregation, or explainability, and production RAG systems routinely add a graph for exactly those gaps. The opposite misconception, that a knowledge graph can replace vector search, ignores how much useful information never gets modeled as a triple. A third confusion concerns “knowledge graph embeddings”, which are vectors learned from the graph’s own structure (TransE, RotatE, and similar) for link prediction, not text embeddings in a vector database. Finally, storing embeddings as node properties in a graph database does not make it a vector database; it is one engine supporting two index types.

FAQ

Should I use a knowledge graph or a vector database for RAG?

Use a vector database when the source is unstructured text and questions are open-ended. Use a knowledge graph when questions involve exact relationships, multi-hop logic, or must be explainable. Most production RAG systems over a real domain end up using both: vector search for recall over passages, and a graph for entity identity, verified facts, and traversal. This combination is generally called GraphRAG.

What is the difference between graph search and vector search?

Vector search compares a query embedding against stored embeddings and returns the nearest neighbors, so it finds semantically similar content without knowing why it is similar. Graph search follows explicit, typed edges between identified entities, so it answers structural questions exactly and can show each stored fact behind the result. Vector search tolerates wording variation; graph search supports aggregation, completeness, and explanation.

Can a graph database store vector embeddings?

Yes. Neo4j, Amazon Neptune Analytics, and several RDF stores support vector indexes on node properties, so a single query can combine similarity search with traversal. This does not turn the graph database into a dedicated vector database, which is typically faster and more scalable for pure nearest-neighbor workloads, but it removes the need for a second system in many GraphRAG deployments.

Is Pinecone a graph database?

No. Pinecone is a managed vector database that stores embeddings with metadata and serves approximate nearest-neighbor queries. It has no notion of nodes, edges, or traversal. Weaviate, Qdrant, Milvus, and pgvector belong to the same category. Graph databases such as Neo4j, GraphDB, and Amazon Neptune store explicit relationships and are queried with Cypher, SPARQL, or Gremlin.

Sources and further reading

Hogan et al., Knowledge Graphs (book and online text): https://kgbook.org/
Microsoft Research, GraphRAG project page: https://www.microsoft.com/en-us/research/project/graphrag/
Neo4j, Vector indexes documentation: https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/
pgvector, PostgreSQL extension for vector similarity search: https://github.com/pgvector/pgvector
W3C, SPARQL 1.1 Query Language: https://www.w3.org/TR/sparql11-query/
Wikidata Query Service user manual: https://www.mediawiki.org/wiki/Wikidata_Query_Service/User_Manual