A vector database retrieves by semantic similarity over unstructured text; a knowledge graph models explicit entities and the relationships between them. Vector search is the faster path to RAG over documents; a knowledge graph shines when multi-hop reasoning and explainable relationships matter. Many production systems combine the two — a pattern often called GraphRAG.
By Evgeny Aleksandrov, Founder, BlackGrid ·
At a glance
Dimension
Vector database
Knowledge graph
Data model
Embeddings of text chunks
Entities + typed relationships
Retrieval
Semantic similarity
Graph traversal / queries
Multi-hop reasoning
Weak
Strong
Explainability
Opaque similarity
Explicit, traceable paths
Setup effort
Lower
Higher (modeling the ontology)
Best for
Unstructured documents
Structured, connected facts
When to choose Vector database
Knowledge is mostly unstructured text
You need fuzzy, semantic recall
Fast similarity search over large corpora
You want a quick path to RAG
When to choose Knowledge graph
Relationships and multi-hop reasoning matter
Facts must be explicit and consistent
You need explainable, traceable connections
The domain has a rich, structured ontology
Can you use both?
They are not mutually exclusive. A common pattern — GraphRAG — uses a knowledge graph for explicit relationships and a vector store for semantic recall over text, letting an agent both traverse facts and retrieve passages.
Usually not to start. Most RAG systems begin with a vector database over documents. A knowledge graph adds value when relationships and multi-hop reasoning matter, or when explicit, explainable connections are required.
What is GraphRAG?
A pattern that combines a knowledge graph with retrieval, so an agent can traverse explicit relationships and retrieve relevant text — improving multi-hop reasoning and explainability over vector search alone.
Which is more explainable?
A knowledge graph. Its relationships are explicit and traceable, whereas vector similarity is opaque — which matters in regulated settings that demand auditable reasoning.