---
Title: "What Is a Vector Database? Definition and Uses"
Url: "https://computer.io/glossary/vector-database"
Topic: "AI foundations"
Description: "A vector database stores embeddings and finds items by similarity, powering semantic search and RAG. How it works and vector database vs knowledge graph."
---

# What is a vector database?

A vector database stores data as embeddings, numerical vectors that capture meaning, and finds items that are most similar to a query vector. It powers semantic search, recommendations and retrieval-augmented generation by finding relevant content even when wording differs.

## How it works

1. Content is converted to embeddings.
2. Vectors are indexed.
3. A query is embedded.
4. The database returns nearest neighbors.

## Vector database vs knowledge graph

|  | Vector database | Knowledge graph |
| --- | --- | --- |
| Stores | Similarity | Relationships |
| Best for | "Find similar content" | "How are these connected?" |

Many enterprise AI systems use both.

## FAQ

### Why do AI apps use vector databases?

To retrieve relevant context for LLMs quickly.

### Do you need a dedicated vector database?

Not always; many databases now support vector search.

### What is approximate nearest neighbor search?

A fast method for finding similar vectors without checking every item.

## Related terms

- [Embeddings](https://computer.io/glossary/embeddings)
- [Semantic search](https://computer.io/glossary/semantic-search)
- [Knowledge graph](https://computer.io/glossary/knowledge-graph)
- [Retrieval-augmented generation (RAG)](https://computer.io/glossary/retrieval-augmented-generation)

## How Computer helps

- [Enterprise search and answers](https://devrev.ai/enterprise-search-and-answers)

## Further reading

- [Knowledge graph vs vector database](https://devrev.ai/blog/knowledge-graph-vs-vector-database)