Vector database
A vector database is a database optimised for storing and searching embeddings — the foundation of RAG systems and AI search.
Also known as: vector database, vector store
A vector database is a specialised database that stores embeddings (numerical representations of text or images) and is optimised for fast nearest-neighbour search. In practice, that means it can answer queries like "show me the 10 documents semantically closest to this question" in milliseconds, even in databases with millions of documents. Common examples are Pinecone, Weaviate, Qdrant, and PostgreSQL with the pgvector extension. Vector databases are the foundation of RAG systems, AI search, and recommendation engines.
Related terms
- Embedding — An embedding is a numerical representation of text (or image) that lets AI systems compare semantic similarity.
- RAG (Retrieval-Augmented Generation) — RAG is a technique where a language model answers based on the business's own documents — instead of only its general training.