Guide
Glossary
Vector database
A vector database stores information as embeddings: long lists of numbers that capture what a passage of text means, so software can find content by meaning rather than by exact keywords. When someone asks a question, the question is turned into an embedding too, and the database returns the passages whose numbers sit closest to it. This is the search step behind most retrieval-augmented generation (RAG) tools and document assistants. Examples include dedicated products such as Pinecone, Qdrant and Weaviate, and pgvector, an extension that adds vector search to PostgreSQL. A vector database holds copies of your content, so where it is hosted and who can query it matter as much as for the original documents.
Also called vector store, vector index
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Example
In a law firm
For example, a 30-person commercial law firm loads its precedent bank into a vector database. A lawyer asks for "a clause limiting liability for data breaches in a software licence", and the search returns the closest precedents even though none uses those exact words. The assistant drafts from them, and the lawyer checks the source documents before relying on the result.
Guides that explain it in context
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