Vector database (pgvector and others)
A database that stores embeddings and finds the nearest ones to a query vector quickly, which is the search step behind most RAG systems.
Dedicated products (Pinecone, Qdrant, Weaviate) compete with extensions to databases you already run, such as pgvector for Postgres. Keeping vectors next to the rows they describe means one backup, one permission model, transactions, and filters on ordinary columns in the same query. A dedicated store pays off at very large scale or with specialized features; most products never reach that point.
Related terms
- Embeddings: Vectors of numbers that place texts with similar meaning close together.
- Hybrid search: Combining keyword search (such as BM25) with vector search, so results match both exact terms and meaning.
- RAG (retrieval-augmented generation): Finding relevant documents first and putting them into the prompt, so the model answers from them.
