Vector database
A vector database stores embeddings and finds the nearest vectors to a query efficiently, powering semantic search and RAG.
A vector database (or vector index) is built to store high-dimensional embedding vectors and answer nearest-neighbor queries quickly — “find the documents whose meaning is closest to this question.”
Options range from dedicated managed services to extensions that add vector search to a database you already run, such as pgvector for Postgres. For small-to-mid corpora, an extension on your existing database is often plenty; very large or high-throughput workloads may justify a dedicated system.
The vector store is one piece of a retrieval pipeline; embedding quality, chunking, and re-ranking matter just as much for end-to-end results.