A vector database is a database built to store and search vectors. In AI systems, those vectors often represent documents, chunks, images, products, or user questions.
In practice
Vector databases are used in RAG, recommendation, duplicate detection, clustering, and semantic search. They can return the most similar items to a query vector at high speed.
What to watch
A vector database is infrastructure, not the whole knowledge system. Document quality, chunking, metadata, permissions, and evaluation still matter.