A vector database stores and searches embeddings, enabling fast retrieval of content based on semantic similarity rather than exact keyword matching.
Traditional databases search for exact matches. Vector databases search for meaning — finding stored content whose embedding is mathematically closest to the query. They are a key component of retrieval-augmented generation systems.