Vector Database
A vector database stores embeddings and does similarity search, acting as the storage layer behind RAG and semantic search that finds the most relevant pieces.
A vector database stores the embeddings of text, images and more, searching by similarity instead of keywords to match meaning even when wording differs. It underpins semantic search, recommendation and RAG, using embeddings to place questions and documents in one space and pull back the closest passages fast.
