A vector database is a database built to store and search embeddings—the high-dimensional numeric vectors that represent the meaning of text, images, or other data. Instead of matching records by exact values like a traditional database, a vector database finds the records whose vectors are most similar to a query, which is what makes meaning-based (semantic) search possible at scale.
In short: a vector database stores embeddings and finds the most similar ones to a query—the storage-and-search engine behind semantic search and RAG.
How a vector database works
The job is to store vectors and find the nearest ones to a query, fast:
Store vectors: Each item is converted into an embedding and stored, usually with metadata such as source, title, and tags attached.
