What is the purpose of LanceDB?

LanceDB exists to give AI applications a single place to store and query vectors, raw multimodal data (text, images, audio), and structured metadata together, instead of stitching together a separate vector index, a separate blob store, and a separate metadata database.

Traditional setups for AI retrieval often mean keeping embeddings in one system, source documents in object storage, and filterable metadata in a relational database, then writing glue code to keep all three in sync; LanceDB's purpose is collapsing that into one columnar table that supports vector search, keyword search, and SQL-style filtering directly.

This matters most for RAG pipelines, semantic search, and agent memory systems, where a query commonly needs to combine "find semantically similar items" with "and only ones matching this metadata filter" — a pattern LanceDB is designed to handle natively rather than requiring the application to query two separate systems and merge results itself.