LanceDB — Multimodal Vector Database for AI

Key Features

• Billion-scale vector search: Millisecond query latency across massive datasets
• Multimodal support: Text, images, videos, and point clouds in one database
• Hybrid search: Combine vector similarity, full-text search, and SQL queries
• Zero-copy architecture: Automatic versioning with no data duplication
• GPU-accelerated indexing: Fast index building for large datasets
• Multi-language clients: Python, Node.js, Rust, REST API
• Rich ecosystem: LangChain, LlamaIndex, DuckDB, Polars integrations

FAQ

Q: What is LanceDB? A: LanceDB is a multimodal vector database with 9.7K+ stars for AI/ML applications. Built on the Lance columnar format, it provides millisecond vector search across billions of vectors with support for text, images, and video. Apache 2.0 licensed.

Q: How do I install LanceDB? A: Run pip install lancedb for Python or npm install lancedb for JavaScript. No external server needed — it runs embedded in your application.

Q: How does LanceDB compare to Chroma or Pinecone? A: LanceDB is built on a columnar format (Lance) optimized for ML workloads, supports true multimodal data, and scales to billions of vectors. Chroma is simpler but less scalable. Pinecone is cloud-only; LanceDB runs locally or in the cloud.