LanceDB
LanceDB is an open-source multimodal AI data platform built on the Lance columnar format, designed for fast, scalable, and production-ready vector search. It solves the problem of storing, indexing, and searching over petabytes of multimodal data and vectors, and is intended for developers building AI/ML applications.
✨ Key features
- Fast vector search: billions of vectors in milliseconds
- Comprehensive search: vector, full-text, and SQL
- Multimodal support: text, images, videos, point clouds
- Zero-copy, automatic versioning, GPU index support
- Open source, local or cloud, no vendor lock-in
- Integrations with LangChain, LlamaIndex, Pandas, DuckDB
🎯 Use cases
- Build multimodal search applications by keyword, vector, or SQL
- Manage and version large-scale AI training datasets
- Implement production-ready vector similarity search
- Integrate with LangChain or LlamaIndex for RAG pipelines
📦 Installation
🧰 Requirements: No specific runtime or OS requirements stated; supports Python, TypeScript, Rust, and REST APIs.
Follow the Quickstart doc to set up LanceDB locally.
For Python SDK, install via pip:
pip install lancedb
For TypeScript SDK, install via npm:
npm install @lancedb/lancedb
For Rust SDK, add to Cargo.toml:
lancedb = "0.1"
🚀 Usage
import lancedb
db = lancedb.connect("~/lancedb")
table = db.create_table("my_table", data=[{"vector": [1.1, 2.1], "item": "foo"}, {"vector": [0.1, 1.2], "item": "bar"}])
result = table.search([0.1, 0.3]).limit(1).to_pandas()
print(result)
❓ FAQ
What data formats does LanceDB support?
LanceDB supports multimodal data including text, images, videos, point clouds, and more, stored in the Lance columnar format.
Can I use LanceDB for full-text search?
Yes, LanceDB supports vector similarity search, full-text search, and SQL.
What integrations are available?
LanceDB integrates with LangChain, LlamaIndex, Apache-Arrow, Pandas, Polars, DuckDB, and more.
Is LanceDB open source?
Yes, the open source version is 100% open source and can run locally or in your cloud.
📊 Repository
🤖 Overview, features, install steps and FAQ were generated from the project's README on Sep 4, 2026. Always check the original source before running commands.