Brendan Clement 62a4edc654 docs: say which tagged versions stop cleanup, and what zero retention keeps
The cleanup page said timedelta(0) prunes everything except the latest
version, and the three pages said any tag on a version older than the
window stops cleanup. In the pinned Lance, cleanup also keeps the
versions branches were forked from, and only a tagged version that is
older than the window and is not the latest stops it; a tag on the
latest version is kept without an error. optimize() can still turn
such a tag into a blocking one by committing a compaction first.

The pages now say so, with moving the tag inside the window or widening
the window as remedies alongside deleting it. Each case was checked
locally against the baseline SDK.
2026-10-09 09:42:55 -07:00
2023-03-17 18:15:19 -07:00
2025-03-10 09:01:23 -07:00

Reverie LanceDB Website Blog Discord Twitter LinkedIn

LanceDB

The Multimodal Lakehouse for AI

How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors

The ultimate multimodal data platform for AI/ML applications.

LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.


Demo: Multimodal Search by Keyword, Vector or with SQL

LanceDB Multimodal Search

Star LanceDB to get updates!

⭐ Click here ⭐ to see how fast we're growing!

Key Features:

  • Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
  • Comprehensive Search: Support for vector similarity search, full-text search and SQL.
  • Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
  • Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.

Products:

  • Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
  • Cloud and Enterprise: Production-scale vector search with no servers to manage. Complete data sovereignty and security.

Ecosystem:

  • Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
  • Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
  • Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.

How to Install:

Follow the Quickstart doc to set up LanceDB locally.

API & SDK: We also support Python, Typescript and Rust SDKs

Interface Documentation
Python SDK https://lancedb.github.io/lancedb/python/python/
Typescript SDK https://lancedb.github.io/lancedb/js/globals/
Rust SDK https://docs.rs/lancedb/latest/lancedb/index.html
REST API https://docs.lancedb.com/api-reference/rest

Join Us and Contribute

We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.

If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.

Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.

Contributors

Stay in Touch With Us


Website Blog Discord Twitter LinkedIn

S
Description
Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.
Readme Apache-2.0
107 MiB
Languages
Rust 42.4%
Python 25.9%
HTML 22.8%
TypeScript 7.9%
Java 0.7%
Other 0.2%