Xuanwo
2d70bec53a
Add a central documentation link registry (`lancedb::docs::DocLink`) in the Rust core that builds `docs.lancedb.com` URLs with UTM parameters (`utm_source=lancedb_sdk`, `utm_medium=warning|error`, `utm_campaign=<id>`, `utm_content=<sdk>-<version>`) placed before any `#anchor` fragment and without user-identifying data. - Initialize `utm_content` per language binding (`python`, `nodejs`, `cli`, defaulting to `rust`) and expose the link builder to Python so `DBConnection` and `Job` stubs reuse the core definitions. - Append published documentation links to Enterprise-only `NotSupported` / `NotImplementedError` errors across database, job, catalog, and table operations. - Emit rate-limited maintenance warnings (once per table per process when `warn` logging is enabled) when opening a native table with at least 100 small fragments (`< 100k` physical rows each) or at least 1,000,000 unindexed rows on an optimizable user index, reading only manifest metadata without opening data or `_indices/` files, and document the thresholds in the compaction and reindexing guides. - Verify published/in-repo `docs/web/` targets and `#anchor` fragments in unit tests and include extracted SDK links in the scheduled `docs-link-check` workflow. Linear: OSS-2772
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
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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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