Xuanwo
8a4effa06a
Published SDK builds currently expose local SQL computed columns and materialized-view execution that are not ready for release. Add the opt-in `experimental-functions` Cargo feature to the Rust core and forward it through the Python and Node bindings; published packages leave it disabled. Local declarations and refreshes now return `NotSupported` before mutating tables or spawning jobs, including prepared materialized-view creation. Remote Cloud/Enterprise client operations, Python UDF packaging, ordinary eager `add_columns`, and access to existing data remain available. Stored metadata and write protections stay active, and shared server commit helpers remain usable. CI covers both feature configurations. Existing local execution tests run with the feature enabled; default-build regressions cover early rejection, unchanged table versions, existing metadata protections, and normal eager transforms. API and source-build documentation explain the opt-in. Validation: Rust library tests pass with the feature disabled (1,869 tests) and enabled (1,947 tests), plus three default-build integration regressions. Python and TypeScript bindings were rebuilt and tested in both configurations, including mocked remote requests; Rust formatting, Python package lint, TypeScript lint/build, and generated docs were checked. Repository-wide Ruff still reports 48 pre-existing errors in unchanged `docs/web-tests` files.
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
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⭐ 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
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