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Yang Cen
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Lance beta.11 calibrates lower `search_effort` tiers by metric and requested `k`. With the Rust dependency and Python/Node execution tests already on main via #4881, document the tiers across Rust, Python, Node and the search guide, extend the remote request matrix to cover `0.1` and `0.25`, and align the Python test package with beta.11. `0.1` and `0.25` target empirical mean partition-routing recall of 80% and 90% for L2, cosine and dot through `k=100000`. These are not per-query or final Recall@k guarantees. Default Auto is unchanged; Hamming and larger `k` remain uncalibrated, and remote queries require corresponding server support. This PR makes no additional performance claim. See [Lance #10695](https://github.com/lance-format/lance/pull/10695) for measurements and limitations. Validation: both strict workspace Clippy configurations, Rust formatting, 13 Rust effort tests, 208 Python query tests, 41 Node query tests, native binding builds, TypeScript, Node lint/generated docs, Python lint/format, full lockfile check and two dependency guards passed on `a8b09170`. All validated Rust/Python/Node source and dependency files are byte-identical at `2a82d07c`; the only subsequent change restores the Java POM to main. Root Ruff has 59 existing diagnostics and six formatting files, all byte-identical to the base; the Python package passes. The Java REST/Namespace client has no changed API here, and its dependency remains identical to main. The separate [Java beta publication failure](https://github.com/lance-format/lance/issues/10773) is not claimed resolved or validated. Official SDK release, automatic mirror, Sophon adoption and measured consumer acceptance remain follow-up stages.
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:
- 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.
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