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Yang Cen
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Lance supports `search_effort`, but LanceDB cannot pass it through its query APIs. Expose the parameter in Rust, Python sync/async and TypeScript, including remote requests and hybrid query conversion, using the [Lance v14.0.0-beta.13](https://github.com/lance-format/lance/releases/tag/v14.0.0-beta.13) dependency already on main. The default is `None` (Auto, internally 0.5). Any explicit effort, including 0.5, overrides fixed `nprobes` in either setter order; explicit minimum/maximum bounds remain effective. Invalid values are rejected in the Rust core. Hybrid conversion now retains the vector probe parameters and handles an inferred FTS column without panicking. Namespace queries with explicit effort execute locally because the namespace wire model cannot carry it. MemWAL LSM queries reject it explicitly because Lance's LSM scanner does not yet expose this option. Maturin is included in the dev extra so the documented build command works in the project environment. BREAKING CHANGE: The public Rust `VectorQueryRequest` struct now includes `search_effort: Option<f64>`. Downstream code that initializes every field explicitly must add `search_effort: None` or use `..Default::default()`. Exhaustive destructuring patterns must include the new field or `..`. Query-builder calls and struct literals that already use `..Default::default()` require no source changes. The new effort setters in Python and TypeScript are additive. This PR does not change the persisted file format. Using explicit effort with remote tables requires server support for `search_effort` and its precedence over `nprobes`; the client wire tests do not establish that a deployed server supports it. Server-side support is tracked in lancedb/sophon#8607. Validation on AWS after merging current main (Lance beta.13 / SDK beta.5): - 14 Rust regressions and the API doctest pass. - 60 parameter regressions, the full 47-test namespace suite, and three API doctests pass. The parameter selection includes some namespace cases. - 40 TypeScript query tests and 54 remote tests pass; build, lint and generated docs pass. - Rust Clippy, Python package lint/format, Pyright and lockfile checks pass. Repository-wide Ruff reports 49 lint errors and 11 formatting failures in unchanged `docs/web-tests` files; those files are byte-identical to main and outside this change. All 1,103 Git-tracked files match the AWS validation checkout. No benchmark scripts or result artifacts are included, and no performance improvement is claimed. Uses lance-format/lance#9798. Server-side support is in lancedb/sophon#8607.
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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