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lancedb-gatefixer[bot]andXuanwo
66357275bb
`IvfRq()` forced `num_bits=1` through Python's Rust binding, overriding Lance's five-bit default. The shared native index creation path also substituted one bit when the Rust or Node builder left the value unset. Make Python's bit count optional and forward only explicit values. Use `RQBuildParams::default()` for an unset native bit count, and omit unset values when dispatching to pylance for accelerator builds. Update the Python, Rust, Node, and vector-index documentation. New indexes with an unset bit count now inherit Lance's default, currently five bits, across Python, Rust, and Node. Five-bit vector codes use roughly five times the storage of one-bit codes, plus other index overhead. This also changes reader compatibility: indexes created with `num_bits >= 2` use a newer on-disk layout that some older LanceDB versions cannot read. Upgrade readers to versions that support multi-bit IVF_RQ, or explicitly set `num_bits=1` (for example, `IvfRq(num_bits=1)` in Python) when sharing indexes with readers limited to one-bit IVF_RQ. Validation: - Reproduced six regression failures before the fix: one-bit default metadata, rejected `None`, and incorrect remote/accelerator forwarding. - After merging `main` through `b762ec22`, rebuilt both native bindings against Lance 15.0.0-beta.1. Python validation passed with 106 tests and 15 optional Ollama skips. Coverage includes the full index suite, omitted and explicit bit counts, reading Lance-created IVF_RQ indexes, accelerator forwarding, CUDA-unavailability handling, timeout validation, remote requests, and table/namespace listing behavior. - Rust formatting and Python package Ruff lint/format checks pass. Node `pnpm lint`, `pnpm build` (including TypeScript checks), `pnpm run docs`, the full connection suite, and the existing IVF_RQ regression pass. Generated documentation adds no further changes. The merge preserves upstream's explicit RaBitQ heading anchor alongside the `IvfRq()` example and five-bit default/reader-compatibility guidance. Repository-wide `ruff check .` still reports 49 existing errors in unchanged documentation examples under `docs/web-tests`; root formatting also finds 11 unchanged documentation example files. The Python package checks pass. Fixes #4582 <!-- lance-gatekeeper-fix:v1 agent=6ef9406f0142b1ab66d53d9d56098223 generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io>
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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