adenzhou1350andaden
b205e7c628
## Summary Prevent large JavaScript table-listing limits from wrapping during the native `u32` conversion. With two tables, `limit: 2 ** 32` currently returns no tables and `2 ** 32 + 1` returns only one. Validate nonnegative safe integers using the existing helper and clamp valid limits above `u32::MAX` before calling the native binding. Apply the same normalization to `tableNames` and `listTables`, including namespace overloads. Undefined, zero, small limits and `u32::MAX` keep their existing behavior. ## Validation Built the real Node native extension from main `0aa842b17f13d1f8ffd2851bc57c222bc5a376ba` with the repository's `pnpm build`, using locked dependencies and reusable build caches. No mocked/released native extension or disabled TypeScript diagnostics. With identical regression tests and that native library in fresh Jest processes: - Baseline: 40 passed, 8 failed; candidate: **48 passed**, no failures. - Both arms: `pnpm run tsc` and `pnpm run docs` passed. - Changed-file Biome check passed. Tests cover wrapping limits, invalid numbers, zero/small/u32 limits, defaults and both overload forms. Validation is the focused connection suite, not the complete repository suite. No performance claim. AI-assisted implementation and verification; the patch and exact tested source bytes were reviewed before publication. Signed-off-by: aden <aden@example.com> Co-authored-by: aden <aden@example.com>
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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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.
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