Xuanwo
b1cfe6edb1
The docs have no link checking at all, so external links rot silently: a trial run already found `docs/src/python/python.md` pointing at `lancedb.github.io/lance-namespace`, which returns 404 since the repository moved to the lance-format org. Checking external links on the blocking path would be the wrong trade: third-party hosts rate-limit automated clients, reject non-browser user agents, and go down temporarily, so any of them having a bad minute would turn unrelated PRs red. Following lance-format/lance#8315, this adds a daily `lychee` run that reports broken links into a single tracking issue, rewritten in place on each run and closed automatically once every link resolves. The scan job runs the downloaded lychee binary with a read-only token; everything that writes lives in a separate report job, and a non-verdict lychee exit fails the run instead of publishing a bogus report. The check is restricted to http(s) links because much of `docs/src` is generated API reference (the `js/` tree comes from `npm run docs`) and the hand-written pages use mkdocstrings cross-references and nav-relative paths that only resolve in the site mkdocs builds, so relative links would be reported as broken on every run. The one broken link the trial run surfaced is fixed here; after the fix, a local run over all 154 files reports 0 errors across 216 unique links.
The Multimodal AI Lakehouse
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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