Will JonesandClaude Opus 5
ed6be12ad6
`mkdocs build` emitted 61 warnings on main, and rendering the previously undocumented classes in this PR pushed that to 158. That backlog is what blocks turning on strict mode (#3707), so clear it here rather than leave it worse than we found it. Most of it was one systematic false positive: griffe cannot see the generated `__init__` of a pydantic dataclass, so every documented parameter looked unknown. `warn_unknown_params` turns that check off. The rest were real docstring bugs, in 15 docstrings: * Prose trailing a `Parameters` section is read as parameter names, which invented parameters called `The`, `you` and `To`. Moved into `Notes` or the summary. * numpydoc only reads a type when the colon has spaces around it. Where the documented name is a pydantic attribute rather than a signature parameter, griffe has no signature to fall back on and the type was dropped. Affects nine embedding classes. * `num_partitions, default sqrt(num_rows)` and friends parse as a list of names, rendering a bogus `default` parameter. * One parameter indented five spaces instead of four. `nodejs/CONTRIBUTING.md` links to the repo-root CONTRIBUTING.md, which does not resolve once typedoc copies the file into `docs/src/js/_media/`; an absolute URL works from both places. `mkdocs build --strict` now exits 0. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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
Star LanceDB to get updates!
⭐ 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
Stay in Touch With Us