Jack Ye
70c10e4aa4
lancedb now holds a page at every path the site publishes, including the twelve Enterprise ones. Those are real open-source pages -- what the capability is, and what the embedded form does instead -- rather than blank placeholders, so the open-source site is complete on its own. sophon becomes an overlay whose page replaces the one at the same path, which means navigation lives in one place and the two repositories only have to agree on a path. That removes the whole class of problem where a page could only be reached from one of the two trees. The open-source site went from twenty-four dangling Enterprise links to one, and that one points at the dataset pages the build repository owns. Ports four changes that landed on the docs repository after this branch left it: #344 indexing and quantization rewritten #354 LeRobot native reader #361 training/index renamed to training/data-loading and rewritten #362 distance metric mismatch falls back to a brute-force scan None applied as patches, because these files were merged or split here -- quantization into the vector index page, multi-vector into vector search, performance into four pages. Each was ported by taking the upstream page and re-applying the restructure on top, which also picked up a correction that would otherwise have been lost: the stricter brute-force threshold for multi-vector columns is a Cloud limit, not an Enterprise one. The upstream LeRobot page linked to a Geneva page that no longer exists; it now points at the feature-engineering pages that replaced it.
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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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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