2734e10767
Thanks @vincentkoc for starting this! I was just looking into this myself. I started with your PR and added: - make arrow 21 the default - widen the peer range to <22 so if they release 21.3 that'll be included - have to do skipLibCheck in tsconfig.json, because of flatbuffers 25 which needs TypeScript 5.7 - fix override in Symbol.hasInstance - support new types in Arrow 20-21 (LargeList, Utf8View, BinaryView, IntervalMonthDayNano) - arrow-compat job in nodejs.yml that runs tests with arrow 19 and 20 (adds 2 sec) - put the list of arrow types in one place - update NODEJS_THIRD_PARTY_LICENSES.md - regenerated typescript docs (sorry about reorderings, one time cost) - reformat arrow.test.ts (sorry, almost all whitespace, thanks to biome. git diff -w shows 110 lines edited) - fixed Sliced Utf8View / BinaryView serialization error, by just converting them to Utf8 and Binary (as our rust code would anyway) --------- Co-authored-by: Vincent Koc <vincentkoc@ieee.org> Co-authored-by: Claude Fable 5.1 <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
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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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