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**Blocked on the corrected Lance release:** https://github.com/lance-format/lance/pull/10370 restores the `Send` contract required by downstream Function workers. Refresh this upgrade with that release before merging. Upgrade the Rust, Java, and Python Lance dependencies to https://github.com/lance-format/lance/releases/tag/v15.0.0-beta.2. This brings in bounded vector-index consolidation without retraining (https://github.com/lance-format/lance/pull/10335) and deletion filtering for index entries whose fragment coverage was pruned (https://github.com/lance-format/lance/pull/10325). Synchronize the vendored `lance-arrow` with the release and retain its offset-safe validity patch. The existing `nullable_struct_merge` regression still panics with the unpatched release, so removing that patch would regress nullable nested queries. Refresh both lockfiles, including the `zstd-sys` version required by the new Lance encoding dependency. Update the custom memtable bitmap test adapter to train on row addresses, matching the new bitmap contract; remove its obsolete row-ID conversion. Preserve the Blob APIs' existing missing-row error messages when newer Lance rejects a missing fragment earlier in the read path. Retain upstream attribution in vendored sources while satisfying the repository license-header check. Validation of the compatibility fixes: all 58 Rust Blob integration tests and 219 Python Blob tests pass (one Python test skipped). Strict workspace Clippy, formatting, and diff checks pass.
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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⭐ 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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