Xuanwo
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An input-preserving merge can fail freshness preservation after a successful data write: the replaced fragment loses its signature, but its known/failed-row records remain and fail sidecar validation. This affects tables with persisted Function outcome metadata. Prune both outcome maps with the same live-fragment set used for signatures and row state. Outcomes on untouched fragments remain intact, and inserted rows remain pending fill. The regression performs a real mixed update/insert and reopens the resulting snapshot to check both output columns. Native validation on Linux: the exact regression fails before the fix with `failure evidence names a fragment without a signature`. At `20b10d80408cb455d9e5827f5d7d36a117622dea`, all 24 discovered `table::freshness::tests::function_write_` cases pass, including the new real merge regression. Test lists, counts and logs were retained and SHA-256 verified. [Current-head CI passed both workspace Clippy configurations](https://github.com/lancedb/lancedb/actions/runs/43274052568/job/131377627811). A separate package-only, default-feature Clippy command reported dead-code errors in seven files unchanged from the base; that local failure is retained rather than represented as a pass. Other hosted gates are pending. This does not claim downstream Sophon integration or deployed acceptance.
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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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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