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Yang CenandXuanwo
10625f6dda
Update Rust Lance dependencies to [v15.0.0-beta.9](https://github.com/lance-format/lance/releases/tag/v15.0.0-beta.9), including the scoped stable-row-ID conversion fix. Python tests use published beta.9 wheels on Linux/Windows while macOS retains beta.4. Java stays on beta.8, matching the original base, so this update does not depend on beta.9 Maven publication or macOS Java packaging. The dependency updater preserves workspace inheritance, the WAL test wrapper uses the new StoredBatch module, and Linux/remote Rust CI uses its installed mold linker. The Python lockfile also records the already-declared OCI extra. CLI CI now caches Cargo downloads under a separate key instead of restoring compiled targets: the previous runner exhausted its disk during cache restoration, before compilation. Its test matrix and commands are unchanged. This branch includes #4820's row-freshness changes: successful rows become current while skipped rows retain their values and remain eligible for retry. Repair identities, replay handling and Restore fencing protect publication completion. BREAKING CHANGE: `StalenessPlan::row_dirty` is a new public field. Rust callers using exhaustive struct literals must initialize it. Validation: - All reported non-Java checks passed on `f5fa66fcbcf31a8eb188935f728735560c77c873`, including Rust lint/Linux/remote, Python, NodeJS and CLI checks. Current head `c660bc0ac9fd5920ad4b64be9c19f732ef4c56a5` preserves those product sources, manifests, lockfiles and compiler/test commands; only the Java pin restoration and CLI cache configuration differ. Current-head CI is tracked separately. - The complete Java directory is identical to the original PR base. The scope correction reuses [Xuanwo's prepared and validated commit](https://github.com/lancedb/lancedb/pull/4814#issuecomment-6626088466); no new Java build was run. - CLI workflow YAML parses successfully, its inputs match the pinned rust-cache action schema, and the existing jobs, matrix and commands are unchanged. Hosted execution must confirm the cache-restoration repair. - Existing validation includes the updater regressions and 32 CI-script tests, dependency-policy checks, count/WAL coverage, and #4820's 42 freshness tests, strict core Clippy and Rust doctest. Root Ruff previously reported unchanged documentation-example issues; Python-package Ruff passed. This PR adopts the existing released Lance beta.9. A subsequent official SDK release and normal Sophon dependency adoption remain separate delivery steps. --------- Co-authored-by: Xuanwo <github@xuanwo.io>
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
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:
- 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
Stay in Touch With Us