lancedb-gatefixer[bot]
de19ecbf63
Fixes #4390 Lance accepts column alterations with every change field unset, so a path-only Python call could commit a new local version without changing the schema. The Python binding also silently discarded unknown keys such as `nulable`. Validate every alteration in the shared Rust `Table` API before dispatching to either backend, and reject unknown dictionary keys in the Python binding while preserving the legacy `name` alias. Invalid batches leave local schema and version unchanged and send no remote request. Validation: - Reproduced both reported calls on the original extension: versions advanced from 1 to 2 to 3 with an unchanged schema; all 13 new invalid-input Python cases failed before the fix. - Rebuilt the extension with `uv run --extra tests --extra dev maturin develop --extras tests,dev`; all 18 focused Python cases pass across sync/async and local/mocked remote tables. - `cargo test --quiet --features remote -p lancedb --lib alter_column`: all eight tests pass, including both new regressions. - `cargo clippy --quiet --features remote --tests --examples` passes across the workspace. - `cargo fmt --all`, `ruff check .`, and `ruff format .` pass. - `typos` 1.26.0 passes across the repository; intentional misspellings in regression fixtures use the existing line-specific spelling exemptions. <!-- lance-gatekeeper-fix:v1 agent=36a547cd11108898893456042e38628b generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.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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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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