lancedb-gatefixer[bot]andXuanwo
1edfa6077b
Quoted async full-text queries without `columns` carry an unset phrase column, but converting them to Python unwrapped it and raised `PanicException`. Python also required `PhraseQuery.column`, preventing callers from constructing the equivalent query object. Make the Python column optional and preserve `Option<String>` in both Rust conversion directions, leaving column selection to query planning. Regression tests cover quoted strings and structured queries with omitted, `None`, and explicit columns, query reuse, JSON serialization, slop, and document granularity. Validation: - Reproduced both missing-column failures before the fix; explicit-column controls passed. - Rebuilt the extension with `uv run --extra tests --extra dev maturin develop --extras tests,dev`. - `cd python && uv run --no-sync --extra tests pytest python/tests/test_fts.py python/tests/test_query.py::test_query_serialization_async -q`: 85 passed. - `cargo fmt --all -- --check` and `ruff check python` pass; touched Python files pass formatting checks. - `cargo clippy --quiet -p lancedb-python --lib --features remote` passes. - Root-wide `ruff check .` reports 49 existing findings in unchanged documentation examples. Fixes #4651 <!-- lance-gatekeeper-fix:v1 agent=62d36fad70c8717236283b8e97dd6b30 generation=1 --> Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> 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
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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 |
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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.
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