lancedb-gatefixer[bot]
bcf5ee263f
Python's FTS bindings always forwarded `stem=True` and `remove_stop_words=True`, overriding Lance's code tokenizer defaults. As a result, code indexes stemmed identifiers and discarded stop words, and `lancedb.tokenize` reproduced the same behavior. Use `None` for omitted filter options and only apply explicit booleans in the Rust bindings. This preserves tokenizer defaults across local and remote index creation, the deprecated `create_fts_index` helpers, and standalone tokenization. Update the signatures and documentation to describe the inherited defaults. Regression tests cover stored local settings, token output, stop-word searches, remote request payloads, explicit overrides, and the existing simple tokenizer behavior. The regression suite produced 16 failures before the fix and passes all 28 cases after it. Validation: - Rebuilt the Python extension using the uv-managed development environment. - Full Python FTS and index test files plus relevant remote index tests: 105 passed. - `cargo clippy --quiet --features remote --tests --examples -p lancedb-python` - `cargo fmt --all`, Ruff format/lint with CI's pinned version 0.9.9, and Pyright (zero errors). Fixes #4365 <!-- lance-gatekeeper-fix:v1 agent=9c1db2d3fc25c49c5b340f8debf43a0f generation=1 --> Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
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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