Yang Cen
f7feed48c3
## What Expose custom FTS stop-word lists in the Python and TypeScript public APIs, including their standalone tokenize helpers and remote index creation. This PR supports concrete string lists only. It does not add file or LanceDB-table stop-word sources. ## Why Rust already exposes Lance's custom stop-word list option. The Python and TypeScript APIs did not pass it through, and local index details did not retain the full tokenizer parameters needed by index-backed tokenization after reopening a table. ## How - Add `custom_stop_words` / `customStopWords` to the Python and TypeScript FTS and tokenize options. - Preserve `None` / `undefined`, empty lists, and list contents without normalization. - Load the persisted FTS segment parameters when returning local index details. - Serialize the concrete list in remote create-index requests. - Keep Python and TypeScript tests thin; behavior, persistence, query tokenization, and remote JSON coverage live primarily in Rust. ## Validation - `cargo check --quiet --features remote --tests --examples` - `cargo clippy --quiet --features remote --tests --examples` - `cargo test --quiet --features remote --tests` - Python extension rebuild with `uv` and `maturin` - Targeted Python tests: 4 passed - Python `ruff format --check` and `ruff check` - TypeScript build, typecheck, Biome lint, generated docs, and targeted tests --------- Co-authored-by: Yang Cen <yangcen@Yangs-Mac-mini.local>
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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⭐ 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.
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