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Yang Cen
9dc5ec03aa
## What changed - add `block_size` to Python FTS configuration and the deprecated local/remote helpers - add `blockSize` to the TypeScript FTS options and propagate it through the NAPI binding - serialize the value as `block_size` for remote index creation - document the existing Rust builder API and generate the TypeScript API reference - add local, remote, metadata, search, and invalid-value regression coverage ## Why Lance supports configuring the number of documents per compressed FTS posting block, but LanceDB's Python and TypeScript APIs did not expose the setting. This made the experimental FTS V3 layout unavailable through those clients and allowed the value to be dropped before index creation. ## How it works The default remains `128`. Supported values are `128` and `256`; selecting `256` uses the experimental FTS V3 format. Invalid values are rejected by the Lance builder and surfaced as Python or JavaScript errors. ## Validation - `cargo check --quiet --features remote --tests --examples` - `cargo +1.94.0 clippy --quiet --features remote --tests --examples -- -D warnings` - targeted Rust local and remote index tests - Rust doctests: 34 passed - Python Ruff checks, doctest, and targeted local/remote tests: 5 passed - TypeScript build, Biome lint, generated docs, and targeted Jest tests: 9 passed - `git diff --check` ## Limitations The Java client remains unchanged because its external remote REST model does not currently expose `block_size`. 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.
Contributors
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