geoffgscott
7ea10fa1e0
The Node SDK's vector index options only take `numPartitions`. The Rust index builders (`target_partition_size` in `rust/lancedb/src/index/vector.rs`) and the Python SDK already support a target partition size, so this adds `targetPartitionSize` to `IvfPqOptions`, `IvfRqOptions`, `IvfFlatOptions`, `HnswPqOptions` and `HnswSqOptions` and passes it to their builders. When both are set, `numPartitions` is used, as it is for Rust and Python callers. The reason to want it: an index built from a partition count records no target partition size, so `table.optimize()` splits and joins it against the index type's default and can merge small partitions down to one or two. Building with a target records it in the index details, and optimize keeps to it. https://github.com/lance-format/lance/pull/10154 fixes this on the Lance side for indexes built from a partition count; this lets Node users set the size directly. The new tests build IvfPq, IvfFlat, IvfRq, IvfHnswPq and IvfHnswSq indexes with `targetPartitionSize` and check the size recorded in the index details, and check that `numPartitions` wins when both are set. Each option's doc comment states that index type's default target. Docs regenerated with `pnpm run docs`.
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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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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