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lancedb-gatefixer[bot]
9804f26bc3
Python column refresh methods rejected `batch_size`, and the Rust remote backend never included it in the backfill request. Callers could not reduce the rows passed to a slow Function to stay within the executor's per-call deadline. Add optional, keyword-only `batch_size` arguments throughout the synchronous and asynchronous Python APIs and bindings. Add compatible Rust `RefreshColumnOptions` APIs while preserving existing Rust method signatures. Reject non-positive sizes before submitting a job, send positive sizes in `backfill_column`, and omit the field when unset while retaining branch scoping. Batch size controls remote Function calls. Embedded expression refreshes accept and validate the option without changing execution, and blocking remote refreshes retain their existing unsupported behavior; remote callers use `refresh_column_async` and wait on the returned job. Validation: - Reproduced the original Python `TypeError` from the committed method. - Five focused Rust tests passed, covering request contents, omission, branch scoping, and rejection before any request. - `cargo check --quiet --features remote --tests --examples` passed. - `cargo clippy --quiet --features remote --tests --examples -- -D warnings` passed. - Nineteen targeted Python tests passed against the rebuilt extension, including synchronous and asynchronous remote requests, invalid sizes, and default-build behavior. Eight embedded refresh cases require the experimental-functions build and were skipped. - Rust formatting and Python formatting/lint checks passed, including CI's pinned Ruff version. Repository-wide Ruff reports 48 existing findings in untouched documentation files; none are in changed files. Fixes #4776 <!-- lance-gatekeeper-fix:v1 agent=4906ff3f5f7797a1d49a13803302021c 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:
- 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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