Packaged UDFs omit globals used only in postponed annotations, so a `BlobReader` input has a correct wire signature but `typing.get_type_hints()` on the loaded function raises `NameError`. Use the resolved input signature to bind reader annotations to an imported `lance_blob.BlobReader` alias. Preserve signature nullability and avoid collisions with existing globals. Regression tests cover functions and callable classes, eager and postponed annotations, aliases, forward references, qualified names, and body globals. Validation: - Confirmed the new regression fails with `NameError: name 'BlobReader' is not defined` against the original packager at `057a39a1c70e15c78461bb7e3db8cef4c067cd3e`. - The packaging, first-class Function slice 1 and 2, and BlobReader suites pass all 159 tests with Python file-import directory caches refreshed before lookups. The standard run passes 156 tests; three temporary-module import failures reproduce unchanged on the original packager because directory timestamps do not advance between file writes in this environment. All 37 new regression cases pass in the standard run. - Python Ruff lint and formatting checks pass. Repository-wide Ruff lint reports 48 existing errors in documentation examples. Fixes #4718 <!-- lance-gatekeeper-fix:v1 agent=a32bb6efa79217b3c1c5c9d7a7ff9928 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
Star LanceDB to get updates!
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.

