lancedb-gatefixer[bot]
ec80acb668
## Summary - publish the PEP 561 `py.typed` marker so downstream type checkers consume the inline public annotations - add a Pyright contract test that distinguishes synchronous `connect` from awaited `connect_async` - verify the marker is present in the installed package ## Root cause The public Python module already annotated `lancedb.connect` as synchronous and `lancedb.connect_async` as asynchronous. The private native `_lancedb.connect` stub is intentionally awaitable because it backs `connect_async`. However, the distribution did not include a PEP 561 marker, so downstream tools such as mypy could ignore the public inline annotations and expose misleading or incomplete type information. ## Validation - `python/.venv/bin/ruff format --check python/python/tests/test_db.py python/python/type_tests/connect.py` - `python/.venv/bin/ruff check .` - `cd python && .venv/bin/pytest python/tests/test_db.py::test_package_includes_pep_561_marker -q` - `cd python && .venv/bin/pyright --pythonpath .venv/bin/python` - downstream mypy contract check for both public connection functions Fixes #2159 <!-- lance-gatekeeper-fix:v1 agent=b07901451487187fc03f61890d3aa6bb generation=1 --> Co-authored-by: lancedb-gatefixer[bot] <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
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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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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