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Tobias
1ad97b148b
Closes #4246. lancedb publishes no source distribution, so a platform without a prebuilt wheel (#4104, #4239) has nothing to install. This adds an sdist to the Python release. ## The sdist job The release workflow builds an sdist with the maturin version of the wheel jobs. On a tag, the publish job uploads it with the wheels: to PyPI, and to Fury for beta tags. The job also runs on the pull request dry run, which now starts on a change to `python/pyproject.toml`. ## Checks - The job builds a wheel from the sdist with `--locked` and smoke-tests it in a fresh venv. - `ci/check_sdist.py` fails if a tracked file of the Python package is missing from the tarball. - `twine check --strict` checks that the metadata renders. `pydistcheck` enforces PyPI's 100 MiB size limit. - The unit tests of both check scripts run in the CI scripts workflow. ## Cargo.lock maturin ships the repository lockfile, which pins crates that only the dropped `nodejs` member uses, so `cargo --locked` fails (PyO3/maturin#2609). The job first runs `cargo metadata --locked` on the repository workspace, so a lockfile that has drifted from `Cargo.toml` fails there. It then removes the nodejs-only pins and repacks the sdist. `ci/check_cargo_lock.py` fails if the prune changes any package it keeps, or any dependency of one. The published file is the repacked one. ## Behavior change After the first release with an sdist, pip on a platform with no current wheel builds the newest version from source, which needs a Rust toolchain and `protoc`. Before, pip installed the last version with a wheel for that platform.
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:
- 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 |
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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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