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The Windows wheel for 0.39.0 exceeded PyPI's 100 MiB file-size limit, preventing the original release upload. Restore the repository's release profile (`fat` LTO, 1 codegen unit) by removing the Windows-only `thin`/16 overrides introduced in #3716. Keep `rust-lld` as the linker. This recovers wheel-size headroom at the cost of the longer fat-LTO build. The already-published 0.39.0 Windows wheel was recovered separately by recompressing the original artifact; this change addresses the build configuration for future releases. Fixes #4239. ### Windows size comparison Built the same v0.38.0 source (`8c68e0c619f2b1febe92e51a29d72c968d24a5c9`) twice on one AWS `m7i.4xlarge` Windows Server 2025 machine, changing only the LTO/codegen-unit overrides: | Artifact | thin LTO / 16 units | fat LTO / 1 unit | | --- | ---: | ---: | | Installable Windows wheel | 104,129,447 bytes (99.31 MiB) | 73,875,399 bytes (70.45 MiB) | | Uncompressed native module | 309,969,408 bytes (295.61 MiB) | 206,609,408 bytes (197.04 MiB) | The thin/16 configuration increased wheel size by **40.95%** and native-module size by **50.03%** relative to fat/1. The compressed native module accounts for all but 3 bytes of the wheel increase. Both builds used Rust 1.97.0, maturin 1.12.4, Python 3.13.5, MSVC 14.44.35207, Windows SDK 10.0.26100.0, rust-lld, static CRT, default features, and `maturin build --release --strip --locked --verbose`. They ran sequentially with separate empty target directories and identical locked third-party dependencies. The tag's stale workspace package versions were normalized once before both builds. This controlled pair used VS2022 Build Tools; the historical GitHub runner used VS2026. Both wheels passed ZIP/RECORD integrity checks and installed successfully. Native smoke checks covered import, database creation, row count, nearest-vector query, and reopening the database. This establishes the combined configuration effect; it does not isolate LTO mode from codegen-unit count or establish a runtime-performance difference. The figures above are the v0.38.0 reproduction, **not measurements of this PR head**. The existing PyPI Publish pull-request workflow rebuilds the current revision without publishing; its result is pending. Workflow validation passed with `actionlint -shellcheck=`. Full actionlint reports the same three pre-existing ShellCheck diagnostics in the unchanged repository-selection step as on `main`.
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
Star LanceDB to get updates!
⭐ 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:
- 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.
Contributors
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