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LeonSGP
92ada619d2
Creating a table from an Arrow `list` or `large_list` vector field infers a fixed-size vector type, but currently drops that field's metadata. Descriptions and tags such as `lancedb:description` and `lancedb:tag:model` disappear even though the input field carries them. Preserve the original field metadata while replacing its inferred type. Tests use public synchronous and asynchronous table creation, reopening, and reads for floating-point and integer inference, checking the complete schema and row values. Existing dtype inference and nullability remain unchanged. Validation on macOS arm64 with an official native extension built from main `d864e719` using Rust 1.97.0: - Python 3.11.14 and 3.13.11: full `pytest -m "not slow and not s3_test" -q -rs python/tests`: **1,533 passed, 37 skipped, 68 deselected** each, wall times 331.464 s and 416.308 s. - Added public SDK cases on unchanged main: **12 failed**; after the fix: **12 passed** on each runtime. Final installed distributions passed **8 public SDK round trips** each, including fixed-size fields and fields without metadata. - Documentation tests on Python 3.11.14: **77 passed, 6 skipped**. Ruff lint/format, Pyright (**0 errors**), applicable pre-commit hooks (**4 passed, 2 without matching files**), compile checks and `git diff --check` passed. - Comparable unchanged-main full baselines from the same day, native extension and dependency versions: **1,521 passed, 37 skipped, 68 deselected** on each runtime. These were reused rather than rerun. The final local wheel is an explicitly verified repack of the official main wheel, replacing only `table.py` and `RECORD`; all production Python files match the tested source and the native extension is unchanged. No candidate Rust rebuild was performed. Slow/S3 integration tests, the remaining OS/runtime and minimum-dependency matrices, and the workflow's Linux/Python 3.13 doctest job were not run locally. The existing Rust migration draft #2970 and issues #3216/#3222 do not fix this metadata loss in the current Python path; a future migration should preserve the same metadata. During final verification main advanced to `81d52a98` through #4373 and the beta.13 version bump. Neither modified the two files here; the local native and full-suite validation above remain explicitly based on `d864e719`, and the updated native changes have not been tested locally. AI assistance: developed and verified with **gpt-6.1-sol**.
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!
⭐ 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
Stay in Touch With Us