lancedb-gatefixer[bot]
dd5cb4d805
## Summary - exercise float16 sanitization through the reported direct Arrow-data table creation path - assert that the inferred fixed-size vector schema remains float16 - retain end-to-end index creation and vector search coverage ## Root cause and fix PyArrow 16 does not provide an is_nan kernel for half-float arrays, so passing float16 vector values directly to that kernel raises ArrowNotImplementedError. LanceDB's sanitizer already carries the compatibility fix from #837: it casts float16 values to float32 only for NaN detection while preserving the stored vector type. The existing end-to-end regression created an empty schema-defined table and added data afterward. This change aligns that regression with the issue reproduction by creating a table directly from a FixedSizeList<float16> Arrow table and verifying the persisted schema. ## Validation - uv run --extra tests pytest python/tests/test_table.py::test_create_f16_table_from_arrow_data -q - direct 1,000-row by 128-dimension float16 Arrow-table reproduction - PyArrow 16.1 half-float is_nan kernel reproduction - uvx ruff@0.15.20 format --check python/python/tests/test_table.py - uvx ruff@0.15.20 check . Fixes #835 <!-- lance-gatekeeper-fix:v1 agent=dd0a32a959f691f49de958d4333fb29d generation=1 --> --------- Co-authored-by: Gatefixer <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.
Contributors
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