lancedb-gatefixer[bot]
4048150fdd
## Summary - add regression coverage for adding dictionary rows with a nullable fixed-size-list column - verify ordinary list columns remain aligned alongside the null fixed-size-list value ## Root cause PyArrow infers an all-`None` dictionary column as the generic `null` type. The original schema-alignment path treated the target fixed-size-list type as proof that the inferred source was also list-like and unconditionally accessed `value_field`, which raised `AttributeError`. Current alignment logic correctly falls back to the target type when the source is not list-like; this test locks in that repair for the reported ingestion path. ## Validation - `uv run --extra tests pytest python/tests/test_table.py::test_add_with_empty_fixed_size_list_drops_bad_rows python/tests/test_table.py::test_add_nullable_fixed_size_list_with_none python/tests/test_table.py::test_add_nullable_struct_with_none -q` - `uv run --with pyarrow==19.0.1 --extra tests pytest python/tests/test_table.py::test_add_nullable_fixed_size_list_with_none -q` - `uv run --project python --extra dev ruff format --check python/python/tests/test_table.py` - `uv run --project python --extra dev ruff check .` Fixes #2340 <!-- lance-gatekeeper-fix:v1 agent=cb0475e85e764f79bd03b35eb8955ec4 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
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