lancedb-gatefixer[bot]
123c921c4f
## Summary - add a Python regression for vector search over a sliced Arrow table with nullable scalar columns - verify the nearest row retains its non-null score values after the table is written ## Root cause Lance 0.19.2 deep-copied a validity bitmap without preserving its non-zero bit offset. For a sliced nullable table, scalar values and vectors began at the slice while the copied validity bitmap began at the parent table's first row. That made valid score values appear null even though the corresponding vector stayed intact. The upstream Lance repair is already present in the current dependency; this adds a LanceDB-level guard for the reported create/search path. ## Validation - reproduced on Python 3.12 with LanceDB 0.16.0, pylance 0.19.2, PyArrow 18.0.0, and Polars 1.14.0 - `uv run --project python --extra dev ruff format --check python/python/tests/test_table.py` - `uv run --project python --extra dev ruff check .` - `cd python && uv run --extra tests pytest python/tests/test_table.py::test_search_preserves_nulls_from_sliced_arrow_table -q` Fixes #1879 <!-- lance-gatekeeper-fix:v1 agent=bfa0551793f8e3cf3980cf64ad89908a 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.
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