lancedb-gatefixer[bot]
0ba82873c5
## Summary - add a minimized regression for mostly-null `list<float32>` data at the v2.2 structural page boundary - verify scans preserve all 64,885 rows, including 64,668 null list values ## Root cause Lance 3.0.0 sliced repetition/definition state using top-level row offsets in the complex all-null decoder. At this page boundary, the list and validity children were materialized at different lengths. The current Lance dependency contains the upstream decoder repair; this test locks that behavior into the LanceDB Python suite without duplicating decoder logic. ## Validation - reproduced the attached 1,892,466-row case on `lancedb==0.30.0` with `expected 1024 got 285` - verified the full attachment reads on the current branch - `python/.venv/bin/ruff format --check python/python/tests/test_table.py` - `python/.venv/bin/ruff check .` - `cd python && uv run --extra tests pytest python/tests/test_table.py::test_read_mostly_null_list_v2_2_page_boundary -q` - `cd python && uv run --extra tests pytest python/tests/test_table.py -q` (137 passed) Fixes #3194 <!-- lance-gatekeeper-fix:v1 agent=0445adc5303a3302152cea3d2110bed1 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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