lancedb-gatefixer[bot]
c3176a47ce
## Summary - add Python regression coverage for an IVF build that cannot form all requested non-empty partitions - verify hierarchical k-means returns an actionable RuntimeError instead of panicking or silently creating a degenerate index - exercise the current Lance v10.1.0-beta.1 dependency, which contains the upstream error-return fix ## Root cause Hierarchical k-means previously guarded a shortfall in generated clusters with only a debug assertion. Debug builds panicked, while release builds could silently publish an index with many empty partitions. The upstream Lance fix now returns a descriptive error and is already included in the dependency pinned on main; this test locks in propagation through the LanceDB Python API. ## Validation - uv run --extra tests pytest python/tests/test_index.py -q (24 passed) - uv run --extra tests pytest python/tests/test_index.py::test_create_ivf_index_reports_unsplittable_partitions -q (1 passed) - python/.venv/bin/ruff format python/python/tests/test_index.py - python/.venv/bin/ruff check . - git diff --check Fixes #3649 <!-- lance-gatekeeper-fix:v1 agent=a4d34448a9d350a3e2e659f33f5db6f2 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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⭐ 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
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