lancedb-gatefixer[bot]andXuanwo
7952ee2a71
Keep FP16 vectors through IVF-PQ index creation and search in the synchronous and asynchronous quickstarts. Add native and conditional CUDA regression coverage using a float64 query against an explicitly typed FP16 column. This is test and documentation-example coverage for dtype preservation already present in Lance; it does not change production code. Related #2120. Validation on `bbad20940632b21a9ee85bdcb46b4cc19dbd65e3` with Python 3.12.12, Lance 15.0.0-beta.1, PyArrow 24.0.0, and Torch 2.14.1: - The native FP16 parameter, Arrow FP16 ingestion test, and both quickstarts passed: 4 passed, 1 CUDA-only skip. - The two affected test files passed: 386 passed, 1 CUDA-only skip (`-m "not slow and not s3_test"`). - Ruff 0.9.9 lint and formatting checks passed for the Python package. Real CUDA execution has not been performed. Torch is installed, but `torch.cuda.is_available()` is false; `test_create_f16_table[cuda]` skips with `CUDA not available`. The native CPU result does not establish that the original GPU failure is resolved. Issue #2120 remains open for verification on an actual CUDA device. Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io>
The Multimodal Lakehouse for AI
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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