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GPU Functions that use a scalar callable currently invoke the callable once per row. The SDK documents Arrow-vectorized callables, but silently accepts a scalar GPU callable, which makes it easy to assume that the runtime will batch model work automatically. Warn during Function construction when a GPU callable does not use the documented vectorized form. A callable is recognized as vectorized when every input is annotated as `pyarrow.Array`, or when a single input is annotated as `pyarrow.RecordBatch`. The warning explains the resulting per-row invocation and shows how to opt into batch execution. CPU Functions and the vectorized GPU forms do not emit it. The warning is advisory and does not change registration serialization, the artifact, the runtime adapter, or execution behavior. It is one part of the ENT-3522 documentation-and-warning alternative; it does not claim that a single A10G meets the original batch throughput requirement. Validation on the designated EC2 host reused the compatible existing native extension from `ent3563-sdk-freshness-9336cfb`; this does not claim a new native package or GPU performance run. The final head has a clean source tree and ran: ```bash PYTHONPATH="$sdk/python/python" /home/ec2-user/.venvs/ent3563-0a839fa/bin/python -m pytest -q python/python/tests/test_first_class_function_slice2.py -k 'gpu_marker_uses_gpu_runtime or gpu_scalar_warning_names_the_vectorized_alternative' /home/ec2-user/.venvs/ent3563-0a839fa/bin/ruff format --check python/python/lancedb/functions.py python/python/tests/test_first_class_function_slice2.py /home/ec2-user/.venvs/ent3563-0a839fa/bin/ruff check python/python/lancedb/functions.py python/python/tests/test_first_class_function_slice2.py ``` The actual result was `2 passed, 123 deselected`; both Ruff commands passed. The original 2,000-row single-A10G throughput requirement remains a separate ENT-3522 acceptance gate.
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
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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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