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Xuanwo
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## Summary - Add sync/async `DBConnection.list_gpu_classes()` returning typed `GpuClass` records for `GET /v1/gpu/classes`. Records include the class name, Kubernetes resource, GPU model and memory, per-worker GPU maximum, and worker CPU/memory/ephemeral-storage limits. - Add `udf(gpu_class=...)` for immutable Function-level GPU placement. It requires `gpu=True`; Function versions expose their registered `gpu_class`. - Add `gpu_class` and `gpu_count` to `refresh_column` / `refresh_column_async` and Rust `RefreshColumnOptions`. Both are sent as top-level `backfill_column` fields and validated before any request. `gpu_count` is the aggregate GPU budget across Function workers. - Validate GPU class names with the server lowercase 63-character DNS-label rule. - Document the discovery-to-registration flow, configured hardware versus free capacity, System Usage permission, and per-refresh GPU placement in the API and web guides. Older servers without `/v1/gpu/classes` produce a clear `NotImplementedError` instead of a bare 404. Part of ENT-3470. The client follows the in-flight Sophon contract on `xuanwo/ent-3470-fix` for GPU class discovery and backfill placement options. ## Breaking change `PythonRuntimeSpec::PythonV2` adds `gpu_class`, and `RefreshColumnOptions` adds `gpu_class` and `gpu_count`. Rust callers constructing either value with all fields explicitly must supply the new fields. ## Validation At `7685c968`: Rust GPU tests (5 passed), Rust/PyO3 clippy, Python extension build, pyright, ruff, and Python Function/runtime, discovery, refresh placement, and refresh regressions (147 passed, 6 skipped). At `b22428fd`: Rust GPU tests (5 passed), rustfmt, ruff, pyright, Function slice 2 (85 passed), and remote GPU/refresh tests (20 passed). The final commit only updates documentation and Rust doc comments.
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.
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