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## Depends on merged #4951 Phase 1 merged as `4d22c0ce2b6825c880c818e737b079db494b4053`, so this PR now targets `main`. It reuses the canonical `FunctionWorkerResourceBudget` introduced there rather than copying the type. For Function `add_columns` declarations, Python and Rust now persist `auto_refresh_resource_budget` in computed-column metadata and send it as `auto_refresh_resource_budget` in the remote request. CPU milliseconds and memory bytes are required positive integers. Omitting `gpu_count` is accepted as zero and the canonical outgoing JSON still contains `"gpu_count": 0`. Unknown fields, missing CPU or memory, wrong types, zero CPU or memory, negative values, and overflow are rejected. The same budget is applied to every public output binding of a multi-output Function declaration. Existing metadata is retained, and SQL-expression declarations or local execution reject the budget instead of silently ignoring it. Node is unchanged because its current `addColumns` surface only accepts SQL computed expressions and has no Function declaration path. This change does not implement fleet call concurrency limits or enforce the budget in the runtime. The Enterprise server must support the nested Function worker resource-budget contract before these values can act as execution limits. ## Validation - Python extension build passed. - Remote Function declaration and local unsupported-path rejection each passed. - Canonical metadata round-trip passed, including omitted GPU decoding to zero and explicit `gpu_count: 0` on encode. - Function multi-output remote wire test passed with `--features remote`. - Repository-scoped Python type check passed with 0 errors. - All runtime source blobs match the native-tested source. The post-native deltas are one documentation example line and an existing routing-test stub update for the new optional argument; the affected parameterized test passed 3/3 against the existing extension build. No private test runners are part of this PR.
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
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