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Stacked on #4253. Remote Python Functions could not share helper code, take typed parameters, or keep per-process state: helpers became `from <module> import ...` lines a worker cannot resolve, every parameter was an `env=` string, and state had to be stashed on imported modules. MMLB's OpenAI preset shows the cost — 24 env vars, a 302-line callable, and a rate limiter duplicated between the scalar and batch variants, one of which never constructed it (ENT-2516). **Class Functions.** `@udf` accepts a class. Each remote instance runs `__init__` once, calls `__call__` for every row or batch it processes (row or batch mode is inferred from annotations as before), and calls `close()` once if defined. Calling the definition locally constructs the class, so unit tests stay ordinary. **Initialization.** The annotated `__init__` parameters become the Function's initialization fields. A binding passes their values beside its column inputs: `fn(text=col("body"), model="small", dimensions=512)`. Values are constants of that binding; another column can bind the same Function version with other values. Types are limited to booleans, integers, floats, strings, and lists or structs of them, which have one unambiguous JSON and SQL spelling. A parameter with a default may be omitted; a null value then takes the default. Initialization and input names must be disjoint because both are keyword arguments of one call. Secrets stay on `EnvVarSecret`. **Helper code.** `code=[package, ...]` ships top-level modules or packages as Python source in a `python_bundle` artifact (a canonical JSON object of path → source), imported normally on the worker. Changing a helper changes the artifact digest and so the Function version; the environment from `pip`/`conda` is reused. Functions and classes defined in `__main__` (notebook cells, scripts) are packaged by source, recursively and dependencies first. An import of a module that lives in a local source tree, is not shipped with `code=`, and names no declared package is now rejected at registration instead of failing on the worker. Closures, lambdas, and nested definitions are still rejected, with the reason. A Function without these features packages byte-identically to before (`python_callable`, same digest, no `initialization` on the wire). Class sources are located through a method's code object, because `inspect.getsource` cannot find a class defined in a notebook cell or doctest. The packaging contract is documented on `udf`. Sophon changes that build and execute these artifacts are in lancedb/sophon; they were verified end to end on a Linux local server (registration → REST, SQL, and materialized-view bindings with different initialization → refresh → query, one construction per instance).
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
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:
- 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
Stay in Touch With Us