auroflow opened a new pull request, #29478: URL: https://github.com/apache/flink/pull/29478
## What is the purpose of the change Add `df.llm.predict()` so DataFrame users can invoke registered model providers and append prediction columns in one call. The accessor creates an anonymous model descriptor and reuses Flink's existing model inference machinery. This addresses [FLINK-40440](https://issues.apache.org/jira/browse/FLINK-40440). ## Brief change log - Add `DataFrame.llm` and `LLMAccessor.predict()`. - Expose Java's existing anonymous `fromModel(ModelDescriptor)` overload in Python while preserving named-model calls and the `model_path` keyword. ## Verifying this change This change added tests and can be verified as follows: - The focused suites `test_model_provider.py`, `test_llm.py`, `test_model.py`, and `test_llm_integration.py` passed: **39 tests**. - Flake8 passed for all changed Python files; mypy passed for the four changed production Python files. A full Maven package build with tests skipped passed. Full `mvn clean verify` has not been run. ## Does this pull request potentially affect one of the following parts: - Dependencies (does it add or upgrade a dependency): no - The public API, i.e., is any changed class annotated with `@Public(Evolving)`: yes - The serializers: no - The runtime per-record code paths (performance sensitive): no - Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn, ZooKeeper: no - The S3 file system connector: no ## Documentation - Does this pull request introduce a new feature? yes - If yes, how is the feature documented? Python API docstrings and Sphinx reference documentation, including prediction, provider overrides, and output naming examples. --- ##### Was generative AI tooling used to co-author this PR? - [X] Yes (please specify the tool below) Generated-by: Codex (GPT-6) -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
