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Fix prompt registration with model_config on Databricks#19617

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TomeHirata merged 2 commits intomlflow:masterfrom
TomeHirata:fix/databricks-lm-config
Dec 25, 2025
Merged

Fix prompt registration with model_config on Databricks#19617
TomeHirata merged 2 commits intomlflow:masterfrom
TomeHirata:fix/databricks-lm-config

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@TomeHirata TomeHirata commented Dec 25, 2025 •
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Install mlflow from this PR

# mlflow
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/19617/merge
# mlflow-skinny
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/19617/merge#subdirectory=libs/skinny

For Databricks, use the following command:

%sh curl -LsSf https://raw.githubusercontent.com/mlflow/mlflow/HEAD/dev/install-skinny.sh | sh -s pull/19617/merge

Related Issues/PRs

n/a

What changes are proposed in this pull request?

Fix the following error happening when a prompt is registered on Databricks

TypeError: ModelRegistryClient.create_prompt_version() got an unexpected keyword argument 'model_config'

How is this PR tested?

  • Existing unit/integration tests
  • New unit/integration tests
  • Manual tests

Does this PR require documentation update?

  • No. You can skip the rest of this section.
  • Yes. I've updated:
    • Examples
    • API references
    • Instructions

Release Notes

Is this a user-facing change?

  • No. You can skip the rest of this section.
  • Yes. Give a description of this change to be included in the release notes for MLflow users.

What component(s), interfaces, languages, and integrations does this PR affect?

Components

  • area/tracking: Tracking Service, tracking client APIs, autologging
  • area/models: MLmodel format, model serialization/deserialization, flavors
  • area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
  • area/scoring: MLflow Model server, model deployment tools, Spark UDFs
  • area/evaluation: MLflow model evaluation features, evaluation metrics, and evaluation workflows
  • area/gateway: MLflow AI Gateway client APIs, server, and third-party integrations
  • area/prompts: MLflow prompt engineering features, prompt templates, and prompt management
  • area/tracing: MLflow Tracing features, tracing APIs, and LLM tracing functionality
  • area/projects: MLproject format, project running backends
  • area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
  • area/build: Build and test infrastructure for MLflow
  • area/docs: MLflow documentation pages

How should the PR be classified in the release notes? Choose one:

  • rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
  • rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
  • rn/feature - A new user-facing feature worth mentioning in the release notes
  • rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
  • rn/documentation - A user-facing documentation change worth mentioning in the release notes

Should this PR be included in the next patch release?

Yes should be selected for bug fixes, documentation updates, and other small changes. No should be selected for new features and larger changes. If you're unsure about the release classification of this PR, leave this unchecked to let the maintainers decide.

What is a minor/patch release?
  • Minor release: a release that increments the second part of the version number (e.g., 1.2.0 -> 1.3.0).
    Bug fixes, doc updates and new features usually go into minor releases.
  • Patch release: a release that increments the third part of the version number (e.g., 1.2.0 -> 1.2.1).
    Bug fixes and doc updates usually go into patch releases.
  • Yes (this PR will be cherry-picked and included in the next patch release)
  • No (this PR will be included in the next minor release)

@TomeHirata
Fix prompt registration with model_config on Databricks
f40ea58 
Signed-off-by: Tomu Hirata <tomu.hirata@gmail.com>
Copilot AI review requested due to automatic review settings December 25, 2025 00:52
@github-actions github-actions bot added v3.8.1 area/prompts MLflow Prompt Registry and Optimization rn/bug-fix Mention under Bug Fixes in Changelogs. labels Dec 25, 2025
Copilot AI reviewed Dec 25, 2025
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Pull request overview

This PR fixes a TypeError that occurred when registering prompts on Databricks with the model_config parameter. The issue was that ModelRegistryClient.create_prompt_version() was missing the model_config parameter, even though the underlying store implementations already supported it.

Key Changes:

  • Added model_config parameter to ModelRegistryClient.create_prompt_version() method signature
  • Converted positional arguments to keyword arguments in both MlflowClient and ModelRegistryClient to ensure proper parameter passing
  • Added comprehensive test coverage for the model_config parameter with Unity Catalog store

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated no comments.

File Description
mlflow/tracking/client.py Added model_config parameter to create_prompt_version() method and converted positional arguments to keyword arguments
mlflow/tracking/_model_registry/client.py Added model_config parameter to create_prompt_version() method signature and converted positional arguments to keyword arguments when calling the store
tests/store/_unity_catalog/model_registry/test_unity_catalog_rest_store.py Added new test test_create_prompt_version_with_model_config_uc to verify model_config is properly passed to Unity Catalog store

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github-actions bot commented Dec 25, 2025 •
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Documentation preview for 9fd2edd is available at:

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@TomeHirata
Add model_config support in proto_to_mlflow_prompt function
9fd2edd 
Enhance the proto_to_mlflow_prompt function to extract and include model configuration from version tags. Update tests to verify the correct handling of response_format and model_config, ensuring user tags are preserved while filtering out internal _mlflow tags.

Signed-off-by: Tomu Hirata <tomu.hirata@gmail.com>
WeichenXu123
WeichenXu123 approved these changes Dec 25, 2025
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LGTM!

@TomeHirata TomeHirata enabled auto-merge December 25, 2025 01:21
@TomeHirata TomeHirata disabled auto-merge December 25, 2025 02:55
@TomeHirata TomeHirata merged commit 50b3d6b into mlflow:master Dec 25, 2025
46 of 50 checks passed
WeichenXu123 pushed a commit to WeichenXu123/mlflow that referenced this pull request Dec 26, 2025
@TomeHirata @WeichenXu123
Fix prompt registration with model_config on Databricks (mlflow#19617)
1411f5d 
Signed-off-by: Tomu Hirata <tomu.hirata@gmail.com>
WeichenXu123 pushed a commit that referenced this pull request Dec 26, 2025
@TomeHirata @WeichenXu123
Fix prompt registration with model_config on Databricks (#19617)
1178f67 
Signed-off-by: Tomu Hirata <tomu.hirata@gmail.com>
omarfarhoud pushed a commit to omarfarhoud/mlflow that referenced this pull request Jan 20, 2026
@TomeHirata @omarfarhoud
Fix prompt registration with model_config on Databricks (mlflow#19617)
5056e6d 
Signed-off-by: Tomu Hirata <tomu.hirata@gmail.com>
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area/prompts MLflow Prompt Registry and Optimization rn/bug-fix Mention under Bug Fixes in Changelogs. v3.8.1

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