Feature/user defined evaluation prompt template

[Cokral]

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pip install git+https://github.com/mlflow/mlflow.git@refs/pull/11085/merge

Checkout with GitHub CLI

gh pr checkout 11085

Related Issues/PRs

#10808

What changes are proposed in this pull request?

Provide a way for users to override the grading_system_prompt_template used in the genai prompt v1.

• Add parameter grading_system_prompt_template parameter in make_genai_metric which replaces the default value
• Update EvaluationModel accordingly

How is this PR tested?

• [ ] Existing unit/integration tests
• [X] 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
• [X] Unsure -- maybe the docstring is sufficient as this will not be used massively.

Release Notes

Is this a user-facing change?

• [X] 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/artifacts: Artifact stores and artifact logging
• [ ] area/build: Build and test infrastructure for MLflow
• [ ] area/deployments: MLflow Deployments client APIs, server, and third-party Deployments integrations
• [ ] area/docs: MLflow documentation pages
• [ ] area/examples: Example code
• [ ] area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• [ ] area/models: MLmodel format, model serialization/deserialization, flavors
• [ ] area/recipes: Recipes, Recipe APIs, Recipe configs, Recipe Templates
• [ ] area/projects: MLproject format, project running backends
• [ ] area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• [ ] area/server-infra: MLflow Tracking server backend
• [ ] area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• [ ] area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• [ ] area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• [ ] area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• [ ] area/windows: Windows support

Language

• [ ] language/r: R APIs and clients
• [ ] language/java: Java APIs and clients
• [ ] language/new: Proposals for new client languages

Integrations

• [ ] integrations/azure: Azure and Azure ML integrations
• [ ] integrations/sagemaker: SageMaker integrations
• [ ] integrations/databricks: Databricks integrations

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
• [X] 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

[Cokral]

Not exactly sure how to classify it! It's just a small python API update.

[github-actions[bot]]

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