# mlflow
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/19478/merge
# mlflow-skinny
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/19478/merge#subdirectory=libs/skinny

%sh curl -LsSf https://raw.githubusercontent.com/mlflow/mlflow/HEAD/dev/install-skinny.sh | sh -s pull/19478/merge

Related Issues/PRs

Related to #19436

What changes are proposed in this pull request?

This PR adds documentation for the new KnowledgeRetention scorer that evaluates whether AI assistants correctly retain, contradict, or distort information provided by users in earlier conversation turns.

Changes include:

• Added KnowledgeRetention to the Multi-Turn Scorers table in predefined.mdx
• Added KnowledgeRetention to the Built-in Scorers list in multi-turn.mdx
• Positioned alphabetically between ConversationalToolCallEfficiency and UserFrustration
• Used consistent terminology ("assistant" instead of "AI") to match existing scorer descriptions

How is this PR tested?

• Manual tests

Documentation changes were manually reviewed to ensure:

• Correct formatting and alignment in the markdown tables
• Consistent terminology with existing scorers
• Proper APILink references
• Accurate descriptions matching the scorer implementation

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.

Added documentation for the new KnowledgeRetention scorer in the conversational evaluation guides and predefined scorers reference.

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 (this PR will be cherry-picked and included in the next patch release)
• No (this PR will be included in the next minor release)