Skip to content

Fix retrieval_relevance assessments logged to wrong span with missing chunk index#20998

Merged
smoorjani merged 2 commits intomlflow:masterfrom
smoorjani:fix-retrieval-relevance-span-id
Feb 19, 2026
Merged

Fix retrieval_relevance assessments logged to wrong span with missing chunk index#20998
smoorjani merged 2 commits intomlflow:masterfrom
smoorjani:fix-retrieval-relevance-span-id

Conversation

@smoorjani
Copy link
Collaborator

@smoorjani smoorjani commented Feb 19, 2026

Related Issues/PRs

#xxx

What changes are proposed in this pull request?

Two bugs:

  1. _log_assessments unconditionally overwrote assessment span_id to root span, even when the scorer already set it to the retriever span. Now only falls back to root span when span_id is not already set.
  2. Non-Databricks model path in RetrievalRelevance did not include chunk_index in feedback metadata. Now adds it consistent with the Databricks path.

How is this PR tested?

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

MLflow OSS UI:
image

DBX UI:
image

Does this PR require documentation update?

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

Does this PR require updating the MLflow Skills repository?

  • No. You can skip the rest of this section.
  • Yes. Please link the corresponding PR or explain how you plan to update it.

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)

… chunk_index

Two bugs:
1. _log_assessments unconditionally overwrote assessment span_id to root span,
   even when the scorer already set it to the retriever span. Now only falls back
   to root span when span_id is not already set.
2. Non-Databricks model path in RetrievalRelevance did not include chunk_index
   in feedback metadata. Now adds it consistent with the Databricks path.

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
@github-actions github-actions bot added size/S Small PR (10-49 LoC) v3.10.0 labels Feb 19, 2026
@github-actions
Copy link
Contributor

🛠 DevTools 🛠

Install mlflow from this PR

# mlflow
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/20998/merge
# mlflow-skinny
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/20998/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/20998/merge

@github-actions github-actions bot added area/evaluation MLflow Evaluation rn/bug-fix Mention under Bug Fixes in Changelogs. labels Feb 19, 2026
@smoorjani smoorjani force-pushed the fix-retrieval-relevance-span-id branch 3 times, most recently from b8b8f3a to 7360908 Compare February 19, 2026 18:20
- Test that non-Databricks model path includes chunk_index in metadata
  and assigns correct span_id to chunk-level feedbacks
- Test that _log_assessments preserves existing span_id instead of
  unconditionally overwriting to root span

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
@smoorjani smoorjani force-pushed the fix-retrieval-relevance-span-id branch from 7360908 to f6587db Compare February 19, 2026 18:22
@github-actions
Copy link
Contributor

Documentation preview for f6587db is available at:

More info
  • Ignore this comment if this PR does not change the documentation.
  • The preview is updated when a new commit is pushed to this PR.
  • This comment was created by this workflow run.
  • The documentation was built by this workflow run.

@smoorjani smoorjani enabled auto-merge February 19, 2026 18:45
@smoorjani smoorjani added this pull request to the merge queue Feb 19, 2026
Merged via the queue into mlflow:master with commit 9e773a9 Feb 19, 2026
53 of 55 checks passed
@smoorjani smoorjani deleted the fix-retrieval-relevance-span-id branch February 19, 2026 19:08
@github-actions github-actions bot added size/M and removed size/S Small PR (10-49 LoC) labels Feb 19, 2026
daniellok-db pushed a commit to daniellok-db/mlflow that referenced this pull request Feb 20, 2026
… chunk index (mlflow#20998)

Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
Co-authored-by: Claude <noreply@anthropic.com>
daniellok-db pushed a commit that referenced this pull request Feb 20, 2026
… chunk index (#20998)

Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
Co-authored-by: Claude <noreply@anthropic.com>
cluster2600 pushed a commit to cluster2600/mlflow that referenced this pull request Feb 20, 2026
… chunk index (mlflow#20998)

Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
Co-authored-by: Claude <noreply@anthropic.com>
Signed-off-by: Maxime Grenu <maxime.grenu@gmail.com>
cluster2600 pushed a commit to cluster2600/mlflow that referenced this pull request Feb 20, 2026
… chunk index (mlflow#20998)

Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
Co-authored-by: Claude <noreply@anthropic.com>
Signed-off-by: Maxime Grenu <maxime.grenu@gmail.com>
cluster2600 pushed a commit to cluster2600/mlflow that referenced this pull request Feb 21, 2026
… chunk index (mlflow#20998)

Signed-off-by: Maxime Grenu <maxime.grenu@gmail.com>
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

area/evaluation MLflow Evaluation rn/bug-fix Mention under Bug Fixes in Changelogs. size/M v3.10.0

Projects

None yet

Development

Successfully merging this pull request may close these issues.

2 participants