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Fix token usage for embeddings API in mlflow typescript sdk#19239

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BenWilson2 merged 1 commit intomlflow:masterfrom
serena-ruan:fix_embeddings_token_typescript
Dec 5, 2025
Merged

Fix token usage for embeddings API in mlflow typescript sdk#19239
BenWilson2 merged 1 commit intomlflow:masterfrom
serena-ruan:fix_embeddings_token_typescript

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@serena-ruan serena-ruan commented Dec 5, 2025

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Install mlflow from this PR

# mlflow
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/19239/merge
# mlflow-skinny
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/19239/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/19239/merge

Related Issues/PRs

#xxx

What changes are proposed in this pull request?

usage.completion_tokens could be empty, and in the old implementation this causes the token usage to be generated like:

{'input_tokens': 20, 'output_tokens': '0[Undefined]', 'total_tokens': 20}

This makes output_tokens an invalid string that cannot be converted to int.

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)

Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
Copilot AI review requested due to automatic review settings December 5, 2025 07:42
@github-actions github-actions bot added v3.7.0 rn/none List under Small Changes in Changelogs. labels Dec 5, 2025
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Pull request overview

This PR fixes token usage reporting for the embeddings API in the MLflow TypeScript SDK. The embeddings API response doesn't include completion_tokens in its usage object, which was causing invalid token usage strings like '0[Undefined]' to be generated.

Key Changes:

  • Added null-safety handling for completion_tokens using the nullish coalescing operator
  • Updated tests to verify that embeddings API correctly reports 0 for output_tokens

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated no comments.

File Description
libs/typescript/integrations/openai/src/index.ts Fixed extractTokenUsage to use nullish coalescing operator (?? 0) for completion_tokens to handle embeddings API responses where this field is undefined
libs/typescript/integrations/openai/tests/index.test.ts Added test assertions to verify token usage is correctly extracted for embeddings API, expecting output_tokens to be 0 when completion_tokens is missing

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looks good!

@BenWilson2 BenWilson2 added this pull request to the merge queue Dec 5, 2025
Merged via the queue into mlflow:master with commit 9d4c409 Dec 5, 2025
59 of 61 checks passed
@serena-ruan serena-ruan deleted the fix_embeddings_token_typescript branch December 8, 2025 06:12
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