Related Issues/PRs

N/A

What changes are proposed in this pull request?

RestException.__init__ crashes with TypeError when the JSON response contains "error_code": null. Two bugs contribute:

1. Null handling: dict.get("error_code", default) returns None when the key exists with null value, bypassing the default
2. Exception handling: except ValueError or KeyError: only catches ValueError (Python evaluates or first, returning ValueError instead of a tuple)

Changes:

• Line 122: json.get("error_code") or ErrorCode.Name(INTERNAL_ERROR) - handles both missing keys and null values
• Line 136: except (ValueError, KeyError, TypeError): - correctly catches all three exception types
• Added test cases for null and missing error_code scenarios

Example:

# Previously crashed with TypeError
RestException({"error_code": None, "message": "test"})

# Now defaults to INTERNAL_ERROR
# error_code="INTERNAL_ERROR", message="INTERNAL_ERROR: test"

How is this PR tested?

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

Added test_rest_exception_with_null_error_code and test_rest_exception_with_missing_error_code.

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.

Fixed RestException crash when REST API returns "error_code": null in error responses.

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)

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