[APO-2934] Make _parse_ml_models resilient to validation errors#3702
[APO-2934] Make _parse_ml_models resilient to validation errors#3702dvargas92495 merged 2 commits intomainfrom
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Co-Authored-By: vargas@vellum.ai <vargas@vellum.ai>
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Co-Authored-By: vargas@vellum.ai <vargas@vellum.ai>
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| try: | ||
| parsed_models.append(MLModel.model_validate(item)) | ||
| except Exception as e: | ||
| model_name = item.get("name") if isinstance(item, dict) else None | ||
| logger.warning(f"Skipping ML model '{model_name}' due to validation error: {type(e).__name__}") |
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Catching Exception here will silently skip models on any unexpected error raised inside MLModel.model_validate, including programming errors or runtime issues unrelated to input validation. That can mask real regressions (e.g., a bug in validation logic) by downgrading them to warnings and dropping models, which makes failures harder to detect. Since the intent is only to ignore validation failures, consider catching the specific Pydantic validation exception(s) instead.
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Makes
_parse_ml_modelsinBaseWorkflowDisplayresilient to validation errors by catching exceptions for individual models, skipping invalid ones, and logging warnings instead of failing the entire serialization process.