The documentation states that 'checked_text' is required in the info dict, but the validation logic doesn't match the docstring. The docstring says 'Must include' but doesn't explain why this field is mandatory or what it should contain.

Double error handling creates redundant validation error messages. The validate_dataset function already logs errors, so this catch-and-re-raise pattern results in duplicate logging and error wrapping.

Setting global matplotlib and seaborn styles in the constructor can affect other parts of the application. Consider using context managers or style parameters in individual plotting methods to avoid global side effects.

[nitpick] The supported file types set uses mixed line formatting that makes it harder to maintain. Consider formatting each extension on its own line for better version control diffs and easier maintenance.

The__post_init__ method validation will fail for existing code that createsGuardrailResult instances without the required 'checked_text' field. This represents a breaking change that could cause runtime errors in production code that was previously working.

The ROC AUC calculation usingnp.trapz(tpr, fpr) is incorrect. The correct calculation should benp.trapz(tpr, fpr) with reversed order or usesklearn.metrics.auc(fpr, tpr) for proper AUC computation.

SettingAsyncAzureOpenAI = None after import failure creates potential runtime errors when the code later checksif not AsyncAzureOpenAI:. A more robust approach would be to use a proper sentinel value or define a stub class.