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33 lines
1.6 KiB
Python
33 lines
1.6 KiB
Python
from promptflow.core import tool
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@tool
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def validate_input(question: str, answer: str, context: str, ground_truth: str, selected_metrics: dict) -> dict:
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input_data = {"question": question, "answer": answer, "context": context, "ground_truth": ground_truth}
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expected_input_cols = set(input_data.keys())
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dict_metric_required_fields = {"answer_relevance": set(["question", "answer"]),
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"answer_quality": set(["question", "answer"]),
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"creativity": set(["question", "answer"]),
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"grounding": set(["answer", "context"]),
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"context_recall": set(["question", "context", "ground_truth"]),
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"context_precision": set(["question", "context", "ground_truth"]),
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"answer_similarity": set(["question", "answer", "ground_truth"]),
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"answer_correctness": set(["question", "answer", "ground_truth"])}
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actual_input_cols = set()
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for col in expected_input_cols:
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if input_data[col] and input_data[col].strip():
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actual_input_cols.add(col)
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data_validation = selected_metrics
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for metric in selected_metrics:
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if selected_metrics[metric]:
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metric_required_fields = dict_metric_required_fields[metric]
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if metric_required_fields <= actual_input_cols:
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data_validation[metric] = True
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else:
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data_validation[metric] = False
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if data_validation['answer_correctness']:
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data_validation['answer_similarity'] = True
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return data_validation
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