src/deepeval/faithfulness_task.py

from datetime import datetime
from src.deepeval.base_task import BaseTask
from deepeval.metrics import FaithfulnessMetric
from deepeval.test_case import LLMTestCase
from typing import Any

class FaithfulnessTask(BaseTask):
    def __init__(self, model_name: str):
        super().__init__("metunlp/sosyoloji_faithfulness", model_name=model_name)

    def load_dataset_from_hf(self):
        dataset = super().load_dataset_lmjudge_from_hf()
        return dataset

    def evaluate(self) -> dict[str, Any]:

        results = []
        total_model_time = 0
        total_judge_time = 0

        for i, row in enumerate(self.dataset):
            start_model = datetime.now()
            context = row["context"]
            question = row["question"]

            prompt = (
                f"Context: {context}\n"
                f"Question: {question}\n"
                f"Answer:"
            )

            generated_answer = self.generate_response(prompt, max_new_tokens=100)
            end_model = datetime.now()
            total_model_time += (end_model - start_model).total_seconds()

            start_judge = datetime.now()
            test_case = LLMTestCase(
                input=question,
                actual_output=generated_answer,
                retrieval_context=[context]
            )

            metric = FaithfulnessMetric(
                threshold=0.0,
                model="gpt-4o-mini",
                include_reason=True
            )
            metric.measure(test_case)
            end_judge = datetime.now()
            total_judge_time += (end_judge - start_judge).total_seconds()

            results.append({
                "index": i,
                "score": metric.score,
                "reason": metric.reason,
                "score_breakdown": metric.score_breakdown,
                "context": context,
                "question": question,
                "answer": generated_answer
            })

            #Sum all scores in results and divide to nubmer of results
            overallScore = (sum([result["score"] for result in results]) / len(results)) * 100 

        print(f"Total model time: {total_model_time} seconds")
        print(f"Total judge time: {total_judge_time} seconds")  
        return {"results": overallScore}