3494521cae
* QA eval dataset as argument, with hotpot and 2wikimultihop as options. Json schema validation for datasets. * Load dataset file by filename, outsource utilities * restructure metric selection * Add comprehensiveness, diversity and empowerment metrics * add promptfoo as an option * refactor RAG solution in eval;2C * LLM as a judge metrics implemented in a uniform way * Use requests.get instead of wget * clean up promptfoo config template * minor fixes * get promptfoo path instead of hardcoding * minor fixes * Add LLM as a judge prompts * Support 4 different rag options in eval * Minor refactor and logger usage
99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
import argparse
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import asyncio
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import statistics
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from deepeval.dataset import EvaluationDataset
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from deepeval.test_case import LLMTestCase
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from tqdm import tqdm
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import logging
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from cognee.infrastructure.llm.get_llm_client import get_llm_client
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from cognee.infrastructure.llm.prompts import read_query_prompt, render_prompt
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from evals.qa_dataset_utils import load_qa_dataset
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from evals.qa_metrics_utils import get_metric
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from evals.qa_context_provider_utils import qa_context_providers
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logger = logging.getLogger(__name__)
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async def answer_qa_instance(instance, context_provider):
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context = await context_provider(instance)
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args = {
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"question": instance["question"],
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"context": context,
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}
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user_prompt = render_prompt("context_for_question.txt", args)
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system_prompt = read_query_prompt("answer_hotpot_using_cognee_search.txt")
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llm_client = get_llm_client()
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answer_prediction = await llm_client.acreate_structured_output(
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text_input=user_prompt,
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system_prompt=system_prompt,
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response_model=str,
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)
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return answer_prediction
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async def deepeval_answers(instances, answers, eval_metric):
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test_cases = []
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for instance, answer in zip(instances, answers):
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test_case = LLMTestCase(
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input=instance["question"], actual_output=answer, expected_output=instance["answer"]
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)
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test_cases.append(test_case)
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eval_set = EvaluationDataset(test_cases)
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eval_results = eval_set.evaluate([eval_metric])
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return eval_results
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async def deepeval_on_instances(instances, context_provider, eval_metric):
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answers = []
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for instance in tqdm(instances, desc="Getting answers"):
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answer = await answer_qa_instance(instance, context_provider)
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answers.append(answer)
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eval_results = await deepeval_answers(instances, answers, eval_metric)
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avg_score = statistics.mean(
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[result.metrics_data[0].score for result in eval_results.test_results]
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)
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return avg_score
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async def eval_on_QA_dataset(
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dataset_name_or_filename: str, context_provider_name, num_samples, eval_metric_name
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):
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dataset = load_qa_dataset(dataset_name_or_filename)
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context_provider = qa_context_providers[context_provider_name]
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eval_metric = get_metric(eval_metric_name)
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instances = dataset if not num_samples else dataset[:num_samples]
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if eval_metric_name.startswith("promptfoo"):
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return await eval_metric.measure(instances, context_provider)
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else:
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return await deepeval_on_instances(instances, context_provider, eval_metric)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--dataset", type=str, required=True, help="Which dataset to evaluate on")
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parser.add_argument(
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"--rag_option",
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type=str,
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choices=qa_context_providers.keys(),
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required=True,
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help="RAG option to use for providing context",
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)
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parser.add_argument("--num_samples", type=int, default=500)
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parser.add_argument("--metric_name", type=str, default="Correctness")
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args = parser.parse_args()
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avg_score = asyncio.run(
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eval_on_QA_dataset(args.dataset, args.rag_option, args.num_samples, args.metric_name)
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)
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logger.info(f"Average {args.metric_name}: {avg_score}")
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