134 lines
No EOL
4.3 KiB
Python
134 lines
No EOL
4.3 KiB
Python
import argparse
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import asyncio
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import json
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import statistics
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from pathlib import Path
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import deepeval.metrics
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import wget
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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 cognee
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import evals.deepeval_metrics
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from cognee.api.v1.search import SearchType
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from cognee.base_config import get_base_config
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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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async def answer_without_cognee(instance):
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args = {
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"question": instance["question"],
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"context": instance["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_question.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 answer_with_cognee(instance):
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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for (title, sentences) in instance["context"]:
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await cognee.add("\n".join(sentences), dataset_name = "HotPotQA")
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await cognee.cognify("HotPotQA")
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search_results = await cognee.search(
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SearchType.INSIGHTS, query_text=instance["question"]
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)
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args = {
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"question": instance["question"],
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"context": search_results,
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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 eval_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"],
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actual_output=answer,
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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 eval_on_hotpotQA(answer_provider, num_samples, eval_metric):
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base_config = get_base_config()
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data_root_dir = base_config.data_root_directory
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if not Path(data_root_dir).exists():
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Path(data_root_dir).mkdir()
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filepath = data_root_dir / Path("hotpot_dev_fullwiki_v1.json")
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if not filepath.exists():
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url = 'http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_fullwiki_v1.json'
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wget.download(url, out=data_root_dir)
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with open(filepath, "r") as file:
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dataset = json.load(file)
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instances = dataset if not num_samples else dataset[:num_samples]
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answers = []
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for instance in tqdm(instances, desc="Getting answers"):
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answer = await answer_provider(instance)
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answers.append(answer)
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eval_results = await eval_answers(instances, answers, eval_metric)
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avg_score = statistics.mean([result.metrics_data[0].score for result in eval_results.test_results])
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return avg_score
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--with_cognee", action="store_true")
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parser.add_argument("--num_samples", type=int, default=500)
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parser.add_argument("--metric", type=str, default="correctness_metric",
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help="Valid options are Deepeval metrics (e.g. AnswerRelevancyMetric) \
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and metrics defined in evals/deepeval_metrics.py, e.g. f1_score_metric")
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args = parser.parse_args()
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try:
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metric_cls = getattr(deepeval.metrics, args.metric)
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metric = metric_cls()
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except AttributeError:
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metric = getattr(evals.deepeval_metrics, args.metric)
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if isinstance(metric, type):
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metric = metric()
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if args.with_cognee:
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answer_provider = answer_with_cognee
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else:
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answer_provider = answer_without_cognee
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avg_score = asyncio.run(eval_on_hotpotQA(answer_provider, args.num_samples, metric))
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print(f"Average {args.metric}: {avg_score}") |