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import argparse
from ..rag_pipeline import multichoice_qa_prompt
from ..rag_pipeline import ChatAssistant
from ..utils import paralelize, load_qa_dataset, load_prepared_retrieve_docs
from datetime import datetime
from typing import List, Optional
from langchain.schema import Document
def get_answer_from_response(llm_response: str) -> chr:
"""
Get the answer from the LLM response.
"""
return llm_response[llm_response.lower().rfind("the answer is ") + 14]
def build_multichoice_qa_prompt(question: str, options: str, document: Optional[List[Document]]) -> str:
"""
Build the prompt for the multichoice QA task.
"""
if document is not None:
document = '\n'.join([f"Document {i+1}:\n" + doc.page_content for i,doc in enumerate(document)])
return multichoice_qa_prompt.format(question=question, options=options, document=document)
def process_question(question, prompt, answer, id, args, llm):
llm_response = ""
for j in range(args.retries):
try:
llm_response = llm.get_response("", prompt)
ans = get_answer_from_response(llm_response)
if ans in ["A", "B", "C", "D", "E"]:
with open("log.txt", "a", encoding="utf-8") as f:
f.write(f"ID: {id}\n")
f.write(prompt)
f.write(f"LLM Response:\n{llm_response}\n")
f.write(f"Answer: {answer} {ans}\n\n")
break
except Exception as e:
print(f"Error: {e}")
ans = "#"
with open("log_score.txt", "a", encoding="utf-8") as f:
f.write("1" if ans == answer else "0")
return 1 if ans == answer else 0
def evaluate_qa(questions, prompts, answers, ids, args, llm):
import concurrent.futures
from tqdm import tqdm
correct = 0
with concurrent.futures.ThreadPoolExecutor(max_workers=args.max_workers) as executor:
futures = [executor.submit(process_question, questions[i], prompts[i], answers[i], ids[i], args, llm) for i in range(len(questions))]
for future in tqdm(concurrent.futures.as_completed(futures), total=len(questions)):
correct += future.result()
return correct / len(questions)
def main(args):
ids, questions, options, answers = load_qa_dataset(args.qa_file)
if ids is None:
raise ValueError(f"No id field in {args.qa_file}.")
if args.num_docs > 0:
if args.prepared_retrieve_docs_path is not None:
documents = load_prepared_retrieve_docs(args.prepared_retrieve_docs_path)
docs = [d[:args.num_docs] for i,d in enumerate(documents)]
else:
raise ValueError(f"No prepared retrieve docs found.")
else:
docs = [None]*len(questions)
prompts = [build_multichoice_qa_prompt(questions[i], options[i], docs[i]) for i in range(len(questions))]
# print(prompts[0])
llm = ChatAssistant(args.model_name, args.provider)
with open("log_score.txt", "a", encoding="utf-8") as f:
f.write(f"\n{datetime.now()} {args}\n")
acc = evaluate_qa(questions, prompts, answers, ids, args, llm)
print(f"Accuracy: {acc}")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# parser.add_argument("--qa_file", type=str, default="dataset/QA Data/MedAB/MedABv2.jsonl")
# parser.add_argument("--prepared_retrieve_docs_path", type=str, default="dataset/QA Data/MedAB/prepared_retrieve_docs_full.pkl")
parser.add_argument("--qa_file", type=str, default="dataset/QA Data/MedMCQA/translated_hard_questions.jsonl")
parser.add_argument("--prepared_retrieve_docs_path", type=str, default="dataset/QA Data/MedMCQA/prepared_retrieve_docs_full.pkl")
# Eval params
parser.add_argument("--model_name", type=str, default="mistral-medium")
parser.add_argument("--provider", type=str, default="mistral")
parser.add_argument("--max_workers", type=int, default=4)
parser.add_argument("--num_docs", type=int, default=0)
parser.add_argument("--retries", type=int, default=4)
# Dataset params
parser.add_argument("--dataset_path", type=str)
args = parser.parse_args()
print(f"Log:{args}")
main(args) |