Spaces:
Configuration error
Configuration error
| import argparse | |
| from ..rag_pipeline import qa_prompt | |
| from ..rag_pipeline import ChatAssistant | |
| from ..utils import load_qa_dataset, load_prepared_retrieve_docs | |
| from typing import List, Optional | |
| from langchain.schema import Document | |
| def get_answer_from_response(llm_response: str) -> str: | |
| return llm_response.strip() | |
| def build_qa_prompt(question: str, document: Optional[List[Document]]) -> str: | |
| if document is not None: | |
| document = '\n'.join([f"Document {i+1}:\n" + doc.page_content for i,doc in enumerate(document)]) | |
| return qa_prompt.format(question=question, document=document) | |
| def process_question(question, prompt, answer, id, args, llm): | |
| llm_response = llm.get_response("", prompt) | |
| # ans = get_answer_from_response(llm_response) | |
| 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} \n\n") | |
| # 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 | |
| return llm_response | |
| def evaluate_qa(questions, prompts, answers, ids, args, llm): | |
| import concurrent.futures | |
| from tqdm import tqdm | |
| ans = [] | |
| 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)): | |
| ans.append(future.result()) | |
| return ans | |
| 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_qa_prompt(questions[i], docs[i]) for i in range(len(questions))] | |
| llm = ChatAssistant(args.model_name, args.provider) | |
| with open("log_score.txt", "a", encoding="utf-8") as f: | |
| f.write("\n") | |
| qa_results = evaluate_qa(questions, prompts, answers, ids, args, llm) | |
| qa_results = [qa_results[i][qa_results[i].rfind("[")+1:qa_results[i].rfind("]")] for i in range(len(qa_results))] | |
| # print(f"{qa_results}") | |
| import pyperclip | |
| pyperclip.copy('\n'.join(qa_results)) | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--qa_file", type=str, default="dataset/QA Data/random.jsonl") | |
| parser.add_argument("--prepared_retrieve_docs_path", type=str, default="prepared_retrieve_docs.pkl") | |
| 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("--dataset_path", type=str) | |
| args = parser.parse_args() | |
| print(args) | |
| main(args) |