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| """Generate answers with GPT-3.5""" | |
| # Note: you need to be using OpenAI Python v0.27.0 for the code below to work | |
| import argparse | |
| import json | |
| import os | |
| import time | |
| import concurrent.futures | |
| import openai | |
| import tqdm | |
| import shortuuid | |
| MODEL = 'gpt-3.5-turbo' | |
| MODEL_ID = 'gpt-3.5-turbo:20230327' | |
| def get_answer(question_id: int, question: str, max_tokens: int): | |
| ans = { | |
| 'answer_id': shortuuid.uuid(), | |
| 'question_id': question_id, | |
| 'model_id': MODEL_ID, | |
| } | |
| for _ in range(3): | |
| try: | |
| response = openai.ChatCompletion.create( | |
| model=MODEL, | |
| messages=[{ | |
| 'role': 'system', | |
| 'content': 'You are a helpful assistant.' | |
| }, { | |
| 'role': 'user', | |
| 'content': question, | |
| }], | |
| max_tokens=max_tokens, | |
| ) | |
| ans['text'] = response['choices'][0]['message']['content'] | |
| return ans | |
| except Exception as e: | |
| print('[ERROR]', e) | |
| ans['text'] = '#ERROR#' | |
| time.sleep(1) | |
| return ans | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser(description='ChatGPT answer generation.') | |
| parser.add_argument('-q', '--question') | |
| parser.add_argument('-o', '--output') | |
| parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output') | |
| args = parser.parse_args() | |
| questions_dict = {} | |
| with open(os.path.expanduser(args.question)) as f: | |
| for line in f: | |
| if not line: | |
| continue | |
| q = json.loads(line) | |
| questions_dict[q['question_id']] = q['text'] | |
| answers = [] | |
| with concurrent.futures.ThreadPoolExecutor(max_workers=32) as executor: | |
| futures = [] | |
| for qid, question in questions_dict.items(): | |
| future = executor.submit(get_answer, qid, question, args.max_tokens) | |
| futures.append(future) | |
| for future in tqdm.tqdm(concurrent.futures.as_completed(futures), total=len(futures)): | |
| answers.append(future.result()) | |
| answers.sort(key=lambda x: x['question_id']) | |
| with open(os.path.expanduser(args.output), 'w') as f: | |
| table = [json.dumps(ans) for ans in answers] | |
| f.write('\n'.join(table)) | |