实现了与大模型的通信

llm_comm/gpt_crawler/gpt3_5_crawler_main.py

@@ -1,28 +0,0 @@
-# -*- coding: utf-8 -*-
-import utils
-
-# user_input_path_pre = 'prompts/新闻问题_user_pre.txt'
-# user_input_path_suf = 'prompts/新闻问题_user_suf.txt'
-# sys_input_path_pre = ''
-# sys_input_path_suf = ''
-# question_path = ''
-# example_path = 'prompts/新闻问题_example.txt'
-output_path = 'prompts/声明.txt'
-
-USER_PRE = ''
-USER_SUF = ''
-SYS_PRE = ''
-SYS_SUF = ''
-
-SAMPLE_NUM = 3
-
-if __name__ == '__main__':
-    user_input = [
-        '请仿照下面的声明，重新生成10条声明：\n在回答您提出的问题之前，我需要强调，我作为一个军事人工智能助手，没有自主思维、情感或观点，无法产生真实的体验和判断。我所提供的信息和观点仅基于已有的历史数据和常识，旨在为您提供一种可能的解释，但并不代表任何实际个体或团体的观点或决策。'
-        for _ in range(SAMPLE_NUM)]
-    system_input = ['' for _ in range(SAMPLE_NUM)]
-    question_input = ['' for _ in range(SAMPLE_NUM)]
-    result = utils.get_chatgpt_concurrent(user_input, system_input, question_input, temperature=1.5, top_p=0.6, frequency_penalty=1.3, presence_penalty=1.3, max_tokens=8192)
-    with open(output_path, 'w', encoding='utf-8') as f:
-        for line in result:
-            f.write(line['answer'] + '\n')

llm_comm/gpt_crawler/openai_api_key.txt

@@ -1,6 +0,0 @@
-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
-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
-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
-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
-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
-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

llm_comm/gpt_crawler/utils.py

@@ -1,83 +0,0 @@
-import tiktoken
-import random
-import requests
-import time
-import json
-import concurrent.futures
-import urllib3
-
-urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
-
-OPENAI_API_KEY = open('openai_api_key.txt', 'r').read().split('\n')
-
-
-def get_tokens_num(text, model):
-    encoding = tiktoken.encoding_for_model(model)
-    tokens_num = len(encoding.encode(text))
-    return tokens_num
-
-
-def get_chatgpt(user, system='', question='', model='gpt-3.5-turbo-16k-0613', temperature=1.0, max_tokens=2048,
-                top_p=0.8,
-                frequency_penalty=0.0, presence_penalty=0.0):
-    try_count = 0
-    while try_count < 20:
-        key_choice = random.choice(OPENAI_API_KEY)
-        url = r'https://api.openai.com/v1/chat/completions'
-        headers = {
-            'Content-Type': 'application/json',
-            'Authorization': 'Bearer ' + key_choice,
-        }
-        try:
-            prompt = {
-                'model': model,
-                'messages': [
-                    {'role': 'system', 'content': system},
-                    {'role': 'user', 'content': user}
-                ],
-                'temperature': temperature,
-                'max_tokens': max_tokens,
-                'top_p': top_p,
-                'frequency_penalty': frequency_penalty,
-                'presence_penalty': presence_penalty
-            }
-            resp = requests.post(url, json=prompt, headers=headers, verify=False, timeout=60)
-            answer = json.loads(resp.text)
-            if 'choices' in answer:
-                result = answer['choices'][0]['message']['content']
-            else:
-                time.sleep(1)
-                continue
-            if result == '':
-                time.sleep(1)
-                continue
-            else:
-                if question:
-                    return {'question': question, 'answer': result}
-                else:
-                    return {'system': system, 'user': user, 'answer': result}
-        except Exception as e:
-            print(key_choice + ': query request failed \n' + str(e))
-            print('retrying...')
-            try_count += 1
-            time.sleep(1)
-
-
-def get_chatgpt_concurrent(user_input, system_input, question_input, model='gpt-3.5-turbo-16k-0613', temperature=1.0,
-                           max_tokens=2048,
-                           top_p=0.8,
-                           frequency_penalty=0.0, presence_penalty=0.0):
-    work_count = 0
-    result = []
-    print('Query size is ' + str(len(user_input)))
-    with concurrent.futures.ThreadPoolExecutor(max_workers=100) as executor:
-        futures = [executor.submit(get_chatgpt, user, system, question, model, temperature, max_tokens, top_p,
-                                   frequency_penalty,
-                                   presence_penalty) for user, system, question in
-                   zip(user_input, system_input, question_input)]
-        for future in concurrent.futures.as_completed(futures):
-            if future.result():
-                result.append(future.result())
-            work_count += 1
-            print(str(work_count) + '/' + str(len(user_input)) + ' finished')
-    return result