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| # 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目 | |
| """ | |
| 该文件中主要包含2个函数 | |
| 不具备多线程能力的函数： | |
| 1. predict: 正常对话时使用，具备完备的交互功能，不可多线程 | |
| 具备多线程调用能力的函数 | |
| 2. predict_no_ui_long_connection：支持多线程 | |
| """ | |
| import os | |
| import json | |
| import time | |
| import gradio as gr | |
| import logging | |
| import traceback | |
| import requests | |
| import importlib | |
| # config_private.py放自己的秘密如API和代理网址 | |
| # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件 | |
| from toolbox import get_conf, update_ui, trimmed_format_exc, ProxyNetworkActivate | |
| proxies, TIMEOUT_SECONDS, MAX_RETRY, ANTHROPIC_API_KEY = \ | |
| get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'ANTHROPIC_API_KEY') | |
| timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \ | |
| '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。' | |
| def get_full_error(chunk, stream_response): | |
| """ | |
| 获取完整的从Openai返回的报错 | |
| """ | |
| while True: | |
| try: | |
| chunk += next(stream_response) | |
| except: | |
| break | |
| return chunk | |
| def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False): | |
| """ | |
| 发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。 | |
| inputs： | |
| 是本次问询的输入 | |
| sys_prompt: | |
| 系统静默prompt | |
| llm_kwargs： | |
| chatGPT的内部调优参数 | |
| history： | |
| 是之前的对话列表 | |
| observe_window = None： | |
| 用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗 | |
| """ | |
| from anthropic import Anthropic | |
| watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可 | |
| prompt = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True) | |
| retry = 0 | |
| if len(ANTHROPIC_API_KEY) == 0: | |
| raise RuntimeError("没有设置ANTHROPIC_API_KEY选项") | |
| while True: | |
| try: | |
| # make a POST request to the API endpoint, stream=False | |
| from .bridge_all import model_info | |
| anthropic = Anthropic(api_key=ANTHROPIC_API_KEY) | |
| # endpoint = model_info[llm_kwargs['llm_model']]['endpoint'] | |
| # with ProxyNetworkActivate() | |
| stream = anthropic.completions.create( | |
| prompt=prompt, | |
| max_tokens_to_sample=4096, # The maximum number of tokens to generate before stopping. | |
| model=llm_kwargs['llm_model'], | |
| stream=True, | |
| temperature = llm_kwargs['temperature'] | |
| ) | |
| break | |
| except Exception as e: | |
| retry += 1 | |
| traceback.print_exc() | |
| if retry > MAX_RETRY: raise TimeoutError | |
| if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……') | |
| result = '' | |
| try: | |
| for completion in stream: | |
| result += completion.completion | |
| if not console_slience: print(completion.completion, end='') | |
| if observe_window is not None: | |
| # 观测窗，把已经获取的数据显示出去 | |
| if len(observe_window) >= 1: observe_window[0] += completion.completion | |
| # 看门狗，如果超过期限没有喂狗，则终止 | |
| if len(observe_window) >= 2: | |
| if (time.time()-observe_window[1]) > watch_dog_patience: | |
| raise RuntimeError("用户取消了程序。") | |
| except Exception as e: | |
| traceback.print_exc() | |
| return result | |
| def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): | |
| """ | |
| 发送至chatGPT，流式获取输出。 | |
| 用于基础的对话功能。 | |
| inputs 是本次问询的输入 | |
| top_p, temperature是chatGPT的内部调优参数 | |
| history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误） | |
| chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容 | |
| additional_fn代表点击的哪个按钮，按钮见functional.py | |
| """ | |
| from anthropic import Anthropic | |
| if len(ANTHROPIC_API_KEY) == 0: | |
| chatbot.append((inputs, "没有设置ANTHROPIC_API_KEY")) | |
| yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面 | |
| return | |
| if additional_fn is not None: | |
| from core_functional import handle_core_functionality | |
| inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot) | |
| raw_input = inputs | |
| logging.info(f'[raw_input] {raw_input}') | |
| chatbot.append((inputs, "")) | |
| yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面 | |
| try: | |
| prompt = generate_payload(inputs, llm_kwargs, history, system_prompt, stream) | |
| except RuntimeError as e: | |
| chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。") | |
| yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面 | |
| return | |
| history.append(inputs); history.append("") | |
| retry = 0 | |
| while True: | |
| try: | |
| # make a POST request to the API endpoint, stream=True | |
| from .bridge_all import model_info | |
| anthropic = Anthropic(api_key=ANTHROPIC_API_KEY) | |
| # endpoint = model_info[llm_kwargs['llm_model']]['endpoint'] | |
| # with ProxyNetworkActivate() | |
| stream = anthropic.completions.create( | |
| prompt=prompt, | |
| max_tokens_to_sample=4096, # The maximum number of tokens to generate before stopping. | |
| model=llm_kwargs['llm_model'], | |
| stream=True, | |
| temperature = llm_kwargs['temperature'] | |
| ) | |
| break | |
| except: | |
| retry += 1 | |
| chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg)) | |
| retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else "" | |
| yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面 | |
| if retry > MAX_RETRY: raise TimeoutError | |
| gpt_replying_buffer = "" | |
| for completion in stream: | |
| try: | |
| gpt_replying_buffer = gpt_replying_buffer + completion.completion | |
| history[-1] = gpt_replying_buffer | |
| chatbot[-1] = (history[-2], history[-1]) | |
| yield from update_ui(chatbot=chatbot, history=history, msg='正常') # 刷新界面 | |
| except Exception as e: | |
| from toolbox import regular_txt_to_markdown | |
| tb_str = '```\n' + trimmed_format_exc() + '```' | |
| chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str}") | |
| yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + tb_str) # 刷新界面 | |
| return | |
| # https://github.com/jtsang4/claude-to-chatgpt/blob/main/claude_to_chatgpt/adapter.py | |
| def convert_messages_to_prompt(messages): | |
| prompt = "" | |
| role_map = { | |
| "system": "Human", | |
| "user": "Human", | |
| "assistant": "Assistant", | |
| } | |
| for message in messages: | |
| role = message["role"] | |
| content = message["content"] | |
| transformed_role = role_map[role] | |
| prompt += f"\n\n{transformed_role.capitalize()}: {content}" | |
| prompt += "\n\nAssistant: " | |
| return prompt | |
| def generate_payload(inputs, llm_kwargs, history, system_prompt, stream): | |
| """ | |
| 整合所有信息，选择LLM模型，生成http请求，为发送请求做准备 | |
| """ | |
| from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT | |
| conversation_cnt = len(history) // 2 | |
| messages = [{"role": "system", "content": system_prompt}] | |
| if conversation_cnt: | |
| for index in range(0, 2*conversation_cnt, 2): | |
| what_i_have_asked = {} | |
| what_i_have_asked["role"] = "user" | |
| what_i_have_asked["content"] = history[index] | |
| what_gpt_answer = {} | |
| what_gpt_answer["role"] = "assistant" | |
| what_gpt_answer["content"] = history[index+1] | |
| if what_i_have_asked["content"] != "": | |
| if what_gpt_answer["content"] == "": continue | |
| if what_gpt_answer["content"] == timeout_bot_msg: continue | |
| messages.append(what_i_have_asked) | |
| messages.append(what_gpt_answer) | |
| else: | |
| messages[-1]['content'] = what_gpt_answer['content'] | |
| what_i_ask_now = {} | |
| what_i_ask_now["role"] = "user" | |
| what_i_ask_now["content"] = inputs | |
| messages.append(what_i_ask_now) | |
| prompt = convert_messages_to_prompt(messages) | |
| return prompt | |