# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目 """ 该文件中主要包含三个函数 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：高级实验性功能模块调用，不会实时显示在界面上，参数简单，可以多线程并行，方便实现复杂的功能逻辑 3. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程 """ 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 proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL = \ get_conf('proxies', 'API_URL', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'LLM_MODEL') 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 """ watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可 headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True) retry = 0 while True: try: # make a POST request to the API endpoint, stream=False response = requests.post(API_URL, headers=headers, proxies=proxies, json=payload, stream=True, timeout=TIMEOUT_SECONDS); break except requests.exceptions.ReadTimeout 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}) ……') stream_response = response.iter_lines() result = '' while True: try: chunk = next(stream_response).decode() except StopIteration: break except requests.exceptions.ConnectionError: chunk = next(stream_response).decode() # 失败了，重试一次？再失败就没办法了。 if len(chunk)==0: continue if not chunk.startswith('data:'): error_msg = get_full_error(chunk.encode('utf8'), stream_response).decode() if "reduce the length" in error_msg: raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg) else: raise RuntimeError("OpenAI拒绝了请求：" + error_msg) json_data = json.loads(chunk.lstrip('data:'))['choices'][0] delta = json_data["delta"] if len(delta) == 0: break if "role" in delta: continue if "content" in delta: result += delta["content"] if not console_slience: print(delta["content"], 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] += delta["content"] # 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("程序终止。") else: raise RuntimeError("意外Json结构："+delta) if json_data['finish_reason'] == 'length': raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。") 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 """ if inputs.startswith('sk-') and len(inputs) == 51: chatbot._cookies['api_key'] = inputs chatbot.append(("输入已识别为openai的api_key", "api_key已导入")) yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面 return elif len(chatbot._cookies['api_key']) != 51: chatbot.append((inputs, "缺少api_key。\n\n1. 解决方案：直接在输入区键入api_key，然后回车提交。")) yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面 return if additional_fn is not None: import core_functional importlib.reload(core_functional) # 热更新prompt core_functional = core_functional.get_core_functions() if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话） inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"] if stream: 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 headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream) history.append(inputs); history.append(" ") retry = 0 while True: try: # make a POST request to the API endpoint, stream=True response = requests.post(API_URL, headers=headers, proxies=proxies, json=payload, stream=True, timeout=TIMEOUT_SECONDS);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 = "" is_head_of_the_stream = True if stream: stream_response = response.iter_lines() while True: chunk = next(stream_response) # print(chunk.decode()[6:]) if is_head_of_the_stream: # 数据流的第一帧不携带content is_head_of_the_stream = False; continue if chunk: try: if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0: # 判定为数据流的结束，gpt_replying_buffer也写完了 logging.info(f'[response] {gpt_replying_buffer}') break # 处理数据流的主体 chunkjson = json.loads(chunk.decode()[6:]) status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}" # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出 gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"] history[-1] = gpt_replying_buffer chatbot[-1] = (history[-2], history[-1]) yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面 except Exception as e: traceback.print_exc() yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面 chunk = get_full_error(chunk, stream_response) error_msg = chunk.decode() if "reduce the length" in error_msg: chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长，或历史数据过长. 历史缓存数据现已释放，您可以请再次尝试.") history = [] # 清除历史 elif "Incorrect API key" in error_msg: chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由，拒绝服务.") elif "exceeded your current quota" in error_msg: chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由，拒绝服务.") else: from toolbox import regular_txt_to_markdown tb_str = '```\n' + traceback.format_exc() + '```' chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk.decode()[4:])}") yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面 return 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 """ if len(llm_kwargs['api_key']) != 51: raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。") headers = { "Content-Type": "application/json", "Authorization": f"Bearer {llm_kwargs['api_key']}" } 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) payload = { "model": llm_kwargs['llm_model'], "messages": messages, "temperature": llm_kwargs['temperature'], # 1.0, "top_p": llm_kwargs['top_p'], # 1.0, "n": 1, "stream": stream, "presence_penalty": 0, "frequency_penalty": 0, } try: print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........") except: print('输入中可能存在乱码。') return headers,payload