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| # 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_long_connection：支持多线程 | |
| """ | |
| import json | |
| import time | |
| import gradio as gr | |
| import logging | |
| import traceback | |
| import requests | |
| import importlib | |
| import random | |
| # 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, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder | |
| proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \ | |
| get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY') | |
| 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 decode_chunk(chunk): | |
| # 提前读取一些信息 （用于判断异常） | |
| chunk_decoded = chunk.decode() | |
| chunkjson = None | |
| has_choices = False | |
| choice_valid = False | |
| has_content = False | |
| has_role = False | |
| try: | |
| chunkjson = json.loads(chunk_decoded[6:]) | |
| has_choices = 'choices' in chunkjson | |
| if has_choices: choice_valid = (len(chunkjson['choices']) > 0) | |
| if has_choices and choice_valid: has_content = ("content" in chunkjson['choices'][0]["delta"]) | |
| if has_content: has_content = (chunkjson['choices'][0]["delta"]["content"] is not None) | |
| if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"] | |
| except: | |
| pass | |
| return chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role | |
| from functools import lru_cache | |
| def verify_endpoint(endpoint): | |
| """ | |
| 检查endpoint是否可用 | |
| """ | |
| if "你亲手写的api名称" in endpoint: | |
| raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint) | |
| return endpoint | |
| 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 | |
| from .bridge_all import model_info | |
| endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint']) | |
| response = requests.post(endpoint, 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 = '' | |
| json_data = None | |
| while True: | |
| try: chunk = next(stream_response) | |
| except StopIteration: | |
| break | |
| except requests.exceptions.ConnectionError: | |
| chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。 | |
| chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk) | |
| if len(chunk_decoded)==0: continue | |
| if not chunk_decoded.startswith('data:'): | |
| error_msg = get_full_error(chunk, stream_response).decode() | |
| if "reduce the length" in error_msg: | |
| raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg) | |
| elif """type":"upstream_error","param":"307""" in error_msg: | |
| 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。") | |
| else: | |
| raise RuntimeError("OpenAI拒绝了请求：" + error_msg) | |
| if ('data: [DONE]' in chunk_decoded): break # api2d 正常完成 | |
| # 提前读取一些信息 （用于判断异常） | |
| if has_choices and not choice_valid: | |
| # 一些垃圾第三方接口的出现这样的错误 | |
| continue | |
| json_data = chunkjson['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("用户取消了程序。") | |
| else: raise RuntimeError("意外Json结构："+delta) | |
| if json_data and json_data['finish_reason'] == 'content_filter': | |
| raise RuntimeError("由于提问含不合规内容被Azure过滤。") | |
| if json_data and 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 is_any_api_key(inputs): | |
| chatbot._cookies['api_key'] = inputs | |
| chatbot.append(("输入已识别为openai的api_key", what_keys(inputs))) | |
| yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面 | |
| return | |
| elif not is_any_api_key(chatbot._cookies['api_key']): | |
| chatbot.append((inputs, "缺少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。")) | |
| yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面 | |
| return | |
| user_input = inputs | |
| 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 | |
| # check mis-behavior | |
| if is_the_upload_folder(user_input): | |
| chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。") | |
| yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面 | |
| time.sleep(2) | |
| try: | |
| headers, payload = 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 | |
| # 检查endpoint是否合法 | |
| try: | |
| from .bridge_all import model_info | |
| endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint']) | |
| except: | |
| tb_str = '```\n' + trimmed_format_exc() + '```' | |
| chatbot[-1] = (inputs, tb_str) | |
| yield from update_ui(chatbot=chatbot, history=history, msg="Endpoint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 | |
| response = requests.post(endpoint, 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: | |
| try: | |
| chunk = next(stream_response) | |
| except StopIteration: | |
| # 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里 | |
| chunk_decoded = chunk.decode() | |
| error_msg = chunk_decoded | |
| # 首先排除一个one-api没有done数据包的第三方Bug情形 | |
| if len(gpt_replying_buffer.strip()) > 0 and len(error_msg) == 0: | |
| yield from update_ui(chatbot=chatbot, history=history, msg="检测到有缺陷的非OpenAI官方接口，建议选择更稳定的接口。") | |
| break | |
| # 其他情况，直接返回报错 | |
| chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg) | |
| yield from update_ui(chatbot=chatbot, history=history, msg="非OpenAI官方接口返回了错误:" + chunk.decode()) # 刷新界面 | |
| return | |
| # 提前读取一些信息 （用于判断异常） | |
| chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk) | |
| if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"content" not in chunk_decoded): | |
| # 数据流的第一帧不携带content | |
| is_head_of_the_stream = False; continue | |
| if chunk: | |
| try: | |
| if has_choices and not choice_valid: | |
| # 一些垃圾第三方接口的出现这样的错误 | |
| continue | |
| if ('data: [DONE]' not in chunk_decoded) and len(chunk_decoded) > 0 and (chunkjson is None): | |
| # 传递进来一些奇怪的东西 | |
| raise ValueError(f'无法读取以下数据，请检查配置。\n\n{chunk_decoded}') | |
| # 前者是API2D的结束条件，后者是OPENAI的结束条件 | |
| if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['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 | |
| status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}" | |
| # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出 | |
| if has_content: | |
| # 正常情况 | |
| gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"] | |
| elif has_role: | |
| # 一些第三方接口的出现这样的错误，兼容一下吧 | |
| continue | |
| else: | |
| # 一些垃圾第三方接口的出现这样的错误 | |
| gpt_replying_buffer = gpt_replying_buffer + chunkjson['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: | |
| 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) | |
| chunk_decoded = chunk.decode() | |
| error_msg = chunk_decoded | |
| chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg) | |
| yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面 | |
| print(error_msg) | |
| return | |
| def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg): | |
| from .bridge_all import model_info | |
| openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup' | |
| if "reduce the length" in error_msg: | |
| if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出 | |
| history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'], | |
| max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一 | |
| chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)") | |
| elif "does not exist" in error_msg: | |
| chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.") | |
| 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. " + openai_website) | |
| 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." + openai_website) | |
| elif "account is not active" in error_msg: | |
| chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website) | |
| elif "associated with a deactivated account" in error_msg: | |
| chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website) | |
| elif "API key has been deactivated" in error_msg: | |
| chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. OpenAI以账户失效为由, 拒绝服务." + openai_website) | |
| elif "bad forward key" in error_msg: | |
| chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.") | |
| elif "Not enough point" in error_msg: | |
| chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.") | |
| else: | |
| 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} \n\n{regular_txt_to_markdown(chunk_decoded)}") | |
| return chatbot, history | |
| 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 not is_any_api_key(llm_kwargs['api_key']): | |
| 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。") | |
| api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model']) | |
| headers = { | |
| "Content-Type": "application/json", | |
| "Authorization": f"Bearer {api_key}" | |
| } | |
| if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG}) | |
| if llm_kwargs['llm_model'].startswith('azure-'): | |
| headers.update({"api-key": api_key}) | |
| if llm_kwargs['llm_model'] in AZURE_CFG_ARRAY.keys(): | |
| azure_api_key_unshared = AZURE_CFG_ARRAY[llm_kwargs['llm_model']]["AZURE_API_KEY"] | |
| headers.update({"api-key": azure_api_key_unshared}) | |
| 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) | |
| model = llm_kwargs['llm_model'] | |
| if llm_kwargs['llm_model'].startswith('api2d-'): | |
| model = llm_kwargs['llm_model'][len('api2d-'):] | |
| if model == "gpt-3.5-random": # 随机选择, 绕过openai访问频率限制 | |
| model = random.choice([ | |
| "gpt-3.5-turbo", | |
| "gpt-3.5-turbo-16k", | |
| "gpt-3.5-turbo-1106", | |
| "gpt-3.5-turbo-0613", | |
| "gpt-3.5-turbo-16k-0613", | |
| "gpt-3.5-turbo-0301", | |
| ]) | |
| logging.info("Random select model:" + model) | |
| payload = { | |
| "model": 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 | |