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| """ | |
| 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 logging | |
| import traceback | |
| import importlib | |
| import openai | |
| import time | |
| # 读取config.py文件中关于AZURE OPENAI API的信息 | |
| from toolbox import get_conf, update_ui, clip_history, trimmed_format_exc | |
| TIMEOUT_SECONDS, MAX_RETRY, AZURE_ENGINE, AZURE_ENDPOINT, AZURE_API_VERSION, AZURE_API_KEY = \ | |
| get_conf('TIMEOUT_SECONDS', 'MAX_RETRY',"AZURE_ENGINE","AZURE_ENDPOINT", "AZURE_API_VERSION", "AZURE_API_KEY") | |
| 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(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): | |
| """ | |
| 发送至azure openai api，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 | |
| """ | |
| print(llm_kwargs["llm_model"]) | |
| 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"] | |
| 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 | |
| payload = generate_azure_payload(inputs, llm_kwargs, history, system_prompt, stream) | |
| history.append(inputs); history.append("") | |
| retry = 0 | |
| while True: | |
| try: | |
| openai.api_type = "azure" | |
| openai.api_version = AZURE_API_VERSION | |
| openai.api_base = AZURE_ENDPOINT | |
| openai.api_key = AZURE_API_KEY | |
| response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break | |
| except: | |
| retry += 1 | |
| chatbot[-1] = ((chatbot[-1][0], "获取response失败，重试中。。。")) | |
| 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 | |
| while True: | |
| try: | |
| chunk = next(stream_response) | |
| except StopIteration: | |
| from toolbox import regular_txt_to_markdown; tb_str = '```\n' + trimmed_format_exc() + '```' | |
| chatbot[-1] = (chatbot[-1][0], f"[Local Message] 远程返回错误: \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk)}") | |
| yield from update_ui(chatbot=chatbot, history=history, msg="远程返回错误:" + chunk) # 刷新界面 | |
| return | |
| if is_head_of_the_stream and (r'"object":"error"' not in chunk): | |
| # 数据流的第一帧不携带content | |
| is_head_of_the_stream = False; continue | |
| if chunk: | |
| #print(chunk) | |
| try: | |
| if "delta" in chunk["choices"][0]: | |
| if chunk["choices"][0]["finish_reason"] == "stop": | |
| logging.info(f'[response] {gpt_replying_buffer}') | |
| break | |
| status_text = f"finish_reason: {chunk['choices'][0]['finish_reason']}" | |
| gpt_replying_buffer = gpt_replying_buffer + chunk["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 | |
| yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面 | |
| return | |
| def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False): | |
| """ | |
| 发送至AZURE OPENAI API，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 | |
| payload = generate_azure_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True) | |
| retry = 0 | |
| while True: | |
| try: | |
| openai.api_type = "azure" | |
| openai.api_version = AZURE_API_VERSION | |
| openai.api_base = AZURE_ENDPOINT | |
| openai.api_key = AZURE_API_KEY | |
| response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break | |
| except: | |
| 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 | |
| result = '' | |
| while True: | |
| try: chunk = next(stream_response) | |
| except StopIteration: | |
| break | |
| except: | |
| chunk = next(stream_response) # 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, stream_response) | |
| if "reduce the length" in error_msg: | |
| raise ConnectionAbortedError("AZURE OPENAI API拒绝了请求:" + error_msg) | |
| else: | |
| raise RuntimeError("AZURE OPENAI API拒绝了请求：" + error_msg) | |
| if ('data: [DONE]' in chunk): break | |
| delta = chunk["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 chunk['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 generate_azure_payload(inputs, llm_kwargs, history, system_prompt, stream): | |
| """ | |
| 整合所有信息，选择LLM模型，生成 azure openai api请求，为发送请求做准备 | |
| """ | |
| 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 | |
| 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, | |
| "engine": AZURE_ENGINE | |
| } | |
| try: | |
| print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........") | |
| except: | |
| print('输入中可能存在乱码。') | |
| return payload | |