diff --git a/config.py b/config.py index 87e0ec9..cb26cbb 100644 --- a/config.py +++ b/config.py @@ -1,6 +1,13 @@ # [step 1]>> 例如： API_KEY = "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" （此key无效） API_KEY = "sk-此处填API密钥" # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey1,fkxxxx-api2dkey2" +#增加关于AZURE的配置信息， 可以在AZURE网页中找到 +AZURE_ENDPOINT = "https://你的api名称.openai.azure.com/" +AZURE_API_KEY = "填入azure openai api的密钥" +AZURE_API_VERSION = "填入api版本" +AZURE_ENGINE = "填入ENGINE" + + # [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改 USE_PROXY = False if USE_PROXY: diff --git a/request_llm/bridge_all.py b/request_llm/bridge_all.py index a27407c..8656ee5 100644 --- a/request_llm/bridge_all.py +++ b/request_llm/bridge_all.py @@ -16,6 +16,9 @@ from toolbox import get_conf, trimmed_format_exc from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui from .bridge_chatgpt import predict as chatgpt_ui +from .bridge_azure_test import predict_no_ui_long_connection as azure_noui +from .bridge_azure_test import predict as azure_ui + from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui from .bridge_chatglm import predict as chatglm_ui @@ -102,6 +105,16 @@ model_info = { "token_cnt": get_token_num_gpt4, }, + # azure openai + "azure-gpt35":{ + "fn_with_ui": azure_ui, + "fn_without_ui": azure_noui, + "endpoint": get_conf("AZURE_ENDPOINT"), + "max_token": 4096, + "tokenizer": tokenizer_gpt35, + "token_cnt": get_token_num_gpt35, + }, + # api_2d "api2d-gpt-3.5-turbo": { "fn_with_ui": chatgpt_ui, diff --git a/request_llm/bridge_azure_test.py b/request_llm/bridge_azure_test.py new file mode 100644 index 0000000..edc68f7 --- /dev/null +++ b/request_llm/bridge_azure_test.py @@ -0,0 +1,241 @@ +""" + 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 + +