import time, requests, json from multiprocessing import Process, Pipe from functools import wraps from datetime import datetime, timedelta from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, get_conf model_name = '千帆大模型平台' timeout_bot_msg = '[Local Message] Request timeout. Network error.' def cache_decorator(timeout): cache = {} def decorator(func): @wraps(func) def wrapper(*args, **kwargs): key = (func.__name__, args, frozenset(kwargs.items())) # Check if result is already cached and not expired if key in cache: result, timestamp = cache[key] if datetime.now() - timestamp < timedelta(seconds=timeout): return result # Call the function and cache the result result = func(*args, **kwargs) cache[key] = (result, datetime.now()) return result return wrapper return decorator @cache_decorator(timeout=3600) def get_access_token(): """ 使用 AK，SK 生成鉴权签名（Access Token） :return: access_token，或是None(如果错误) """ # if (access_token_cache is None) or (time.time() - last_access_token_obtain_time > 3600): BAIDU_CLOUD_API_KEY, BAIDU_CLOUD_SECRET_KEY = get_conf('BAIDU_CLOUD_API_KEY', 'BAIDU_CLOUD_SECRET_KEY') if len(BAIDU_CLOUD_SECRET_KEY) == 0: raise RuntimeError("没有配置BAIDU_CLOUD_SECRET_KEY") if len(BAIDU_CLOUD_API_KEY) == 0: raise RuntimeError("没有配置BAIDU_CLOUD_API_KEY") url = "https://aip.baidubce.com/oauth/2.0/token" params = {"grant_type": "client_credentials", "client_id": BAIDU_CLOUD_API_KEY, "client_secret": BAIDU_CLOUD_SECRET_KEY} access_token_cache = str(requests.post(url, params=params).json().get("access_token")) return access_token_cache # else: # return access_token_cache def generate_message_payload(inputs, llm_kwargs, history, system_prompt): conversation_cnt = len(history) // 2 messages = [{"role": "user", "content": system_prompt}] messages.append({"role": "assistant", "content": 'Certainly!'}) 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) return messages def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt): BAIDU_CLOUD_QIANFAN_MODEL, = get_conf('BAIDU_CLOUD_QIANFAN_MODEL') url_lib = { "ERNIE-Bot": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions" , "ERNIE-Bot-turbo": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant" , "BLOOMZ-7B": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/bloomz_7b1", "Llama-2-70B-Chat": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_70b", "Llama-2-13B-Chat": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_13b", "Llama-2-7B-Chat": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_7b", } url = url_lib[BAIDU_CLOUD_QIANFAN_MODEL] url += "?access_token=" + get_access_token() payload = json.dumps({ "messages": generate_message_payload(inputs, llm_kwargs, history, system_prompt), "stream": True }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload, stream=True) buffer = "" for line in response.iter_lines(): if len(line) == 0: continue try: dec = line.decode().lstrip('data:') dec = json.loads(dec) incoming = dec['result'] buffer += incoming yield buffer except: if ('error_code' in dec) and ("max length" in dec['error_msg']): raise ConnectionAbortedError(dec['error_msg']) # 上下文太长导致 token 溢出 elif ('error_code' in dec): raise RuntimeError(dec['error_msg']) def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): """ ⭐多线程方法 函数的说明请见 request_llm/bridge_all.py """ watch_dog_patience = 5 response = "" for response in generate_from_baidu_qianfan(inputs, llm_kwargs, history, sys_prompt): if len(observe_window) >= 1: observe_window[0] = response if len(observe_window) >= 2: if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。") return response def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): """ ⭐单线程方法 函数的说明请见 request_llm/bridge_all.py """ chatbot.append((inputs, "")) if additional_fn is not None: from core_functional import handle_core_functionality inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot) yield from update_ui(chatbot=chatbot, history=history) # 开始接收回复 try: for response in generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt): chatbot[-1] = (inputs, response) yield from update_ui(chatbot=chatbot, history=history) except ConnectionAbortedError as e: from .bridge_all import model_info 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.)") yield from update_ui(chatbot=chatbot, history=history, msg="异常") # 刷新界面 return # 总结输出 response = f"[Local Message]: {model_name}响应异常 ..." if response == f"[Local Message]: 等待{model_name}响应中 ...": response = f"[Local Message]: {model_name}响应异常 ..." history.extend([inputs, response]) yield from update_ui(chatbot=chatbot, history=history)