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| 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): | |
| 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 | |
| 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 | |
| if system_prompt == "": system_prompt = "Hello" | |
| 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] if history[index]!="" else "Hello" | |
| what_gpt_answer = {} | |
| what_gpt_answer["role"] = "assistant" | |
| what_gpt_answer["content"] = history[index+1] if history[index]!="" else "Hello" | |
| 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-4": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro", | |
| "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_llms/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_llms/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: | |
| response = f"[Local Message] 等待{model_name}响应中 ..." | |
| 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) | |
| history.extend([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 | |