# encoding:utf-8 import json import time from typing import List, Tuple import openai import openai.error import broadscope_bailian from broadscope_bailian import ChatQaMessage from bot.bot import Bot from bot.baidu.baidu_wenxin_session import BaiduWenxinSession from bot.session_manager import SessionManager from bridge.context import ContextType from bridge.reply import Reply, ReplyType from common.log import logger from config import conf, load_config class TongyiQwenBot(Bot): def __init__(self): super().__init__() self.access_key_id = conf().get("qwen_access_key_id") self.access_key_secret = conf().get("qwen_access_key_secret") self.agent_key = conf().get("qwen_agent_key") self.app_id = conf().get("qwen_app_id") self.node_id = conf().get("qwen_node_id") or "" self.api_key_client = broadscope_bailian.AccessTokenClient(access_key_id=self.access_key_id, access_key_secret=self.access_key_secret) self.api_key_expired_time = self.set_api_key() self.sessions = SessionManager(BaiduWenxinSession, model=conf().get("model") or "qwen") self.temperature = conf().get("temperature", 0.2) # 值在[0,1]之间，越大表示回复越具有不确定性 self.top_p = conf().get("top_p", 1) def reply(self, query, context=None): # acquire reply content if context.type == ContextType.TEXT: logger.info("[TONGYI] query={}".format(query)) session_id = context["session_id"] reply = None clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"]) if query in clear_memory_commands: self.sessions.clear_session(session_id) reply = Reply(ReplyType.INFO, "记忆已清除") elif query == "#清除所有": self.sessions.clear_all_session() reply = Reply(ReplyType.INFO, "所有人记忆已清除") elif query == "#更新配置": load_config() reply = Reply(ReplyType.INFO, "配置已更新") if reply: return reply session = self.sessions.session_query(query, session_id) logger.debug("[TONGYI] session query={}".format(session.messages)) reply_content = self.reply_text(session) logger.debug( "[TONGYI] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format( session.messages, session_id, reply_content["content"], reply_content["completion_tokens"], ) ) if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0: reply = Reply(ReplyType.ERROR, reply_content["content"]) elif reply_content["completion_tokens"] > 0: self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"]) reply = Reply(ReplyType.TEXT, reply_content["content"]) else: reply = Reply(ReplyType.ERROR, reply_content["content"]) logger.debug("[TONGYI] reply {} used 0 tokens.".format(reply_content)) return reply else: reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type)) return reply def reply_text(self, session: BaiduWenxinSession, retry_count=0) -> dict: """ call bailian's ChatCompletion to get the answer :param session: a conversation session :param retry_count: retry count :return: {} """ try: prompt, history = self.convert_messages_format(session.messages) self.update_api_key_if_expired() # NOTE 阿里百炼的call()函数参数比较奇怪, top_k参数表示top_p, top_p参数表示temperature, 可以参考文档 https://help.aliyun.com/document_detail/2587502.htm response = broadscope_bailian.Completions().call(app_id=self.app_id, prompt=prompt, history=history, top_k=self.top_p, top_p=self.temperature) completion_content = self.get_completion_content(response, self.node_id) completion_tokens, total_tokens = self.calc_tokens(session.messages, completion_content) return { "total_tokens": total_tokens, "completion_tokens": completion_tokens, "content": completion_content, } except Exception as e: need_retry = retry_count < 2 result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"} if isinstance(e, openai.error.RateLimitError): logger.warn("[TONGYI] RateLimitError: {}".format(e)) result["content"] = "提问太快啦，请休息一下再问我吧" if need_retry: time.sleep(20) elif isinstance(e, openai.error.Timeout): logger.warn("[TONGYI] Timeout: {}".format(e)) result["content"] = "我没有收到你的消息" if need_retry: time.sleep(5) elif isinstance(e, openai.error.APIError): logger.warn("[TONGYI] Bad Gateway: {}".format(e)) result["content"] = "请再问我一次" if need_retry: time.sleep(10) elif isinstance(e, openai.error.APIConnectionError): logger.warn("[TONGYI] APIConnectionError: {}".format(e)) need_retry = False result["content"] = "我连接不到你的网络" else: logger.exception("[TONGYI] Exception: {}".format(e)) need_retry = False self.sessions.clear_session(session.session_id) if need_retry: logger.warn("[TONGYI] 第{}次重试".format(retry_count + 1)) return self.reply_text(session, retry_count + 1) else: return result def set_api_key(self): api_key, expired_time = self.api_key_client.create_token(agent_key=self.agent_key) broadscope_bailian.api_key = api_key return expired_time def update_api_key_if_expired(self): if time.time() > self.api_key_expired_time: self.api_key_expired_time = self.set_api_key() def convert_messages_format(self, messages) -> Tuple[str, List[ChatQaMessage]]: history = [] user_content = '' assistant_content = '' for message in messages: role = message.get('role') if role == 'user': user_content += message.get('content') elif role == 'assistant': assistant_content = message.get('content') history.append(ChatQaMessage(user_content, assistant_content)) user_content = '' assistant_content = '' if user_content == '': raise Exception('no user message') return user_content, history def get_completion_content(self, response, node_id): text = response['Data']['Text'] if node_id == '': return text # TODO: 当使用流程编排创建大模型应用时，响应结构如下，最终结果在['finalResult'][node_id]['response']['text']中，暂时先这么写 # { # 'Success': True, # 'Code': None, # 'Message': None, # 'Data': { # 'ResponseId': '9822f38dbacf4c9b8daf5ca03a2daf15', # 'SessionId': 'session_id', # 'Text': '{"finalResult":{"LLM_T7islK":{"params":{"modelId":"qwen-plus-v1","prompt":"${systemVars.query}${bizVars.Text}"},"response":{"text":"作为一个AI语言模型，我没有年龄，因为我没有生日。\n我只是一个程序，没有生命和身体。"}}}}', # 'Thoughts': [], # 'Debug': {}, # 'DocReferences': [] # }, # 'RequestId': '8e11d31551ce4c3f83f49e6e0dd998b0', # 'Failed': None # } text_dict = json.loads(text) completion_content = text_dict['finalResult'][node_id]['response']['text'] return completion_content def calc_tokens(self, messages, completion_content): completion_tokens = len(completion_content) prompt_tokens = 0 for message in messages: prompt_tokens += len(message["content"]) return completion_tokens, prompt_tokens + completion_tokens