ai-on-wechat/bot/tongyi/tongyi_qwen_bot.py

# 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("tongyi_access_key_id")
        self.access_key_secret = conf().get("tongyi_access_key_secret")
        self.agent_key = conf().get("tongyi_agent_key")
        self.app_id = conf().get("tongyi_app_id")
        self.node_id = conf().get("tongyi_node_id")
        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 "tongyi")
        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