AgentCoreService/agent_service.py

#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8

import sys
import time
from typing import Dict, List, Tuple, Any, Optional
import logging
from datetime import datetime, timedelta

import apscheduler.triggers.cron
from apscheduler.schedulers.background import BackgroundScheduler

import chat_service
import global_flags
import logging_service
from chat_service import CozeChat, ChatServiceType
from dialogue_manager import DialogueManager, DialogueState
from user_manager import UserManager, LocalUserManager
from openai import OpenAI
from message_queue_backend import MessageQueueBackend, MemoryQueueBackend
from user_profile_extractor import UserProfileExtractor
import threading
from message import MessageType, Message, MessageChannel
from logging_service import ColoredFormatter


class AgentService:
    def __init__(
        self,
        receive_backend: MessageQueueBackend,
        send_backend: MessageQueueBackend,
        human_backend: MessageQueueBackend,
        user_manager: UserManager,
        chat_service_type: ChatServiceType = ChatServiceType.OPENAI_COMPATIBLE
    ):
        self.receive_queue = receive_backend
        self.send_queue = send_backend
        self.human_queue = human_backend

        # 核心服务模块
        self.user_manager = user_manager
        self.user_profile_extractor = UserProfileExtractor()
        self.agent_registry: Dict[str, DialogueManager] = {}

        self.llm_client = OpenAI(
            api_key=chat_service.VOLCENGINE_API_TOKEN,
            base_url=chat_service.VOLCENGINE_BASE_URL
        )
        # DeepSeek on Volces
        self.model_name = chat_service.VOLCENGINE_MODEL_DEEPSEEK_V3
        self.coze_client = CozeChat(
            token=chat_service.COZE_API_TOKEN,
            base_url=chat_service.COZE_CN_BASE_URL
        )
        self.chat_service_type = chat_service_type

        # 定时任务调度器
        self.scheduler = BackgroundScheduler()
        self.scheduler.start()

    def setup_initiative_conversations(self, schedule_params: Optional[Dict] = None):
        if not schedule_params:
            schedule_params = {'hour': '8,16,20'}
        self.scheduler.add_job(
            self._check_initiative_conversations,
            apscheduler.triggers.cron.CronTrigger(**schedule_params)
        )

    def _get_agent_instance(self, staff_id: str, user_id: str) -> DialogueManager:
        """获取Agent实例"""
        agent_key = 'agent_{}_{}'.format(staff_id, user_id)
        if agent_key not in self.agent_registry:
            self.agent_registry[agent_key] = DialogueManager(
                staff_id, user_id, self.user_manager)
        return self.agent_registry[agent_key]

    def process_messages(self):
        """持续处理接收队列消息"""
        while True:
            message = self.receive_queue.consume()
            if message:
                self.process_single_message(message)
            time.sleep(1)  # 避免CPU空转

    def _update_user_profile(self, user_id, user_profile, message: str):
        profile_to_update = self.user_profile_extractor.extract_profile_info(user_profile, message)
        if not profile_to_update:
            logging.debug("user_id: {}, no profile info extracted".format(user_id))
            return
        logging.warning("update user profile: {}".format(profile_to_update))
        merged_profile = self.user_profile_extractor.merge_profile_info(user_profile, profile_to_update)
        self.user_manager.save_user_profile(user_id, merged_profile)
        return merged_profile

    def _schedule_aggregation_trigger(self, staff_id: str, user_id: str, delay_sec: int):
        logging.debug("user: {}, schedule trigger message after {} seconds".format(user_id, delay_sec))
        message_ts = int((time.time() + delay_sec) * 1000)
        message = Message.build(MessageType.AGGREGATION_TRIGGER, MessageChannel.SYSTEM, user_id, staff_id, None, message_ts)
        message.msgId = -MessageType.AGGREGATION_TRIGGER.code
        self.scheduler.add_job(lambda: self.receive_queue.produce(message),
                               'date',
                               run_date=datetime.now() + timedelta(seconds=delay_sec))

    def process_single_message(self, message: Message):
        user_id = message.sender
        staff_id = message.receiver

        # 获取用户信息和Agent实例
        user_profile = self.user_manager.get_user_profile(user_id)
        agent = self._get_agent_instance(staff_id, user_id)

        # 更新对话状态
        logging.debug("process message: {}".format(message))
        dialogue_state, message_text = agent.update_state(message)
        logging.debug("user: {}, next state: {}".format(user_id, dialogue_state))

        # 根据状态路由消息
        if agent.is_in_human_intervention():
            self._route_to_human_intervention(user_id, message)
        elif dialogue_state == DialogueState.MESSAGE_AGGREGATING:
            if message.type != MessageType.AGGREGATION_TRIGGER:
                # 产生一个触发器，但是不能由触发器递归产生
                logging.debug("user: {}, waiting next message for aggregation".format(user_id))
                self._schedule_aggregation_trigger(staff_id, user_id, agent.message_aggregation_sec)
            return
        else:
            # 先更新用户画像再处理回复
            self._update_user_profile(user_id, user_profile, message_text)
            self._get_chat_response(user_id, agent, message_text)

    def _route_to_human_intervention(self, user_id: str, origin_message: Message):
        """路由到人工干预"""
        self.human_queue.produce(Message.build(
            MessageType.TEXT,
            origin_message.channel,
            origin_message.sender,
            origin_message.receiver,
            "用户对话需人工介入，用户名：{}".format(user_id),
            int(time.time() * 1000)
        ))

    def _check_initiative_conversations(self):
        """定时检查主动发起对话"""
        for user_id in self.user_manager.list_all_users():
            #FIXME(zhoutian): 需要企微账号与用户关系
            agent = self._get_agent_instance('staff_id_0', user_id)
            should_initiate = agent.should_initiate_conversation()

            if should_initiate:
                logging.warning("user: {}, initiate conversation".format(user_id))
                self._get_chat_response(user_id, agent, None)
            else:
                logging.debug("user: {}, do not initiate conversation".format(user_id))

    def _get_chat_response(self, user_id: str, agent: DialogueManager,
                           user_message: str):
        """处理LLM响应"""
        chat_config = agent.build_chat_configuration(user_message, self.chat_service_type)
        logging.debug(chat_config)
        chat_response = self._call_chat_api(chat_config)

        if response := agent.generate_response(chat_response):
            logging.warning("user: {}, response: {}".format(user_id, response))
            current_ts = int(time.time() * 1000)
            self.send_queue.produce(
                Message.build(MessageType.TEXT, MessageChannel.CORP_WECHAT,
                              agent.staff_id, user_id, response, current_ts)
            )

    def _call_chat_api(self, chat_config: Dict) -> str:
        if global_flags.DISABLE_LLM_API_CALL:
            return 'LLM模拟回复'
        if self.chat_service_type == ChatServiceType.OPENAI_COMPATIBLE:
            chat_completion = self.llm_client.chat.completions.create(
                messages=chat_config['messages'],
                model=self.model_name,
            )
            response = chat_completion.choices[0].message.content
        elif self.chat_service_type == ChatServiceType.COZE_CHAT:
            bot_user_id = 'dev_user'
            response = self.coze_client.create(
                chat_config['bot_id'], bot_user_id, chat_config['messages'],
                chat_config['custom_variables']
            )
        else:
            raise Exception('Unsupported chat service type: {}'.format(self.chat_service_type))
        return response

if __name__ == "__main__":
    logging_service.setup_root_logger()
    scheduler_logger = logging.getLogger('apscheduler')
    scheduler_logger.setLevel(logging.WARNING)

    # 初始化不同队列的后端
    receive_queue = MemoryQueueBackend()
    send_queue = MemoryQueueBackend()
    human_queue = MemoryQueueBackend()

    # 初始化用户管理服务
    user_manager = LocalUserManager()

    global_flags.DISABLE_LLM_API_CALL = True

    # 创建Agent服务
    service = AgentService(
        receive_backend=receive_queue,
        send_backend=send_queue,
        human_backend=human_queue,
        user_manager=user_manager,
        chat_service_type=ChatServiceType.COZE_CHAT
    )
    # 只有企微场景需要主动发起
    service.setup_initiative_conversations({'second': '5,35'})

    process_thread = threading.Thread(target=service.process_messages)
    process_thread.start()

    message_id = 0
    while True:
        print("Input next message: ")
        text = sys.stdin.readline().strip()
        if not text:
            continue
        message_id += 1
        message = Message.build(MessageType.TEXT, MessageChannel.CORP_WECHAT,
            'user_id_1','staff_id_0', text, int(time.time() * 1000)
        )
        message.msgId = message_id
        receive_queue.produce(message)
        time.sleep(0.1)