mirror of https://github.com/Mai-with-u/MaiBot.git
523 lines
27 KiB
Python
523 lines
27 KiB
Python
import time
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import traceback
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from typing import List, Optional, Dict
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import asyncio
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from asyncio import Lock
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from ...moods.moods import MoodManager
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from ....config.config import global_config
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from ...chat.emoji_manager import emoji_manager
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from .heartFC__generator import ResponseGenerator
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from ...chat.message import MessageSending, MessageRecv, MessageThinking, MessageSet
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from .messagesender import MessageManager
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from ...chat.utils_image import image_path_to_base64
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from ...message import UserInfo, Seg
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from src.heart_flow.heartflow import heartflow
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from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
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from ...person_info.relationship_manager import relationship_manager
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from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
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from ...utils.timer_calculater import Timer
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from src.do_tool.tool_use import ToolUser
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from .interest import InterestManager
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from src.plugins.chat.chat_stream import chat_manager
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from src.plugins.chat.message import BaseMessageInfo
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from .pf_chatting import PFChatting
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# 定义日志配置
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chat_config = LogConfig(
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console_format=CHAT_STYLE_CONFIG["console_format"],
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file_format=CHAT_STYLE_CONFIG["file_format"],
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)
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logger = get_module_logger("heartFC_chat", config=chat_config)
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# 新增常量
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INTEREST_MONITOR_INTERVAL_SECONDS = 1
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class HeartFC_Chat:
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_instance = None # For potential singleton access if needed by MessageManager
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def __init__(self):
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# --- Updated Init ---
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if HeartFC_Chat._instance is not None:
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# Prevent re-initialization if used as a singleton
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return
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self.logger = logger # Make logger accessible via self
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self.gpt = ResponseGenerator()
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self.mood_manager = MoodManager.get_instance()
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self.mood_manager.start_mood_update()
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self.tool_user = ToolUser()
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self.interest_manager = InterestManager()
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self._interest_monitor_task: Optional[asyncio.Task] = None
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# --- New PFChatting Management ---
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self.pf_chatting_instances: Dict[str, PFChatting] = {}
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self._pf_chatting_lock = Lock()
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# --- End New PFChatting Management ---
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HeartFC_Chat._instance = self # Register instance
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# --- End Updated Init ---
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# --- Added Class Method for Singleton Access ---
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@classmethod
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def get_instance(cls):
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return cls._instance
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# --- End Added Class Method ---
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async def start(self):
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"""启动异步任务,如兴趣监控器"""
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logger.info("HeartFC_Chat 正在启动异步任务...")
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await self.interest_manager.start_background_tasks()
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self._initialize_monitor_task()
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logger.info("HeartFC_Chat 异步任务启动完成")
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def _initialize_monitor_task(self):
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"""启动后台兴趣监控任务，可以检查兴趣是否足以开启心流对话"""
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if self._interest_monitor_task is None or self._interest_monitor_task.done():
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try:
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loop = asyncio.get_running_loop()
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self._interest_monitor_task = loop.create_task(self._interest_monitor_loop())
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logger.info(f"兴趣监控任务已创建。监控间隔: {INTEREST_MONITOR_INTERVAL_SECONDS}秒。")
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except RuntimeError:
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logger.error("创建兴趣监控任务失败：没有运行中的事件循环。")
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raise
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else:
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logger.warning("跳过兴趣监控任务创建：任务已存在或正在运行。")
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# --- Added PFChatting Instance Manager ---
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async def _get_or_create_pf_chatting(self, stream_id: str) -> Optional[PFChatting]:
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"""获取现有PFChatting实例或创建新实例。"""
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async with self._pf_chatting_lock:
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if stream_id not in self.pf_chatting_instances:
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self.logger.info(f"为流 {stream_id} 创建新的PFChatting实例")
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# 传递 self (HeartFC_Chat 实例) 进行依赖注入
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instance = PFChatting(stream_id, self)
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# 执行异步初始化
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if not await instance._initialize():
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self.logger.error(f"为流 {stream_id} 初始化PFChatting失败")
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return None
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self.pf_chatting_instances[stream_id] = instance
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return self.pf_chatting_instances[stream_id]
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# --- End Added PFChatting Instance Manager ---
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async def _interest_monitor_loop(self):
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"""后台任务，定期检查兴趣度变化并触发回复"""
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logger.info("兴趣监控循环开始...")
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while True:
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await asyncio.sleep(INTEREST_MONITOR_INTERVAL_SECONDS)
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try:
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active_stream_ids = list(heartflow.get_all_subheartflows_streams_ids())
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# logger.trace(f"检查 {len(active_stream_ids)} 个活跃流是否足以开启心流对话...") # 调试日志
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for stream_id in active_stream_ids:
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stream_name = chat_manager.get_stream_name(stream_id) or stream_id # 获取流名称
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sub_hf = heartflow.get_subheartflow(stream_id)
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if not sub_hf:
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logger.warning(f"监控循环: 无法获取活跃流 {stream_name} 的 sub_hf")
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continue
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should_trigger = False
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try:
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interest_chatting = self.interest_manager.get_interest_chatting(stream_id)
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if interest_chatting:
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should_trigger = interest_chatting.should_evaluate_reply()
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# if should_trigger:
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# logger.info(f"[{stream_name}] 基于兴趣概率决定启动交流模式 (概率: {interest_chatting.current_reply_probability:.4f})。")
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else:
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logger.trace(f"[{stream_name}] 没有找到对应的 InterestChatting 实例，跳过基于兴趣的触发检查。")
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except Exception as e:
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logger.error(f"检查兴趣触发器时出错 流 {stream_name}: {e}")
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logger.error(traceback.format_exc())
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if should_trigger:
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pf_instance = await self._get_or_create_pf_chatting(stream_id)
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if pf_instance:
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# logger.info(f"[{stream_name}] 触发条件满足, 委托给PFChatting.")
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asyncio.create_task(pf_instance.add_time())
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else:
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logger.error(f"[{stream_name}] 无法获取或创建PFChatting实例。跳过触发。")
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except asyncio.CancelledError:
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logger.info("兴趣监控循环已取消。")
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break
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except Exception as e:
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logger.error(f"兴趣监控循环错误: {e}")
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logger.error(traceback.format_exc())
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await asyncio.sleep(5) # 发生错误时等待
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async def _create_thinking_message(self, anchor_message: Optional[MessageRecv]):
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"""创建思考消息 (尝试锚定到 anchor_message)"""
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if not anchor_message or not anchor_message.chat_stream:
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logger.error("无法创建思考消息，缺少有效的锚点消息或聊天流。")
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return None
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chat = anchor_message.chat_stream
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messageinfo = anchor_message.message_info
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bot_user_info = UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=messageinfo.platform,
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)
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thinking_time_point = round(time.time(), 2)
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thinking_id = "mt" + str(thinking_time_point)
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thinking_message = MessageThinking(
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message_id=thinking_id,
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chat_stream=chat,
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bot_user_info=bot_user_info,
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reply=anchor_message, # 回复的是锚点消息
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thinking_start_time=thinking_time_point,
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)
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MessageManager().add_message(thinking_message)
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return thinking_id
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async def _send_response_messages(self, anchor_message: Optional[MessageRecv], response_set: List[str], thinking_id) -> Optional[MessageSending]:
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"""发送回复消息 (尝试锚定到 anchor_message)"""
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if not anchor_message or not anchor_message.chat_stream:
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logger.error("无法发送回复，缺少有效的锚点消息或聊天流。")
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return None
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chat = anchor_message.chat_stream
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container = MessageManager().get_container(chat.stream_id)
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thinking_message = None
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for msg in container.messages:
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if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id:
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thinking_message = msg
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container.messages.remove(msg)
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break
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if not thinking_message:
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stream_name = chat_manager.get_stream_name(chat.stream_id) or chat.stream_id # 获取流名称
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logger.warning(f"[{stream_name}] 未找到对应的思考消息 {thinking_id}，可能已超时被移除")
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return None
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thinking_start_time = thinking_message.thinking_start_time
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message_set = MessageSet(chat, thinking_id)
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mark_head = False
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first_bot_msg = None
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for msg_text in response_set:
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message_segment = Seg(type="text", data=msg_text)
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bot_message = MessageSending(
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message_id=thinking_id, # 使用 thinking_id 作为批次标识
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chat_stream=chat,
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bot_user_info=UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=anchor_message.message_info.platform,
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),
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sender_info=anchor_message.message_info.user_info, # 发送给锚点消息的用户
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message_segment=message_segment,
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reply=anchor_message, # 回复锚点消息
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is_head=not mark_head,
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is_emoji=False,
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thinking_start_time=thinking_start_time,
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)
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if not mark_head:
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mark_head = True
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first_bot_msg = bot_message
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message_set.add_message(bot_message)
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if message_set.messages: # 确保有消息才添加
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MessageManager().add_message(message_set)
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return first_bot_msg
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else:
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stream_name = chat_manager.get_stream_name(chat.stream_id) or chat.stream_id # 获取流名称
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logger.warning(f"[{stream_name}] 没有生成有效的回复消息集，无法发送。")
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return None
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async def _handle_emoji(self, anchor_message: Optional[MessageRecv], response_set, send_emoji=""):
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"""处理表情包 (尝试锚定到 anchor_message)"""
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if not anchor_message or not anchor_message.chat_stream:
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logger.error("无法处理表情包，缺少有效的锚点消息或聊天流。")
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return
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chat = anchor_message.chat_stream
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if send_emoji:
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emoji_raw = await emoji_manager.get_emoji_for_text(send_emoji)
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else:
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emoji_text_source = "".join(response_set) if response_set else ""
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emoji_raw = await emoji_manager.get_emoji_for_text(emoji_text_source)
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if emoji_raw:
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emoji_path, description = emoji_raw
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emoji_cq = image_path_to_base64(emoji_path)
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# 使用当前时间戳，因为没有原始消息的时间戳
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thinking_time_point = round(time.time(), 2)
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message_segment = Seg(type="emoji", data=emoji_cq)
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bot_message = MessageSending(
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message_id="me" + str(thinking_time_point), # 使用不同的 ID 前缀?
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chat_stream=chat,
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bot_user_info=UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=anchor_message.message_info.platform,
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),
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sender_info=anchor_message.message_info.user_info,
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message_segment=message_segment,
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reply=anchor_message, # 回复锚点消息
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is_head=False,
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is_emoji=True,
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)
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MessageManager().add_message(bot_message)
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async def _update_relationship(self, anchor_message: Optional[MessageRecv], response_set):
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"""更新关系情绪 (尝试基于 anchor_message)"""
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if not anchor_message or not anchor_message.chat_stream:
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logger.error("无法更新关系情绪，缺少有效的锚点消息或聊天流。")
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return
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# 关系更新依赖于理解回复是针对谁的，以及原始消息的上下文
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# 这里的实现可能需要调整，取决于关系管理器如何工作
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ori_response = ",".join(response_set)
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# 注意：anchor_message.processed_plain_text 是锚点消息的文本，不一定是思考的全部上下文
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stance, emotion = await self.gpt._get_emotion_tags(ori_response, anchor_message.processed_plain_text)
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await relationship_manager.calculate_update_relationship_value(
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chat_stream=anchor_message.chat_stream, # 使用锚点消息的流
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label=emotion,
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stance=stance
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)
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self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
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async def trigger_reply_generation(self, stream_id: str, observed_messages: List[dict]):
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"""根据 SubHeartflow 的触发信号生成回复 (基于观察)"""
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stream_name = chat_manager.get_stream_name(stream_id) or stream_id # <--- 在开始时获取名称
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chat = None
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sub_hf = None
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anchor_message: Optional[MessageRecv] = None # <--- 重命名，用于锚定回复的消息对象
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userinfo: Optional[UserInfo] = None
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messageinfo: Optional[BaseMessageInfo] = None
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timing_results = {}
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current_mind = None
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response_set = None
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thinking_id = None
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info_catcher = None
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try:
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# --- 1. 获取核心对象：ChatStream 和 SubHeartflow ---
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try:
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with Timer("获取聊天流和子心流", timing_results):
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chat = chat_manager.get_stream(stream_id)
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if not chat:
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logger.error(f"[{stream_name}] 无法找到聊天流对象，无法生成回复。")
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return
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sub_hf = heartflow.get_subheartflow(stream_id)
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if not sub_hf:
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logger.error(f"[{stream_name}] 无法找到子心流对象，无法生成回复。")
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return
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except Exception as e:
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logger.error(f"[{stream_name}] 获取 ChatStream 或 SubHeartflow 时出错: {e}")
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logger.error(traceback.format_exc())
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return
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# --- 2. 尝试从 observed_messages 重建最后一条消息作为锚点, 失败则创建占位符 --- #
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try:
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with Timer("获取或创建锚点消息", timing_results):
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reconstruction_failed = False
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if observed_messages:
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try:
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last_msg_dict = observed_messages[-1]
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logger.debug(f"[{stream_name}] Attempting to reconstruct MessageRecv from last observed message.")
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anchor_message = MessageRecv(last_msg_dict, chat_stream=chat)
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if not (anchor_message and anchor_message.message_info and anchor_message.message_info.message_id and anchor_message.message_info.user_info):
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raise ValueError("Reconstructed MessageRecv missing essential info.")
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userinfo = anchor_message.message_info.user_info
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messageinfo = anchor_message.message_info
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logger.debug(f"[{stream_name}] Successfully reconstructed anchor message: ID={messageinfo.message_id}, Sender={userinfo.user_nickname}")
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except Exception as e_reconstruct:
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logger.warning(f"[{stream_name}] Reconstructing MessageRecv from observed message failed: {e_reconstruct}. Will create placeholder.")
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reconstruction_failed = True
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else:
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logger.warning(f"[{stream_name}] observed_messages is empty. Will create placeholder anchor message.")
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reconstruction_failed = True # Treat empty observed_messages as a failure to reconstruct
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# 如果重建失败或 observed_messages 为空，创建占位符
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if reconstruction_failed:
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placeholder_id = f"mid_{int(time.time() * 1000)}" # 使用毫秒时间戳增加唯一性
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placeholder_user = UserInfo(user_id="system_trigger", user_nickname="系统触发")
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placeholder_msg_info = BaseMessageInfo(
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message_id=placeholder_id,
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platform=chat.platform,
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group_info=chat.group_info,
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user_info=placeholder_user,
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time=time.time()
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# 其他 BaseMessageInfo 可能需要的字段设为默认值或 None
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)
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# 创建 MessageRecv 实例，注意它需要消息字典结构，我们创建一个最小化的
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placeholder_msg_dict = {
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"message_info": placeholder_msg_info.to_dict(),
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"processed_plain_text": "", # 提供空文本
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"raw_message": "",
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"time": placeholder_msg_info.time,
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}
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# 先只用字典创建实例
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anchor_message = MessageRecv(placeholder_msg_dict)
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# 然后调用方法更新 chat_stream
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anchor_message.update_chat_stream(chat)
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userinfo = anchor_message.message_info.user_info
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messageinfo = anchor_message.message_info
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logger.info(f"[{stream_name}] Created placeholder anchor message: ID={messageinfo.message_id}, Sender={userinfo.user_nickname}")
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except Exception as e:
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logger.error(f"[{stream_name}] 获取或创建锚点消息时出错: {e}")
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logger.error(traceback.format_exc())
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anchor_message = None # 确保出错时 anchor_message 为 None
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# --- 4. 检查并发思考限制 (使用 anchor_message 简化获取) ---
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try:
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container = MessageManager().get_container(chat.stream_id)
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thinking_count = container.count_thinking_messages()
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max_thinking_messages = getattr(global_config, 'max_concurrent_thinking_messages', 3)
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if thinking_count >= max_thinking_messages:
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logger.warning(f"聊天流 {stream_name} 已有 {thinking_count} 条思考消息，取消回复。")
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return
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except Exception as e:
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logger.error(f"[{stream_name}] 检查并发思考限制时出错: {e}")
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return
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# --- 5. 创建思考消息 (使用 anchor_message) ---
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try:
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with Timer("创建思考消息", timing_results):
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# 注意：这里传递 anchor_message 给 _create_thinking_message
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thinking_id = await self._create_thinking_message(anchor_message)
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except Exception as e:
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logger.error(f"[{stream_name}] 创建思考消息失败: {e}")
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return
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if not thinking_id:
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logger.error(f"[{stream_name}] 未能成功创建思考消息 ID，无法继续回复流程。")
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return
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# --- 6. 信息捕捉器 (使用 anchor_message) ---
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logger.trace(f"[{stream_name}] 创建捕捉器，thinking_id:{thinking_id}")
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info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
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info_catcher.catch_decide_to_response(anchor_message)
|
||
|
||
# --- 7. 思考前使用工具 --- #
|
||
get_mid_memory_id = []
|
||
tool_result_info = {}
|
||
send_emoji = ""
|
||
observation_context_text = "" # 从 observation 获取上下文文本
|
||
try:
|
||
# --- 使用传入的 observed_messages 构建上下文文本 --- #
|
||
if observed_messages:
|
||
# 可以选择转换全部消息，或只转换最后几条
|
||
# 这里示例转换全部消息
|
||
context_texts = []
|
||
for msg_dict in observed_messages:
|
||
# 假设 detailed_plain_text 字段包含所需文本
|
||
# 你可能需要更复杂的逻辑来格式化，例如添加发送者和时间
|
||
text = msg_dict.get('detailed_plain_text', '')
|
||
if text:
|
||
context_texts.append(text)
|
||
observation_context_text = "\n".join(context_texts)
|
||
logger.debug(f"[{stream_name}] Context for tools:\n{observation_context_text[-200:]}...") # 打印部分上下文
|
||
else:
|
||
logger.warning(f"[{stream_name}] observed_messages 列表为空，无法为工具提供上下文。")
|
||
|
||
if observation_context_text:
|
||
with Timer("思考前使用工具", timing_results):
|
||
tool_result = await self.tool_user.use_tool(
|
||
message_txt=observation_context_text, # <--- 使用观察上下文
|
||
chat_stream=chat,
|
||
sub_heartflow=sub_hf
|
||
)
|
||
if tool_result.get("used_tools", False):
|
||
if "structured_info" in tool_result:
|
||
tool_result_info = tool_result["structured_info"]
|
||
get_mid_memory_id = []
|
||
for tool_name, tool_data in tool_result_info.items():
|
||
if tool_name == "mid_chat_mem":
|
||
for mid_memory in tool_data:
|
||
get_mid_memory_id.append(mid_memory["content"])
|
||
if tool_name == "send_emoji":
|
||
send_emoji = tool_data[0]["content"]
|
||
except Exception as e:
|
||
logger.error(f"[{stream_name}] 思考前工具调用失败: {e}")
|
||
logger.error(traceback.format_exc())
|
||
|
||
# --- 8. 调用 SubHeartflow 进行思考 (不传递具体消息文本和发送者) ---
|
||
try:
|
||
with Timer("生成内心想法(SubHF)", timing_results):
|
||
# 不再传递 message_txt 和 sender_info, SubHeartflow 应基于其内部观察
|
||
current_mind, past_mind = await sub_hf.do_thinking_before_reply(
|
||
# sender_info=userinfo,
|
||
chat_stream=chat,
|
||
extra_info=tool_result_info,
|
||
obs_id=get_mid_memory_id,
|
||
)
|
||
logger.info(f"[{stream_name}] SubHeartflow 思考完成: {current_mind}")
|
||
except Exception as e:
|
||
logger.error(f"[{stream_name}] SubHeartflow 思考失败: {e}")
|
||
logger.error(traceback.format_exc())
|
||
if info_catcher:
|
||
info_catcher.done_catch()
|
||
return # 思考失败则不继续
|
||
if info_catcher:
|
||
info_catcher.catch_afer_shf_step(timing_results.get("生成内心想法(SubHF)"), past_mind, current_mind)
|
||
|
||
# --- 9. 调用 ResponseGenerator 生成回复 (使用 anchor_message 和 current_mind) ---
|
||
try:
|
||
with Timer("生成最终回复(GPT)", timing_results):
|
||
# response_set = await self.gpt.generate_response(anchor_message, thinking_id, current_mind=current_mind)
|
||
response_set = await self.gpt.generate_response(anchor_message, thinking_id)
|
||
except Exception as e:
|
||
logger.error(f"[{stream_name}] GPT 生成回复失败: {e}")
|
||
logger.error(traceback.format_exc())
|
||
if info_catcher:
|
||
info_catcher.done_catch()
|
||
return
|
||
if info_catcher:
|
||
info_catcher.catch_after_generate_response(timing_results.get("生成最终回复(GPT)"))
|
||
if not response_set:
|
||
logger.info(f"[{stream_name}] 回复生成失败或为空。")
|
||
if info_catcher:
|
||
info_catcher.done_catch()
|
||
return
|
||
|
||
# --- 10. 发送消息 (使用 anchor_message) ---
|
||
first_bot_msg = None
|
||
try:
|
||
with Timer("发送消息", timing_results):
|
||
first_bot_msg = await self._send_response_messages(anchor_message, response_set, thinking_id)
|
||
except Exception as e:
|
||
logger.error(f"[{stream_name}] 发送消息失败: {e}")
|
||
logger.error(traceback.format_exc())
|
||
if info_catcher:
|
||
info_catcher.catch_after_response(timing_results.get("发送消息"), response_set, first_bot_msg)
|
||
info_catcher.done_catch() # 完成捕捉
|
||
|
||
# --- 11. 处理表情包 (使用 anchor_message) ---
|
||
try:
|
||
with Timer("处理表情包", timing_results):
|
||
if send_emoji:
|
||
logger.info(f"[{stream_name}] 决定发送表情包 {send_emoji}")
|
||
await self._handle_emoji(anchor_message, response_set, send_emoji)
|
||
except Exception as e:
|
||
logger.error(f"[{stream_name}] 处理表情包失败: {e}")
|
||
logger.error(traceback.format_exc())
|
||
|
||
# --- 12. 记录性能日志 --- #
|
||
timing_str = " | ".join([f"{step}: {duration:.2f}秒" for step, duration in timing_results.items()])
|
||
response_msg = " ".join(response_set) if response_set else "无回复"
|
||
logger.info(f"[{stream_name}] 回复任务完成 (Observation Triggered): | 思维消息: {response_msg[:30]}... | 性能计时: {timing_str}")
|
||
|
||
# --- 13. 更新关系情绪 (使用 anchor_message) ---
|
||
if first_bot_msg: # 仅在成功发送消息后
|
||
try:
|
||
with Timer("更新关系情绪", timing_results):
|
||
await self._update_relationship(anchor_message, response_set)
|
||
except Exception as e:
|
||
logger.error(f"[{stream_name}] 更新关系情绪失败: {e}")
|
||
logger.error(traceback.format_exc())
|
||
|
||
except Exception as e:
|
||
logger.error(f"回复生成任务失败 (trigger_reply_generation V4 - Observation Triggered): {e}")
|
||
logger.error(traceback.format_exc())
|
||
|
||
finally:
|
||
# 可以在这里添加清理逻辑，如果有的话
|
||
pass
|
||
# --- 结束重构 ---
|
||
|
||
# _create_thinking_message, _send_response_messages, _handle_emoji, _update_relationship
|
||
# 这几个辅助方法目前仍然依赖 MessageRecv 对象。
|
||
# 如果无法可靠地从 Observation 获取并重建最后一条消息的 MessageRecv，
|
||
# 或者希望回复不锚定具体消息，那么这些方法也需要进一步重构。
|