mirror of https://github.com/Mai-with-u/MaiBot.git
Update normal_chat.py
2829798842
GitHub
parent
db22882dd9
commit
d767cea165
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import asyncio
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# import statistics # 导入 statistics 模块
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import time
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import json # <--- 确保导入 json
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import traceback
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from random import random
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from typing import List, Optional # 导入 Optional
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from maim_message import UserInfo, Seg
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from src.common.logger_manager import get_logger
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from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info
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from src.manager.mood_manager import mood_manager
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from src.chat.message_receive.chat_stream import ChatStream, chat_manager
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from src.person_info.relationship_manager import relationship_manager
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from src.chat.utils.info_catcher import info_catcher_manager
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from src.chat.utils.timer_calculator import Timer
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from src.chat.utils.prompt_builder import global_prompt_manager
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from .normal_chat_generator import NormalChatGenerator
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from ..message_receive.message import MessageSending, MessageRecv, MessageThinking, MessageSet
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from src.chat.message_receive.message_sender import message_manager
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from src.chat.utils.utils_image import image_path_to_base64
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from src.chat.emoji_system.emoji_manager import emoji_manager
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from src.chat.normal_chat.willing.willing_manager import willing_manager
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from typing import List, Dict, Any, Optional
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from rich.traceback import install
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config
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from src.chat.focus_chat.info.info_base import InfoBase
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from src.chat.focus_chat.info.obs_info import ObsInfo
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from src.chat.focus_chat.info.cycle_info import CycleInfo
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from src.chat.focus_chat.info.mind_info import MindInfo
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from src.chat.focus_chat.info.action_info import ActionInfo
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from src.chat.focus_chat.info.structured_info import StructuredInfo
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from src.chat.focus_chat.info.self_info import SelfInfo
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from src.common.logger_manager import get_logger
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.individuality.individuality import individuality
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from src.chat.focus_chat.planners.action_manager import ActionManager
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logger = get_logger("normal_chat")
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logger = get_logger("planner")
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install(extra_lines=3)
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class NormalChat:
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def __init__(self, chat_stream: ChatStream, interest_dict: dict = None, on_switch_to_focus_callback=None):
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"""初始化 NormalChat 实例。只进行同步操作。"""
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self.chat_stream = chat_stream
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self.stream_id = chat_stream.stream_id
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self.stream_name = chat_manager.get_stream_name(self.stream_id) or self.stream_id
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# Interest dict
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self.interest_dict = interest_dict
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self.is_group_chat: bool = False
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self.chat_target_info: Optional[dict] = None
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# Other sync initializations
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self.gpt = NormalChatGenerator()
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self.mood_manager = mood_manager
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self.start_time = time.time()
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self._chat_task: Optional[asyncio.Task] = None
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self._initialized = False # Track initialization status
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# 记录最近的回复内容，每项包含: {time, user_message, response, is_mentioned, is_reference_reply}
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self.recent_replies = []
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self.max_replies_history = 20 # 最多保存最近20条回复记录
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# 添加回调函数，用于在满足条件时通知切换到focus_chat模式
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self.on_switch_to_focus_callback = on_switch_to_focus_callback
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self._disabled = False # 增加停用标志
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async def initialize(self):
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"""异步初始化，获取聊天类型和目标信息。"""
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if self._initialized:
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return
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self.is_group_chat, self.chat_target_info = await get_chat_type_and_target_info(self.stream_id)
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self.stream_name = chat_manager.get_stream_name(self.stream_id) or self.stream_id
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self._initialized = True
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logger.info(f"[{self.stream_name}] NormalChat 实例 initialize 完成 (异步部分)。")
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# 改为实例方法
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async def _create_thinking_message(self, message: MessageRecv, timestamp: Optional[float] = None) -> str:
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"""创建思考消息"""
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messageinfo = message.message_info
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bot_user_info = UserInfo(
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user_id=global_config.bot.qq_account,
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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=self.chat_stream,
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bot_user_info=bot_user_info,
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reply=message,
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thinking_start_time=thinking_time_point,
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timestamp=timestamp if timestamp is not None else None,
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)
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await message_manager.add_message(thinking_message)
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return thinking_id
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# 改为实例方法
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async def _add_messages_to_manager(
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self, message: MessageRecv, response_set: List[str], thinking_id
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) -> Optional[MessageSending]:
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"""发送回复消息"""
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container = await message_manager.get_container(self.stream_id) # 使用 self.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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logger.warning(f"[{self.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(self.chat_stream, thinking_id) # 使用 self.chat_stream
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mark_head = False
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first_bot_msg = None
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for msg in response_set:
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if global_config.experimental.debug_show_chat_mode:
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msg += "ⁿ"
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message_segment = Seg(type="text", data=msg)
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bot_message = MessageSending(
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message_id=thinking_id,
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chat_stream=self.chat_stream, # 使用 self.chat_stream
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bot_user_info=UserInfo(
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user_id=global_config.bot.qq_account,
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user_nickname=global_config.bot.nickname,
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platform=message.message_info.platform,
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),
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sender_info=message.message_info.user_info,
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message_segment=message_segment,
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reply=message,
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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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apply_set_reply_logic=True,
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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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await message_manager.add_message(message_set)
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return first_bot_msg
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# 改为实例方法
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async def _handle_emoji(self, message: MessageRecv, response: str):
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"""处理表情包"""
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if random() < global_config.normal_chat.emoji_chance:
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emoji_raw = await emoji_manager.get_emoji_for_text(response)
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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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thinking_time_point = round(message.message_info.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="mt" + str(thinking_time_point),
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chat_stream=self.chat_stream, # 使用 self.chat_stream
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bot_user_info=UserInfo(
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user_id=global_config.bot.qq_account,
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user_nickname=global_config.bot.nickname,
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platform=message.message_info.platform,
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),
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sender_info=message.message_info.user_info,
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message_segment=message_segment,
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reply=message,
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is_head=False,
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is_emoji=True,
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apply_set_reply_logic=True,
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)
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await message_manager.add_message(bot_message)
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# 改为实例方法 (虽然它只用 message.chat_stream, 但逻辑上属于实例)
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async def _update_relationship(self, message: MessageRecv, response_set):
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"""更新关系情绪"""
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ori_response = ",".join(response_set)
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stance, emotion = await self.gpt._get_emotion_tags(ori_response, message.processed_plain_text)
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user_info = message.message_info.user_info
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platform = user_info.platform
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await relationship_manager.calculate_update_relationship_value(
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user_info,
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platform,
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label=emotion,
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stance=stance, # 使用 self.chat_stream
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)
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self.mood_manager.update_mood_from_emotion(emotion, global_config.mood.mood_intensity_factor)
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async def _reply_interested_message(self) -> None:
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def init_prompt():
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Prompt(
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"""
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后台任务方法，轮询当前实例关联chat的兴趣消息
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通常由start_monitoring_interest()启动
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Your self-awareness is:
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{self_info_block}
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{extra_info_block}
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You need to decide how to participate in the conversation based on the following information
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These information may conflict, please integrate these information, and choose the most suitable action:
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{chat_content_block}
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{mind_info_block}
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{cycle_info_block}
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IMPORTANT: The following tool call information has the highest priority and should be heavily weighted in your decision-making:
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{structured_info_block}
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Please analyze the conversation content and new messages you see, refer to the conversation plan, and choose the appropriate action:
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{action_options_text}
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You must choose one from the available actions above and explain why.
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Your decision must be output in strict JSON format, and only contain JSON content, no other text or explanation.
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Please output your decision JSON in the following format:
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{{
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"action": "action_name",
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"reasoning": "Your decision reason",
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"parameter1": "parameter1 value",
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"parameter2": "parameter2 value",
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"parameter3": "parameter3 value",
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...
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}}
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Please output your decision JSON: """,
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"planner_prompt",
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)
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Prompt(
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"""
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while True:
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async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
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await asyncio.sleep(0.5) # 每秒检查一次
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# 检查任务是否已被取消
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if self._chat_task is None or self._chat_task.cancelled():
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logger.info(f"[{self.stream_name}] 兴趣监控任务被取消或置空，退出")
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break
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action_name: {action_name}
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描述：{action_description}
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参数：
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{action_parameters}
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动作要求：
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{action_require}""",
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"action_prompt",
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)
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items_to_process = list(self.interest_dict.items())
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if not items_to_process:
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continue
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# 处理每条兴趣消息
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for msg_id, (message, interest_value, is_mentioned) in items_to_process:
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try:
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# 处理消息
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await self.normal_response(
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message=message,
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is_mentioned=is_mentioned,
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interested_rate=interest_value,
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rewind_response=False,
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)
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except Exception as e:
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logger.error(f"[{self.stream_name}] 处理兴趣消息{msg_id}时出错: {e}\n{traceback.format_exc()}")
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finally:
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self.interest_dict.pop(msg_id, None)
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# 改为实例方法, 移除 chat 参数
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async def normal_response(
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self, message: MessageRecv, is_mentioned: bool, interested_rate: float, rewind_response: bool = False
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) -> None:
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# 新增：如果已停用，直接返回
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if self._disabled:
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logger.info(f"[{self.stream_name}] 已停用，忽略 normal_response。")
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return
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# 检查收到的消息是否属于当前实例处理的 chat stream
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if message.chat_stream.stream_id != self.stream_id:
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logger.error(
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f"[{self.stream_name}] normal_response 收到不匹配的消息 (来自 {message.chat_stream.stream_id})，预期 {self.stream_id}。已忽略。"
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)
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return
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timing_results = {}
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reply_probability = 1.0 if is_mentioned else 0.0 # 如果被提及，基础概率为1，否则需要意愿判断
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# 意愿管理器：设置当前message信息
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willing_manager.setup(message, self.chat_stream, is_mentioned, interested_rate)
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# 获取回复概率
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is_willing = False
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# 仅在未被提及或基础概率不为1时查询意愿概率
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if reply_probability < 1: # 简化逻辑，如果未提及 (reply_probability 为 0)，则获取意愿概率
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is_willing = True
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reply_probability = await willing_manager.get_reply_probability(message.message_info.message_id)
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if message.message_info.additional_config:
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if "maimcore_reply_probability_gain" in message.message_info.additional_config.keys():
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reply_probability += message.message_info.additional_config["maimcore_reply_probability_gain"]
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reply_probability = min(max(reply_probability, 0), 1) # 确保概率在 0-1 之间
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# 打印消息信息
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mes_name = self.chat_stream.group_info.group_name if self.chat_stream.group_info else "私聊"
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current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time))
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# 使用 self.stream_id
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willing_log = f"[回复意愿:{await willing_manager.get_willing(self.stream_id):.2f}]" if is_willing else ""
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logger.info(
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f"[{current_time}][{mes_name}]"
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f"{message.message_info.user_info.user_nickname}:" # 使用 self.chat_stream
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f"{message.processed_plain_text}{willing_log}[概率:{reply_probability * 100:.1f}%]"
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class ActionPlanner:
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def __init__(self, log_prefix: str, action_manager: ActionManager):
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self.log_prefix = log_prefix
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# LLM规划器配置
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self.planner_llm = LLMRequest(
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model=global_config.model.focus_planner,
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max_tokens=1000,
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request_type="action_planning", # 用于动作规划
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)
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do_reply = False
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response_set = None # 初始化 response_set
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if random() < reply_probability:
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do_reply = True
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# 回复前处理
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await willing_manager.before_generate_reply_handle(message.message_info.message_id)
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self.action_manager = action_manager
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with Timer("创建思考消息", timing_results):
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if rewind_response:
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thinking_id = await self._create_thinking_message(message, message.message_info.time)
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else:
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thinking_id = await self._create_thinking_message(message)
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async def plan(self, all_plan_info: List[InfoBase], cycle_timers: dict) -> Dict[str, Any]:
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"""
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规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
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logger.debug(f"[{self.stream_name}] 创建捕捉器，thinking_id:{thinking_id}")
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参数:
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all_plan_info: 所有计划信息
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cycle_timers: 计时器字典
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"""
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info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
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info_catcher.catch_decide_to_response(message)
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action = "no_reply" # 默认动作
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reasoning = "规划器初始化默认"
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action_data = {}
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# 初始化所有可能用到的变量，避免UnboundLocalError
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observed_messages = []
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observed_messages_str = ""
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current_mind = ""
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cycle_info = ""
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self_info = ""
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is_group_chat = True # 默认为群聊
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prompt = "未生成prompt" # 初始化prompt变量
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try:
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with Timer("生成回复", timing_results):
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response_set = await self.gpt.generate_response(
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message=message,
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thinking_id=thinking_id,
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)
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try:
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# 获取观察信息
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extra_info: list[str] = []
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_structured_info_list = []
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info_catcher.catch_after_generate_response(timing_results["生成回复"])
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except Exception as e:
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logger.error(f"[{self.stream_name}] 回复生成出现错误：{str(e)} {traceback.format_exc()}")
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response_set = None # 确保出错时 response_set 为 None
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# 首先处理动作变更
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for info in all_plan_info:
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if isinstance(info, ActionInfo) and info.has_changes():
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add_actions = info.get_add_actions()
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remove_actions = info.get_remove_actions()
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reason = info.get_reason()
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if not response_set:
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logger.info(f"[{self.stream_name}] 模型未生成回复内容")
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# 如果模型未生成回复，移除思考消息
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container = await message_manager.get_container(self.stream_id) # 使用 self.stream_id
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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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container.messages.remove(msg)
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logger.debug(f"[{self.stream_name}] 已移除未产生回复的思考消息 {thinking_id}")
|
||||
break
|
||||
# 需要在此处也调用 not_reply_handle 和 delete 吗？
|
||||
# 如果是因为模型没回复，也算是一种 "未回复"
|
||||
await willing_manager.not_reply_handle(message.message_info.message_id)
|
||||
willing_manager.delete(message.message_info.message_id)
|
||||
return # 不执行后续步骤
|
||||
# 处理动作的增加
|
||||
for action_name in add_actions:
|
||||
if action_name in self.action_manager.get_registered_actions():
|
||||
self.action_manager.add_action_to_using(action_name)
|
||||
logger.debug(f"{self.log_prefix}添加动作: {action_name}, 原因: {reason}")
|
||||
|
||||
logger.info(f"[{self.stream_name}] 回复内容: {response_set}")
|
||||
|
||||
if self._disabled:
|
||||
logger.info(f"[{self.stream_name}] 已停用，忽略 normal_response。")
|
||||
return
|
||||
# 处理动作的移除
|
||||
for action_name in remove_actions:
|
||||
self.action_manager.remove_action_from_using(action_name)
|
||||
logger.debug(f"{self.log_prefix}移除动作: {action_name}, 原因: {reason}")
|
||||
|
||||
# 发送回复 (不再需要传入 chat)
|
||||
with Timer("消息发送", timing_results):
|
||||
first_bot_msg = await self._add_messages_to_manager(message, response_set, thinking_id)
|
||||
# 如果当前选择的动作被移除了，更新为no_reply
|
||||
if action in remove_actions:
|
||||
action = "no_reply"
|
||||
reasoning = f"之前选择的动作{action}已被移除，原因: {reason}"
|
||||
|
||||
# 检查 first_bot_msg 是否为 None (例如思考消息已被移除的情况)
|
||||
if first_bot_msg:
|
||||
info_catcher.catch_after_response(timing_results["消息发送"], response_set, first_bot_msg)
|
||||
# 继续处理其他信息
|
||||
for info in all_plan_info:
|
||||
if isinstance(info, ObsInfo):
|
||||
observed_messages = info.get_talking_message()
|
||||
observed_messages_str = info.get_talking_message_str_truncate()
|
||||
chat_type = info.get_chat_type()
|
||||
is_group_chat = chat_type == "group"
|
||||
elif isinstance(info, MindInfo):
|
||||
current_mind = info.get_current_mind()
|
||||
elif isinstance(info, CycleInfo):
|
||||
cycle_info = info.get_observe_info()
|
||||
elif isinstance(info, SelfInfo):
|
||||
self_info = info.get_processed_info()
|
||||
elif isinstance(info, StructuredInfo):
|
||||
_structured_info = info.get_data()
|
||||
# 收集工具调用的结构化信息
|
||||
if _structured_info and isinstance(_structured_info, dict):
|
||||
# StructuredInfo 的数据结构是 {tool_type: tool_content}
|
||||
for tool_type, tool_content in _structured_info.items():
|
||||
if tool_content: # 确保内容不为空
|
||||
_structured_info_list.append(f"{tool_type}: {str(tool_content)}")
|
||||
elif not isinstance(info, ActionInfo): # 跳过已处理的ActionInfo
|
||||
extra_info.append(info.get_processed_info())
|
||||
|
||||
# 记录回复信息到最近回复列表中
|
||||
reply_info = {
|
||||
"time": time.time(),
|
||||
"user_message": message.processed_plain_text,
|
||||
"user_info": {
|
||||
"user_id": message.message_info.user_info.user_id,
|
||||
"user_nickname": message.message_info.user_info.user_nickname,
|
||||
},
|
||||
"response": response_set,
|
||||
"is_mentioned": is_mentioned,
|
||||
"is_reference_reply": message.reply is not None, # 判断是否为引用回复
|
||||
"timing": {k: round(v, 2) for k, v in timing_results.items()},
|
||||
# 获取当前可用的动作
|
||||
current_available_actions = self.action_manager.get_using_actions()
|
||||
|
||||
# 如果没有可用动作，直接返回no_reply
|
||||
if not current_available_actions:
|
||||
logger.warning(f"{self.log_prefix}没有可用的动作，将使用no_reply")
|
||||
action = "no_reply"
|
||||
reasoning = "没有可用的动作"
|
||||
return {
|
||||
"action_result": {"action_type": action, "action_data": action_data, "reasoning": reasoning},
|
||||
"current_mind": current_mind,
|
||||
"observed_messages": observed_messages,
|
||||
}
|
||||
self.recent_replies.append(reply_info)
|
||||
# 保持最近回复历史在限定数量内
|
||||
if len(self.recent_replies) > self.max_replies_history:
|
||||
self.recent_replies = self.recent_replies[-self.max_replies_history :]
|
||||
|
||||
# 检查是否需要切换到focus模式
|
||||
await self._check_switch_to_focus()
|
||||
|
||||
# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
|
||||
# 构建工具信息块
|
||||
structured_info_block_str = "\n".join(_structured_info_list)
|
||||
if structured_info_block_str:
|
||||
structured_info_block_str = f"The following is basic information returned by tool calls. Please use this information as the basis for subsequent decision-making and actions:\n{structured_info_block_str}"
|
||||
else:
|
||||
logger.warning(f"[{self.stream_name}] 思考消息 {thinking_id} 在发送前丢失，无法记录 info_catcher")
|
||||
structured_info_block_str = "No tool information available for reference."
|
||||
|
||||
info_catcher.done_catch()
|
||||
|
||||
# 处理表情包 (不再需要传入 chat)
|
||||
with Timer("处理表情包", timing_results):
|
||||
await self._handle_emoji(message, response_set[0])
|
||||
|
||||
# 更新关系情绪 (不再需要传入 chat)
|
||||
with Timer("关系更新", timing_results):
|
||||
await self._update_relationship(message, response_set)
|
||||
|
||||
# 回复后处理
|
||||
await willing_manager.after_generate_reply_handle(message.message_info.message_id)
|
||||
|
||||
# 输出性能计时结果
|
||||
if do_reply and response_set: # 确保 response_set 不是 None
|
||||
timing_str = " | ".join([f"{step}: {duration:.2f}秒" for step, duration in timing_results.items()])
|
||||
trigger_msg = message.processed_plain_text
|
||||
response_msg = " ".join(response_set)
|
||||
logger.info(
|
||||
f"[{self.stream_name}] 触发消息: {trigger_msg[:20]}... | 推理消息: {response_msg[:20]}... | 性能计时: {timing_str}"
|
||||
prompt = await self.build_planner_prompt(
|
||||
self_info_block=self_info,
|
||||
is_group_chat=is_group_chat, # <-- Pass HFC state
|
||||
chat_target_info=None,
|
||||
observed_messages_str=observed_messages_str, # <-- Pass local variable
|
||||
current_mind=current_mind, # <-- Pass argument
|
||||
structured_info_block=structured_info_block_str,
|
||||
current_available_actions=current_available_actions, # <-- Pass determined actions
|
||||
cycle_info=cycle_info, # <-- Pass cycle info
|
||||
extra_info=extra_info,
|
||||
)
|
||||
elif not do_reply:
|
||||
# 不回复处理
|
||||
await willing_manager.not_reply_handle(message.message_info.message_id)
|
||||
|
||||
# 意愿管理器：注销当前message信息 (无论是否回复，只要处理过就删除)
|
||||
willing_manager.delete(message.message_info.message_id)
|
||||
|
||||
# 改为实例方法, 移除 chat 参数
|
||||
|
||||
async def start_chat(self):
|
||||
"""先进行异步初始化，然后启动聊天任务。"""
|
||||
if not self._initialized:
|
||||
await self.initialize() # Ensure initialized before starting tasks
|
||||
|
||||
self._disabled = False # 启动时重置停用标志
|
||||
|
||||
if self._chat_task is None or self._chat_task.done():
|
||||
logger.info(f"[{self.stream_name}] 开始处理兴趣消息...")
|
||||
polling_task = asyncio.create_task(self._reply_interested_message())
|
||||
polling_task.add_done_callback(lambda t: self._handle_task_completion(t))
|
||||
self._chat_task = polling_task
|
||||
else:
|
||||
logger.info(f"[{self.stream_name}] 聊天轮询任务已在运行中。")
|
||||
|
||||
def _handle_task_completion(self, task: asyncio.Task):
|
||||
"""任务完成回调处理"""
|
||||
if task is not self._chat_task:
|
||||
logger.warning(f"[{self.stream_name}] 收到未知任务回调")
|
||||
return
|
||||
try:
|
||||
if exc := task.exception():
|
||||
logger.error(f"[{self.stream_name}] 任务异常: {exc}")
|
||||
traceback.print_exc()
|
||||
except asyncio.CancelledError:
|
||||
logger.debug(f"[{self.stream_name}] 任务已取消")
|
||||
except Exception as e:
|
||||
logger.error(f"[{self.stream_name}] 回调处理错误: {e}")
|
||||
finally:
|
||||
if self._chat_task is task:
|
||||
self._chat_task = None
|
||||
logger.debug(f"[{self.stream_name}] 任务清理完成")
|
||||
|
||||
# 改为实例方法, 移除 stream_id 参数
|
||||
async def stop_chat(self):
|
||||
"""停止当前实例的兴趣监控任务。"""
|
||||
self._disabled = True # 停止时设置停用标志
|
||||
if self._chat_task and not self._chat_task.done():
|
||||
task = self._chat_task
|
||||
logger.debug(f"[{self.stream_name}] 尝试取消normal聊天任务。")
|
||||
task.cancel()
|
||||
# --- 调用 LLM (普通文本生成) ---
|
||||
llm_content = None
|
||||
try:
|
||||
await task # 等待任务响应取消
|
||||
except asyncio.CancelledError:
|
||||
logger.info(f"[{self.stream_name}] 结束一般聊天模式。")
|
||||
except Exception as e:
|
||||
# 回调函数 _handle_task_completion 会处理异常日志
|
||||
logger.warning(f"[{self.stream_name}] 等待监控任务取消时捕获到异常 (可能已在回调中记录): {e}")
|
||||
finally:
|
||||
# 确保任务状态更新，即使等待出错 (回调函数也会尝试更新)
|
||||
if self._chat_task is task:
|
||||
self._chat_task = None
|
||||
llm_content, _, _ = await self.planner_llm.generate_response(prompt=prompt)
|
||||
logger.debug(f"{self.log_prefix}[Planner] LLM 原始 JSON 响应 (预期): {llm_content}")
|
||||
except Exception as req_e:
|
||||
logger.error(f"{self.log_prefix}[Planner] LLM 请求执行失败: {req_e}")
|
||||
reasoning = f"LLM 请求失败，你的模型出现问题: {req_e}"
|
||||
action = "no_reply"
|
||||
|
||||
# 清理所有未处理的思考消息
|
||||
try:
|
||||
container = await message_manager.get_container(self.stream_id)
|
||||
if container:
|
||||
# 查找并移除所有 MessageThinking 类型的消息
|
||||
thinking_messages = [msg for msg in container.messages[:] if isinstance(msg, MessageThinking)]
|
||||
if thinking_messages:
|
||||
for msg in thinking_messages:
|
||||
container.messages.remove(msg)
|
||||
logger.info(f"[{self.stream_name}] 清理了 {len(thinking_messages)} 条未处理的思考消息。")
|
||||
except Exception as e:
|
||||
logger.error(f"[{self.stream_name}] 清理思考消息时出错: {e}")
|
||||
if llm_content:
|
||||
try:
|
||||
# 尝试去除可能的 markdown 代码块标记
|
||||
cleaned_content = (
|
||||
llm_content.strip().removeprefix("```json").removeprefix("```").removesuffix("```").strip()
|
||||
)
|
||||
if not cleaned_content:
|
||||
raise json.JSONDecodeError("Cleaned content is empty", cleaned_content, 0)
|
||||
parsed_json = json.loads(cleaned_content)
|
||||
|
||||
# 提取决策，提供默认值
|
||||
extracted_action = parsed_json.get("action", "no_reply")
|
||||
extracted_reasoning = parsed_json.get("reasoning", "LLM未提供理由")
|
||||
|
||||
# 将所有其他属性添加到action_data
|
||||
action_data = {}
|
||||
for key, value in parsed_json.items():
|
||||
if key not in ["action", "reasoning"]:
|
||||
action_data[key] = value
|
||||
|
||||
# 对于reply动作不需要额外处理，因为相关字段已经在上面的循环中添加到action_data
|
||||
|
||||
if extracted_action not in current_available_actions:
|
||||
logger.warning(
|
||||
f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{extracted_action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_reply'"
|
||||
)
|
||||
action = "no_reply"
|
||||
reasoning = f"LLM 返回了当前不可用的动作 '{extracted_action}' (可用: {list(current_available_actions.keys())})。原始理由: {extracted_reasoning}"
|
||||
else:
|
||||
# 动作有效且可用
|
||||
action = extracted_action
|
||||
reasoning = extracted_reasoning
|
||||
|
||||
except Exception as json_e:
|
||||
logger.warning(
|
||||
f"{self.log_prefix}解析LLM响应JSON失败，模型返回不标准: {json_e}. LLM原始输出: '{llm_content}'"
|
||||
)
|
||||
reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_reply'."
|
||||
action = "no_reply"
|
||||
|
||||
except Exception as outer_e:
|
||||
logger.error(f"{self.log_prefix}Planner 处理过程中发生意外错误，规划失败，将执行 no_reply: {outer_e}")
|
||||
traceback.print_exc()
|
||||
action = "no_reply"
|
||||
reasoning = f"Planner 内部处理错误: {outer_e}"
|
||||
|
||||
# 获取最近回复记录的方法
|
||||
def get_recent_replies(self, limit: int = 10) -> List[dict]:
|
||||
"""获取最近的回复记录
|
||||
logger.debug(
|
||||
f"{self.log_prefix}规划器Prompt:\n{prompt}\n\n决策动作:{action},\n动作信息: '{action_data}'\n理由: {reasoning}"
|
||||
)
|
||||
|
||||
Args:
|
||||
limit: 最大返回数量，默认10条
|
||||
# 恢复原始动作集
|
||||
self.action_manager.restore_actions()
|
||||
logger.debug(
|
||||
f"{self.log_prefix}恢复了原始动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}"
|
||||
)
|
||||
|
||||
Returns:
|
||||
List[dict]: 最近的回复记录列表，每项包含：
|
||||
time: 回复时间戳
|
||||
user_message: 用户消息内容
|
||||
user_info: 用户信息(user_id, user_nickname)
|
||||
response: 回复内容
|
||||
is_mentioned: 是否被提及(@)
|
||||
is_reference_reply: 是否为引用回复
|
||||
timing: 各阶段耗时
|
||||
"""
|
||||
# 返回最近的limit条记录，按时间倒序排列
|
||||
return sorted(self.recent_replies[-limit:], key=lambda x: x["time"], reverse=True)
|
||||
action_result = {"action_type": action, "action_data": action_data, "reasoning": reasoning}
|
||||
|
||||
async def _check_switch_to_focus(self) -> None:
|
||||
"""检查是否满足切换到focus模式的条件"""
|
||||
if not self.on_switch_to_focus_callback:
|
||||
return # 如果没有设置回调函数，直接返回
|
||||
current_time = time.time()
|
||||
plan_result = {
|
||||
"action_result": action_result,
|
||||
"current_mind": current_mind,
|
||||
"observed_messages": observed_messages,
|
||||
}
|
||||
|
||||
time_threshold = 120 / global_config.chat.auto_focus_threshold
|
||||
reply_threshold = 6 * global_config.chat.auto_focus_threshold
|
||||
return plan_result
|
||||
|
||||
one_minute_ago = current_time - time_threshold
|
||||
async def build_planner_prompt(
|
||||
self,
|
||||
self_info_block: str,
|
||||
is_group_chat: bool, # Now passed as argument
|
||||
chat_target_info: Optional[dict], # Now passed as argument
|
||||
observed_messages_str: str,
|
||||
current_mind: Optional[str],
|
||||
structured_info_block: str,
|
||||
current_available_actions: Dict[str, ActionInfo],
|
||||
cycle_info: Optional[str],
|
||||
extra_info: list[str],
|
||||
) -> str:
|
||||
"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
|
||||
try:
|
||||
# --- Determine chat context ---
|
||||
chat_context_description = "你现在正在一个群聊中"
|
||||
chat_target_name = None # Only relevant for private
|
||||
if not is_group_chat and chat_target_info:
|
||||
chat_target_name = (
|
||||
chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or "对方"
|
||||
)
|
||||
chat_context_description = f"你正在和 {chat_target_name} 私聊"
|
||||
|
||||
# 统计1分钟内的回复数量
|
||||
recent_reply_count = sum(1 for reply in self.recent_replies if reply["time"] > one_minute_ago)
|
||||
# print(111111111111111333333333333333333333333331111111111111111111111111111111111)
|
||||
# print(recent_reply_count)
|
||||
# 如果1分钟内回复数量大于8，触发切换到focus模式
|
||||
if recent_reply_count > reply_threshold:
|
||||
logger.info(
|
||||
f"[{self.stream_name}] 检测到1分钟内回复数量({recent_reply_count})大于{reply_threshold}，触发切换到focus模式"
|
||||
chat_content_block = ""
|
||||
if observed_messages_str:
|
||||
chat_content_block = f"聊天记录：\n{observed_messages_str}"
|
||||
else:
|
||||
chat_content_block = "你还未开始聊天"
|
||||
|
||||
mind_info_block = ""
|
||||
if current_mind:
|
||||
mind_info_block = f"对聊天的规划：{current_mind}"
|
||||
else:
|
||||
mind_info_block = "你刚参与聊天"
|
||||
|
||||
personality_block = individuality.get_prompt(x_person=2, level=2)
|
||||
|
||||
action_options_block = ""
|
||||
for using_actions_name, using_actions_info in current_available_actions.items():
|
||||
# print(using_actions_name)
|
||||
# print(using_actions_info)
|
||||
# print(using_actions_info["parameters"])
|
||||
# print(using_actions_info["require"])
|
||||
# print(using_actions_info["description"])
|
||||
|
||||
using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt")
|
||||
|
||||
param_text = ""
|
||||
for param_name, param_description in using_actions_info["parameters"].items():
|
||||
param_text += f" {param_name}: {param_description}\n"
|
||||
|
||||
require_text = ""
|
||||
for require_item in using_actions_info["require"]:
|
||||
require_text += f" - {require_item}\n"
|
||||
|
||||
using_action_prompt = using_action_prompt.format(
|
||||
action_name=using_actions_name,
|
||||
action_description=using_actions_info["description"],
|
||||
action_parameters=param_text,
|
||||
action_require=require_text,
|
||||
)
|
||||
|
||||
action_options_block += using_action_prompt
|
||||
|
||||
extra_info_block = "\n".join(extra_info) # 将局部变量名从 extra_info_block_str 改为 extra_info_block
|
||||
if extra_info_block: # 使用新的变量名进行检查
|
||||
extra_info_block = f"The following is some additional information. Please read the following content to make a decision:\n{extra_info_block}\nEnd of additional information." # 使用新的变量名进行格式化
|
||||
else:
|
||||
extra_info_block = "No additional information available." # 使用新的变量名进行赋值
|
||||
|
||||
planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
|
||||
prompt = planner_prompt_template.format(
|
||||
self_info_block=self_info_block,
|
||||
# bot_name=global_config.bot.nickname,
|
||||
prompt_personality=personality_block,
|
||||
chat_context_description=chat_context_description,
|
||||
chat_content_block=chat_content_block,
|
||||
mind_info_block=mind_info_block,
|
||||
structured_info_block=structured_info_block,
|
||||
cycle_info_block=cycle_info,
|
||||
action_options_text=action_options_block,
|
||||
extra_info_block=extra_info_block, # 确保这里传递的是修改后的变量名
|
||||
)
|
||||
try:
|
||||
# 调用回调函数通知上层切换到focus模式
|
||||
await self.on_switch_to_focus_callback()
|
||||
except Exception as e:
|
||||
logger.error(f"[{self.stream_name}] 触发切换到focus模式时出错: {e}\n{traceback.format_exc()}")
|
||||
return prompt
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"构建 Planner 提示词时出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
return "构建 Planner Prompt 时出错"
|
||||
|
||||
|
||||
init_prompt()
|
||||
|
|
|
|||
Loading…
Reference in New Issue