feat：非常好的早期聊天记录压缩系统，麦麦现在有5倍上文记忆量（真的吗？
This commit is contained in:
SengokuCola
@@ -3,7 +3,6 @@ from ..person_info.relationship_manager import relationship_manager
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from .chat_stream import chat_manager
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from .message_sender import message_manager
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from ..storage.storage import MessageStorage
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from .auto_speak import auto_speak_manager
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__all__ = [
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@@ -12,5 +11,4 @@ __all__ = [
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"chat_manager",
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"message_manager",
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"MessageStorage",
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"auto_speak_manager",
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]
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@@ -1,184 +0,0 @@
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import time
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import asyncio
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import random
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from random import random as random_float
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from typing import Dict
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from ..config.config import global_config
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from .message import MessageSending, MessageThinking, MessageSet, MessageRecv
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from ..message.message_base import UserInfo, Seg
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from .message_sender import message_manager
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from ..moods.moods import MoodManager
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from ..chat_module.reasoning_chat.reasoning_generator import ResponseGenerator
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from src.common.logger import get_module_logger
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from src.heart_flow.heartflow import heartflow
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from ...common.database import db
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logger = get_module_logger("auto_speak")
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class AutoSpeakManager:
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def __init__(self):
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self._last_auto_speak_time: Dict[str, float] = {} # 记录每个聊天流上次自主发言的时间
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self.mood_manager = MoodManager.get_instance()
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self.gpt = ResponseGenerator() # 添加gpt实例
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self._started = False
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self._check_task = None
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self.db = db
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async def get_chat_info(self, chat_id: str) -> dict:
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"""从数据库获取聊天流信息"""
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chat_info = await self.db.chat_streams.find_one({"stream_id": chat_id})
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return chat_info
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async def start_auto_speak_check(self):
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"""启动自动发言检查任务"""
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if not self._started:
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self._check_task = asyncio.create_task(self._periodic_check())
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self._started = True
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logger.success("自动发言检查任务已启动")
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async def _periodic_check(self):
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"""定期检查是否需要自主发言"""
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while True and global_config.enable_think_flow:
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# 获取所有活跃的子心流
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active_subheartflows = []
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for chat_id, subheartflow in heartflow._subheartflows.items():
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if (
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subheartflow.is_active and subheartflow.current_state.willing > 0
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): # 只考虑活跃且意愿值大于0.5的子心流
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active_subheartflows.append((chat_id, subheartflow))
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logger.debug(
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f"发现活跃子心流 - 聊天ID: {chat_id}, 意愿值: {subheartflow.current_state.willing:.2f}"
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)
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if not active_subheartflows:
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logger.debug("当前没有活跃的子心流")
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await asyncio.sleep(20) # 添加异步等待
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continue
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# 随机选择一个活跃的子心流
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chat_id, subheartflow = random.choice(active_subheartflows)
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logger.info(f"随机选择子心流 - 聊天ID: {chat_id}, 意愿值: {subheartflow.current_state.willing:.2f}")
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# 检查是否应该自主发言
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if await self.check_auto_speak(subheartflow):
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logger.info(f"准备自主发言 - 聊天ID: {chat_id}")
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# 生成自主发言
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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="qq", # 默认使用qq平台
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)
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# 创建一个空的MessageRecv对象作为上下文
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message = MessageRecv(
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{
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"message_info": {
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"user_info": {"user_id": chat_id, "user_nickname": "", "platform": "qq"},
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"group_info": None,
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"platform": "qq",
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"time": time.time(),
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},
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"processed_plain_text": "",
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"raw_message": "",
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"is_emoji": False,
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}
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)
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await self.generate_auto_speak(
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subheartflow, message, bot_user_info, message.message_info["user_info"], message.message_info
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)
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else:
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logger.debug(f"不满足自主发言条件 - 聊天ID: {chat_id}")
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# 每分钟检查一次
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await asyncio.sleep(20)
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# await asyncio.sleep(5) # 发生错误时等待5秒再继续
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async def check_auto_speak(self, subheartflow) -> bool:
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"""检查是否应该自主发言"""
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if not subheartflow:
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return False
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current_time = time.time()
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chat_id = subheartflow.observe_chat_id
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# 获取上次自主发言时间
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if chat_id not in self._last_auto_speak_time:
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self._last_auto_speak_time[chat_id] = 0
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last_speak_time = self._last_auto_speak_time.get(chat_id, 0)
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# 如果距离上次自主发言不到5分钟，不发言
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if current_time - last_speak_time < 30:
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logger.debug(
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f"距离上次发言时间太短 - 聊天ID: {chat_id}, 剩余时间: {30 - (current_time - last_speak_time):.1f}秒"
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)
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return False
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# 获取当前意愿值
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current_willing = subheartflow.current_state.willing
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if current_willing > 0.1 and random_float() < 0.5:
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self._last_auto_speak_time[chat_id] = current_time
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logger.info(f"满足自主发言条件 - 聊天ID: {chat_id}, 意愿值: {current_willing:.2f}")
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return True
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logger.debug(f"不满足自主发言条件 - 聊天ID: {chat_id}, 意愿值: {current_willing:.2f}")
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return False
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async def generate_auto_speak(self, subheartflow, message, bot_user_info: UserInfo, userinfo, messageinfo):
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"""生成自主发言内容"""
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thinking_time_point = round(time.time(), 2)
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think_id = "mt" + str(thinking_time_point)
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thinking_message = MessageThinking(
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message_id=think_id,
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chat_stream=None, # 不需要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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)
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message_manager.add_message(thinking_message)
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# 生成自主发言内容
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try:
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response, raw_content = await self.gpt.generate_response(message)
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except Exception as e:
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logger.error(f"生成自主发言内容时发生错误: {e}")
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return False
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if response:
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message_set = MessageSet(None, think_id) # 不需要chat_stream
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mark_head = False
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for msg in response:
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message_segment = Seg(type="text", data=msg)
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bot_message = MessageSending(
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message_id=think_id,
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chat_stream=None, # 不需要chat_stream
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bot_user_info=bot_user_info,
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sender_info=userinfo,
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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_time_point,
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)
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if not mark_head:
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mark_head = True
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message_set.add_message(bot_message)
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message_manager.add_message(message_set)
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# 更新情绪和关系
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stance, emotion = await self.gpt._get_emotion_tags(raw_content, message.processed_plain_text)
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self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
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return True
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return False
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# 创建全局AutoSpeakManager实例
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auto_speak_manager = AutoSpeakManager()
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@@ -236,7 +236,7 @@ class ThinkFlowChat:
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if random() < reply_probability:
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try:
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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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@@ -252,80 +252,77 @@ class ThinkFlowChat:
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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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# 观察
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try:
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# 观察
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with Timer("观察", timing_results):
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await heartflow.get_subheartflow(chat.stream_id).do_observe()
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except Exception as e:
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logger.error(f"心流观察失败: {e}")
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traceback.print_exc()
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info_catcher.catch_after_observe(timing_results["观察"])
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# 思考前使用工具
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update_relationship = ""
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get_mid_memory_id = []
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tool_result_info = {}
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try:
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with Timer("思考前使用工具", timing_results):
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tool_result = await self.tool_user.use_tool(message.processed_plain_text, message.message_info.user_info.user_nickname, chat)
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tool_result = await self.tool_user.use_tool(
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message.processed_plain_text,
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message.message_info.user_info.user_nickname,
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chat,
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heartflow.get_subheartflow(chat.stream_id))
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# 如果工具被使用且获得了结果，将收集到的信息合并到思考中
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collected_info = ""
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# collected_info = ""
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if tool_result.get("used_tools", False):
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# 如果有收集到的结构化信息，将其格式化后添加到当前思考中
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if "structured_info" in tool_result:
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info = tool_result["structured_info"]
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# 处理记忆信息
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if info["memory"]:
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collected_info += "\n记忆相关信息:\n"
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for mem in info["memory"]:
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collected_info += f"- {mem['name']}: {mem['content']}\n"
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tool_result_info = tool_result["structured_info"]
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# collected_info = ""
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get_mid_memory_id = []
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update_relationship = ""
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# 处理日程信息
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if info["schedule"]:
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collected_info += "\n日程相关信息:\n"
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for sch in info["schedule"]:
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collected_info += f"- {sch['name']}: {sch['content']}\n"
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# 处理知识信息
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if info["knowledge"]:
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collected_info += "\n知识相关信息:\n"
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for know in info["knowledge"]:
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collected_info += f"- {know['name']}: {know['content']}\n"
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# 处理关系信息
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if info["change_relationship"]:
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collected_info += "\n关系相关信息:\n"
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for rel in info["change_relationship"]:
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collected_info += f"- {rel['name']}: {rel['content']}\n"
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# print("11111111111111111111111111111")
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update_relationship += rel["content"]
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# print(f"11111111111111111111111111111{update_relationship}")
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# 动态解析工具结果
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for tool_name, tool_data in tool_result_info.items():
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# tool_result_info += f"\n{tool_name} 相关信息:\n"
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# for item in tool_data:
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# tool_result_info += f"- {item['name']}: {item['content']}\n"
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# 特殊判定：mid_chat_mem
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if tool_name == "mid_chat_mem":
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for mid_memory in tool_data:
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get_mid_memory_id.append(mid_memory['content'])
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# 特殊判定：change_mood
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if tool_name == "change_mood":
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for mood in tool_data:
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self.mood_manager.update_mood_from_emotion(
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mood['content'],
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global_config.mood_intensity_factor
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)
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# 特殊判定：change_relationship
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if tool_name == "change_relationship":
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update_relationship = tool_data[0]["content"]
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# 处理心情信息
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if info["change_mood"]:
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collected_info += "\n心情相关信息:\n"
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for mood in info["change_mood"]:
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collected_info += f"- {mood['name']}: {mood['content']}\n"
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# 处理其他信息
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if info["other"]:
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collected_info += "\n其他相关信息:\n"
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for other in info["other"]:
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collected_info += f"- {other['name']}: {other['content']}\n"
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except Exception as e:
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logger.error(f"思考前工具调用失败: {e}")
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logger.error(traceback.format_exc())
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# 处理关系更新
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if update_relationship:
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# ori_response = ",".join(response_set)
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# print("22222222222222222222222222222")
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stance, emotion = await self.gpt._get_emotion_tags_with_reason("你还没有回复", message.processed_plain_text,update_relationship)
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stance, emotion = await self.gpt._get_emotion_tags_with_reason(
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"你还没有回复",
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message.processed_plain_text,
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update_relationship
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)
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await relationship_manager.calculate_update_relationship_value(
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chat_stream=message.chat_stream, label=emotion, stance=stance
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)
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print("33333333333333333333333333333")
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chat_stream=message.chat_stream,
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label=emotion,
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stance=stance
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)
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# 思考前脑内状态
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try:
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with Timer("思考前脑内状态", timing_results):
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@@ -335,7 +332,8 @@ class ThinkFlowChat:
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message_txt=message.processed_plain_text,
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sender_name=message.message_info.user_info.user_nickname,
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chat_stream=chat,
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extra_info=collected_info
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obs_id = get_mid_memory_id,
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extra_info=tool_result_info
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)
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except Exception as e:
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logger.error(f"心流思考前脑内状态失败: {e}")
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@@ -371,66 +369,6 @@ class ThinkFlowChat:
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logger.error(f"心流处理表情包失败: {e}")
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# 思考后使用工具
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try:
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with Timer("思考后使用工具", timing_results):
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tool_result = await self.tool_user.use_tool(message.processed_plain_text, message.message_info.user_info.user_nickname, chat)
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# 如果工具被使用且获得了结果，将收集到的信息合并到思考中
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collected_info = ""
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if tool_result.get("used_tools", False):
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# 如果有收集到的结构化信息，将其格式化后添加到当前思考中
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if "structured_info" in tool_result:
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info = tool_result["structured_info"]
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# 处理记忆信息
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if info["memory"]:
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collected_info += "\n记忆相关信息:\n"
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for mem in info["memory"]:
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collected_info += f"- {mem['name']}: {mem['content']}\n"
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# 处理日程信息
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if info["schedule"]:
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collected_info += "\n日程相关信息:\n"
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for sch in info["schedule"]:
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collected_info += f"- {sch['name']}: {sch['content']}\n"
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# 处理知识信息
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if info["knowledge"]:
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collected_info += "\n知识相关信息:\n"
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for know in info["knowledge"]:
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collected_info += f"- {know['name']}: {know['content']}\n"
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# 处理关系信息
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if info["change_relationship"]:
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collected_info += "\n关系相关信息:\n"
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for rel in info["change_relationship"]:
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collected_info += f"- {rel['name']}: {rel['content']}\n"
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# 处理心情信息
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if info["change_mood"]:
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collected_info += "\n心情相关信息:\n"
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for mood in info["change_mood"]:
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collected_info += f"- {mood['name']}: {mood['content']}\n"
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# 处理其他信息
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if info["other"]:
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collected_info += "\n其他相关信息:\n"
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for other in info["other"]:
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collected_info += f"- {other['name']}: {other['content']}\n"
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except Exception as e:
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logger.error(f"思考后工具调用失败: {e}")
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logger.error(traceback.format_exc())
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# 更新关系
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if info["change_relationship"]:
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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,info["change_relationship"]["content"])
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await relationship_manager.calculate_update_relationship_value(
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chat_stream=message.chat_stream, label=emotion, stance=stance
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)
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try:
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with Timer("思考后脑内状态更新", timing_results):
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stream_id = message.chat_stream.stream_id
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@@ -440,7 +378,7 @@ class ThinkFlowChat:
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stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
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)
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await heartflow.get_subheartflow(stream_id).do_thinking_after_reply(response_set, chat_talking_prompt,collected_info)
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await heartflow.get_subheartflow(stream_id).do_thinking_after_reply(response_set, chat_talking_prompt,tool_result_info)
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except Exception as e:
|
||||
logger.error(f"心流思考后脑内状态更新失败: {e}")
|
||||
|
||||
|
||||
@@ -43,7 +43,7 @@ def init_prompt():
|
||||
{chat_talking_prompt}
|
||||
现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
|
||||
你刚刚脑子里在想：{current_mind_info}
|
||||
现在请你读读之前的聊天记录，然后给出日常，口语化且简短的回复内容，只给出文字的回复内容，不要有内心独白:
|
||||
现在请你读读之前的聊天记录，然后给出日常，口语化且简短的回复内容，请只对一个话题进行回复，只给出文字的回复内容，不要有内心独白:
|
||||
""",
|
||||
"heart_flow_prompt_simple",
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user