mirror of https://github.com/Mai-with-u/MaiBot.git
feat:情绪可开关，默认关
SengokuCola
parent
9781b5f09c
commit
993f85bfeb
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@ -20,6 +20,8 @@ MaiBot-Napcat-Adapter
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nonebot-maibot-adapter/
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MaiMBot-LPMM
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*.zip
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run_bot.bat
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run_na.bat
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run.bat
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log_debug/
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run_amds.bat
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21
bot.py
21
bot.py
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@ -5,16 +5,29 @@ import sys
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import time
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import platform
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import traceback
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import shutil
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from dotenv import load_dotenv
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from pathlib import Path
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from rich.traceback import install
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if os.path.exists(".env"):
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load_dotenv(".env", override=True)
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env_path = Path(__file__).parent / ".env"
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template_env_path = Path(__file__).parent / "template" / "template.env"
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if env_path.exists():
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load_dotenv(str(env_path), override=True)
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print("成功加载环境变量配置")
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else:
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print("未找到.env文件，请确保程序所需的环境变量被正确设置")
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raise FileNotFoundError(".env 文件不存在，请创建并配置所需的环境变量")
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try:
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if template_env_path.exists():
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shutil.copyfile(template_env_path, env_path)
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print("未找到.env，已从 template/template.env 自动创建")
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load_dotenv(str(env_path), override=True)
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else:
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print("未找到.env文件，也未找到模板 template/template.env")
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raise FileNotFoundError(".env 文件不存在，请创建并配置所需的环境变量")
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except Exception as e:
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print(f"自动创建 .env 失败: {e}")
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raise
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# 最早期初始化日志系统，确保所有后续模块都使用正确的日志格式
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from src.common.logger import initialize_logging, get_logger, shutdown_logging
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@ -20,6 +20,7 @@ from src.chat.message_receive.uni_message_sender import UniversalMessageSender
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from src.chat.utils.timer_calculator import Timer # <--- Import Timer
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from src.chat.utils.utils import get_chat_type_and_target_info
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from src.chat.utils.prompt_builder import global_prompt_manager
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from src.mood.mood_manager import mood_manager
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from src.chat.utils.chat_message_builder import (
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build_readable_messages,
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get_raw_msg_before_timestamp_with_chat,
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@ -283,38 +284,13 @@ class DefaultReplyer:
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expression_habits_block += f"{style_habits_str}\n"
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return f"{expression_habits_title}\n{expression_habits_block}", selected_ids
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# async def build_memory_block(self, chat_history: List[DatabaseMessages], target: str) -> str:
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# """构建记忆块
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# Args:
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# chat_history: 聊天历史记录
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# target: 目标消息内容
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# Returns:
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# str: 记忆信息字符串
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# """
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# if not global_config.memory.enable_memory:
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# return ""
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# instant_memory = None
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# running_memories = await self.memory_activator.activate_memory_with_chat_history(
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# target_message=target, chat_history=chat_history
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# )
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# if not running_memories:
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# return ""
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# memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
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# for running_memory in running_memories:
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# keywords, content = running_memory
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# memory_str += f"- {keywords}：{content}\n"
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# if instant_memory:
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# memory_str += f"- {instant_memory}\n"
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# return memory_str
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async def build_mood_state_prompt(self) -> str:
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"""构建情绪状态提示"""
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if not global_config.mood.enable_mood:
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return ""
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mood_state = await mood_manager.get_mood_by_chat_id(self.chat_stream.stream_id).get_mood()
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return f"你现在的心情是：{mood_state}"
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async def build_memory_block(self) -> str:
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"""构建记忆块
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@ -526,50 +502,6 @@ class DefaultReplyer:
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return core_dialogue_prompt, all_dialogue_prompt
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def build_mai_think_context(
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self,
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chat_id: str,
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memory_block: str,
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relation_info: str,
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time_block: str,
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chat_target_1: str,
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chat_target_2: str,
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identity_block: str,
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sender: str,
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target: str,
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chat_info: str,
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) -> Any:
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"""构建 mai_think 上下文信息
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Args:
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chat_id: 聊天ID
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memory_block: 记忆块内容
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relation_info: 关系信息
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time_block: 时间块内容
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chat_target_1: 聊天目标1
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chat_target_2: 聊天目标2
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identity_block: 身份块内容
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sender: 发送者名称
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target: 目标消息内容
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chat_info: 聊天信息
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Returns:
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Any: mai_think 实例
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"""
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mai_think = mai_thinking_manager.get_mai_think(chat_id)
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mai_think.memory_block = memory_block
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mai_think.relation_info_block = relation_info
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mai_think.time_block = time_block
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mai_think.chat_target = chat_target_1
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mai_think.chat_target_2 = chat_target_2
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mai_think.chat_info = chat_info
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mai_think.identity = identity_block
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mai_think.sender = sender
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mai_think.target = target
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return mai_think
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async def build_actions_prompt(
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self, available_actions: Dict[str, ActionInfo], chosen_actions_info: Optional[List[ActionPlannerInfo]] = None
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) -> str:
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@ -717,6 +649,7 @@ class DefaultReplyer:
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self._time_and_run_task(self.get_prompt_info(chat_talking_prompt_short, sender, target), "prompt_info"),
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self._time_and_run_task(self.build_actions_prompt(available_actions, chosen_actions), "actions_info"),
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self._time_and_run_task(self.build_personality_prompt(), "personality_prompt"),
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self._time_and_run_task(self.build_mood_state_prompt(), "mood_state_prompt"),
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)
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# 任务名称中英文映射
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@ -729,6 +662,7 @@ class DefaultReplyer:
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"prompt_info": "获取知识",
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"actions_info": "动作信息",
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"personality_prompt": "人格信息",
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"mood_state_prompt": "情绪状态",
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}
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# 处理结果
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@ -759,6 +693,7 @@ class DefaultReplyer:
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actions_info: str = results_dict["actions_info"]
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personality_prompt: str = results_dict["personality_prompt"]
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keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
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mood_state_prompt: str = results_dict["mood_state_prompt"]
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if extra_info:
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extra_info_block = f"以下是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策\n{extra_info}\n以上是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策"
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@ -789,6 +724,7 @@ class DefaultReplyer:
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tool_info_block=tool_info,
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memory_block=memory_block,
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knowledge_prompt=prompt_info,
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mood_state=mood_state_prompt,
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# memory_block=memory_block,
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# relation_info_block=relation_info,
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extra_info_block=extra_info_block,
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@ -809,6 +745,7 @@ class DefaultReplyer:
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tool_info_block=tool_info,
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memory_block=memory_block,
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knowledge_prompt=prompt_info,
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mood_state=mood_state_prompt,
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# memory_block=memory_block,
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# relation_info_block=relation_info,
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extra_info_block=extra_info_block,
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@ -26,6 +26,7 @@ from src.chat.utils.chat_message_builder import (
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replace_user_references,
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)
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from src.chat.express.expression_selector import expression_selector
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from src.mood.mood_manager import mood_manager
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# from src.memory_system.memory_activator import MemoryActivator
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@ -281,37 +282,12 @@ class PrivateReplyer:
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return f"{expression_habits_title}\n{expression_habits_block}", selected_ids
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# async def build_memory_block(self, chat_history: List[DatabaseMessages], target: str) -> str:
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# """构建记忆块
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# Args:
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# chat_history: 聊天历史记录
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# target: 目标消息内容
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# Returns:
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# str: 记忆信息字符串
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# """
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# if not global_config.memory.enable_memory:
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# return ""
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# instant_memory = None
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# running_memories = await self.memory_activator.activate_memory_with_chat_history(
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# target_message=target, chat_history=chat_history
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# )
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# if not running_memories:
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# return ""
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# memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
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# for running_memory in running_memories:
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# keywords, content = running_memory
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# memory_str += f"- {keywords}：{content}\n"
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# if instant_memory:
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# memory_str += f"- {instant_memory}\n"
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# return memory_str
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async def build_mood_state_prompt(self) -> str:
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"""构建情绪状态提示"""
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if not global_config.mood.enable_mood:
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return ""
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mood_state = await mood_manager.get_mood_by_chat_id(self.chat_stream.stream_id).get_mood()
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return f"你现在的心情是：{mood_state}"
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async def build_memory_block(self) -> str:
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@ -600,6 +576,7 @@ class PrivateReplyer:
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self._time_and_run_task(self.get_prompt_info(chat_talking_prompt_short, sender, target), "prompt_info"),
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self._time_and_run_task(self.build_actions_prompt(available_actions, chosen_actions), "actions_info"),
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self._time_and_run_task(self.build_personality_prompt(), "personality_prompt"),
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self._time_and_run_task(self.build_mood_state_prompt(), "mood_state_prompt"),
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)
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# 任务名称中英文映射
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@ -611,6 +588,7 @@ class PrivateReplyer:
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"prompt_info": "获取知识",
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"actions_info": "动作信息",
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"personality_prompt": "人格信息",
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"mood_state_prompt": "情绪状态",
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}
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# 处理结果
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@ -639,6 +617,7 @@ class PrivateReplyer:
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prompt_info: str = results_dict["prompt_info"] # 直接使用格式化后的结果
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actions_info: str = results_dict["actions_info"]
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personality_prompt: str = results_dict["personality_prompt"]
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mood_state_prompt: str = results_dict["mood_state_prompt"]
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keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
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if extra_info:
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@ -660,12 +639,12 @@ class PrivateReplyer:
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expression_habits_block=expression_habits_block,
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tool_info_block=tool_info,
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knowledge_prompt=prompt_info,
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mood_state=mood_state_prompt,
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memory_block=memory_block,
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relation_info_block=relation_info,
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extra_info_block=extra_info_block,
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identity=personality_prompt,
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action_descriptions=actions_info,
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dialogue_prompt=dialogue_prompt,
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time_block=time_block,
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target=target,
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@ -681,6 +660,7 @@ class PrivateReplyer:
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expression_habits_block=expression_habits_block,
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tool_info_block=tool_info,
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knowledge_prompt=prompt_info,
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mood_state=mood_state_prompt,
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memory_block=memory_block,
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relation_info_block=relation_info,
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extra_info_block=extra_info_block,
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@ -22,7 +22,7 @@ def init_replyer_prompt():
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{reply_target_block}。
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{identity}
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你正在群里聊天,现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，
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你正在群里聊天,现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，{mood_state}
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尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。
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{reply_style}
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请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
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@ -41,7 +41,7 @@ def init_replyer_prompt():
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{background_dialogue_prompt}
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你现在想补充说明你刚刚自己的发言内容：{target}，原因是{reason}
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请你根据聊天内容，组织一条新回复。注意，{target} 是刚刚你自己的发言，你要在这基础上进一步发言，请按照你自己的角度来继续进行回复。注意保持上下文的连贯性。
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请你根据聊天内容，组织一条新回复。注意，{target} 是刚刚你自己的发言，你要在这基础上进一步发言，请按照你自己的角度来继续进行回复。注意保持上下文的连贯性。{mood_state}
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{identity}
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尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。
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{reply_style}
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@ -63,7 +63,7 @@ def init_replyer_prompt():
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{reply_target_block}。
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{identity}
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你正在和{sender_name}聊天,现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，
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你正在和{sender_name}聊天,现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，{mood_state}
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尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。
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{reply_style}
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请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
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@ -81,7 +81,7 @@ def init_replyer_prompt():
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{dialogue_prompt}
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你现在想补充说明你刚刚自己的发言内容：{target}，原因是{reason}
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请你根据聊天内容，组织一条新回复。注意，{target} 是刚刚你自己的发言，你要在这基础上进一步发言，请按照你自己的角度来继续进行回复。注意保持上下文的连贯性。
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请你根据聊天内容，组织一条新回复。注意，{target} 是刚刚你自己的发言，你要在这基础上进一步发言，请按照你自己的角度来继续进行回复。注意保持上下文的连贯性。{mood_state}
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{identity}
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尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。
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{reply_style}
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@ -151,7 +151,7 @@ class Prompt(str):
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@staticmethod
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def _process_escaped_braces(template) -> str:
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"""处理模板中的转义花括号，将 \{ 和 \} 替换为临时标记""" # type: ignore
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"""处理模板中的转义花括号,替换为临时标记""" # type: ignore
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# 如果传入的是列表，将其转换为字符串
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if isinstance(template, list):
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template = "\n".join(str(item) for item in template)
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@ -30,6 +30,7 @@ from src.config.official_configs import (
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RelationshipConfig,
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ToolConfig,
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VoiceConfig,
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MoodConfig,
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MemoryConfig,
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DebugConfig,
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)
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@ -54,7 +55,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template")
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# 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
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# 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/
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MMC_VERSION = "0.11.0-snapshot.1"
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MMC_VERSION = "0.11.0-snapshot.2"
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def get_key_comment(toml_table, key):
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@ -356,6 +357,7 @@ class Config(ConfigBase):
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tool: ToolConfig
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memory: MemoryConfig
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debug: DebugConfig
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mood: MoodConfig
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voice: VoiceConfig
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@ -38,9 +38,6 @@ class PersonalityConfig(ConfigBase):
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personality: str
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"""人格"""
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emotion_style: str
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"""情感特征"""
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reply_style: str = ""
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"""表达风格"""
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@ -278,6 +275,19 @@ class ToolConfig(ConfigBase):
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"""是否在聊天中启用工具"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class MoodConfig(ConfigBase):
|
||||
"""情绪配置类"""
|
||||
|
||||
enable_mood: bool = True
|
||||
"""是否启用情绪系统"""
|
||||
|
||||
mood_update_threshold: float = 1
|
||||
"""情绪更新阈值,越高，更新越慢"""
|
||||
|
||||
emotion_style: str = "情绪较为稳定，但遭遇特定事件的时候起伏较大"
|
||||
"""情感特征，影响情绪的变化情况"""
|
||||
|
||||
@dataclass
|
||||
class VoiceConfig(ConfigBase):
|
||||
"""语音识别配置类"""
|
||||
|
|
|
|||
10
src/main.py
10
src/main.py
|
|
@ -14,7 +14,6 @@ from src.common.server import get_global_server, Server
|
|||
from src.mood.mood_manager import mood_manager
|
||||
from src.chat.knowledge import lpmm_start_up
|
||||
from src.memory_system.Hippocampus import hippocampus_manager
|
||||
from src.memory_system.hippocampus_to_memory_chest_task import HippocampusToMemoryChestTask
|
||||
from src.memory_system.memory_management_task import MemoryManagementTask
|
||||
from rich.traceback import install
|
||||
from src.migrate_helper.migrate import check_and_run_migrations
|
||||
|
|
@ -86,8 +85,9 @@ class MainSystem:
|
|||
logger.info("表情包管理器初始化成功")
|
||||
|
||||
# 启动情绪管理器
|
||||
await mood_manager.start()
|
||||
logger.info("情绪管理器初始化成功")
|
||||
if global_config.mood.enable_mood:
|
||||
await mood_manager.start()
|
||||
logger.info("情绪管理器初始化成功")
|
||||
|
||||
# 初始化聊天管理器
|
||||
await get_chat_manager()._initialize()
|
||||
|
|
@ -99,10 +99,6 @@ class MainSystem:
|
|||
hippocampus_manager.initialize()
|
||||
logger.info("记忆系统初始化成功")
|
||||
|
||||
# 添加海马体到记忆仓库的转换任务
|
||||
await async_task_manager.add_task(HippocampusToMemoryChestTask())
|
||||
logger.info("海马体到记忆仓库转换任务已启动")
|
||||
|
||||
# 添加记忆管理任务
|
||||
await async_task_manager.add_task(MemoryManagementTask())
|
||||
logger.info("记忆管理任务已启动")
|
||||
|
|
|
|||
|
|
@ -207,343 +207,6 @@ class Hippocampus:
|
|||
|
||||
|
||||
|
||||
|
||||
def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
|
||||
"""从关键词获取相关记忆。
|
||||
|
||||
Args:
|
||||
keyword (str): 关键词
|
||||
max_depth (int, optional): 记忆检索深度，默认为2。1表示只获取直接相关的记忆，2表示获取间接相关的记忆。
|
||||
|
||||
Returns:
|
||||
list: 记忆列表，每个元素是一个元组 (topic, memory_content, similarity)
|
||||
- topic: str, 记忆主题
|
||||
- memory_content: str, 该主题下的完整记忆内容
|
||||
- similarity: float, 与关键词的相似度
|
||||
"""
|
||||
if not keyword:
|
||||
return []
|
||||
|
||||
# 获取所有节点
|
||||
all_nodes = list(self.memory_graph.G.nodes())
|
||||
memories = []
|
||||
|
||||
# 计算关键词的词集合
|
||||
keyword_words = set(jieba.cut(keyword))
|
||||
|
||||
# 遍历所有节点，计算相似度
|
||||
for node in all_nodes:
|
||||
node_words = set(jieba.cut(node))
|
||||
all_words = keyword_words | node_words
|
||||
v1 = [1 if word in keyword_words else 0 for word in all_words]
|
||||
v2 = [1 if word in node_words else 0 for word in all_words]
|
||||
similarity = cosine_similarity(v1, v2)
|
||||
|
||||
# 如果相似度超过阈值，获取该节点的记忆
|
||||
if similarity >= 0.3: # 可以调整这个阈值
|
||||
node_data = self.memory_graph.G.nodes[node]
|
||||
# 直接使用完整的记忆内容
|
||||
if memory_items := node_data.get("memory_items", ""):
|
||||
memories.append((node, memory_items, similarity))
|
||||
|
||||
# 按相似度降序排序
|
||||
memories.sort(key=lambda x: x[2], reverse=True)
|
||||
return memories
|
||||
|
||||
async def get_keywords_from_text(self, text: str) -> Tuple[List[str], List]:
|
||||
"""从文本中提取关键词。
|
||||
|
||||
Args:
|
||||
text (str): 输入文本
|
||||
fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
|
||||
如果为True，使用jieba分词提取关键词，速度更快但可能不够准确。
|
||||
如果为False，使用LLM提取关键词，速度较慢但更准确。
|
||||
"""
|
||||
if not text:
|
||||
return [], []
|
||||
|
||||
# 使用LLM提取关键词 - 根据详细文本长度分布优化topic_num计算
|
||||
text_length = len(text)
|
||||
topic_num: int | list[int] = 0
|
||||
|
||||
keywords_lite = cut_key_words(text)
|
||||
if keywords_lite:
|
||||
logger.debug(f"提取关键词极简版: {keywords_lite}")
|
||||
|
||||
if text_length <= 12:
|
||||
topic_num = [1, 3] # 6-10字符: 1个关键词 (27.18%的文本)
|
||||
elif text_length <= 20:
|
||||
topic_num = [2, 4] # 11-20字符: 2个关键词 (22.76%的文本)
|
||||
elif text_length <= 30:
|
||||
topic_num = [3, 5] # 21-30字符: 3个关键词 (10.33%的文本)
|
||||
elif text_length <= 50:
|
||||
topic_num = [4, 5] # 31-50字符: 4个关键词 (9.79%的文本)
|
||||
else:
|
||||
topic_num = 5 # 51+字符: 5个关键词 (其余长文本)
|
||||
|
||||
topics_response, _ = await self.model_small.generate_response_async(self.find_topic_llm(text, topic_num))
|
||||
|
||||
# 提取关键词
|
||||
keywords = re.findall(r"<([^>]+)>", topics_response)
|
||||
if not keywords:
|
||||
keywords = []
|
||||
else:
|
||||
keywords = [
|
||||
keyword.strip()
|
||||
for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||||
if keyword.strip()
|
||||
]
|
||||
|
||||
if keywords:
|
||||
logger.debug(f"提取关键词: {keywords}")
|
||||
|
||||
return keywords, keywords_lite
|
||||
|
||||
async def get_memory_from_topic(
|
||||
self,
|
||||
keywords: list[str],
|
||||
max_memory_num: int = 3,
|
||||
max_memory_length: int = 2,
|
||||
max_depth: int = 3,
|
||||
) -> list:
|
||||
"""从文本中提取关键词并获取相关记忆。
|
||||
|
||||
Args:
|
||||
keywords (list): 输入文本
|
||||
max_memory_num (int, optional): 返回的记忆条目数量上限。默认为3，表示最多返回3条与输入文本相关度最高的记忆。
|
||||
max_memory_length (int, optional): 每个主题最多返回的记忆条目数量。默认为2，表示每个主题最多返回2条相似度最高的记忆。
|
||||
max_depth (int, optional): 记忆检索深度。默认为3。值越大，检索范围越广，可以获取更多间接相关的记忆，但速度会变慢。
|
||||
|
||||
Returns:
|
||||
list: 记忆列表，每个元素是一个元组 (topic, memory_content)
|
||||
- topic: str, 记忆主题
|
||||
- memory_content: str, 该主题下的完整记忆内容
|
||||
"""
|
||||
if not keywords:
|
||||
return []
|
||||
|
||||
logger.info(f"提取的关键词: {', '.join(keywords)}")
|
||||
|
||||
# 过滤掉不存在于记忆图中的关键词
|
||||
valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
|
||||
if not valid_keywords:
|
||||
logger.debug("没有找到有效的关键词节点")
|
||||
return []
|
||||
|
||||
logger.debug(f"有效的关键词: {', '.join(valid_keywords)}")
|
||||
|
||||
# 从每个关键词获取记忆
|
||||
activate_map = {} # 存储每个词的累计激活值
|
||||
|
||||
# 对每个关键词进行扩散式检索
|
||||
for keyword in valid_keywords:
|
||||
logger.debug(f"开始以关键词 '{keyword}' 为中心进行扩散检索 (最大深度: {max_depth}):")
|
||||
# 初始化激活值
|
||||
activation_values = {keyword: 1.0}
|
||||
# 记录已访问的节点
|
||||
visited_nodes = {keyword}
|
||||
# 待处理的节点队列，每个元素是(节点, 激活值, 当前深度)
|
||||
nodes_to_process = [(keyword, 1.0, 0)]
|
||||
|
||||
while nodes_to_process:
|
||||
current_node, current_activation, current_depth = nodes_to_process.pop(0)
|
||||
|
||||
# 如果激活值小于0或超过最大深度，停止扩散
|
||||
if current_activation <= 0 or current_depth >= max_depth:
|
||||
continue
|
||||
|
||||
# 获取当前节点的所有邻居
|
||||
neighbors = list(self.memory_graph.G.neighbors(current_node))
|
||||
|
||||
for neighbor in neighbors:
|
||||
if neighbor in visited_nodes:
|
||||
continue
|
||||
|
||||
# 获取连接强度
|
||||
edge_data = self.memory_graph.G[current_node][neighbor]
|
||||
strength = edge_data.get("strength", 1)
|
||||
|
||||
# 计算新的激活值
|
||||
new_activation = current_activation - (1 / strength)
|
||||
|
||||
if new_activation > 0:
|
||||
activation_values[neighbor] = new_activation
|
||||
visited_nodes.add(neighbor)
|
||||
nodes_to_process.append((neighbor, new_activation, current_depth + 1))
|
||||
# logger.debug(
|
||||
# f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})"
|
||||
# ) # noqa: E501
|
||||
|
||||
# 更新激活映射
|
||||
for node, activation_value in activation_values.items():
|
||||
if activation_value > 0:
|
||||
if node in activate_map:
|
||||
activate_map[node] += activation_value
|
||||
else:
|
||||
activate_map[node] = activation_value
|
||||
|
||||
# 基于激活值平方的独立概率选择
|
||||
remember_map = {}
|
||||
# logger.info("基于激活值平方的归一化选择:")
|
||||
|
||||
# 计算所有激活值的平方和
|
||||
total_squared_activation = sum(activation**2 for activation in activate_map.values())
|
||||
if total_squared_activation > 0:
|
||||
# 计算归一化的激活值
|
||||
normalized_activations = {
|
||||
node: (activation**2) / total_squared_activation for node, activation in activate_map.items()
|
||||
}
|
||||
|
||||
# 按归一化激活值排序并选择前max_memory_num个
|
||||
sorted_nodes = sorted(normalized_activations.items(), key=lambda x: x[1], reverse=True)[:max_memory_num]
|
||||
|
||||
# 将选中的节点添加到remember_map
|
||||
for node, normalized_activation in sorted_nodes:
|
||||
remember_map[node] = activate_map[node] # 使用原始激活值
|
||||
logger.debug(
|
||||
f"节点 '{node}' (归一化激活值: {normalized_activation:.2f}, 激活值: {activate_map[node]:.2f})"
|
||||
)
|
||||
else:
|
||||
logger.info("没有有效的激活值")
|
||||
|
||||
# 从选中的节点中提取记忆
|
||||
all_memories = []
|
||||
# logger.info("开始从选中的节点中提取记忆:")
|
||||
for node, activation in remember_map.items():
|
||||
logger.debug(f"处理节点 '{node}' (激活值: {activation:.2f}):")
|
||||
node_data = self.memory_graph.G.nodes[node]
|
||||
if memory_items := node_data.get("memory_items", ""):
|
||||
logger.debug("节点包含完整记忆")
|
||||
# 计算记忆与关键词的相似度
|
||||
memory_words = set(jieba.cut(memory_items))
|
||||
text_words = set(keywords)
|
||||
if all_words := memory_words | text_words:
|
||||
# 计算相似度（虽然这里没有使用，但保持逻辑一致性）
|
||||
v1 = [1 if word in memory_words else 0 for word in all_words]
|
||||
v2 = [1 if word in text_words else 0 for word in all_words]
|
||||
_ = cosine_similarity(v1, v2) # 计算但不使用，用_表示
|
||||
|
||||
# 添加完整记忆到结果中
|
||||
all_memories.append((node, memory_items, activation))
|
||||
else:
|
||||
logger.info("节点没有记忆")
|
||||
|
||||
# 去重（基于记忆内容）
|
||||
logger.debug("开始记忆去重:")
|
||||
seen_memories = set()
|
||||
unique_memories = []
|
||||
for topic, memory_items, activation_value in all_memories:
|
||||
# memory_items现在是完整的字符串格式
|
||||
memory = memory_items or ""
|
||||
if memory not in seen_memories:
|
||||
seen_memories.add(memory)
|
||||
unique_memories.append((topic, memory_items, activation_value))
|
||||
logger.debug(f"保留记忆: {memory} (来自节点: {topic}, 激活值: {activation_value:.2f})")
|
||||
else:
|
||||
logger.debug(f"跳过重复记忆: {memory} (来自节点: {topic})")
|
||||
|
||||
# 转换为(关键词, 记忆)格式
|
||||
result = []
|
||||
for topic, memory_items, _ in unique_memories:
|
||||
# memory_items现在是完整的字符串格式
|
||||
memory = memory_items or ""
|
||||
result.append((topic, memory))
|
||||
logger.debug(f"选中记忆: {memory} (来自节点: {topic})")
|
||||
|
||||
return result
|
||||
|
||||
async def get_activate_from_text(
|
||||
self, text: str, max_depth: int = 3, fast_retrieval: bool = False
|
||||
) -> tuple[float, list[str], list[str]]:
|
||||
"""从文本中提取关键词并获取相关记忆。
|
||||
|
||||
Args:
|
||||
text (str): 输入文本
|
||||
max_depth (int, optional): 记忆检索深度。默认为2。
|
||||
fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
|
||||
如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。
|
||||
如果为False，使用LLM提取关键词，速度较慢但更准确。
|
||||
|
||||
Returns:
|
||||
float: 激活节点数与总节点数的比值
|
||||
list[str]: 有效的关键词
|
||||
"""
|
||||
keywords, keywords_lite = await self.get_keywords_from_text(text)
|
||||
|
||||
# 过滤掉不存在于记忆图中的关键词
|
||||
valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
|
||||
if not valid_keywords:
|
||||
# logger.info("没有找到有效的关键词节点")
|
||||
return 0, keywords, keywords_lite
|
||||
|
||||
logger.debug(f"有效的关键词: {', '.join(valid_keywords)}")
|
||||
|
||||
# 从每个关键词获取记忆
|
||||
activate_map = {} # 存储每个词的累计激活值
|
||||
|
||||
# 对每个关键词进行扩散式检索
|
||||
for keyword in valid_keywords:
|
||||
logger.debug(f"开始以关键词 '{keyword}' 为中心进行扩散检索 (最大深度: {max_depth}):")
|
||||
# 初始化激活值
|
||||
activation_values = {keyword: 1.5}
|
||||
# 记录已访问的节点
|
||||
visited_nodes = {keyword}
|
||||
# 待处理的节点队列，每个元素是(节点, 激活值, 当前深度)
|
||||
nodes_to_process = [(keyword, 1.0, 0)]
|
||||
|
||||
while nodes_to_process:
|
||||
current_node, current_activation, current_depth = nodes_to_process.pop(0)
|
||||
|
||||
# 如果激活值小于0或超过最大深度，停止扩散
|
||||
if current_activation <= 0 or current_depth >= max_depth:
|
||||
continue
|
||||
|
||||
# 获取当前节点的所有邻居
|
||||
neighbors = list(self.memory_graph.G.neighbors(current_node))
|
||||
|
||||
for neighbor in neighbors:
|
||||
if neighbor in visited_nodes:
|
||||
continue
|
||||
|
||||
# 获取连接强度
|
||||
edge_data = self.memory_graph.G[current_node][neighbor]
|
||||
strength = edge_data.get("strength", 1)
|
||||
|
||||
# 计算新的激活值
|
||||
new_activation = current_activation - (1 / strength)
|
||||
|
||||
if new_activation > 0:
|
||||
activation_values[neighbor] = new_activation
|
||||
visited_nodes.add(neighbor)
|
||||
nodes_to_process.append((neighbor, new_activation, current_depth + 1))
|
||||
# logger.debug(
|
||||
# f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})") # noqa: E501
|
||||
|
||||
# 更新激活映射
|
||||
for node, activation_value in activation_values.items():
|
||||
if activation_value > 0:
|
||||
if node in activate_map:
|
||||
activate_map[node] += activation_value
|
||||
else:
|
||||
activate_map[node] = activation_value
|
||||
|
||||
# 输出激活映射
|
||||
# logger.info("激活映射统计:")
|
||||
# for node, total_activation in sorted(activate_map.items(), key=lambda x: x[1], reverse=True):
|
||||
# logger.info(f"节点 '{node}': 累计激活值 = {total_activation:.2f}")
|
||||
|
||||
# 计算激活节点数与总节点数的比值
|
||||
total_activation = sum(activate_map.values())
|
||||
# logger.debug(f"总激活值: {total_activation:.2f}")
|
||||
total_nodes = len(self.memory_graph.G.nodes())
|
||||
# activated_nodes = len(activate_map)
|
||||
activation_ratio = total_activation / total_nodes if total_nodes > 0 else 0
|
||||
activation_ratio = activation_ratio * 50
|
||||
logger.debug(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
|
||||
|
||||
return activation_ratio, keywords, keywords_lite
|
||||
|
||||
|
||||
# 负责海马体与其他部分的交互
|
||||
class EntorhinalCortex:
|
||||
def __init__(self, hippocampus: Hippocampus):
|
||||
|
|
@ -905,11 +568,6 @@ class ParahippocampalGyrus:
|
|||
self.memory_graph = hippocampus.memory_graph
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class HippocampusManager:
|
||||
def __init__(self):
|
||||
self._hippocampus: Hippocampus = None # type: ignore
|
||||
|
|
@ -942,41 +600,6 @@ class HippocampusManager:
|
|||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||||
return self._hippocampus
|
||||
|
||||
|
||||
async def get_memory_from_topic(
|
||||
self, valid_keywords: list[str], max_memory_num: int = 3, max_memory_length: int = 2, max_depth: int = 3
|
||||
) -> list:
|
||||
"""从文本中获取相关记忆的公共接口"""
|
||||
if not self._initialized:
|
||||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||||
try:
|
||||
response = await self._hippocampus.get_memory_from_topic(
|
||||
valid_keywords, max_memory_num, max_memory_length, max_depth
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"文本激活记忆失败: {e}")
|
||||
response = []
|
||||
return response
|
||||
|
||||
async def get_activate_from_text(
|
||||
self, text: str, max_depth: int = 3, fast_retrieval: bool = False
|
||||
) -> tuple[float, list[str], list[str]]:
|
||||
"""从文本中获取激活值的公共接口"""
|
||||
if not self._initialized:
|
||||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||||
try:
|
||||
return await self._hippocampus.get_activate_from_text(text, max_depth, fast_retrieval)
|
||||
except Exception as e:
|
||||
logger.error(f"文本产生激活值失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
return 0.0, [], []
|
||||
|
||||
def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
|
||||
"""从关键词获取相关记忆的公共接口"""
|
||||
if not self._initialized:
|
||||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||||
return self._hippocampus.get_memory_from_keyword(keyword, max_depth)
|
||||
|
||||
def get_all_node_names(self) -> list:
|
||||
"""获取所有节点名称的公共接口"""
|
||||
if not self._initialized:
|
||||
|
|
|
|||
|
|
@ -21,20 +21,6 @@ def init_prompt():
|
|||
{chat_talking_prompt}
|
||||
以上是群里正在进行的聊天记录
|
||||
|
||||
{identity_block}
|
||||
你刚刚的情绪状态是：{mood_state}
|
||||
|
||||
现在，发送了消息，引起了你的注意，你对其进行了阅读和思考，请你输出一句话描述你新的情绪状态
|
||||
你的情绪特点是:{emotion_style}
|
||||
请只输出新的情绪状态，不要输出其他内容：
|
||||
""",
|
||||
"change_mood_prompt",
|
||||
)
|
||||
Prompt(
|
||||
"""
|
||||
{chat_talking_prompt}
|
||||
以上是群里正在进行的聊天记录
|
||||
|
||||
{identity_block}
|
||||
你先前的情绪状态是：{mood_state}
|
||||
你的情绪特点是:{emotion_style}
|
||||
|
|
@ -81,78 +67,6 @@ class ChatMood:
|
|||
|
||||
self.last_change_time: float = 0
|
||||
|
||||
async def update_mood_by_message(self, message: MessageRecv):
|
||||
self.regression_count = 0
|
||||
|
||||
during_last_time = message.message_info.time - self.last_change_time # type: ignore
|
||||
|
||||
base_probability = 0.05
|
||||
time_multiplier = 4 * (1 - math.exp(-0.01 * during_last_time))
|
||||
|
||||
# 基于消息长度计算基础兴趣度
|
||||
message_length = len(message.processed_plain_text or "")
|
||||
interest_multiplier = min(2.0, 1.0 + message_length / 100)
|
||||
|
||||
logger.debug(
|
||||
f"base_probability: {base_probability}, time_multiplier: {time_multiplier}, interest_multiplier: {interest_multiplier}"
|
||||
)
|
||||
update_probability = global_config.mood.mood_update_threshold * min(
|
||||
1.0, base_probability * time_multiplier * interest_multiplier
|
||||
)
|
||||
|
||||
if random.random() > update_probability:
|
||||
return
|
||||
|
||||
logger.debug(f"{self.log_prefix} 更新情绪状态，更新概率: {update_probability:.2f}")
|
||||
|
||||
message_time: float = message.message_info.time # type: ignore
|
||||
message_list_before_now = get_raw_msg_by_timestamp_with_chat_inclusive(
|
||||
chat_id=self.chat_id,
|
||||
timestamp_start=self.last_change_time,
|
||||
timestamp_end=message_time,
|
||||
limit=int(global_config.chat.max_context_size / 3),
|
||||
limit_mode="last",
|
||||
)
|
||||
|
||||
chat_talking_prompt = build_readable_messages(
|
||||
message_list_before_now,
|
||||
replace_bot_name=True,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
read_mark=0.0,
|
||||
truncate=True,
|
||||
show_actions=True,
|
||||
)
|
||||
|
||||
bot_name = global_config.bot.nickname
|
||||
if global_config.bot.alias_names:
|
||||
bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
|
||||
else:
|
||||
bot_nickname = ""
|
||||
|
||||
identity_block = f"你的名字是{bot_name}{bot_nickname}"
|
||||
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
"change_mood_prompt",
|
||||
chat_talking_prompt=chat_talking_prompt,
|
||||
identity_block=identity_block,
|
||||
mood_state=self.mood_state,
|
||||
emotion_style=global_config.personality.emotion_style,
|
||||
)
|
||||
|
||||
response, (reasoning_content, _, _) = await self.mood_model.generate_response_async(
|
||||
prompt=prompt, temperature=0.7
|
||||
)
|
||||
if global_config.debug.show_prompt:
|
||||
logger.info(f"{self.log_prefix} prompt: {prompt}")
|
||||
logger.info(f"{self.log_prefix} response: {response}")
|
||||
logger.info(f"{self.log_prefix} reasoning_content: {reasoning_content}")
|
||||
|
||||
logger.info(f"{self.log_prefix} 情绪状态更新为: {response}")
|
||||
|
||||
self.mood_state = response
|
||||
|
||||
self.last_change_time = message_time
|
||||
|
||||
async def get_mood(self) -> str:
|
||||
self.regression_count = 0
|
||||
|
||||
|
|
@ -189,7 +103,7 @@ class ChatMood:
|
|||
chat_talking_prompt=chat_talking_prompt,
|
||||
identity_block=identity_block,
|
||||
mood_state=self.mood_state,
|
||||
emotion_style=global_config.personality.emotion_style,
|
||||
emotion_style=global_config.mood.emotion_style,
|
||||
)
|
||||
|
||||
response, (reasoning_content, _, _) = await self.mood_model.generate_response_async(
|
||||
|
|
@ -240,7 +154,7 @@ class ChatMood:
|
|||
chat_talking_prompt=chat_talking_prompt,
|
||||
identity_block=identity_block,
|
||||
mood_state=self.mood_state,
|
||||
emotion_style=global_config.personality.emotion_style,
|
||||
emotion_style=global_config.mood.emotion_style,
|
||||
)
|
||||
|
||||
response, (reasoning_content, _, _) = await self.mood_model.generate_response_async(
|
||||
|
|
@ -290,7 +204,6 @@ class MoodManager:
|
|||
if self.task_started:
|
||||
return
|
||||
|
||||
logger.info("启动情绪回归任务...")
|
||||
task = MoodRegressionTask(self)
|
||||
await async_task_manager.add_task(task)
|
||||
self.task_started = True
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
[inner]
|
||||
version = "6.16.0"
|
||||
version = "6.17.0"
|
||||
|
||||
#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
|
||||
#如果你想要修改配置文件，请递增version的值
|
||||
|
|
@ -23,8 +23,7 @@ personality = "是一个女大学生，现在在读大二，会刷贴吧。"
|
|||
#アイデンティティがない 生まれないらららら
|
||||
# 描述麦麦说话的表达风格，表达习惯，如要修改，可以酌情新增内容
|
||||
reply_style = "请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景。可以参考贴吧，知乎和微博的回复风格。"
|
||||
# 情感特征，影响情绪的变化情况
|
||||
emotion_style = "情绪较为稳定，但遭遇特定事件的时候起伏较大"
|
||||
|
||||
# 麦麦的兴趣，会影响麦麦对什么话题进行回复
|
||||
interest = "对技术相关话题，游戏和动漫相关话题感兴趣，也对日常话题感兴趣，不喜欢太过沉重严肃的话题"
|
||||
|
||||
|
|
@ -90,6 +89,12 @@ enable_relationship = true # 是否启用关系系统
|
|||
[tool]
|
||||
enable_tool = true # 是否启用回复工具
|
||||
|
||||
[mood]
|
||||
enable_mood = false # 是否启用情绪系统
|
||||
mood_update_threshold = 1 # 情绪更新阈值,越高，更新越慢
|
||||
# 情感特征，影响情绪的变化情况
|
||||
emotion_style = "情绪较为稳定，但遭遇特定事件的时候起伏较大"
|
||||
|
||||
[emoji]
|
||||
emoji_chance = 0.4 # 麦麦激活表情包动作的概率
|
||||
max_reg_num = 100 # 表情包最大注册数量
|
||||
|
|
@ -172,7 +177,7 @@ file_log_level = "DEBUG" # 文件日志级别，可选: DEBUG, INFO, WARNING, ER
|
|||
|
||||
# 第三方库日志控制
|
||||
suppress_libraries = ["faiss","httpx", "urllib3", "asyncio", "websockets", "httpcore", "requests", "peewee", "openai","uvicorn","jieba"] # 完全屏蔽的库
|
||||
library_log_levels = { "aiohttp" = "WARNING"} # 设置特定库的日志级别
|
||||
library_log_levels = { aiohttp = "WARNING"} # 设置特定库的日志级别
|
||||
|
||||
[debug]
|
||||
show_prompt = false # 是否显示prompt
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ base_url = "https://api.deepseek.com/v1" # API服务商的BaseURL
|
|||
api_key = "your-api-key-here" # API密钥（请替换为实际的API密钥）
|
||||
client_type = "openai" # 请求客户端（可选，默认值为"openai"，使用gimini等Google系模型时请配置为"gemini"）
|
||||
max_retry = 2 # 最大重试次数（单个模型API调用失败，最多重试的次数）
|
||||
timeout = 30 # API请求超时时间（单位：秒）
|
||||
timeout = 120 # API请求超时时间（单位：秒）
|
||||
retry_interval = 10 # 重试间隔时间（单位：秒）
|
||||
|
||||
[[api_providers]] # 阿里 百炼 API服务商配置
|
||||
|
|
@ -18,7 +18,7 @@ base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
|||
api_key = "your-bailian-key"
|
||||
client_type = "openai"
|
||||
max_retry = 2
|
||||
timeout = 15
|
||||
timeout = 120
|
||||
retry_interval = 5
|
||||
|
||||
[[api_providers]] # 特殊：Google的Gimini使用特殊API，与OpenAI格式不兼容，需要配置client为"gemini"
|
||||
|
|
@ -27,7 +27,7 @@ base_url = "https://generativelanguage.googleapis.com/v1beta"
|
|||
api_key = "your-google-api-key-1"
|
||||
client_type = "gemini"
|
||||
max_retry = 2
|
||||
timeout = 30
|
||||
timeout = 120
|
||||
retry_interval = 10
|
||||
|
||||
[[api_providers]] # SiliconFlow的API服务商配置
|
||||
|
|
@ -36,7 +36,7 @@ base_url = "https://api.siliconflow.cn/v1"
|
|||
api_key = "your-siliconflow-api-key"
|
||||
client_type = "openai"
|
||||
max_retry = 2
|
||||
timeout = 60
|
||||
timeout = 120
|
||||
retry_interval = 10
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue