Merge branch 'dev' of https://github.com/Windpicker-owo/MaiBot into dev

README.md

@@ -46,7 +46,7 @@
 
 ## 🔥 更新和安装
 
-**最新版本: v0.10.0** ([更新日志](changelogs/changelog.md))
+**最新版本: v0.10.1** ([更新日志](changelogs/changelog.md))
 
 可前往 [Release](https://github.com/MaiM-with-u/MaiBot/releases/) 页面下载最新版本
 可前往 [启动器发布页面](https://github.com/MaiM-with-u/mailauncher/releases/)下载最新启动器
@@ -59,9 +59,8 @@
 - [🚀 最新版本部署教程](https://docs.mai-mai.org/manual/deployment/mmc_deploy_windows.html) - 基于 MaiCore 的新版本部署方式(与旧版本不兼容)
 
 > [!WARNING]
-> - 从 0.6.x 旧版本升级前请务必阅读：[升级指南](https://docs.mai-mai.org/faq/maibot/update_to_07.html)
 > - 项目处于活跃开发阶段，功能和 API 可能随时调整。
-> - 文档未完善，有问题可以提交 Issue 或者 Discussion。
+> - 有问题可以提交 Issue 或者 Discussion。
 > - QQ 机器人存在被限制风险，请自行了解，谨慎使用。
 > - 由于程序处于开发中，可能消耗较多 token。
 

bot.py

@@ -1,7 +1,13 @@
 import asyncio
 import hashlib
 import os
+import sys
+import time
+import platform
+import traceback
 from dotenv import load_dotenv
+from pathlib import Path
+from rich.traceback import install
 
 if os.path.exists(".env"):
     load_dotenv(".env", override=True)
@@ -9,24 +15,16 @@ if os.path.exists(".env"):
 else:
     print("未找到.env文件，请确保程序所需的环境变量被正确设置")
     raise FileNotFoundError(".env 文件不存在，请创建并配置所需的环境变量")
-import sys
-import time
-import platform
-import traceback
-from pathlib import Path
-from rich.traceback import install
-
-# maim_message imports for console input
 
 # 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
 from src.common.logger import initialize_logging, get_logger, shutdown_logging
+
 initialize_logging()
 
 from src.main import MainSystem  # noqa
 from src.manager.async_task_manager import async_task_manager  # noqa
 
 
-
 logger = get_logger("main")
 
 
@@ -48,21 +46,6 @@ app = None
 loop = None
 
 
-async def request_shutdown() -> bool:
-    """请求关闭程序"""
-    try:
-        if loop and not loop.is_closed():
-            try:
-                loop.run_until_complete(graceful_shutdown())
-            except Exception as ge:  # 捕捉优雅关闭时可能发生的错误
-                logger.error(f"优雅关闭时发生错误: {ge}")
-                return False
-        return True
-    except Exception as e:
-        logger.error(f"请求关闭程序时发生错误: {e}")
-        return False
-
-
 def easter_egg():
     # 彩蛋
     from colorama import init, Fore
@@ -76,15 +59,15 @@ def easter_egg():
     print(rainbow_text)
 
 
-
+async def graceful_shutdown():  # sourcery skip: use-named-expression
-async def graceful_shutdown():
     try:
         logger.info("正在优雅关闭麦麦...")
-        # 触发 ON_STOP 事件
+        
         from src.plugin_system.core.events_manager import events_manager
         from src.plugin_system.base.component_types import EventType
-        asyncio.run(events_manager.handle_mai_events(event_type=EventType.ON_STOP))     
+        # 触发 ON_STOP 事件
-        # logger.info("已触发 ON_STOP 事件")
+        _ = await events_manager.handle_mai_events(event_type=EventType.ON_STOP)
+
         # 停止所有异步任务
         await async_task_manager.stop_and_wait_all_tasks()
 

changelogs/changelog.md

@@ -1,6 +1,16 @@
 # Changelog
 
-## [0.10.0] - 2025-7-1
+## [0.10.1] - 2025-8-
+
+- 为planner添加单独控制的提示词
+- 修复激活值计算异常的BUG
+- 修复lpmm日志错误
+- 修复首句不回复的问题
+- 修复emoji管理器的一个BUG
+- 优化对模型请求的处理
+- 重构内部代码
+
+## [0.10.0] - 2025-8-18
 ### 🌟 主要功能更改
 - 优化的回复生成，现在的回复对上下文把控更加精准
 - 新的回复逻辑控制，现在合并了normal和focus模式，更加统一

scripts/log_viewer_optimized.py

@@ -110,7 +110,6 @@ class LogFormatter:
             "plugin_system": "#FF0080",
             "experimental": "#FFFFFF",
             "person_info": "#008000",
-            "individuality": "#000080",
             "manager": "#800080",
             "llm_models": "#008080",
             "plugins": "#800000",

src/chat/express/expression_learner.py

@@ -65,7 +65,6 @@ class ExpressionLearner:
         self.chat_id = chat_id
         self.chat_name = get_chat_manager().get_stream_name(chat_id) or chat_id
 
-        
         # 维护每个chat的上次学习时间
         self.last_learning_time: float = time.time()
 
@@ -73,9 +72,6 @@ class ExpressionLearner:
         self.min_messages_for_learning = 25  # 触发学习所需的最少消息数
         self.min_learning_interval = 300  # 最短学习时间间隔（秒）
 
-
-
-
     def can_learn_for_chat(self) -> bool:
         """
         检查指定聊天流是否允许学习表达
@@ -107,7 +103,9 @@ class ExpressionLearner:
 
         # 获取该聊天流的学习强度
         try:
-            _, enable_learning, learning_intensity = global_config.expression.get_expression_config_for_chat(self.chat_id)
+            _, enable_learning, learning_intensity = global_config.expression.get_expression_config_for_chat(
+                self.chat_id
+            )
         except Exception as e:
             logger.error(f"获取聊天流 {self.chat_id} 的学习配置失败: {e}")
             return False
@@ -169,33 +167,6 @@ class ExpressionLearner:
             logger.error(f"为聊天流 {self.chat_name} 触发学习失败: {e}")
             return False
 
-    # def get_expression_by_chat_id(self) -> Tuple[List[Dict[str, float]], List[Dict[str, float]]]:
-    #     """
-    #     获取指定chat_id的style表达方式（已禁用grammar的获取）
-    #     返回的每个表达方式字典中都包含了source_id, 用于后续的更新操作
-    #     """
-    #     learnt_style_expressions = []
-
-    #     # 直接从数据库查询
-    #     style_query = Expression.select().where((Expression.chat_id == self.chat_id) & (Expression.type == "style"))
-    #     for expr in style_query:
-    #         # 确保create_date存在，如果不存在则使用last_active_time
-    #         create_date = expr.create_date if expr.create_date is not None else expr.last_active_time
-    #         learnt_style_expressions.append(
-    #             {
-    #                 "situation": expr.situation,
-    #                 "style": expr.style,
-    #                 "count": expr.count,
-    #                 "last_active_time": expr.last_active_time,
-    #                 "source_id": self.chat_id,
-    #                 "type": "style",
-    #                 "create_date": create_date,
-    #             }
-    #         )
-    #     return learnt_style_expressions
-
-
-
     def _apply_global_decay_to_database(self, current_time: float) -> None:
         """
         对数据库中的所有表达方式应用全局衰减
@@ -414,6 +385,7 @@ class ExpressionLearner:
 
 init_prompt()
 
+
 class ExpressionLearnerManager:
     def __init__(self):
         self.expression_learners = {}
@@ -445,7 +417,6 @@ class ExpressionLearnerManager:
             except Exception as e:
                 logger.error(f"创建目录失败 {directory}: {e}")
 
-
     def _auto_migrate_json_to_db(self):
         """
         自动将/data/expression/learnt_style 和 learnt_grammar 下所有expressions.json迁移到数据库。

src/chat/heart_flow/heartFC_chat.py

@@ -16,8 +16,8 @@ from src.chat.utils.timer_calculator import Timer
 from src.chat.planner_actions.planner import ActionPlanner
 from src.chat.planner_actions.action_modifier import ActionModifier
 from src.chat.planner_actions.action_manager import ActionManager
-from src.chat.chat_loop.hfc_utils import CycleDetail
+from src.chat.heart_flow.hfc_utils import CycleDetail
-from src.chat.chat_loop.hfc_utils import send_typing, stop_typing
+from src.chat.heart_flow.hfc_utils import send_typing, stop_typing
 from src.chat.memory_system.Hippocampus import hippocampus_manager
 from src.chat.frequency_control.talk_frequency_control import talk_frequency_control
 from src.chat.frequency_control.focus_value_control import focus_value_control
@@ -29,6 +29,7 @@ from src.plugin_system.core import events_manager
 from src.plugin_system.apis import generator_api, send_api, message_api, database_api
 from src.mais4u.mai_think import mai_thinking_manager
 from src.mais4u.s4u_config import s4u_config
+from src.chat.utils.chat_message_builder import build_readable_messages_with_id, build_readable_actions, get_actions_by_timestamp_with_chat, get_raw_msg_before_timestamp_with_chat
 
 if TYPE_CHECKING:
     from src.common.data_models.database_data_model import DatabaseMessages
@@ -402,8 +403,8 @@ class HeartFChatting:
                     )
                 ]
             else:
-                # 第一步：动作修改
+                # 第一步：动作检查
-                with Timer("动作修改", cycle_timers):
+                with Timer("动作检查", cycle_timers):
                     try:
                         await self.action_modifier.modify_actions()
                         available_actions = self.action_manager.get_using_actions()
@@ -412,10 +413,45 @@ class HeartFChatting:
 
                 # 执行planner
                 planner_info = self.action_planner.get_necessary_info()
+                
+                
+                
+                
+                
+                message_list_before_now = get_raw_msg_before_timestamp_with_chat(
+                    chat_id=self.stream_id,
+                    timestamp=time.time(),
+                    limit=int(global_config.chat.max_context_size * 0.6),
+                )
+                chat_content_block, message_id_list = build_readable_messages_with_id(
+                    messages=message_list_before_now,
+                    timestamp_mode="normal_no_YMD",
+                    read_mark=self.action_planner.last_obs_time_mark,
+                    truncate=True,
+                    show_actions=True,
+                )
+
+                actions_before_now = get_actions_by_timestamp_with_chat(
+                    chat_id=self.stream_id,
+                    timestamp_start=time.time() - 600,
+                    timestamp_end=time.time(),
+                    limit=5,
+                )
+
+                actions_before_now_block = build_readable_actions(
+                    actions=actions_before_now,
+                )
+                                
+                
+                
+                
                 prompt_info = await self.action_planner.build_planner_prompt(
                     is_group_chat=planner_info[0],
                     chat_target_info=planner_info[1],
                     current_available_actions=planner_info[2],
+                    chat_content_block=chat_content_block,
+                    actions_before_now_block=actions_before_now_block,
+                    message_id_list=message_id_list,
                 )
                 if not await events_manager.handle_mai_events(
                     EventType.ON_PLAN, None, prompt_info[0], None, self.chat_stream.stream_id
@@ -428,6 +464,9 @@ class HeartFChatting:
                         available_actions=available_actions,
                     )
                     
+                    for action in action_to_use_info:
+                        print(action.action_type)
+
             # 3. 并行执行所有动作
             action_tasks = [
                 asyncio.create_task(
@@ -679,7 +718,7 @@ class HeartFChatting:
                 }
             else:
                 try:
-                    success, response_set, prompt, selected_expressions = await generator_api.generate_reply(
+                    success, llm_response = await generator_api.generate_reply(
                         chat_stream=self.chat_stream,
                         reply_message=action_planner_info.action_message,
                         available_actions=available_actions,
@@ -688,10 +727,9 @@ class HeartFChatting:
                         enable_tool=global_config.tool.enable_tool,
                         request_type="replyer",
                         from_plugin=False,
-                        return_expressions=True,
                     )
 
-                    if not success or not response_set:
+                    if not success or not llm_response or not llm_response.reply_set:
                         if action_planner_info.action_message:
                             logger.info(f"对 {action_planner_info.action_message.processed_plain_text} 的回复生成失败")
                         else:
@@ -701,7 +739,8 @@ class HeartFChatting:
                 except asyncio.CancelledError:
                     logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消")
                     return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
-
+                response_set = llm_response.reply_set
+                selected_expressions = llm_response.selected_expressions
                 loop_info, reply_text, _ = await self._send_and_store_reply(
                     response_set=response_set,
                     action_message=action_planner_info.action_message,  # type: ignore

src/chat/heart_flow/heartflow.py

@@ -2,36 +2,29 @@ import traceback
 from typing import Any, Optional, Dict
 
 from src.common.logger import get_logger
-from src.chat.heart_flow.sub_heartflow import SubHeartflow
+from src.chat.heart_flow.heartFC_chat import HeartFChatting
-
 logger = get_logger("heartflow")
 
-
 class Heartflow:
     """主心流协调器，负责初始化并协调聊天"""
 
     def __init__(self):
-        self.subheartflows: Dict[Any, "SubHeartflow"] = {}
+        self.heartflow_chat_list: Dict[Any, HeartFChatting] = {}
-
-    async def get_or_create_subheartflow(self, subheartflow_id: Any) -> Optional["SubHeartflow"]:
-        """获取或创建一个新的SubHeartflow实例"""
-        if subheartflow_id in self.subheartflows:
-            if subflow := self.subheartflows.get(subheartflow_id):
-                return subflow
         
+    async def get_or_create_heartflow_chat(self, chat_id: Any) -> Optional[HeartFChatting]:
+        """获取或创建一个新的HeartFChatting实例"""
         try:
-            new_subflow = SubHeartflow(subheartflow_id)
+            if chat_id in self.heartflow_chat_list:
-
+                if chat := self.heartflow_chat_list.get(chat_id):
-            await new_subflow.initialize()
+                    return chat
-
+            else:
-            # 注册子心流
+                new_chat = HeartFChatting(chat_id = chat_id)
-            self.subheartflows[subheartflow_id] = new_subflow
+                await new_chat.start()
-
+                self.heartflow_chat_list[chat_id] = new_chat
-            return new_subflow
+                return new_chat
         except Exception as e:
-            logger.error(f"创建子心流 {subheartflow_id} 失败: {e}", exc_info=True)
+            logger.error(f"创建心流聊天 {chat_id} 失败: {e}", exc_info=True)
             traceback.print_exc()
             return None
 
-
 heartflow = Heartflow()

src/chat/heart_flow/heartflow_message_processor.py

@@ -18,7 +18,7 @@ from src.mood.mood_manager import mood_manager
 from src.person_info.person_info import Person
 
 if TYPE_CHECKING:
-    from src.chat.heart_flow.sub_heartflow import SubHeartflow
+    from src.chat.heart_flow.heartFC_chat import HeartFChatting
 
 logger = get_logger("chat")
 
@@ -38,7 +38,7 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool, list[s
         interested_rate, keywords,keywords_lite = await hippocampus_manager.get_activate_from_text(
             message.processed_plain_text,
             max_depth= 4,
-            fast_retrieval=False,
+            fast_retrieval=global_config.chat.interest_rate_mode == "fast",
         )
         message.key_words = keywords
         message.key_words_lite = keywords_lite
@@ -78,10 +78,14 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool, list[s
     interested_rate += base_interest
 
     if is_mentioned:
-        interest_increase_on_mention = 1
+        interest_increase_on_mention = 2
         interested_rate += interest_increase_on_mention
         
-    return interested_rate, is_mentioned, keywords
+        
+    message.interest_value = interested_rate
+    message.is_mentioned = is_mentioned
+
+    return interested_rate, keywords
 
 
 class HeartFCMessageReceiver:
@@ -110,17 +114,16 @@ class HeartFCMessageReceiver:
             chat = message.chat_stream
 
             # 2. 兴趣度计算与更新
-            interested_rate, is_mentioned, keywords = await _calculate_interest(message)
+            interested_rate, keywords = await _calculate_interest(message)
-            message.interest_value = interested_rate
+            
-            message.is_mentioned = is_mentioned
 
             await self.storage.store_message(message, chat)
 
-            subheartflow: SubHeartflow = await heartflow.get_or_create_subheartflow(chat.stream_id)  # type: ignore
+            heartflow_chat: HeartFChatting = await heartflow.get_or_create_heartflow_chat(chat.stream_id)  # type: ignore
 
             # subheartflow.add_message_to_normal_chat_cache(message, interested_rate, is_mentioned)
             if global_config.mood.enable_mood:  
-                chat_mood = mood_manager.get_mood_by_chat_id(subheartflow.chat_id)
+                chat_mood = mood_manager.get_mood_by_chat_id(heartflow_chat.stream_id)
                 asyncio.create_task(chat_mood.update_mood_by_message(message, interested_rate))
 
             # 3. 日志记录

src/chat/heart_flow/sub_heartflow.py

@@ -1,41 +0,0 @@
-from rich.traceback import install
-
-from src.common.logger import get_logger
-from src.chat.message_receive.chat_stream import get_chat_manager
-from src.chat.chat_loop.heartFC_chat import HeartFChatting
-from src.chat.utils.utils import get_chat_type_and_target_info
-
-logger = get_logger("sub_heartflow")
-
-install(extra_lines=3)
-
-
-class SubHeartflow:
-    def __init__(
-        self,
-        subheartflow_id,
-    ):
-        """子心流初始化函数
-
-        Args:
-            subheartflow_id: 子心流唯一标识符
-        """
-        # 基础属性，两个值是一样的
-        self.subheartflow_id = subheartflow_id
-        self.chat_id = subheartflow_id
-
-        self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_id)
-        self.log_prefix = get_chat_manager().get_stream_name(self.subheartflow_id) or self.subheartflow_id
-
-        # focus模式退出冷却时间管理
-        self.last_focus_exit_time: float = 0  # 上次退出focus模式的时间
-
-        # 随便水群 normal_chat 和 认真水群 focus_chat 实例
-        # CHAT模式激活 随便水群  FOCUS模式激活 认真水群
-        self.heart_fc_instance: HeartFChatting = HeartFChatting(
-            chat_id=self.subheartflow_id,
-        )  # 该sub_heartflow的HeartFChatting实例
-
-    async def initialize(self):
-        """异步初始化方法，创建兴趣流并确定聊天类型"""
-        await self.heart_fc_instance.start()

src/chat/planner_actions/action_modifier.py

@@ -2,7 +2,7 @@ import random
 import asyncio
 import hashlib
 import time
-from typing import List, Any, Dict, TYPE_CHECKING, Tuple
+from typing import List, Dict, TYPE_CHECKING, Tuple
 
 from src.common.logger import get_logger
 from src.config.config import global_config, model_config
@@ -60,7 +60,7 @@ class ActionModifier:
 
         removals_s1: List[Tuple[str, str]] = []
         removals_s2: List[Tuple[str, str]] = []
-        removals_s3: List[Tuple[str, str]] = []
+        # removals_s3: List[Tuple[str, str]] = []
 
         self.action_manager.restore_actions()
         all_actions = self.action_manager.get_using_actions()
@@ -103,26 +103,28 @@ class ActionModifier:
             self.action_manager.remove_action_from_using(action_name)
             logger.debug(f"{self.log_prefix}阶段二移除动作: {action_name}，原因: {reason}")
 
+
+
         # === 第三阶段：激活类型判定 ===
-        if chat_content is not None:
+        # if chat_content is not None:
-            logger.debug(f"{self.log_prefix}开始激活类型判定阶段")
+            # logger.debug(f"{self.log_prefix}开始激活类型判定阶段")
 
             # 获取当前使用的动作集（经过第一阶段处理）
-            current_using_actions = self.action_manager.get_using_actions()
+            # current_using_actions = self.action_manager.get_using_actions()
 
             # 获取因激活类型判定而需要移除的动作
-            removals_s3 = await self._get_deactivated_actions_by_type(
+            # removals_s3 = await self._get_deactivated_actions_by_type(
-                current_using_actions,
+                # current_using_actions,
-                chat_content,
+                # chat_content,
-            )
+            # )
 
             # 应用第三阶段的移除
-            for action_name, reason in removals_s3:
+            # for action_name, reason in removals_s3:
-                self.action_manager.remove_action_from_using(action_name)
+                # self.action_manager.remove_action_from_using(action_name)
-                logger.debug(f"{self.log_prefix}阶段三移除动作: {action_name}，原因: {reason}")
+                # logger.debug(f"{self.log_prefix}阶段三移除动作: {action_name}，原因: {reason}")
 
         # === 统一日志记录 ===
-        all_removals = removals_s1 + removals_s2 + removals_s3
+        all_removals = removals_s1 + removals_s2
         removals_summary: str = ""
         if all_removals:
             removals_summary = " | ".join([f"{name}({reason})" for name, reason in all_removals])
@@ -161,7 +163,7 @@ class ActionModifier:
         deactivated_actions = []
 
         # 分类处理不同激活类型的actions
-        llm_judge_actions = {}
+        llm_judge_actions: Dict[str, ActionInfo] = {}
 
         actions_to_check = list(actions_with_info.items())
         random.shuffle(actions_to_check)
@@ -218,7 +220,7 @@ class ActionModifier:
 
     async def _process_llm_judge_actions_parallel(
         self,
-        llm_judge_actions: Dict[str, Any],
+        llm_judge_actions: Dict[str, ActionInfo],
         chat_content: str = "",
     ) -> Dict[str, bool]:
         """
@@ -237,7 +239,7 @@ class ActionModifier:
         current_time = time.time()
 
         results = {}
-        tasks_to_run = {}
+        tasks_to_run: Dict[str, ActionInfo] = {}
 
         # 检查缓存
         for action_name, action_info in llm_judge_actions.items():

src/chat/planner_actions/planner.py

@@ -1,7 +1,7 @@
 import json
 import time
 import traceback
-from typing import Dict, Optional, Tuple, List
+from typing import Dict, Optional, Tuple, List, Any
 from rich.traceback import install
 from datetime import datetime
 from json_repair import repair_json
@@ -21,8 +21,9 @@ from src.chat.utils.chat_message_builder import (
 from src.chat.utils.utils import get_chat_type_and_target_info
 from src.chat.planner_actions.action_manager import ActionManager
 from src.chat.message_receive.chat_stream import get_chat_manager
-from src.plugin_system.base.component_types import ActionInfo, ChatMode, ComponentType
+from src.plugin_system.base.component_types import ActionInfo, ChatMode, ComponentType, ActionActivationType
 from src.plugin_system.core.component_registry import component_registry
+import random
 
 logger = get_logger("planner")
 
@@ -33,7 +34,7 @@ def init_prompt():
     Prompt(
         """
 {time_block}
-{identity_block}
+{name_block}
 你现在需要根据聊天内容，选择的合适的action来参与聊天。
 请你根据以下行事风格来决定action:
 {plan_style}
@@ -84,6 +85,36 @@ def init_prompt():
     )
     
     
+    Prompt(
+    """
+{time_block}
+{name_block}
+请你根据聊天内容，选择一个或多个action来参与聊天。如果没有合适的action，请选择no_action。
+
+{chat_context_description}，以下是具体的聊天内容
+{chat_content_block}
+
+{moderation_prompt}
+
+现在请你根据聊天内容和用户的最新消息选择合适的action和触发action的消息:
+{actions_before_now_block}
+
+no_action：不选择任何动作
+{{
+    "action": "no_action",
+    "reason":"不动作的原因"
+}}
+
+{action_options_text}
+
+请选择，并说明触发action的消息id和选择该action的原因。消息id格式:m+数字
+
+请根据动作示例，以严格的 JSON 格式输出，且仅包含 JSON 内容：
+""",
+    "sub_planner_prompt",
+    )
+
+
 class ActionPlanner:
     def __init__(self, chat_id: str, action_manager: ActionManager):
         self.chat_id = chat_id
@@ -93,6 +124,9 @@ class ActionPlanner:
         self.planner_llm = LLMRequest(
             model_set=model_config.model_task_config.planner, request_type="planner"
         )  # 用于动作规划
+        self.planner_small_llm = LLMRequest(
+            model_set=model_config.model_task_config.planner_small, request_type="planner_small"
+        )  # 用于动作规划
 
         self.last_obs_time_mark = 0.0
         # 添加重试计数器
@@ -100,7 +134,7 @@ class ActionPlanner:
         self.max_plan_retries = 3
 
     def find_message_by_id(
-        self, message_id: str, message_id_list: List[DatabaseMessages]
+        self, message_id: str, message_id_list: List[Tuple[str, DatabaseMessages]]
     ) -> Optional[DatabaseMessages]:
         # sourcery skip: use-next
         """
@@ -114,8 +148,8 @@ class ActionPlanner:
             找到的原始消息字典，如果未找到则返回None
         """
         for item in message_id_list:
-            if item.message_id == message_id:
+            if item[0] == message_id:
-                return item
+                return item[1]
         return None
 
     def get_latest_message(self, message_id_list: List[DatabaseMessages]) -> Optional[DatabaseMessages]:
@@ -130,6 +164,274 @@ class ActionPlanner:
         """
         return message_id_list[-1] if message_id_list else None
     
+    def _parse_single_action(self, action_json: dict, message_id_list: List[Tuple[str, DatabaseMessages]], current_available_actions: List[Tuple[str, ActionInfo]]) -> List[ActionPlannerInfo]:
+        """解析单个action JSON并返回ActionPlannerInfo列表"""
+        action_planner_infos = []
+        
+        try:
+            action = action_json.get("action", "no_action")
+            reasoning = action_json.get("reason", "未提供原因")
+            action_data = {}
+            
+            # 将所有其他属性添加到action_data
+            for key, value in action_json.items():
+                if key not in ["action", "reasoning"]:
+                    action_data[key] = value
+            
+            # 非no_action动作需要target_message_id
+            target_message = None
+            if action != "no_action":
+                if target_message_id := action_json.get("target_message_id"):
+                    # 根据target_message_id查找原始消息
+                    target_message = self.find_message_by_id(target_message_id, message_id_list)
+                    if target_message is None:
+                        logger.warning(f"{self.log_prefix}无法找到target_message_id '{target_message_id}' 对应的消息")
+                        # 选择最新消息作为target_message
+                        target_message = self.get_latest_message(message_id_list)
+                else:
+                    logger.warning(f"{self.log_prefix}动作'{action}'缺少target_message_id")
+            
+            # 验证action是否可用
+            available_action_names = [action_name for action_name, _ in current_available_actions]
+            if action != "no_action" and action != "reply" and action not in available_action_names:
+                logger.warning(
+                    f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (可用: {available_action_names})，将强制使用 'no_action'"
+                )
+                reasoning = f"LLM 返回了当前不可用的动作 '{action}' (可用: {available_action_names})。原始理由: {reasoning}"
+                action = "no_action"
+            
+            # 创建ActionPlannerInfo对象
+            # 将列表转换为字典格式
+            available_actions_dict = dict(current_available_actions)
+            action_planner_infos.append(ActionPlannerInfo(
+                action_type=action,
+                reasoning=reasoning,
+                action_data=action_data,
+                action_message=target_message,
+                available_actions=available_actions_dict,
+            ))
+            
+        except Exception as e:
+            logger.error(f"{self.log_prefix}解析单个action时出错: {e}")
+            # 将列表转换为字典格式
+            available_actions_dict = dict(current_available_actions)
+            action_planner_infos.append(ActionPlannerInfo(
+                action_type="no_action",
+                reasoning=f"解析单个action时出错: {e}",
+                action_data={},
+                action_message=None,
+                available_actions=available_actions_dict,
+            ))
+        
+        return action_planner_infos
+    
+    async def sub_plan(
+        self,
+        action_list: List[Tuple[str, ActionInfo]],
+        actions_before_now: List[Dict[str, Any]],
+        chat_content_block: str,
+        message_id_list: List[Tuple[str, DatabaseMessages]],
+        is_group_chat: bool = False,
+        chat_target_info: Optional[dict] = None,
+        # current_available_actions: Dict[str, ActionInfo] = {},
+    ) -> List[ActionPlannerInfo]:
+        # 构建副planner并执行(单个副planner)
+        try:
+            # 获取最近的actions
+            # 只保留action_type在action_list中的ActionPlannerInfo
+            action_names_in_list = [name for name, _ in action_list]
+            # actions_before_now是List[Dict[str, Any]]格式，需要提取action_type字段
+            filtered_actions = []
+            # print(actions_before_now)
+            # print(action_names_in_list)
+            for action_record in actions_before_now:
+                if isinstance(action_record, dict) and 'action_name' in action_record:
+                    action_type = action_record['action_name']
+                    if action_type in action_names_in_list:
+                        filtered_actions.append(action_record)
+            
+
+
+            actions_before_now_block = build_readable_actions(
+                actions=filtered_actions,
+            )
+            
+            
+            if actions_before_now_block:
+                actions_before_now_block = f"你刚刚选择并执行过的action是，请注意如果相同的内容已经被执行，请不要重复执行：\n{actions_before_now_block}"
+            else:
+                actions_before_now_block = ""
+
+            chat_context_description = "你现在正在一个群聊中"
+            chat_target_name = None
+            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"
+
+            action_options_block = ""
+
+            for using_actions_name, using_actions_info in action_list:
+                if using_actions_info.action_parameters:
+                    param_text = "\n"
+                    for param_name, param_description in using_actions_info.action_parameters.items():
+                        param_text += f'    "{param_name}":"{param_description}"\n'
+                    param_text = param_text.rstrip("\n")
+                else:
+                    param_text = ""
+
+                require_text = ""
+                for require_item in using_actions_info.action_require:
+                    require_text += f"- {require_item}\n"
+                require_text = require_text.rstrip("\n")
+
+                using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt")
+                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
+
+            moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
+            time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
+            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 = ""
+            name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
+
+            planner_prompt_template = await global_prompt_manager.get_prompt_async("sub_planner_prompt")
+            prompt = planner_prompt_template.format(
+                time_block=time_block,
+                chat_context_description=chat_context_description,
+                chat_content_block=chat_content_block,
+                actions_before_now_block=actions_before_now_block,
+                action_options_text=action_options_block,
+                moderation_prompt=moderation_prompt_block,
+                name_block=name_block,
+            )
+            # return prompt, message_id_list
+        except Exception as e:
+            logger.error(f"构建 Planner 提示词时出错: {e}")
+            logger.error(traceback.format_exc())
+            # 返回一个默认的no_action而不是字符串
+            return [ActionPlannerInfo(
+                action_type="no_action",
+                reasoning=f"构建 Planner Prompt 时出错: {e}",
+                action_data={},
+                action_message=None,
+                available_actions=action_list,
+            )]
+
+        # --- 调用 LLM (普通文本生成) ---
+        llm_content = None
+        action_planner_infos = []  # 存储多个ActionPlannerInfo对象
+        
+        try:
+            llm_content, (reasoning_content, _, _) = await self.planner_small_llm.generate_response_async(prompt=prompt)
+
+            if global_config.debug.show_prompt:
+                logger.info(f"{self.log_prefix}副规划器原始提示词: {prompt}")
+                logger.info(f"{self.log_prefix}副规划器原始响应: {llm_content}")
+                if reasoning_content:
+                    logger.info(f"{self.log_prefix}副规划器推理: {reasoning_content}")
+            else:
+                logger.debug(f"{self.log_prefix}副规划器原始提示词: {prompt}")
+                logger.debug(f"{self.log_prefix}副规划器原始响应: {llm_content}")
+                if reasoning_content:
+                    logger.debug(f"{self.log_prefix}副规划器推理: {reasoning_content}")
+
+        except Exception as req_e:
+            logger.error(f"{self.log_prefix}副规划器LLM 请求执行失败: {req_e}")
+            # 返回一个默认的no_action
+            action_planner_infos.append(ActionPlannerInfo(
+                action_type="no_action",
+                reasoning=f"副规划器LLM 请求失败，模型出现问题: {req_e}",
+                action_data={},
+                action_message=None,
+                available_actions=action_list,
+            ))
+            return action_planner_infos
+
+        if llm_content:
+            try:
+                parsed_json = json.loads(repair_json(llm_content))
+                
+                # 处理不同的JSON格式
+                if isinstance(parsed_json, list):
+                    # 如果是列表，处理每个action
+                    if parsed_json:
+                        logger.info(f"{self.log_prefix}LLM返回了{len(parsed_json)}个action")
+                        for action_item in parsed_json:
+                            if isinstance(action_item, dict):
+                                action_planner_infos.extend(self._parse_single_action(
+                                    action_item, message_id_list, action_list
+                                ))
+                            else:
+                                logger.warning(f"{self.log_prefix}列表中的action项不是字典类型: {type(action_item)}")
+                    else:
+                        logger.warning(f"{self.log_prefix}LLM返回了空列表")
+                        action_planner_infos.append(ActionPlannerInfo(
+                            action_type="no_action",
+                            reasoning="LLM返回了空列表，选择no_action",
+                            action_data={},
+                            action_message=None,
+                            available_actions=action_list,
+                        ))
+                elif isinstance(parsed_json, dict):
+                    # 如果是单个字典，处理单个action
+                    action_planner_infos.extend(self._parse_single_action(
+                        parsed_json, message_id_list, action_list
+                    ))
+                else:
+                    logger.error(f"{self.log_prefix}解析后的JSON不是字典或列表类型: {type(parsed_json)}")
+                    action_planner_infos.append(ActionPlannerInfo(
+                        action_type="no_action",
+                        reasoning=f"解析后的JSON类型错误: {type(parsed_json)}",
+                        action_data={},
+                        action_message=None,
+                        available_actions=action_list,
+                    ))
+
+            except Exception as json_e:
+                logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
+                traceback.print_exc()
+                action_planner_infos.append(ActionPlannerInfo(
+                    action_type="no_action",
+                    reasoning=f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_action'.",
+                    action_data={},
+                    action_message=None,
+                    available_actions=action_list,
+                ))
+        else:
+            # 如果没有LLM内容，返回默认的no_action
+            action_planner_infos.append(ActionPlannerInfo(
+                action_type="no_action",
+                reasoning="副规划器没有获得LLM响应",
+                action_data={},
+                action_message=None,
+                available_actions=action_list,
+            ))
+        
+        # 如果没有解析到任何action，返回默认的no_action
+        if not action_planner_infos:
+            action_planner_infos.append(ActionPlannerInfo(
+                action_type="no_action",
+                reasoning="副规划器没有解析到任何有效action",
+                action_data={},
+                action_message=None,
+                available_actions=action_list,
+            ))
+        
+        logger.info(f"{self.log_prefix}副规划器返回了{len(action_planner_infos)}个action")
+        return action_planner_infos
+
+
     async def plan(
         self,
         mode: ChatMode = ChatMode.FOCUS,
@@ -148,16 +450,143 @@ class ActionPlanner:
         prompt: str = ""
         message_id_list: list = []
         
+        message_list_before_now = get_raw_msg_before_timestamp_with_chat(
+            chat_id=self.chat_id,
+            timestamp=time.time(),
+            limit=int(global_config.chat.max_context_size * 0.6),
+        )
+        chat_content_block, message_id_list = build_readable_messages_with_id(
+            messages=message_list_before_now,
+            timestamp_mode="normal_no_YMD",
+            read_mark=self.last_obs_time_mark,
+            truncate=True,
+            show_actions=True,
+        )
+
+        actions_before_now = get_actions_by_timestamp_with_chat(
+            chat_id=self.chat_id,
+            timestamp_start=time.time() - 600,
+            timestamp_end=time.time(),
+            limit=5,
+        )
+
+        actions_before_now_block = build_readable_actions(
+            actions=actions_before_now,
+        )
+    
+        
+        message_list_before_now_short = message_list_before_now[:5]
+        
+        chat_content_block_short, message_id_list_short = build_readable_messages_with_id(
+            messages=message_list_before_now_short,
+            timestamp_mode="normal_no_YMD",
+            truncate=False,
+            show_actions=False,
+        )
+        
+        self.last_obs_time_mark = time.time()
+
         try:
+            logger.info(f"{self.log_prefix}开始构建副Planner")
+            sub_planner_actions = {}
+            
+            for action_name, action_info in available_actions.items():
+                if action_info.activation_type == ActionActivationType.LLM_JUDGE or action_info.activation_type == ActionActivationType.ALWAYS:
+                    sub_planner_actions[action_name] = action_info
+                elif action_info.activation_type == ActionActivationType.RANDOM:
+                    if random.random() < action_info.random_activation_probability:
+                        sub_planner_actions[action_name] = action_info
+                elif action_info.activation_type == ActionActivationType.KEYWORD:
+                    if action_info.activation_keywords:
+                        for keyword in action_info.activation_keywords:
+                            if keyword in chat_content_block_short:
+                                sub_planner_actions[action_name] = action_info
+                elif action_info.activation_type == ActionActivationType.NEVER:
+                    pass
+                else:
+                    logger.warning(f"{self.log_prefix}未知的激活类型: {action_info.activation_type}，跳过处理")
+                    
+            sub_planner_actions_num = len(sub_planner_actions)
+            sub_planner_size = global_config.chat.planner_size
+            if global_config.chat.planner_size > int(global_config.chat.planner_size):
+                if random.random() < global_config.chat.planner_size - int(global_config.chat.planner_size):
+                    sub_planner_size = int(global_config.chat.planner_size) + 1
+            sub_planner_num = int(sub_planner_actions_num / sub_planner_size)
+            if sub_planner_actions_num % sub_planner_size != 0:
+                sub_planner_num += 1
+                
+            logger.info(f"{self.log_prefix}副规划器数量: {sub_planner_num}, 副规划器大小: {sub_planner_size}")
+            
+            # 将sub_planner_actions随机分配到sub_planner_num个List中
+            sub_planner_lists = []
+            if sub_planner_actions_num > 0:
+                # 将actions转换为列表并随机打乱
+                action_items = list(sub_planner_actions.items())
+                random.shuffle(action_items)
+                
+                # 初始化所有子列表
+                for i in range(sub_planner_num):
+                    sub_planner_lists.append([])
+                
+                # 分配actions到各个子列表
+                for i, (action_name, action_info) in enumerate(action_items):
+                    # 确保每个列表至少有一个action
+                    if i < sub_planner_num:
+                        sub_planner_lists[i].append((action_name, action_info))
+                    else:
+                        # 随机选择一个列表添加action，但不超过最大大小限制
+                        available_lists = [j for j, lst in enumerate(sub_planner_lists) 
+                                         if len(lst) < sub_planner_size]
+                        if available_lists:
+                            target_list = random.choice(available_lists)
+                            sub_planner_lists[target_list].append((action_name, action_info))
+                
+                logger.info(f"{self.log_prefix}成功将{len(sub_planner_actions)}个actions分配到{sub_planner_num}个子列表中")
+                for i, lst in enumerate(sub_planner_lists):
+                    logger.debug(f"{self.log_prefix}子列表{i+1}: {len(lst)}个actions")
+            else:
+                logger.info(f"{self.log_prefix}没有可用的actions需要分配")
+                
+                
+            # 先获取必要信息
             is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()
             
+            # 并行执行所有副规划器
+            import asyncio
+            
+            async def execute_sub_plan(action_list):
+                return await self.sub_plan(
+                    action_list=action_list,
+                    actions_before_now=actions_before_now,
+                    chat_content_block=chat_content_block_short,
+                    message_id_list=message_id_list_short,
+                    is_group_chat=is_group_chat,
+                    chat_target_info=chat_target_info,
+                    # current_available_actions=current_available_actions,
+                )
+            
+            # 创建所有任务
+            sub_plan_tasks = [execute_sub_plan(action_list) for action_list in sub_planner_lists]
+            
+            # 并行执行所有任务
+            sub_plan_results = await asyncio.gather(*sub_plan_tasks)
+            
+            # 收集所有结果
+            all_sub_planner_results = []
+            for sub_result in sub_plan_results:
+                all_sub_planner_results.extend(sub_result)
+            
+            logger.info(f"{self.log_prefix}所有副规划器共返回了{len(all_sub_planner_results)}个action")
+
             # --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
             prompt, message_id_list = await self.build_planner_prompt(
                 is_group_chat=is_group_chat,  # <-- Pass HFC state
                 chat_target_info=chat_target_info,  # <-- 传递获取到的聊天目标信息
-                current_available_actions=current_available_actions,  # <-- Pass determined actions
+                current_available_actions="",  # <-- Pass determined actions
                 mode=mode,
-                refresh_time=True,
+                chat_content_block=chat_content_block,
+                actions_before_now_block=actions_before_now_block,
+                message_id_list=message_id_list,
             )
 
             # --- 调用 LLM (普通文本生成) ---
@@ -185,60 +614,54 @@ class ActionPlanner:
                 try:
                     parsed_json = json.loads(repair_json(llm_content))
 
+                    # 处理不同的JSON格式，复用_parse_single_action函数
                     if isinstance(parsed_json, list):
                         if parsed_json:
+                            # 使用最后一个action（保持原有逻辑）
                             parsed_json = parsed_json[-1]
                             logger.warning(f"{self.log_prefix}LLM返回了多个JSON对象，使用最后一个: {parsed_json}")
                         else:
                             parsed_json = {}
 
-                    if not isinstance(parsed_json, dict):
+                    if isinstance(parsed_json, dict):
-                        logger.error(f"{self.log_prefix}解析后的JSON不是字典类型: {type(parsed_json)}")
+                        # 使用_parse_single_action函数解析单个action
-                        parsed_json = {}
+                        # 将字典转换为列表格式
-
+                        current_available_actions_list = list(current_available_actions.items())
-                    action = parsed_json.get("action", "no_action")
+                        action_planner_infos = self._parse_single_action(
-                    reasoning = parsed_json.get("reason", "未提供原因")
+                            parsed_json, message_id_list, current_available_actions_list
-
-                    # 将所有其他属性添加到action_data
-                    for key, value in parsed_json.items():
-                        if key not in ["action", "reasoning"]:
-                            action_data[key] = value
-
-                    # 非no_action动作需要target_message_id
-                    if action != "no_action":
-                        if target_message_id := parsed_json.get("target_message_id"):
-                            # 根据target_message_id查找原始消息
-                            target_message = self.find_message_by_id(target_message_id, message_id_list)
-                            # 如果获取的target_message为None，输出warning并重新plan
-                            if target_message is None:
-                                self.plan_retry_count += 1
-                                logger.warning(
-                                    f"{self.log_prefix}无法找到target_message_id '{target_message_id}' 对应的消息，重试次数: {self.plan_retry_count}/{self.max_plan_retries}"
-                                )
-                                # 仍有重试次数
-                                if self.plan_retry_count < self.max_plan_retries:
-                                    # 递归重新plan
-                                    return await self.plan(mode, loop_start_time, available_actions)
-                                logger.error(
-                                    f"{self.log_prefix}连续{self.max_plan_retries}次plan获取target_message失败，选择最新消息作为target_message"
                         )
+                        
+                        if action_planner_infos:
+                            # 获取第一个（也是唯一一个）action的信息
+                            action_info = action_planner_infos[0]
+                            action = action_info.action_type
+                            reasoning = action_info.reasoning
+                            action_data.update(action_info.action_data)
+                            target_message = action_info.action_message
+                            
+                            # 处理target_message为None的情况（保持原有的重试逻辑）
+                            if target_message is None and action != "no_action":
+                                # 尝试获取最新消息作为target_message
                                 target_message = self.get_latest_message(message_id_list)
-                            self.plan_retry_count = 0  # 重置计数器
+                                if target_message is None:
+                                    logger.warning(f"{self.log_prefix}无法获取任何消息作为target_message")
                         else:
-                            logger.warning(f"{self.log_prefix}动作'{action}'缺少target_message_id")
+                            # 如果没有解析到action，使用默认值
-
-                    if action != "no_action" and action != "reply" and 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: '{action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_action'"
-                        )
-                        reasoning = f"LLM 返回了当前不可用的动作 '{action}' (可用: {list(current_available_actions.keys())})。原始理由: {reasoning}"
                             action = "no_action"
+                            reasoning = "解析action失败"
+                            target_message = None
+                    else:
+                        logger.error(f"{self.log_prefix}解析后的JSON不是字典类型: {type(parsed_json)}")
+                        action = "no_action"
+                        reasoning = f"解析后的JSON类型错误: {type(parsed_json)}"
+                        target_message = None
 
                 except Exception as json_e:
                     logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
                     traceback.print_exc()
-                    reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_action'."
                     action = "no_action"
+                    reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_action'."
+                    target_message = None
 
         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_action: {outer_e}")
@@ -246,30 +669,70 @@ class ActionPlanner:
             action = "no_action"
             reasoning = f"Planner 内部处理错误: {outer_e}"
 
-        is_parallel = False
+        is_parallel = True
         if mode == ChatMode.NORMAL and action in current_available_actions:
+            if is_parallel:
                 is_parallel = current_available_actions[action].parallel_action
 
         action_data["loop_start_time"] = loop_start_time
 
-        actions = [
+        # 过滤掉no_action，除非所有结果都是no_action
-            ActionPlannerInfo(
+        def filter_no_actions(action_list):
+            """过滤no_action，如果所有都是no_action则返回一个"""
+            non_no_actions = [a for a in action_list if a.action_type != "no_action"]
+            if non_no_actions:
+                return non_no_actions
+            else:
+                # 如果所有都是no_action，返回第一个
+                return [action_list[0]] if action_list else []
+        
+        # 根据is_parallel决定返回值
+        if is_parallel:
+            # 如果为真，将主规划器的结果和副规划器的结果都返回
+            main_actions = []
+            
+            # 添加主规划器的action（如果不是no_action）
+            if action != "no_action":
+                main_actions.append(ActionPlannerInfo(
                     action_type=action,
                     reasoning=reasoning,
                     action_data=action_data,
                     action_message=target_message,
                     available_actions=available_actions,
-            )
+                ))
-        ]
             
-        if action != "reply" and is_parallel:
+            # 先合并主副规划器的结果
-            actions.append(
+            all_actions = main_actions + all_sub_planner_results
-                ActionPlannerInfo(
+            
-                    action_type="reply",
+            # 然后统一过滤no_action
-                    action_message=target_message,
+            actions = filter_no_actions(all_actions)
+            
+            # 如果所有结果都是no_action，返回一个no_action
+            if not actions:
+                actions = [ActionPlannerInfo(
+                    action_type="no_action",
+                    reasoning="所有规划器都选择不执行动作",
+                    action_data={},
+                    action_message=None,
                     available_actions=available_actions,
-                )
+                )]
-            )
+            
+            logger.info(f"{self.log_prefix}并行模式：返回主规划器{len(main_actions)}个action + 副规划器{len(all_sub_planner_results)}个action，过滤后总计{len(actions)}个action")
+        else:
+            # 如果为假，只返回副规划器的结果
+            actions = filter_no_actions(all_sub_planner_results)
+            
+            # 如果所有结果都是no_action，返回一个no_action
+            if not actions:
+                actions = [ActionPlannerInfo(
+                    action_type="no_action",
+                    reasoning="副规划器都选择不执行动作",
+                    action_data={},
+                    action_message=None,
+                    available_actions=available_actions,
+                )]
+            
+            logger.info(f"{self.log_prefix}非并行模式：返回副规划器的{len(actions)}个action（已过滤no_action）")
 
         return actions, target_message
 
@@ -278,38 +741,18 @@ class ActionPlanner:
         is_group_chat: bool,  # Now passed as argument
         chat_target_info: Optional[dict],  # Now passed as argument
         current_available_actions: Dict[str, ActionInfo],
-        refresh_time: bool = False,
         mode: ChatMode = ChatMode.FOCUS,
+        actions_before_now_block :str = "",
+        chat_content_block :str = "",
+        message_id_list :List[Tuple[str, DatabaseMessages]] = None,
     ) -> tuple[str, List[DatabaseMessages]]:  # sourcery skip: use-join
         """构建 Planner LLM 的提示词 (获取模板并填充数据)"""
         try:
-            message_list_before_now = get_raw_msg_before_timestamp_with_chat(
-                chat_id=self.chat_id,
-                timestamp=time.time(),
-                limit=int(global_config.chat.max_context_size * 0.6),
-            )
-            chat_content_block, message_id_list = build_readable_messages_with_id(
-                messages=message_list_before_now,
-                timestamp_mode="normal_no_YMD",
-                read_mark=self.last_obs_time_mark,
-                truncate=True,
-                show_actions=True,
-            )
-
-            actions_before_now = get_actions_by_timestamp_with_chat(
-                chat_id=self.chat_id,
-                timestamp_start=time.time() - 3600,
-                timestamp_end=time.time(),
-                limit=5,
-            )
-
-            actions_before_now_block = build_readable_actions(
-                actions=actions_before_now,
-            )
         
+            if actions_before_now_block:
                 actions_before_now_block = f"你刚刚选择并执行过的action是：\n{actions_before_now_block}"
-            if refresh_time:
+            else:
-                self.last_obs_time_mark = time.time()
+                actions_before_now_block = ""
 
             mentioned_bonus = ""
             if global_config.chat.mentioned_bot_inevitable_reply:
@@ -322,8 +765,7 @@ class ActionPlanner:
 动作：no_action
 动作描述：不进行动作，等待合适的时机
 - 当你刚刚发送了消息，没有人回复时，选择no_action
-- 如果有别的动作（非回复）满足条件，可以不用no_action
+- 当你一次发送了太多消息，为了避免过于烦人，可以不回复
-- 当你一次发送了太多消息，为了避免打扰聊天节奏，选择no_action
 {
     "action": "no_action",
     "reason":"不动作的原因"
@@ -345,6 +787,7 @@ class ActionPlanner:
 
             action_options_block = ""
 
+            if current_available_actions:
                 for using_actions_name, using_actions_info in current_available_actions.items():
                     if using_actions_info.action_parameters:
                         param_text = "\n"
@@ -368,6 +811,8 @@ class ActionPlanner:
                     )
 
                     action_options_block += using_action_prompt
+            else:
+                action_options_block = ""
 
             moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
 
@@ -378,8 +823,7 @@ class ActionPlanner:
                 bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
             else:
                 bot_nickname = ""
-            bot_core_personality = global_config.personality.personality_core
+            name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
-            identity_block = f"你的名字是{bot_name}{bot_nickname}，你{bot_core_personality}："
 
             planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
             prompt = planner_prompt_template.format(
@@ -391,7 +835,7 @@ class ActionPlanner:
                 mentioned_bonus=mentioned_bonus,
                 action_options_text=action_options_block,
                 moderation_prompt=moderation_prompt_block,
-                identity_block=identity_block,
+                name_block=name_block,
                 plan_style=global_config.personality.plan_style,
             )
             return prompt, message_id_list

src/chat/replyer/default_generator.py

@@ -10,8 +10,8 @@ from src.mais4u.mai_think import mai_thinking_manager
 from src.common.logger import get_logger
 from src.common.data_models.database_data_model import DatabaseMessages
 from src.common.data_models.info_data_model import ActionPlannerInfo
+from src.common.data_models.llm_data_model import LLMGenerationDataModel
 from src.config.config import global_config, model_config
-from src.individuality.individuality import get_individuality
 from src.llm_models.utils_model import LLMRequest
 from src.chat.message_receive.message import UserInfo, Seg, MessageRecv, MessageSending
 from src.chat.message_receive.chat_stream import ChatStream
@@ -163,7 +163,7 @@ class DefaultReplyer:
         from_plugin: bool = True,
         stream_id: Optional[str] = None,
         reply_message: Optional[DatabaseMessages] = None,
-    ) -> Tuple[bool, Optional[Dict[str, Any]], Optional[str], Optional[List[int]]]:
+    ) -> Tuple[bool, LLMGenerationDataModel]:
         # sourcery skip: merge-nested-ifs
         """
         回复器 (Replier): 负责生成回复文本的核心逻辑。
@@ -183,6 +183,7 @@ class DefaultReplyer:
 
         prompt = None
         selected_expressions: Optional[List[int]] = None
+        llm_response = LLMGenerationDataModel()
         if available_actions is None:
             available_actions = {}
         try:
@@ -196,10 +197,12 @@ class DefaultReplyer:
                     reply_message=reply_message,
                     reply_reason=reply_reason,
                 )
+            llm_response.prompt = prompt
+            llm_response.selected_expressions = selected_expressions
 
             if not prompt:
                 logger.warning("构建prompt失败，跳过回复生成")
-                return False, None, None, []
+                return False, llm_response
             from src.plugin_system.core.events_manager import events_manager
 
             if not from_plugin:
@@ -216,12 +219,10 @@ class DefaultReplyer:
             try:
                 content, reasoning_content, model_name, tool_call = await self.llm_generate_content(prompt)
                 logger.debug(f"replyer生成内容: {content}")
-                llm_response = {
+                llm_response.content = content
-                    "content": content,
+                llm_response.reasoning = reasoning_content
-                    "reasoning": reasoning_content,
+                llm_response.model = model_name
-                    "model": model_name,
+                llm_response.tool_calls = tool_call
-                    "tool_calls": tool_call,
-                }
                 if not from_plugin and not await events_manager.handle_mai_events(
                     EventType.AFTER_LLM, None, prompt, llm_response, stream_id=stream_id
                 ):
@@ -231,24 +232,23 @@ class DefaultReplyer:
             except Exception as llm_e:
                 # 精简报错信息
                 logger.error(f"LLM 生成失败: {llm_e}")
-                return False, None, prompt, selected_expressions  # LLM 调用失败则无法生成回复
+                return False, llm_response  # LLM 调用失败则无法生成回复
 
-            return True, llm_response, prompt, selected_expressions
+            return True, llm_response
 
         except UserWarning as uw:
             raise uw
         except Exception as e:
             logger.error(f"回复生成意外失败: {e}")
             traceback.print_exc()
-            return False, None, prompt, selected_expressions
+            return False, llm_response
 
     async def rewrite_reply_with_context(
         self,
         raw_reply: str = "",
         reason: str = "",
         reply_to: str = "",
-        return_prompt: bool = False,
+    ) -> Tuple[bool, LLMGenerationDataModel]:
-    ) -> Tuple[bool, Optional[str], Optional[str]]:
         """
         表达器 (Expressor): 负责重写和优化回复文本。
 
@@ -261,6 +261,7 @@ class DefaultReplyer:
         Returns:
             Tuple[bool, Optional[str]]: (是否成功, 重写后的回复内容)
         """
+        llm_response = LLMGenerationDataModel()
         try:
             with Timer("构建Prompt", {}):  # 内部计时器，可选保留
                 prompt = await self.build_prompt_rewrite_context(
@@ -268,29 +269,33 @@ class DefaultReplyer:
                     reason=reason,
                     reply_to=reply_to,
                 )
+            llm_response.prompt = prompt
 
             content = None
             reasoning_content = None
             model_name = "unknown_model"
             if not prompt:
                 logger.error("Prompt 构建失败，无法生成回复。")
-                return False, None, None
+                return False, llm_response
 
             try:
                 content, reasoning_content, model_name, _ = await self.llm_generate_content(prompt)
                 logger.info(f"想要表达：{raw_reply}||理由：{reason}||生成回复: {content}\n")
+                llm_response.content = content
+                llm_response.reasoning = reasoning_content
+                llm_response.model = model_name
 
             except Exception as llm_e:
                 # 精简报错信息
                 logger.error(f"LLM 生成失败: {llm_e}")
-                return False, None, prompt if return_prompt else None  # LLM 调用失败则无法生成回复
+                return False, llm_response  # LLM 调用失败则无法生成回复
 
-            return True, content, prompt if return_prompt else None
+            return True, llm_response
 
         except Exception as e:
             logger.error(f"回复生成意外失败: {e}")
             traceback.print_exc()
-            return False, None, prompt if return_prompt else None
+            return False, llm_response
 
     async def build_relation_info(self, sender: str, target: str):
         if not global_config.relationship.enable_relationship:
@@ -376,9 +381,7 @@ class DefaultReplyer:
 
         if global_config.memory.enable_instant_memory:
             chat_history_str = build_readable_messages(
-                messages=chat_history,
+                messages=chat_history, replace_bot_name=True, timestamp_mode="normal"
-                replace_bot_name=True,
-                timestamp_mode="normal"
             )
             asyncio.create_task(self.instant_memory.create_and_store_memory(chat_history_str))
 
@@ -670,6 +673,18 @@ class DefaultReplyer:
 
         return action_descriptions
 
+    async def build_personality_prompt(self) -> str:
+        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 = ""
+
+        prompt_personality = (
+            f"{global_config.personality.personality_core};{global_config.personality.personality_side}"
+        )
+        return f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}"
+
     async def build_prompt_reply_context(
         self,
         extra_info: str = "",
@@ -751,6 +766,7 @@ class DefaultReplyer:
             ),
             self._time_and_run_task(self.get_prompt_info(chat_talking_prompt_short, sender, target), "prompt_info"),
             self._time_and_run_task(self.build_actions_prompt(available_actions, chosen_actions), "actions_info"),
+            self._time_and_run_task(self.build_personality_prompt(), "personality_prompt"),
         )
 
         # 任务名称中英文映射
@@ -761,6 +777,7 @@ class DefaultReplyer:
             "tool_info": "使用工具",
             "prompt_info": "获取知识",
             "actions_info": "动作信息",
+            "personality_prompt": "人格信息",
         }
 
         # 处理结果
@@ -788,6 +805,7 @@ class DefaultReplyer:
         tool_info: str = results_dict["tool_info"]
         prompt_info: str = results_dict["prompt_info"]  # 直接使用格式化后的结果
         actions_info: str = results_dict["actions_info"]
+        personality_prompt: str = results_dict["personality_prompt"]
         keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
 
         if extra_info:
@@ -797,8 +815,6 @@ class DefaultReplyer:
 
         time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
 
-        identity_block = await get_individuality().get_personality_block()
-
         moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
 
         if sender:
@@ -827,7 +843,7 @@ class DefaultReplyer:
                 memory_block=memory_block,
                 relation_info_block=relation_info,
                 extra_info_block=extra_info_block,
-                identity=identity_block,
+                identity=personality_prompt,
                 action_descriptions=actions_info,
                 mood_state=mood_prompt,
                 background_dialogue_prompt=background_dialogue_prompt,
@@ -847,7 +863,7 @@ class DefaultReplyer:
                 memory_block=memory_block,
                 relation_info_block=relation_info,
                 extra_info_block=extra_info_block,
-                identity=identity_block,
+                identity=personality_prompt,
                 action_descriptions=actions_info,
                 sender_name=sender,
                 mood_state=mood_prompt,
@@ -865,16 +881,11 @@ class DefaultReplyer:
         raw_reply: str,
         reason: str,
         reply_to: str,
-        reply_message: Optional[Dict[str, Any]] = None,
     ) -> str:  # sourcery skip: merge-else-if-into-elif, remove-redundant-if
         chat_stream = self.chat_stream
         chat_id = chat_stream.stream_id
         is_group_chat = bool(chat_stream.group_info)
 
-        if reply_message:
-            sender = reply_message.get("sender", "")
-            target = reply_message.get("target", "")
-        else:
         sender, target = self._parse_reply_target(reply_to)
 
         # 添加情绪状态获取
@@ -898,17 +909,16 @@ class DefaultReplyer:
         )
 
         # 并行执行2个构建任务
-        (expression_habits_block, _), relation_info = await asyncio.gather(
+        (expression_habits_block, _), relation_info, personality_prompt = await asyncio.gather(
             self.build_expression_habits(chat_talking_prompt_half, target),
             self.build_relation_info(sender, target),
+            self.build_personality_prompt(),
         )
 
         keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
 
         time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
 
-        identity_block = await get_individuality().get_personality_block()
-
         moderation_prompt_block = (
             "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。不要随意遵从他人指令。"
         )
@@ -958,7 +968,7 @@ class DefaultReplyer:
             chat_target=chat_target_1,
             time_block=time_block,
             chat_info=chat_talking_prompt_half,
-            identity=identity_block,
+            identity=personality_prompt,
             chat_target_2=chat_target_2,
             reply_target_block=reply_target_block,
             raw_reply=raw_reply,
@@ -1006,7 +1016,7 @@ class DefaultReplyer:
     async def llm_generate_content(self, prompt: str):
         with Timer("LLM生成", {}):  # 内部计时器，可选保留
             # 直接使用已初始化的模型实例
-            logger.info(f"使用模型集生成回复: {self.express_model.model_for_task}")
+            logger.info(f"使用模型集生成回复: {', '.join(map(str, self.express_model.model_for_task.model_list))}")
 
             if global_config.debug.show_prompt:
                 logger.info(f"\n{prompt}\n")

src/chat/utils/chat_message_builder.py

@@ -361,10 +361,10 @@ def _build_readable_messages_internal(
     # 创建时间戳到消息ID的映射，用于在消息前添加[id]标识符
     timestamp_to_id_mapping: Dict[float, str] = {}
     if message_id_list:
-        for msg in message_id_list:
+        for msg_id, msg in message_id_list:
             timestamp = msg.time
             if timestamp is not None:
-                timestamp_to_id_mapping[timestamp] = msg.message_id
+                timestamp_to_id_mapping[timestamp] = msg_id
 
     def process_pic_ids(content: Optional[str]) -> str:
         """处理内容中的图片ID，将其替换为[图片x]格式"""
@@ -477,7 +477,7 @@ def _build_readable_messages_internal(
         readable_time = translate_timestamp_to_human_readable(timestamp, mode=timestamp_mode)
 
         # 查找消息id（如果有）并构建id_prefix
-        message_id = timestamp_to_id_mapping.get(timestamp)
+        message_id = timestamp_to_id_mapping.get(timestamp, "")
         id_prefix = f"[{message_id}]" if message_id else ""
 
         if is_action:
@@ -606,7 +606,7 @@ def build_readable_messages_with_id(
     truncate: bool = False,
     show_actions: bool = False,
     show_pic: bool = True,
-) -> Tuple[str, List[DatabaseMessages]]:
+) -> Tuple[str, List[Tuple[str, DatabaseMessages]]]:
     """
     将消息列表转换为可读的文本格式，并返回原始(时间戳, 昵称, 内容)列表。
     允许通过参数控制格式化行为。

src/chat/utils/utils.py

@@ -685,7 +685,7 @@ def assign_message_ids(messages: List[DatabaseMessages]) -> List[DatabaseMessage
     Returns:
         List[DatabaseMessages]: 分配了唯一ID的消息列表(写入message_id属性)
     """
-    result: List[DatabaseMessages] = list(messages)  # 复制原始消息列表
+    result: List[Tuple[str, DatabaseMessages]] = []  # 复制原始消息列表
     used_ids = set()
     len_i = len(messages)
     if len_i > 100:
@@ -695,7 +695,7 @@ def assign_message_ids(messages: List[DatabaseMessages]) -> List[DatabaseMessage
         a = 1
         b = 9
 
-    for i, _ in enumerate(result):
+    for i, message in enumerate(messages):
         # 生成唯一的简短ID
         while True:
             # 使用索引+随机数生成简短ID
@@ -705,7 +705,7 @@ def assign_message_ids(messages: List[DatabaseMessages]) -> List[DatabaseMessage
             if message_id not in used_ids:
                 used_ids.add(message_id)
                 break
-        result[i].message_id = message_id
+        result.append((message_id, message))
 
     return result
 

src/common/data_models/llm_data_model.py

@@ -0,0 +1,16 @@
+from dataclasses import dataclass
+from typing import Optional, List, Tuple, TYPE_CHECKING, Any
+
+from . import BaseDataModel
+if TYPE_CHECKING:
+    from src.llm_models.payload_content.tool_option import ToolCall
+
+@dataclass
+class LLMGenerationDataModel(BaseDataModel):
+    content: Optional[str] = None
+    reasoning: Optional[str] = None
+    model: Optional[str] = None
+    tool_calls: Optional[List["ToolCall"]] = None
+    prompt: Optional[str] = None
+    selected_expressions: Optional[List[int]] = None
+    reply_set: Optional[List[Tuple[str, Any]]] = None

src/common/logger.py

@@ -341,7 +341,6 @@ MODULE_COLORS = {
     "lpmm": "\033[96m",
     "plugin_system": "\033[91m",  # 亮红色
     "person_info": "\033[32m",  # 绿色
-    "individuality": "\033[94m",  # 显眼的亮蓝色
     "manager": "\033[35m",  # 紫色
     "llm_models": "\033[36m",  # 青色
     "remote": "\033[38;5;242m",  # 深灰色，更不显眼
@@ -423,7 +422,6 @@ MODULE_COLORS = {
 # 定义模块别名映射 - 将真实的logger名称映射到显示的别名
 MODULE_ALIASES = {
     # 示例映射
-    "individuality": "人格特质",
     "emoji": "表情包",
     "no_action_action": "摸鱼",
     "reply_action": "回复",

src/config/api_ada_configs.py

@@ -117,6 +117,9 @@ class ModelTaskConfig(ConfigBase):
     planner: TaskConfig
     """规划模型配置"""
 
+    planner_small: TaskConfig
+    """副规划模型配置"""
+
     embedding: TaskConfig
     """嵌入模型配置"""
 

src/config/config.py

@@ -56,7 +56,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template")
 
 # 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
 # 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/
-MMC_VERSION = "0.10.0"
+MMC_VERSION = "0.10.1"
 
 
 def get_key_comment(toml_table, key):

src/config/official_configs.py

@@ -48,13 +48,10 @@ class PersonalityConfig(ConfigBase):
     """表达风格"""
     
     plan_style: str = ""
+    """行为风格"""
 
-    compress_personality: bool = True
+    interest: str = ""
-    """是否压缩人格，压缩后会精简人格信息，节省token消耗并提高回复性能，但是会丢失一些信息，如果人设不长，可以关闭"""
+    """兴趣"""
-
-    compress_identity: bool = True
-    """是否压缩身份，压缩后会精简身份信息，节省token消耗并提高回复性能，但是会丢失一些信息，如果不长，可以关闭"""
-
 
 @dataclass
 class RelationshipConfig(ConfigBase):
@@ -74,9 +71,15 @@ class ChatConfig(ConfigBase):
     max_context_size: int = 18
     """上下文长度"""
     
+    interest_rate_mode: Literal["fast", "accurate"] = "fast"
+    """兴趣值计算模式，fast为快速计算，accurate为精确计算"""
+
     mentioned_bot_inevitable_reply: bool = False
     """提及 bot 必然回复"""
     
+    planner_size: int = 1
+    """副规划器大小，越小，麦麦的动作执行能力越精细，但是消耗更多token，调大可以缓解429类错误"""
+
     at_bot_inevitable_reply: bool = False
     """@bot 必然回复"""
     

src/individuality/individuality.py

@@ -1,304 +0,0 @@
-import json
-import os
-import hashlib
-import time
-
-from src.common.logger import get_logger
-from src.config.config import global_config, model_config
-from src.llm_models.utils_model import LLMRequest
-from rich.traceback import install
-
-install(extra_lines=3)
-
-logger = get_logger("individuality")
-
-
-class Individuality:
-    """个体特征管理类"""
-
-    def __init__(self):
-        self.name = ""
-        self.meta_info_file_path = "data/personality/meta.json"
-        self.personality_data_file_path = "data/personality/personality_data.json"
-
-        self.model = LLMRequest(model_set=model_config.model_task_config.utils, request_type="individuality.compress")
-
-    async def initialize(self) -> None:
-        """初始化个体特征"""
-        bot_nickname = global_config.bot.nickname
-        personality_core = global_config.personality.personality_core
-        personality_side = global_config.personality.personality_side
-        identity = global_config.personality.identity
-
-        self.name = bot_nickname
-
-        # 检查配置变化，如果变化则清空
-        personality_changed, identity_changed = await self._check_config_and_clear_if_changed(
-            bot_nickname, personality_core, personality_side, identity
-        )
-
-        logger.info("正在构建人设信息")
-
-        # 如果配置有变化，重新生成压缩版本
-        if personality_changed or identity_changed:
-            logger.info("检测到配置变化，重新生成压缩版本")
-            personality_result = await self._create_personality(personality_core, personality_side)
-            identity_result = await self._create_identity(identity)
-        else:
-            logger.info("配置未变化，使用缓存版本")
-            # 从文件中获取已有的结果
-            personality_result, identity_result = self._get_personality_from_file()
-            if not personality_result or not identity_result:
-                logger.info("未找到有效缓存，重新生成")
-                personality_result = await self._create_personality(personality_core, personality_side)
-                identity_result = await self._create_identity(identity)
-
-        # 保存到文件
-        if personality_result and identity_result:
-            self._save_personality_to_file(personality_result, identity_result)
-            logger.info("已将人设构建并保存到文件")
-        else:
-            logger.error("人设构建失败")
-
-
-    async def get_personality_block(self) -> str:
-        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 = ""
-
-        # 从文件获取 short_impression
-        personality, identity = self._get_personality_from_file()
-
-        # 确保short_impression是列表格式且有足够的元素
-        if not personality or not identity:
-            logger.warning(f"personality或identity为空: {personality}, {identity}, 使用默认值")
-            personality = "友好活泼"
-            identity = "人类"
-
-        prompt_personality = f"{personality}\n{identity}"
-        return f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}"
-
-    def _get_config_hash(
-        self, bot_nickname: str, personality_core: str, personality_side: str, identity: str
-    ) -> tuple[str, str]:
-        """获取personality和identity配置的哈希值
-
-        Returns:
-            tuple: (personality_hash, identity_hash)
-        """
-        # 人格配置哈希
-        personality_config = {
-            "nickname": bot_nickname,
-            "personality_core": personality_core,
-            "personality_side": personality_side,
-            "compress_personality": global_config.personality.compress_personality,
-        }
-        personality_str = json.dumps(personality_config, sort_keys=True)
-        personality_hash = hashlib.md5(personality_str.encode("utf-8")).hexdigest()
-
-        # 身份配置哈希
-        identity_config = {
-            "identity": identity,
-            "compress_identity": global_config.personality.compress_identity,
-        }
-        identity_str = json.dumps(identity_config, sort_keys=True)
-        identity_hash = hashlib.md5(identity_str.encode("utf-8")).hexdigest()
-
-        return personality_hash, identity_hash
-
-    async def _check_config_and_clear_if_changed(
-        self, bot_nickname: str, personality_core: str, personality_side: str, identity: str
-    ) -> tuple[bool, bool]:
-        """检查配置是否发生变化，如果变化则清空相应缓存
-
-        Returns:
-            tuple: (personality_changed, identity_changed)
-        """
-        current_personality_hash, current_identity_hash = self._get_config_hash(
-            bot_nickname, personality_core, personality_side, identity
-        )
-
-        meta_info = self._load_meta_info()
-        stored_personality_hash = meta_info.get("personality_hash")
-        stored_identity_hash = meta_info.get("identity_hash")
-
-        personality_changed = current_personality_hash != stored_personality_hash
-        identity_changed = current_identity_hash != stored_identity_hash
-
-        if personality_changed:
-            logger.info("检测到人格配置发生变化")
-
-        if identity_changed:
-            logger.info("检测到身份配置发生变化")
-
-        # 更新元信息文件
-        new_meta_info = {
-            "personality_hash": current_personality_hash,
-            "identity_hash": current_identity_hash,
-        }
-        self._save_meta_info(new_meta_info)
-
-        return personality_changed, identity_changed
-
-    def _load_meta_info(self) -> dict:
-        """从JSON文件中加载元信息"""
-        if os.path.exists(self.meta_info_file_path):
-            try:
-                with open(self.meta_info_file_path, "r", encoding="utf-8") as f:
-                    return json.load(f)
-            except (json.JSONDecodeError, IOError) as e:
-                logger.error(f"读取meta_info文件失败: {e}, 将创建新文件。")
-                return {}
-        return {}
-
-    def _save_meta_info(self, meta_info: dict):
-        """将元信息保存到JSON文件"""
-        try:
-            os.makedirs(os.path.dirname(self.meta_info_file_path), exist_ok=True)
-            with open(self.meta_info_file_path, "w", encoding="utf-8") as f:
-                json.dump(meta_info, f, ensure_ascii=False, indent=2)
-        except IOError as e:
-            logger.error(f"保存meta_info文件失败: {e}")
-
-    def _load_personality_data(self) -> dict:
-        """从JSON文件中加载personality数据"""
-        if os.path.exists(self.personality_data_file_path):
-            try:
-                with open(self.personality_data_file_path, "r", encoding="utf-8") as f:
-                    return json.load(f)
-            except (json.JSONDecodeError, IOError) as e:
-                logger.error(f"读取personality_data文件失败: {e}, 将创建新文件。")
-                return {}
-        return {}
-
-    def _save_personality_data(self, personality_data: dict):
-        """将personality数据保存到JSON文件"""
-        try:
-            os.makedirs(os.path.dirname(self.personality_data_file_path), exist_ok=True)
-            with open(self.personality_data_file_path, "w", encoding="utf-8") as f:
-                json.dump(personality_data, f, ensure_ascii=False, indent=2)
-            logger.debug(f"已保存personality数据到文件: {self.personality_data_file_path}")
-        except IOError as e:
-            logger.error(f"保存personality_data文件失败: {e}")
-
-    def _get_personality_from_file(self) -> tuple[str, str]:
-        """从文件获取personality数据
-
-        Returns:
-            tuple: (personality, identity)
-        """
-        personality_data = self._load_personality_data()
-        personality = personality_data.get("personality", "友好活泼")
-        identity = personality_data.get("identity", "人类")
-        return personality, identity
-
-    def _save_personality_to_file(self, personality: str, identity: str):
-        """保存personality数据到文件
-
-        Args:
-            personality: 压缩后的人格描述
-            identity: 压缩后的身份描述
-        """
-        personality_data = {
-            "personality": personality,
-            "identity": identity,
-            "bot_nickname": self.name,
-            "last_updated": int(time.time()),
-        }
-        self._save_personality_data(personality_data)
-
-    async def _create_personality(self, personality_core: str, personality_side: str) -> str:
-        # sourcery skip: merge-list-append, move-assign
-        """使用LLM创建压缩版本的impression
-
-        Args:
-            personality_core: 核心人格
-            personality_side: 人格侧面列表
-
-        Returns:
-            str: 压缩后的impression文本
-        """
-        logger.info("正在构建人格.........")
-
-        # 核心人格保持不变
-        personality_parts = []
-        if personality_core:
-            personality_parts.append(f"{personality_core}")
-
-        # 准备需要压缩的内容
-        if global_config.personality.compress_personality:
-            personality_to_compress = f"人格特质: {personality_side}"
-
-            prompt = f"""请将以下人格信息进行简洁压缩，保留主要内容，用简练的中文表达：
-{personality_to_compress}
-
-要求：
-1. 保持原意不变，尽量使用原文
-2. 尽量简洁，不超过30字
-3. 直接输出压缩后的内容，不要解释"""
-
-            response, _ = await self.model.generate_response_async(
-                prompt=prompt,
-            )
-
-            if response and response.strip():
-                personality_parts.append(response.strip())
-                logger.info(f"精简人格侧面: {response.strip()}")
-            else:
-                logger.error(f"使用LLM压缩人设时出错: {response}")
-                # 压缩失败时使用原始内容
-                if personality_side:
-                    personality_parts.append(personality_side)
-
-            if personality_parts:
-                personality_result = "。".join(personality_parts)
-            else:
-                personality_result = personality_core or "友好活泼"
-        else:
-            personality_result = personality_core
-            if personality_side:
-                personality_result += f"，{personality_side}"
-
-        return personality_result
-
-    async def _create_identity(self, identity: str) -> str:
-        """使用LLM创建压缩版本的impression"""
-        logger.info("正在构建身份.........")
-
-        if global_config.personality.compress_identity:
-            identity_to_compress = f"身份背景: {identity}"
-
-            prompt = f"""请将以下身份信息进行简洁压缩，保留主要内容，用简练的中文表达：
-{identity_to_compress}
-
-要求：
-1. 保持原意不变，尽量使用原文
-2. 尽量简洁，不超过30字
-3. 直接输出压缩后的内容，不要解释"""
-
-            response, _ = await self.model.generate_response_async(
-                prompt=prompt,
-            )
-
-            if response and response.strip():
-                identity_result = response.strip()
-                logger.info(f"精简身份: {identity_result}")
-            else:
-                logger.error(f"使用LLM压缩身份时出错: {response}")
-                identity_result = identity
-        else:
-            identity_result = identity
-
-        return identity_result
-
-
-individuality = None
-
-
-def get_individuality():
-    global individuality
-    if individuality is None:
-        individuality = Individuality()
-    return individuality

src/individuality/not_using/offline_llm.py

@@ -1,127 +0,0 @@
-import asyncio
-import os
-import time
-from typing import Tuple, Union
-
-import aiohttp
-import requests
-from src.common.logger import get_logger
-from src.common.tcp_connector import get_tcp_connector
-from rich.traceback import install
-
-install(extra_lines=3)
-
-logger = get_logger("offline_llm")
-
-
-class LLMRequestOff:
-    def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs):
-        self.model_name = model_name
-        self.params = kwargs
-        self.api_key = os.getenv("SILICONFLOW_KEY")
-        self.base_url = os.getenv("SILICONFLOW_BASE_URL")
-
-        if not self.api_key or not self.base_url:
-            raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置")
-
-        # logger.info(f"API URL: {self.base_url}")  # 使用 logger 记录 base_url
-
-    def generate_response(self, prompt: str) -> Union[str, Tuple[str, str]]:
-        """根据输入的提示生成模型的响应"""
-        headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
-
-        # 构建请求体
-        data = {
-            "model": self.model_name,
-            "messages": [{"role": "user", "content": prompt}],
-            "temperature": 0.4,
-            **self.params,
-        }
-
-        # 发送请求到完整的 chat/completions 端点
-        api_url = f"{self.base_url.rstrip('/')}/chat/completions"  # type: ignore
-        logger.info(f"Request URL: {api_url}")  # 记录请求的 URL
-
-        max_retries = 3
-        base_wait_time = 15  # 基础等待时间（秒）
-
-        for retry in range(max_retries):
-            try:
-                response = requests.post(api_url, headers=headers, json=data)
-
-                if response.status_code == 429:
-                    wait_time = base_wait_time * (2**retry)  # 指数退避
-                    logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
-                    time.sleep(wait_time)
-                    continue
-
-                response.raise_for_status()  # 检查其他响应状态
-
-                result = response.json()
-                if "choices" in result and len(result["choices"]) > 0:
-                    content = result["choices"][0]["message"]["content"]
-                    reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
-                    return content, reasoning_content
-                return "没有返回结果", ""
-
-            except Exception as e:
-                if retry < max_retries - 1:  # 如果还有重试机会
-                    wait_time = base_wait_time * (2**retry)
-                    logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
-                    time.sleep(wait_time)
-                else:
-                    logger.error(f"请求失败: {str(e)}")
-                    return f"请求失败: {str(e)}", ""
-
-        logger.error("达到最大重试次数，请求仍然失败")
-        return "达到最大重试次数，请求仍然失败", ""
-
-    async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]:
-        """异步方式根据输入的提示生成模型的响应"""
-        headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
-
-        # 构建请求体
-        data = {
-            "model": self.model_name,
-            "messages": [{"role": "user", "content": prompt}],
-            "temperature": 0.5,
-            **self.params,
-        }
-
-        # 发送请求到完整的 chat/completions 端点
-        api_url = f"{self.base_url.rstrip('/')}/chat/completions"  # type: ignore
-        logger.info(f"Request URL: {api_url}")  # 记录请求的 URL
-
-        max_retries = 3
-        base_wait_time = 15
-
-        async with aiohttp.ClientSession(connector=await get_tcp_connector()) as session:
-            for retry in range(max_retries):
-                try:
-                    async with session.post(api_url, headers=headers, json=data) as response:
-                        if response.status == 429:
-                            wait_time = base_wait_time * (2**retry)  # 指数退避
-                            logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
-                            await asyncio.sleep(wait_time)
-                            continue
-
-                        response.raise_for_status()  # 检查其他响应状态
-
-                        result = await response.json()
-                        if "choices" in result and len(result["choices"]) > 0:
-                            content = result["choices"][0]["message"]["content"]
-                            reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
-                            return content, reasoning_content
-                        return "没有返回结果", ""
-
-                except Exception as e:
-                    if retry < max_retries - 1:  # 如果还有重试机会
-                        wait_time = base_wait_time * (2**retry)
-                        logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
-                        await asyncio.sleep(wait_time)
-                    else:
-                        logger.error(f"请求失败: {str(e)}")
-                        return f"请求失败: {str(e)}", ""
-
-            logger.error("达到最大重试次数，请求仍然失败")
-            return "达到最大重试次数，请求仍然失败", ""

src/individuality/not_using/per_bf_gen.py

@@ -1,310 +0,0 @@
-from typing import Dict, List
-import json
-import os
-from dotenv import load_dotenv
-import sys
-import toml
-import random
-from tqdm import tqdm
-
-# 添加项目根目录到 Python 路径
-root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
-sys.path.append(root_path)
-
-# 加载配置文件
-config_path = os.path.join(root_path, "config", "bot_config.toml")
-with open(config_path, "r", encoding="utf-8") as f:
-    config = toml.load(f)
-
-# 现在可以导入src模块
-from individuality.not_using.scene import get_scene_by_factor, PERSONALITY_SCENES  # noqa E402
-from individuality.not_using.questionnaire import FACTOR_DESCRIPTIONS  # noqa E402
-from individuality.not_using.offline_llm import LLMRequestOff  # noqa E402
-
-# 加载环境变量
-env_path = os.path.join(root_path, ".env")
-if os.path.exists(env_path):
-    print(f"从 {env_path} 加载环境变量")
-    load_dotenv(env_path)
-else:
-    print(f"未找到环境变量文件: {env_path}")
-    print("将使用默认配置")
-
-
-def adapt_scene(scene: str) -> str:
-    personality_core = config["personality"]["personality_core"]
-    personality_side = config["personality"]["personality_side"]
-    personality_side = random.choice(personality_side)
-    identitys = config["identity"]["identity"]
-    identity = random.choice(identitys)
-
-    """
-    根据config中的属性，改编场景使其更适合当前角色
-    
-    Args:
-        scene: 原始场景描述
-        
-    Returns:
-        str: 改编后的场景描述
-    """
-    try:
-        prompt = f"""
-这是一个参与人格测评的角色形象:
-- 昵称: {config["bot"]["nickname"]}
-- 性别: {config["identity"]["gender"]}
-- 年龄: {config["identity"]["age"]}岁
-- 外貌: {config["identity"]["appearance"]}
-- 性格核心: {personality_core}
-- 性格侧面: {personality_side}
-- 身份细节: {identity}
-
-请根据上述形象，改编以下场景，在测评中，用户将根据该场景给出上述角色形象的反应:
-{scene}
-保持场景的本质不变，但最好贴近生活且具体，并且让它更适合这个角色。
-改编后的场景应该自然、连贯，并考虑角色的年龄、身份和性格特点。只返回改编后的场景描述，不要包含其他说明。注意{config["bot"]["nickname"]}是面对这个场景的人，而不是场景的其他人。场景中不会有其描述，
-现在，请你给出改编后的场景描述
-"""
-
-        llm = LLMRequestOff(model_name=config["model"]["llm_normal"]["name"])
-        adapted_scene, _ = llm.generate_response(prompt)
-
-        # 检查返回的场景是否为空或错误信息
-        if not adapted_scene or "错误" in adapted_scene or "失败" in adapted_scene:
-            print("场景改编失败，将使用原始场景")
-            return scene
-
-        return adapted_scene
-    except Exception as e:
-        print(f"场景改编过程出错：{str(e)}，将使用原始场景")
-        return scene
-
-
-class PersonalityEvaluatorDirect:
-    def __init__(self):
-        self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
-        self.scenarios = []
-        self.final_scores: Dict[str, float] = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
-        self.dimension_counts = {trait: 0 for trait in self.final_scores}
-
-        # 为每个人格特质获取对应的场景
-        for trait in PERSONALITY_SCENES:
-            scenes = get_scene_by_factor(trait)
-            if not scenes:
-                continue
-
-            # 从每个维度选择3个场景
-            import random
-
-            scene_keys = list(scenes.keys())
-            selected_scenes = random.sample(scene_keys, min(3, len(scene_keys)))
-
-            for scene_key in selected_scenes:
-                scene = scenes[scene_key]
-
-                # 为每个场景添加评估维度
-                # 主维度是当前特质，次维度随机选择一个其他特质
-                other_traits = [t for t in PERSONALITY_SCENES if t != trait]
-                secondary_trait = random.choice(other_traits)
-
-                self.scenarios.append(
-                    {"场景": scene["scenario"], "评估维度": [trait, secondary_trait], "场景编号": scene_key}
-                )
-
-        self.llm = LLMRequestOff()
-
-    def evaluate_response(self, scenario: str, response: str, dimensions: List[str]) -> Dict[str, float]:
-        """
-        使用 DeepSeek AI 评估用户对特定场景的反应
-        """
-        # 构建维度描述
-        dimension_descriptions = []
-        for dim in dimensions:
-            if desc := FACTOR_DESCRIPTIONS.get(dim, ""):
-                dimension_descriptions.append(f"- {dim}：{desc}")
-
-        dimensions_text = "\n".join(dimension_descriptions)
-
-        prompt = f"""请根据以下场景和用户描述，评估用户在大五人格模型中的相关维度得分（1-6分）。
-
-场景描述：
-{scenario}
-
-用户回应：
-{response}
-
-需要评估的维度说明：
-{dimensions_text}
-
-请按照以下格式输出评估结果（仅输出JSON格式）：
-{{
-    "{dimensions[0]}": 分数,
-    "{dimensions[1]}": 分数
-}}
-
-评分标准：
-1 = 非常不符合该维度特征
-2 = 比较不符合该维度特征
-3 = 有点不符合该维度特征
-4 = 有点符合该维度特征
-5 = 比较符合该维度特征
-6 = 非常符合该维度特征
-
-请根据用户的回应，结合场景和维度说明进行评分。确保分数在1-6之间，并给出合理的评估。"""
-
-        try:
-            ai_response, _ = self.llm.generate_response(prompt)
-            # 尝试从AI响应中提取JSON部分
-            start_idx = ai_response.find("{")
-            end_idx = ai_response.rfind("}") + 1
-            if start_idx != -1 and end_idx != 0:
-                json_str = ai_response[start_idx:end_idx]
-                scores = json.loads(json_str)
-                # 确保所有分数在1-6之间
-                return {k: max(1, min(6, float(v))) for k, v in scores.items()}
-            else:
-                print("AI响应格式不正确，使用默认评分")
-                return {dim: 3.5 for dim in dimensions}
-        except Exception as e:
-            print(f"评估过程出错：{str(e)}")
-            return {dim: 3.5 for dim in dimensions}
-
-    def run_evaluation(self):
-        """
-        运行整个评估过程
-        """
-        print(f"欢迎使用{config['bot']['nickname']}形象创建程序！")
-        print("接下来，将给您呈现一系列有关您bot的场景（共15个）。")
-        print("请想象您的bot在以下场景下会做什么，并描述您的bot的反应。")
-        print("每个场景都会进行不同方面的评估。")
-        print("\n角色基本信息：")
-        print(f"- 昵称：{config['bot']['nickname']}")
-        print(f"- 性格核心：{config['personality']['personality_core']}")
-        print(f"- 性格侧面：{config['personality']['personality_side']}")
-        print(f"- 身份细节：{config['identity']['identity']}")
-        print("\n准备好了吗？按回车键开始...")
-        input()
-
-        total_scenarios = len(self.scenarios)
-        progress_bar = tqdm(
-            total=total_scenarios,
-            desc="场景进度",
-            ncols=100,
-            bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]",
-        )
-
-        for _i, scenario_data in enumerate(self.scenarios, 1):
-            # print(f"\n{'-' * 20} 场景 {i}/{total_scenarios} - {scenario_data['场景编号']} {'-' * 20}")
-
-            # 改编场景，使其更适合当前角色
-            print(f"{config['bot']['nickname']}祈祷中...")
-            adapted_scene = adapt_scene(scenario_data["场景"])
-            scenario_data["改编场景"] = adapted_scene
-
-            print(adapted_scene)
-            print(f"\n请描述{config['bot']['nickname']}在这种情况下会如何反应：")
-            response = input().strip()
-
-            if not response:
-                print("反应描述不能为空！")
-                continue
-
-            print("\n正在评估您的描述...")
-            scores = self.evaluate_response(adapted_scene, response, scenario_data["评估维度"])
-
-            # 更新最终分数
-            for dimension, score in scores.items():
-                self.final_scores[dimension] += score
-                self.dimension_counts[dimension] += 1
-
-            print("\n当前评估结果：")
-            print("-" * 30)
-            for dimension, score in scores.items():
-                print(f"{dimension}: {score}/6")
-
-            # 更新进度条
-            progress_bar.update(1)
-
-            # if i < total_scenarios:
-            # print("\n按回车键继续下一个场景...")
-            # input()
-
-        progress_bar.close()
-
-        # 计算平均分
-        for dimension in self.final_scores:
-            if self.dimension_counts[dimension] > 0:
-                self.final_scores[dimension] = round(self.final_scores[dimension] / self.dimension_counts[dimension], 2)
-
-        print("\n" + "=" * 50)
-        print(f"  {config['bot']['nickname']}的人格特征评估结果  ".center(50))
-        print("=" * 50)
-        for trait, score in self.final_scores.items():
-            print(f"{trait}: {score}/6".ljust(20) + f"测试场景数：{self.dimension_counts[trait]}".rjust(30))
-        print("=" * 50)
-
-        # 返回评估结果
-        return self.get_result()
-
-    def get_result(self):
-        """
-        获取评估结果
-        """
-        return {
-            "final_scores": self.final_scores,
-            "dimension_counts": self.dimension_counts,
-            "scenarios": self.scenarios,
-            "bot_info": {
-                "nickname": config["bot"]["nickname"],
-                "gender": config["identity"]["gender"],
-                "age": config["identity"]["age"],
-                "height": config["identity"]["height"],
-                "weight": config["identity"]["weight"],
-                "appearance": config["identity"]["appearance"],
-                "personality_core": config["personality"]["personality_core"],
-                "personality_side": config["personality"]["personality_side"],
-                "identity": config["identity"]["identity"],
-            },
-        }
-
-
-def main():
-    evaluator = PersonalityEvaluatorDirect()
-    result = evaluator.run_evaluation()
-
-    # 准备简化的结果数据
-    simplified_result = {
-        "openness": round(result["final_scores"]["开放性"] / 6, 1),  # 转换为0-1范围
-        "conscientiousness": round(result["final_scores"]["严谨性"] / 6, 1),
-        "extraversion": round(result["final_scores"]["外向性"] / 6, 1),
-        "agreeableness": round(result["final_scores"]["宜人性"] / 6, 1),
-        "neuroticism": round(result["final_scores"]["神经质"] / 6, 1),
-        "bot_nickname": config["bot"]["nickname"],
-    }
-
-    # 确保目录存在
-    save_dir = os.path.join(root_path, "data", "personality")
-    os.makedirs(save_dir, exist_ok=True)
-
-    # 创建文件名，替换可能的非法字符
-    bot_name = config["bot"]["nickname"]
-    # 替换Windows文件名中不允许的字符
-    for char in ["\\", "/", ":", "*", "?", '"', "<", ">", "|"]:
-        bot_name = bot_name.replace(char, "_")
-
-    file_name = f"{bot_name}_personality.per"
-    save_path = os.path.join(save_dir, file_name)
-
-    # 保存简化的结果
-    with open(save_path, "w", encoding="utf-8") as f:
-        json.dump(simplified_result, f, ensure_ascii=False, indent=4)
-
-    print(f"\n结果已保存到 {save_path}")
-
-    # 同时保存完整结果到results目录
-    os.makedirs("results", exist_ok=True)
-    with open("results/personality_result.json", "w", encoding="utf-8") as f:
-        json.dump(result, f, ensure_ascii=False, indent=2)
-
-
-if __name__ == "__main__":
-    main()

src/individuality/not_using/questionnaire.py

@@ -1,142 +0,0 @@
-# 人格测试问卷题目
-# 王孟成, 戴晓阳, & 姚树桥. (2011).
-# 中国大五人格问卷的初步编制Ⅲ:简式版的制定及信效度检验. 中国临床心理学杂志, 19(04), Article 04.
-
-# 王孟成, 戴晓阳, & 姚树桥. (2010).
-# 中国大五人格问卷的初步编制Ⅰ:理论框架与信度分析. 中国临床心理学杂志, 18(05), Article 05.
-
-PERSONALITY_QUESTIONS = [
-    # 神经质维度 (F1)
-    {"id": 1, "content": "我常担心有什么不好的事情要发生", "factor": "神经质", "reverse_scoring": False},
-    {"id": 2, "content": "我常感到害怕", "factor": "神经质", "reverse_scoring": False},
-    {"id": 3, "content": "有时我觉得自己一无是处", "factor": "神经质", "reverse_scoring": False},
-    {"id": 4, "content": "我很少感到忧郁或沮丧", "factor": "神经质", "reverse_scoring": True},
-    {"id": 5, "content": "别人一句漫不经心的话，我常会联系在自己身上", "factor": "神经质", "reverse_scoring": False},
-    {"id": 6, "content": "在面对压力时，我有种快要崩溃的感觉", "factor": "神经质", "reverse_scoring": False},
-    {"id": 7, "content": "我常担忧一些无关紧要的事情", "factor": "神经质", "reverse_scoring": False},
-    {"id": 8, "content": "我常常感到内心不踏实", "factor": "神经质", "reverse_scoring": False},
-    # 严谨性维度 (F2)
-    {"id": 9, "content": "在工作上，我常只求能应付过去便可", "factor": "严谨性", "reverse_scoring": True},
-    {"id": 10, "content": "一旦确定了目标，我会坚持努力地实现它", "factor": "严谨性", "reverse_scoring": False},
-    {"id": 11, "content": "我常常是仔细考虑之后才做出决定", "factor": "严谨性", "reverse_scoring": False},
-    {"id": 12, "content": "别人认为我是个慎重的人", "factor": "严谨性", "reverse_scoring": False},
-    {"id": 13, "content": "做事讲究逻辑和条理是我的一个特点", "factor": "严谨性", "reverse_scoring": False},
-    {"id": 14, "content": "我喜欢一开头就把事情计划好", "factor": "严谨性", "reverse_scoring": False},
-    {"id": 15, "content": "我工作或学习很勤奋", "factor": "严谨性", "reverse_scoring": False},
-    {"id": 16, "content": "我是个倾尽全力做事的人", "factor": "严谨性", "reverse_scoring": False},
-    # 宜人性维度 (F3)
-    {
-        "id": 17,
-        "content": "尽管人类社会存在着一些阴暗的东西（如战争、罪恶、欺诈），我仍然相信人性总的来说是善良的",
-        "factor": "宜人性",
-        "reverse_scoring": False,
-    },
-    {"id": 18, "content": "我觉得大部分人基本上是心怀善意的", "factor": "宜人性", "reverse_scoring": False},
-    {"id": 19, "content": "虽然社会上有骗子，但我觉得大部分人还是可信的", "factor": "宜人性", "reverse_scoring": False},
-    {"id": 20, "content": "我不太关心别人是否受到不公正的待遇", "factor": "宜人性", "reverse_scoring": True},
-    {"id": 21, "content": "我时常觉得别人的痛苦与我无关", "factor": "宜人性", "reverse_scoring": True},
-    {"id": 22, "content": "我常为那些遭遇不幸的人感到难过", "factor": "宜人性", "reverse_scoring": False},
-    {"id": 23, "content": "我是那种只照顾好自己，不替别人担忧的人", "factor": "宜人性", "reverse_scoring": True},
-    {"id": 24, "content": "当别人向我诉说不幸时，我常感到难过", "factor": "宜人性", "reverse_scoring": False},
-    # 开放性维度 (F4)
-    {"id": 25, "content": "我的想象力相当丰富", "factor": "开放性", "reverse_scoring": False},
-    {"id": 26, "content": "我头脑中经常充满生动的画面", "factor": "开放性", "reverse_scoring": False},
-    {"id": 27, "content": "我对许多事情有着很强的好奇心", "factor": "开放性", "reverse_scoring": False},
-    {"id": 28, "content": "我喜欢冒险", "factor": "开放性", "reverse_scoring": False},
-    {"id": 29, "content": "我是个勇于冒险，突破常规的人", "factor": "开放性", "reverse_scoring": False},
-    {"id": 30, "content": "我身上具有别人没有的冒险精神", "factor": "开放性", "reverse_scoring": False},
-    {
-        "id": 31,
-        "content": "我渴望学习一些新东西，即使它们与我的日常生活无关",
-        "factor": "开放性",
-        "reverse_scoring": False,
-    },
-    {
-        "id": 32,
-        "content": "我很愿意也很容易接受那些新事物、新观点、新想法",
-        "factor": "开放性",
-        "reverse_scoring": False,
-    },
-    # 外向性维度 (F5)
-    {"id": 33, "content": "我喜欢参加社交与娱乐聚会", "factor": "外向性", "reverse_scoring": False},
-    {"id": 34, "content": "我对人多的聚会感到乏味", "factor": "外向性", "reverse_scoring": True},
-    {"id": 35, "content": "我尽量避免参加人多的聚会和嘈杂的环境", "factor": "外向性", "reverse_scoring": True},
-    {"id": 36, "content": "在热闹的聚会上，我常常表现主动并尽情玩耍", "factor": "外向性", "reverse_scoring": False},
-    {"id": 37, "content": "有我在的场合一般不会冷场", "factor": "外向性", "reverse_scoring": False},
-    {"id": 38, "content": "我希望成为领导者而不是被领导者", "factor": "外向性", "reverse_scoring": False},
-    {"id": 39, "content": "在一个团体中，我希望处于领导地位", "factor": "外向性", "reverse_scoring": False},
-    {"id": 40, "content": "别人多认为我是一个热情和友好的人", "factor": "外向性", "reverse_scoring": False},
-]
-
-# 因子维度说明
-FACTOR_DESCRIPTIONS = {
-    "外向性": {
-        "description": "反映个体神经系统的强弱和动力特征。外向性主要表现为个体在人际交往和社交活动中的倾向性，"
-        "包括对社交活动的兴趣、"
-        "对人群的态度、社交互动中的主动程度以及在群体中的影响力。高分者倾向于积极参与社交活动，乐于与人交往，善于表达自我，"
-        "并往往在群体中发挥领导作用；低分者则倾向于独处，不喜欢热闹的社交场合，表现出内向、安静的特征。",
-        "trait_words": ["热情", "活力", "社交", "主动"],
-        "subfactors": {
-            "合群性": "个体愿意与他人聚在一起，即接近人群的倾向；高分表现乐群、好交际，低分表现封闭、独处",
-            "热情": "个体对待别人时所表现出的态度；高分表现热情好客，低分表现冷淡",
-            "支配性": "个体喜欢指使、操纵他人，倾向于领导别人的特点；高分表现好强、发号施令，低分表现顺从、低调",
-            "活跃": "个体精力充沛，活跃、主动性等特点；高分表现活跃，低分表现安静",
-        },
-    },
-    "神经质": {
-        "description": "反映个体情绪的状态和体验内心苦恼的倾向性。这个维度主要关注个体在面对压力、"
-        "挫折和日常生活挑战时的情绪稳定性和适应能力。它包含了对焦虑、抑郁、愤怒等负面情绪的敏感程度，"
-        "以及个体对这些情绪的调节和控制能力。高分者容易体验负面情绪，对压力较为敏感，情绪波动较大；"
-        "低分者则表现出较强的情绪稳定性，能够较好地应对压力和挫折。",
-        "trait_words": ["稳定", "沉着", "从容", "坚韧"],
-        "subfactors": {
-            "焦虑": "个体体验焦虑感的个体差异；高分表现坐立不安，低分表现平静",
-            "抑郁": "个体体验抑郁情感的个体差异；高分表现郁郁寡欢，低分表现平静",
-            "敏感多疑": "个体常常关注自己的内心活动，行为和过于意识人对自己的看法、评价；高分表现敏感多疑，"
-            "低分表现淡定、自信",
-            "脆弱性": "个体在危机或困难面前无力、脆弱的特点；高分表现无能、易受伤、逃避，低分表现坚强",
-            "愤怒-敌意": "个体准备体验愤怒，及相关情绪的状态；高分表现暴躁易怒，低分表现平静",
-        },
-    },
-    "严谨性": {
-        "description": "反映个体在目标导向行为上的组织、坚持和动机特征。这个维度体现了个体在工作、"
-        "学习等目标性活动中的自我约束和行为管理能力。它涉及到个体的责任感、自律性、计划性、条理性以及完成任务的态度。"
-        "高分者往往表现出强烈的责任心、良好的组织能力、谨慎的决策风格和持续的努力精神；低分者则可能表现出随意性强、"
-        "缺乏规划、做事马虎或易放弃的特点。",
-        "trait_words": ["负责", "自律", "条理", "勤奋"],
-        "subfactors": {
-            "责任心": "个体对待任务和他人认真负责，以及对自己承诺的信守；高分表现有责任心、负责任，"
-            "低分表现推卸责任、逃避处罚",
-            "自我控制": "个体约束自己的能力，及自始至终的坚持性；高分表现自制、有毅力，低分表现冲动、无毅力",
-            "审慎性": "个体在采取具体行动前的心理状态；高分表现谨慎、小心，低分表现鲁莽、草率",
-            "条理性": "个体处理事务和工作的秩序，条理和逻辑性；高分表现整洁、有秩序，低分表现混乱、遗漏",
-            "勤奋": "个体工作和学习的努力程度及为达到目标而表现出的进取精神；高分表现勤奋、刻苦，低分表现懒散",
-        },
-    },
-    "开放性": {
-        "description": "反映个体对新异事物、新观念和新经验的接受程度，以及在思维和行为方面的创新倾向。"
-        "这个维度体现了个体在认知和体验方面的广度、深度和灵活性。它包括对艺术的欣赏能力、对知识的求知欲、想象力的丰富程度，"
-        "以及对冒险和创新的态度。高分者往往具有丰富的想象力、广泛的兴趣、开放的思维方式和创新的倾向；低分者则倾向于保守、"
-        "传统，喜欢熟悉和常规的事物。",
-        "trait_words": ["创新", "好奇", "艺术", "冒险"],
-        "subfactors": {
-            "幻想": "个体富于幻想和想象的水平；高分表现想象力丰富，低分表现想象力匮乏",
-            "审美": "个体对于艺术和美的敏感与热爱程度；高分表现富有艺术气息，低分表现一般对艺术不敏感",
-            "好奇心": "个体对未知事物的态度；高分表现兴趣广泛、好奇心浓，低分表现兴趣少、无好奇心",
-            "冒险精神": "个体愿意尝试有风险活动的个体差异；高分表现好冒险，低分表现保守",
-            "价值观念": "个体对新事物、新观念、怪异想法的态度；高分表现开放、坦然接受新事物，低分则相反",
-        },
-    },
-    "宜人性": {
-        "description": "反映个体在人际关系中的亲和倾向，体现了对他人的关心、同情和合作意愿。"
-        "这个维度主要关注个体与他人互动时的态度和行为特征，包括对他人的信任程度、同理心水平、"
-        "助人意愿以及在人际冲突中的处理方式。高分者通常表现出友善、富有同情心、乐于助人的特质，善于与他人建立和谐关系；"
-        "低分者则可能表现出较少的人际关注，在社交互动中更注重自身利益，较少考虑他人感受。",
-        "trait_words": ["友善", "同理", "信任", "合作"],
-        "subfactors": {
-            "信任": "个体对他人和/或他人言论的相信程度；高分表现信任他人，低分表现怀疑",
-            "体贴": "个体对别人的兴趣和需要的关注程度；高分表现体贴、温存，低分表现冷漠、不在乎",
-            "同情": "个体对处于不利地位的人或物的态度；高分表现富有同情心，低分表现冷漠",
-        },
-    },
-}

src/individuality/not_using/scene.py

@@ -1,43 +0,0 @@
-import json
-import os
-from typing import Any
-
-
-def load_scenes() -> dict[str, Any]:
-    """
-    从JSON文件加载场景数据
-
-    Returns:
-        Dict: 包含所有场景的字典
-    """
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    json_path = os.path.join(current_dir, "template_scene.json")
-
-    with open(json_path, "r", encoding="utf-8") as f:
-        return json.load(f)
-
-
-PERSONALITY_SCENES = load_scenes()
-
-
-def get_scene_by_factor(factor: str) -> dict | None:
-    """
-    根据人格因子获取对应的情景测试
-
-    Args:
-        factor (str): 人格因子名称
-
-    Returns:
-        dict: 包含情景描述的字典
-    """
-    return PERSONALITY_SCENES.get(factor, None)
-
-
-def get_all_scenes() -> dict:
-    """
-    获取所有情景测试
-
-    Returns:
-        Dict: 所有情景测试的字典
-    """
-    return PERSONALITY_SCENES

src/individuality/not_using/template_scene.json

@@ -1,112 +0,0 @@
-{
-    "外向性": {
-        "场景1": {
-            "scenario": "你刚刚搬到一个新的城市工作。今天是你入职的第一天，在公司的电梯里，一位同事微笑着和你打招呼：\n\n同事：「嗨！你是新来的同事吧？我是市场部的小林。」\n\n同事看起来很友善，还主动介绍说：「待会午饭时间，我们部门有几个人准备一起去楼下新开的餐厅，你要一起来吗？可以认识一下其他同事。」",
-            "explanation": "这个场景通过职场社交情境，观察个体对于新环境、新社交圈的态度和反应倾向。"
-        },
-        "场景2": {
-            "scenario": "在大学班级群里，班长发起了一个组织班级联谊活动的投票：\n\n班长：「大家好！下周末我们准备举办一次班级联谊活动，地点在学校附近的KTV。想请大家报名参加，也欢迎大家邀请其他班级的同学！」\n\n已经有几个同学在群里积极响应，有人@你问你要不要一起参加。",
-            "explanation": "通过班级活动场景，观察个体对群体社交活动的参与意愿。"
-        },
-        "场景3": {
-            "scenario": "你在社交平台上发布了一条动态，收到了很多陌生网友的评论和私信：\n\n网友A：「你说的这个观点很有意思！想和你多交流一下。」\n\n网友B：「我也对这个话题很感兴趣，要不要建个群一起讨论？」",
-            "explanation": "通过网络社交场景，观察个体对线上社交的态度。"
-        },
-        "场景4": {
-            "scenario": "你暗恋的对象今天主动来找你：\n\n对方：「那个...我最近在准备一个演讲比赛，听说你口才很好。能不能请你帮我看看演讲稿，顺便给我一些建议？如果你有时间的话，可以一起吃个饭聊聊。」",
-            "explanation": "通过恋爱情境，观察个体在面对心仪对象时的社交表现。"
-        },
-        "场景5": {
-            "scenario": "在一次线下读书会上，主持人突然点名让你分享读后感：\n\n主持人：「听说你对这本书很有见解，能不能和大家分享一下你的想法？」\n\n现场有二十多个陌生的读书爱好者，都期待地看着你。",
-            "explanation": "通过即兴发言场景，观察个体的社交表现欲和公众表达能力。"
-        }
-    },
-    "神经质": {
-        "场景1": {
-            "scenario": "你正在准备一个重要的项目演示，这关系到你的晋升机会。就在演示前30分钟，你收到了主管发来的消息：\n\n主管：「临时有个变动，CEO也会来听你的演示。他对这个项目特别感兴趣。」\n\n正当你准备回复时，主管又发来一条：「对了，能不能把演示时间压缩到15分钟？CEO下午还有其他安排。你之前准备的是30分钟的版本对吧？」",
-            "explanation": "这个场景通过突发的压力情境，观察个体在面对计划外变化时的情绪反应和调节能力。"
-        },
-        "场景2": {
-            "scenario": "期末考试前一天晚上，你收到了好朋友发来的消息：\n\n好朋友：「不好意思这么晚打扰你...我看你平时成绩很好，能不能帮我解答几个问题？我真的很担心明天的考试。」\n\n你看了看时间，已经是晚上11点，而你原本计划的复习还没完成。",
-            "explanation": "通过考试压力场景，观察个体在时间紧张时的情绪管理。"
-        },
-        "场景3": {
-            "scenario": "你在社交媒体上发表的一个观点引发了争议，有不少人开始批评你：\n\n网友A：「这种观点也好意思说出来，真是无知。」\n\n网友B：「建议楼主先去补补课再来发言。」\n\n评论区里的负面评论越来越多，还有人开始人身攻击。",
-            "explanation": "通过网络争议场景，观察个体面对批评时的心理承受能力。"
-        },
-        "场景4": {
-            "scenario": "你和恋人约好今天一起看电影，但在约定时间前半小时，对方发来消息：\n\n恋人：「对不起，我临时有点事，可能要迟到一会儿。」\n\n二十分钟后，对方又发来消息：「可能要再等等，抱歉！」\n\n电影快要开始了，但对方还是没有出现。",
-            "explanation": "通过恋爱情境，观察个体对不确定性的忍耐程度。"
-        },
-        "场景5": {
-            "scenario": "在一次重要的小组展示中，你的组员在演示途中突然卡壳了：\n\n组员小声对你说：「我忘词了，接下来的部分是什么来着...」\n\n台下的老师和同学都在等待，气氛有些尴尬。",
-            "explanation": "通过公开场合的突发状况，观察个体的应急反应和压力处理能力。"
-        }
-    },
-    "严谨性": {
-        "场景1": {
-            "scenario": "你是团队的项目负责人，刚刚接手了一个为期两个月的重要项目。在第一次团队会议上：\n\n小王：「老大，我觉得两个月时间很充裕，我们先做着看吧，遇到问题再解决。」\n\n小张：「要不要先列个时间表？不过感觉太详细的计划也没必要，点到为止就行。」\n\n小李：「客户那边说如果能提前完成有奖励，我觉得我们可以先做快一点的部分。」",
-            "explanation": "这个场景通过项目管理情境，体现个体在工作方法、计划性和责任心方面的特征。"
-        },
-        "场景2": {
-            "scenario": "期末小组作业，组长让大家分工完成一份研究报告。在截止日期前三天：\n\n组员A：「我的部分大概写完了，感觉还行。」\n\n组员B：「我这边可能还要一天才能完成，最近太忙了。」\n\n组员C发来一份没有任何引用出处、可能存在抄袭的内容：「我写完了，你们看看怎么样？」",
-            "explanation": "通过学习场景，观察个体对学术规范和质量要求的重视程度。"
-        },
-        "场景3": {
-            "scenario": "你在一个兴趣小组的群聊中，大家正在讨论举办一次线下活动：\n\n成员A：「到时候见面就知道具体怎么玩了！」\n\n成员B：「对啊，随意一点挺好的。」\n\n成员C：「人来了自然就热闹了。」",
-            "explanation": "通过活动组织场景，观察个体对活动计划的态度。"
-        },
-        "场景4": {
-            "scenario": "你的好友小明邀请你一起参加一个重要的演出活动，他说：\n\n小明：「到时候我们就即兴发挥吧！不用排练了，我相信我们的默契。」\n\n距离演出还有三天，但节目内容、配乐和服装都还没有确定。",
-            "explanation": "通过演出准备场景，观察个体的计划性和对不确定性的接受程度。"
-        },
-        "场景5": {
-            "scenario": "在一个重要的团队项目中，你发现一个同事的工作存在明显错误：\n\n同事：「差不多就行了，反正领导也看不出来。」\n\n这个错误可能不会立即造成问题，但长期来看可能会影响项目质量。",
-            "explanation": "通过工作质量场景，观察个体对细节和标准的坚持程度。"
-        }
-    },
-    "开放性": {
-        "场景1": {
-            "scenario": "周末下午，你的好友小美兴致勃勃地给你打电话：\n\n小美：「我刚发现一个特别有意思的沉浸式艺术展！不是传统那种挂画的展览，而是把整个空间都变成了艺术品。观众要穿特制的服装，还要带上VR眼镜，好像还有AI实时互动！」\n\n小美继续说：「虽然票价不便宜，但听说体验很独特。网上评价两极分化，有人说是前所未有的艺术革新，也有人说是哗众取宠。要不要周末一起去体验一下？」",
-            "explanation": "这个场景通过新型艺术体验，反映个体对创新事物的接受程度和尝试意愿。"
-        },
-        "场景2": {
-            "scenario": "在一节创意写作课上，老师提出了一个特别的作业：\n\n老师：「下周的作业是用AI写作工具协助创作一篇小说。你们可以自由探索如何与AI合作，打破传统写作方式。」\n\n班上随即展开了激烈讨论，有人认为这是对创作的亵渎，也有人对这种新形式感到兴奋。",
-            "explanation": "通过新技术应用场景，观察个体对创新学习方式的态度。"
-        },
-        "场景3": {
-            "scenario": "在社交媒体上，你看到一个朋友分享了一种新的学习方式：\n\n「最近我在尝试'沉浸式学习'，就是完全投入到一个全新的领域。比如学习一门陌生的语言，或者尝试完全不同的职业技能。虽然过程会很辛苦，但这种打破舒适圈的感觉真的很棒！」\n\n评论区里争论不断，有人认为这种学习方式效率高，也有人觉得太激进。",
-            "explanation": "通过新型学习方式，观察个体对创新和挑战的态度。"
-        },
-        "场景4": {
-            "scenario": "你的朋友向你推荐了一种新的饮食方式：\n\n朋友：「我最近在尝试'未来食品'，比如人造肉、3D打印食物、昆虫蛋白等。这不仅对环境友好，营养也很均衡。要不要一起来尝试看看？」\n\n这个提议让你感到好奇又犹豫，你之前从未尝试过这些新型食物。",
-            "explanation": "通过饮食创新场景，观察个体对新事物的接受度和尝试精神。"
-        },
-        "场景5": {
-            "scenario": "在一次朋友聚会上，大家正在讨论未来职业规划：\n\n朋友A：「我准备辞职去做自媒体，专门介绍一些小众的文化和艺术。」\n\n朋友B：「我想去学习生物科技，准备转行做人造肉研发。」\n\n朋友C：「我在考虑加入一个区块链创业项目，虽然风险很大。」",
-            "explanation": "通过职业选择场景，观察个体对新兴领域的探索意愿。"
-        }
-    },
-    "宜人性": {
-        "场景1": {
-            "scenario": "在回家的公交车上，你遇到这样一幕：\n\n一位老奶奶颤颤巍巍地上了车，车上座位已经坐满了。她站在你旁边，看起来很疲惫。这时你听到前排两个年轻人的对话：\n\n年轻人A：「那个老太太好像站不稳，看起来挺累的。」\n\n年轻人B：「现在的老年人真是...我看她包里还有菜，肯定是去菜市场买完菜回来的，这么多人都不知道叫子女开车接送。」\n\n就在这时，老奶奶一个趔趄，差点摔倒。她扶住了扶手，但包里的东西洒了一些出来。",
-            "explanation": "这个场景通过公共场合的助人情境，体现个体的同理心和对他人需求的关注程度。"
-        },
-        "场景2": {
-            "scenario": "在班级群里，有同学发起为生病住院的同学捐款：\n\n同学A：「大家好，小林最近得了重病住院，医药费很贵，家里负担很重。我们要不要一起帮帮他？」\n\n同学B：「我觉得这是他家里的事，我们不方便参与吧。」\n\n同学C：「但是都是同学一场，帮帮忙也是应该的。」",
-            "explanation": "通过同学互助场景，观察个体的助人意愿和同理心。"
-        },
-        "场景3": {
-            "scenario": "在一个网络讨论组里，有人发布了求助信息：\n\n求助者：「最近心情很低落，感觉生活很压抑，不知道该怎么办...」\n\n评论区里已经有一些回复：\n「生活本来就是这样，想开点！」\n「你这样子太消极了，要积极面对。」\n「谁还没点烦心事啊，过段时间就好了。」",
-            "explanation": "通过网络互助场景，观察个体的共情能力和安慰方式。"
-        },
-        "场景4": {
-            "scenario": "你的朋友向你倾诉工作压力：\n\n朋友：「最近工作真的好累，感觉快坚持不下去了...」\n\n但今天你也遇到了很多烦心事，心情也不太好。",
-            "explanation": "通过感情关系场景，观察个体在自身状态不佳时的关怀能力。"
-        },
-        "场景5": {
-            "scenario": "在一次团队项目中，新来的同事小王因为经验不足，造成了一个严重的错误。在部门会议上：\n\n主管：「这个错误造成了很大的损失，是谁负责的这部分？」\n\n小王看起来很紧张，欲言又止。你知道是他造成的错误，同时你也是这个项目的共同负责人。",
-            "explanation": "通过职场情境，观察个体在面对他人过错时的态度和处理方式。"
-        }
-    }
-} 

src/main.py

@@ -10,7 +10,6 @@ from src.chat.message_receive.chat_stream import get_chat_manager
 from src.config.config import global_config
 from src.chat.message_receive.bot import chat_bot
 from src.common.logger import get_logger
-from src.individuality.individuality import get_individuality, Individuality
 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
@@ -44,8 +43,6 @@ class MainSystem:
         else:
             self.hippocampus_manager = None
 
-        self.individuality: Individuality = get_individuality()
-
         # 使用消息API替代直接的FastAPI实例
         self.app: MessageServer = get_global_api()
         self.server: Server = get_global_server()
@@ -122,9 +119,6 @@ class MainSystem:
         # 将bot.py中的chat_bot.message_process消息处理函数注册到api.py的消息处理基类中
         self.app.register_message_handler(chat_bot.message_process)
         
-        # 初始化个体特征
-        await self.individuality.initialize()
-        
         await check_and_run_migrations()
         
 

src/person_info/person_info.py

@@ -637,7 +637,6 @@ class PersonInfoManager:
         current_name_set = set(self.person_name_list.values())
 
         while current_try < max_retries:
-            # prompt_personality =get_individuality().get_prompt(x_person=2, level=1)
             bot_name = global_config.bot.nickname
 
             qv_name_prompt = f"你是{bot_name}，一个伪装成人类的AI，你不能让别人发现这一点，"

src/person_info/relationship_builder.py

@@ -4,9 +4,10 @@ import os
 import pickle
 import random
 import asyncio
-from typing import List, Dict, Any, TYPE_CHECKING
+from typing import List, Dict, Any
 from src.config.config import global_config
 from src.common.logger import get_logger
+from src.common.data_models.database_data_model import DatabaseMessages
 from src.person_info.relationship_manager import get_relationship_manager
 from src.person_info.person_info import Person, get_person_id
 from src.chat.message_receive.chat_stream import get_chat_manager
@@ -17,8 +18,6 @@ from src.chat.utils.chat_message_builder import (
     num_new_messages_since,
 )
 
-if TYPE_CHECKING:
-    from src.common.data_models.database_data_model import DatabaseMessages
 
 logger = get_logger("relationship_builder")
 

src/plugin_system/apis/generator_api.py

@@ -21,6 +21,7 @@ from src.plugin_system.base.component_types import ActionInfo
 if TYPE_CHECKING:
     from src.common.data_models.info_data_model import ActionPlannerInfo
     from src.common.data_models.database_data_model import DatabaseMessages
+    from src.common.data_models.llm_data_model import LLMGenerationDataModel
 
 install(extra_lines=3)
 
@@ -85,11 +86,9 @@ async def generate_reply(
     enable_tool: bool = False,
     enable_splitter: bool = True,
     enable_chinese_typo: bool = True,
-    return_prompt: bool = False,
     request_type: str = "generator_api",
     from_plugin: bool = True,
-    return_expressions: bool = False,
+) -> Tuple[bool, Optional["LLMGenerationDataModel"]]:
-) -> Tuple[bool, List[Tuple[str, Any]], Optional[str], Optional[List[int]]]:
     """生成回复
 
     Args:
@@ -117,7 +116,7 @@ async def generate_reply(
         replyer = get_replyer(chat_stream, chat_id, request_type=request_type)
         if not replyer:
             logger.error("[GeneratorAPI] 无法获取回复器")
-            return False, [], None, None
+            return False, None
 
         if not extra_info and action_data:
             extra_info = action_data.get("extra_info", "")
@@ -126,7 +125,7 @@ async def generate_reply(
             reply_reason = action_data.get("reason", "")
 
         # 调用回复器生成回复
-        success, llm_response_dict, prompt, selected_expressions = await replyer.generate_reply_with_context(
+        success, llm_response = await replyer.generate_reply_with_context(
             extra_info=extra_info,
             available_actions=available_actions,
             chosen_actions=chosen_actions,
@@ -138,43 +137,27 @@ async def generate_reply(
         )
         if not success:
             logger.warning("[GeneratorAPI] 回复生成失败")
-            return False, [], None, None
+            return False, None
-        assert llm_response_dict is not None, "llm_response_dict不应为None"  # 虽然说不会出现llm_response为空的情况
+        if content := llm_response.content:
-        if content := llm_response_dict.get("content", ""):
             reply_set = process_human_text(content, enable_splitter, enable_chinese_typo)
         else:
             reply_set = []
+        llm_response.reply_set = reply_set
         logger.debug(f"[GeneratorAPI] 回复生成成功，生成了 {len(reply_set)} 个回复项")
 
-        # if return_prompt:
+        return success, llm_response
-        #     if return_expressions:
-        #         return success, reply_set, prompt, selected_expressions
-        #     else:
-        #         return success, reply_set, prompt, None
-        # else:
-        #     if return_expressions:
-        #         return success, reply_set, (None, selected_expressions)
-        #     else:
-        #         return success, reply_set, None
-        return (
-            success,
-            reply_set,
-            prompt if return_prompt else None,
-            selected_expressions if return_expressions else None,
-        )
 
     except ValueError as ve:
         raise ve
 
     except UserWarning as uw:
         logger.warning(f"[GeneratorAPI] 中断了生成: {uw}")
-        return False, [], None, None
+        return False, None
 
     except Exception as e:
         logger.error(f"[GeneratorAPI] 生成回复时出错: {e}")
         logger.error(traceback.format_exc())
-        return False, [], None, None
+        return False, None
-
 
 async def rewrite_reply(
     chat_stream: Optional[ChatStream] = None,
@@ -185,9 +168,8 @@ async def rewrite_reply(
     raw_reply: str = "",
     reason: str = "",
     reply_to: str = "",
-    return_prompt: bool = False,
     request_type: str = "generator_api",
-) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]:
+) -> Tuple[bool, Optional["LLMGenerationDataModel"]]:
     """重写回复
 
     Args:
@@ -210,7 +192,7 @@ async def rewrite_reply(
         replyer = get_replyer(chat_stream, chat_id, request_type=request_type)
         if not replyer:
             logger.error("[GeneratorAPI] 无法获取回复器")
-            return False, [], None
+            return False, None
 
         logger.info("[GeneratorAPI] 开始重写回复")
 
@@ -221,29 +203,28 @@ async def rewrite_reply(
             reply_to = reply_to or reply_data.get("reply_to", "")
 
         # 调用回复器重写回复
-        success, content, prompt = await replyer.rewrite_reply_with_context(
+        success, llm_response = await replyer.rewrite_reply_with_context(
             raw_reply=raw_reply,
             reason=reason,
             reply_to=reply_to,
-            return_prompt=return_prompt,
         )
         reply_set = []
-        if content:
+        if success and llm_response and (content := llm_response.content):
             reply_set = process_human_text(content, enable_splitter, enable_chinese_typo)
-
+        llm_response.reply_set = reply_set
         if success:
             logger.info(f"[GeneratorAPI] 重写回复成功，生成了 {len(reply_set)} 个回复项")
         else:
             logger.warning("[GeneratorAPI] 重写回复失败")
 
-        return success, reply_set, prompt if return_prompt else None
+        return success, llm_response
 
     except ValueError as ve:
         raise ve
 
     except Exception as e:
         logger.error(f"[GeneratorAPI] 重写回复时出错: {e}")
-        return False, [], None
+        return False, None
 
 
 def process_human_text(content: str, enable_splitter: bool, enable_chinese_typo: bool) -> List[Tuple[str, Any]]:

src/plugin_system/base/base_events_handler.py

@@ -38,9 +38,10 @@ class BaseEventHandler(ABC):
                 self.subcribe(event_name)
 
     @abstractmethod
-    async def execute(self, message: MaiMessages) -> Tuple[bool, bool, Optional[str]]:
+    async def execute(self, message: MaiMessages | None) -> Tuple[bool, bool, Optional[str]]:
         """执行事件处理的抽象方法，子类必须实现
-
+        Args:
+            message (MaiMessages | None): 事件消息对象，当你注册的事件为ON_START和ON_STOP时message为None
         Returns:
             Tuple[bool, bool, Optional[str]]: (是否执行成功, 是否需要继续处理, 可选的返回消息)
         """

src/plugin_system/base/component_types.py

@@ -116,8 +116,8 @@ class ActionInfo(ComponentInfo):
     action_require: List[str] = field(default_factory=list)  # 动作需求说明
     associated_types: List[str] = field(default_factory=list)  # 关联的消息类型
     # 激活类型相关
-    focus_activation_type: ActionActivationType = ActionActivationType.ALWAYS
+    focus_activation_type: ActionActivationType = ActionActivationType.ALWAYS #已弃用
-    normal_activation_type: ActionActivationType = ActionActivationType.ALWAYS
+    normal_activation_type: ActionActivationType = ActionActivationType.ALWAYS #已弃用
     activation_type: ActionActivationType = ActionActivationType.ALWAYS 
     random_activation_probability: float = 0.0
     llm_judge_prompt: str = ""

src/plugin_system/core/events_manager.py

@@ -1,6 +1,6 @@
 import asyncio
 import contextlib
-from typing import List, Dict, Optional, Type, Tuple, Any, Coroutine
+from typing import List, Dict, Optional, Type, Tuple, Any, TYPE_CHECKING
 
 from src.chat.message_receive.message import MessageRecv
 from src.chat.message_receive.chat_stream import get_chat_manager
@@ -9,6 +9,9 @@ from src.plugin_system.base.component_types import EventType, EventHandlerInfo,
 from src.plugin_system.base.base_events_handler import BaseEventHandler
 from .global_announcement_manager import global_announcement_manager
 
+if TYPE_CHECKING:
+    from src.common.data_models.llm_data_model import LLMGenerationDataModel
+
 logger = get_logger("events_manager")
 
 
@@ -47,7 +50,7 @@ class EventsManager:
         event_type: EventType,
         message: Optional[MessageRecv] = None,
         llm_prompt: Optional[str] = None,
-        llm_response: Optional[Dict[str, Any]] = None,
+        llm_response: Optional["LLMGenerationDataModel"] = None,
         stream_id: Optional[str] = None,
         action_usage: Optional[List[str]] = None,
     ) -> Optional[MaiMessages]:
@@ -64,20 +67,10 @@ class EventsManager:
 
         return None  # ON_START, ON_STOP事件没有消息体
 
-    def _execute_handler(
-        self, 
-        handler: BaseEventHandler, 
-        message: Optional[MaiMessages]
-    ) -> Coroutine[Any, Any, tuple[bool, bool, Any]]:
-        """封装了调用 handler.execute 的逻辑。"""
-        return handler.execute(message) if message else handler.execute()
-        
     def _dispatch_handler_task(self, handler: BaseEventHandler, message: Optional[MaiMessages]):
         """分发一个非阻塞（异步）的事件处理任务。"""
         try:
-            # 无论是否有 message，都统一调用
+            task = asyncio.create_task(handler.execute(message))
-            coro = self._execute_handler(handler, message)
-            task = asyncio.create_task(coro)
 
             task_name = f"{handler.plugin_name}-{handler.handler_name}"
             task.set_name(task_name)
@@ -87,15 +80,10 @@ class EventsManager:
         except Exception as e:
             logger.error(f"创建事件处理器任务 {handler.handler_name} 时发生异常: {e}", exc_info=True)
 
-    async def _dispatch_intercepting_handler(
+    async def _dispatch_intercepting_handler(self, handler: BaseEventHandler, message: Optional[MaiMessages]) -> bool:
-        self, 
-        handler: BaseEventHandler, 
-        message: Optional[MaiMessages]
-    ) -> bool:
         """分发并等待一个阻塞（同步）的事件处理器，返回是否应继续处理。"""
         try:
-            # 统一调用
+            success, continue_processing, result = await handler.execute(message)
-            success, continue_processing, result = await self._execute_handler(handler, message)
 
             if not success:
                 logger.error(f"EventHandler {handler.handler_name} 执行失败: {result}")
@@ -112,7 +100,7 @@ class EventsManager:
         event_type: EventType,
         message: Optional[MessageRecv] = None,
         llm_prompt: Optional[str] = None,
-        llm_response: Optional[Dict[str, Any]] = None,
+        llm_response: Optional["LLMGenerationDataModel"] = None,
         stream_id: Optional[str] = None,
         action_usage: Optional[List[str]] = None,
     ) -> bool:
@@ -137,7 +125,11 @@ class EventsManager:
 
         for handler in handlers:
             # 3. 前置检查和配置加载
-            if current_stream_id and handler.handler_name in global_announcement_manager.get_disabled_chat_event_handlers(current_stream_id):
+            if (
+                current_stream_id
+                and handler.handler_name
+                in global_announcement_manager.get_disabled_chat_event_handlers(current_stream_id)
+            ):
                 continue
 
             # 统一加载插件配置
@@ -181,16 +173,16 @@ class EventsManager:
         return False
 
     def _transform_event_message(
-        self, message: MessageRecv, llm_prompt: Optional[str] = None, llm_response: Optional[Dict[str, Any]] = None
+        self, message: MessageRecv, llm_prompt: Optional[str] = None, llm_response: Optional["LLMGenerationDataModel"] = None
     ) -> MaiMessages:
         """转换事件消息格式"""
         # 直接赋值部分内容
         transformed_message = MaiMessages(
             llm_prompt=llm_prompt,
-            llm_response_content=llm_response.get("content") if llm_response else None,
+            llm_response_content=llm_response.content if llm_response else None,
-            llm_response_reasoning=llm_response.get("reasoning") if llm_response else None,
+            llm_response_reasoning=llm_response.reasoning if llm_response else None,
-            llm_response_model=llm_response.get("model") if llm_response else None,
+            llm_response_model=llm_response.model if llm_response else None,
-            llm_response_tool_call=llm_response.get("tool_calls") if llm_response else None,
+            llm_response_tool_call=llm_response.tool_calls if llm_response else None,
             raw_message=message.raw_message,
             additional_data=message.message_info.additional_config or {},
         )
@@ -234,7 +226,7 @@ class EventsManager:
         return transformed_message
 
     def _build_message_from_stream(
-        self, stream_id: str, llm_prompt: Optional[str] = None, llm_response: Optional[Dict[str, Any]] = None
+        self, stream_id: str, llm_prompt: Optional[str] = None, llm_response: Optional["LLMGenerationDataModel"] = None
     ) -> MaiMessages:
         """从流ID构建消息"""
         chat_stream = get_chat_manager().get_stream(stream_id)
@@ -246,7 +238,7 @@ class EventsManager:
         self,
         stream_id: str,
         llm_prompt: Optional[str] = None,
-        llm_response: Optional[Dict[str, Any]] = None,
+        llm_response: Optional["LLMGenerationDataModel"] = None,
         action_usage: Optional[List[str]] = None,
     ) -> MaiMessages:
         """没有message对象时进行转换"""
@@ -255,10 +247,10 @@ class EventsManager:
         return MaiMessages(
             stream_id=stream_id,
             llm_prompt=llm_prompt,
-            llm_response_content=(llm_response.get("content") if llm_response else None),
+            llm_response_content=(llm_response.content if llm_response else None),
-            llm_response_reasoning=(llm_response.get("reasoning") if llm_response else None),
+            llm_response_reasoning=(llm_response.reasoning if llm_response else None),
-            llm_response_model=llm_response.get("model") if llm_response else None,
+            llm_response_model=(llm_response.model if llm_response else None),
-            llm_response_tool_call=(llm_response.get("tool_calls") if llm_response else None),
+            llm_response_tool_call=(llm_response.tool_calls if llm_response else None),
             is_group_message=(not (not chat_stream.group_info)),
             is_private_message=(not chat_stream.group_info),
             action_usage=action_usage,

template/bot_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "6.5.0"
+version = "6.7.1"
 
 #----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
@@ -22,6 +22,7 @@ alias_names = ["麦叠", "牢麦"] # 麦麦的别名
 personality_core = "是一个女孩子" 
 # 人格的细节，描述人格的一些侧面
 personality_side = "有时候说话不过脑子,喜欢开玩笑, 有时候会表现得无语,有时候会喜欢说一些奇怪的话"
+
 #アイデンティティがない 生まれないらららら
 # 可以描述外貌，性别，身高，职业，属性等等描述
 identity = "年龄为19岁,是女孩子,身高为160cm,有黑色的短发"
@@ -29,10 +30,11 @@ identity = "年龄为19岁,是女孩子,身高为160cm,有黑色的短发"
 # 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 = "回复可以简短一些。可以参考贴吧，知乎和微博的回复风格，回复不要浮夸，不要用夸张修辞，平淡一些。不要浮夸，不要夸张修辞。"
 
+# 描述麦麦的行为风格，会影响麦麦什么时候回复，什么时候使用动作，麦麦考虑的可就多了
 plan_style = "当你刚刚发送了消息，没有人回复时，不要选择action，如果有别的动作（非回复）满足条件，可以选择，当你一次发送了太多消息，为了避免打扰聊天节奏，不要选择动作"
 
-compress_personality = false # 是否压缩人格，压缩后会精简人格信息，节省token消耗并提高回复性能，但是会丢失一些信息，如果人设不长，可以关闭
+# 麦麦的兴趣，会影响麦麦对什么话题进行回复
-compress_identity = true # 是否压缩身份，压缩后会精简身份信息，节省token消耗并提高回复性能，但是会丢失一些信息，如果不长，可以关闭
+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"
 
 [expression]
 # 表达学习配置
@@ -63,6 +65,10 @@ focus_value = 0.5
 
 max_context_size = 20 # 上下文长度
 
+interest_rate_mode = "fast" #激活值计算模式，可选fast或者accurate
+
+planner_size = 2 # 副规划器大小，越小，麦麦的动作执行能力越精细，但是消耗更多token，调大可以缓解429类错误
+
 mentioned_bot_inevitable_reply = true # 提及 bot 大概率回复
 at_bot_inevitable_reply = true # @bot 或 提及bot 大概率回复
 

template/model_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "1.3.1"
+version = "1.4.1"
 
 # 配置文件版本号迭代规则同bot_config.toml
 
@@ -40,14 +40,14 @@ price_out = 8.0                    # 输出价格（用于API调用统计，单
 #force_stream_mode = true          # 强制流式输出模式（若模型不支持非流式输出，请取消该注释，启用强制流式输出，若无该字段，默认值为false）
 
 [[models]]
-model_identifier = "Pro/deepseek-ai/DeepSeek-V3"
+model_identifier = "deepseek-ai/DeepSeek-V3"
 name = "siliconflow-deepseek-v3"
 api_provider = "SiliconFlow"
 price_in = 2.0
 price_out = 8.0
 
 [[models]]
-model_identifier = "Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
+model_identifier = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
 name = "deepseek-r1-distill-qwen-32b"
 api_provider = "SiliconFlow"
 price_in = 4.0
@@ -117,11 +117,16 @@ model_list = ["siliconflow-deepseek-v3"]
 temperature = 0.2                        # 模型温度，新V3建议0.1-0.3
 max_tokens = 800
 
-[model_task_config.planner] #决策：负责决定麦麦该做什么的模型
+[model_task_config.planner] #决策：负责决定麦麦该什么时候回复的模型
 model_list = ["siliconflow-deepseek-v3"]
 temperature = 0.3
 max_tokens = 800
 
+[model_task_config.planner_small] #副决策：负责决定麦麦该做什么的模型
+model_list = ["qwen3-14b"]
+temperature = 0.3
+max_tokens = 800
+
 [model_task_config.emotion] #负责麦麦的情绪变化
 model_list = ["siliconflow-deepseek-v3"]
 temperature = 0.3