Merge branch 'dev' into dev-api-ada to resolve conflicts

src/chat/chat_loop/heartFC_chat.py

@@ -2,7 +2,7 @@ import asyncio
 import time
 import traceback
 import random
-from typing import List, Optional, Dict, Any
+from typing import List, Optional, Dict, Any, Tuple
 from rich.traceback import install
 
 from src.config.config import global_config
@@ -18,11 +18,12 @@ from src.chat.chat_loop.hfc_utils import CycleDetail
 from src.person_info.relationship_builder_manager import relationship_builder_manager
 from src.person_info.person_info import get_person_info_manager
 from src.plugin_system.base.component_types import ActionInfo, ChatMode
-from src.plugin_system.apis import generator_api, send_api, message_api
+from src.plugin_system.apis import generator_api, send_api, message_api, database_api
 from src.chat.willing.willing_manager import get_willing_manager
 from src.mais4u.mai_think import mai_thinking_manager
-from maim_message.message_base import GroupInfo
 from src.mais4u.constant_s4u import ENABLE_S4U
+from src.plugins.built_in.core_actions.no_reply import NoReplyAction
+from src.chat.chat_loop.hfc_utils import send_typing, stop_typing
 
 ERROR_LOOP_INFO = {
     "loop_plan_info": {
@@ -88,11 +89,6 @@ class HeartFChatting:
 
         self.loop_mode = ChatMode.NORMAL  # 初始循环模式为普通模式
 
-        # 新增：消息计数器和疲惫阈值
-        self._message_count = 0  # 发送的消息计数
-        self._message_threshold = max(10, int(30 * global_config.chat.focus_value))
-        self._fatigue_triggered = False  # 是否已触发疲惫退出
-
         self.action_manager = ActionManager()
         self.action_planner = ActionPlanner(chat_id=self.stream_id, action_manager=self.action_manager)
         self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.stream_id)
@@ -112,7 +108,6 @@ class HeartFChatting:
 
         self.last_read_time = time.time() - 1
 
-        self.willing_amplifier = 1
         self.willing_manager = get_willing_manager()
 
         logger.info(f"{self.log_prefix} HeartFChatting 初始化完成")
@@ -182,6 +177,9 @@ class HeartFChatting:
             if self.loop_mode == ChatMode.NORMAL:
                 self.energy_value -= 0.3
                 self.energy_value = max(self.energy_value, 0.3)
+            if self.loop_mode == ChatMode.FOCUS:
+                self.energy_value -= 0.6
+                self.energy_value = max(self.energy_value, 0.3)
 
     def print_cycle_info(self, cycle_timers):
         # 记录循环信息和计时器结果
@@ -200,9 +198,9 @@ class HeartFChatting:
     async def _loopbody(self):
         if self.loop_mode == ChatMode.FOCUS:
             if await self._observe():
-                self.energy_value -= 1 * global_config.chat.focus_value
+                self.energy_value -= 1 / global_config.chat.focus_value
             else:
-                self.energy_value -= 3 * global_config.chat.focus_value
+                self.energy_value -= 3 / global_config.chat.focus_value
             if self.energy_value <= 1:
                 self.energy_value = 1
                 self.loop_mode = ChatMode.NORMAL
@@ -218,15 +216,17 @@ class HeartFChatting:
                 limit_mode="earliest",
                 filter_bot=True,
             )
+            if global_config.chat.focus_value != 0:
+                if len(new_messages_data) > 3 / pow(global_config.chat.focus_value, 0.5):
+                    self.loop_mode = ChatMode.FOCUS
+                    self.energy_value = (
+                        10 + (len(new_messages_data) / (3 / pow(global_config.chat.focus_value, 0.5))) * 10
+                    )
+                    return True
 
-            if len(new_messages_data) > 3 * global_config.chat.focus_value:
-                self.loop_mode = ChatMode.FOCUS
-                self.energy_value = 10 + (len(new_messages_data) / (3 * global_config.chat.focus_value)) * 10
-                return True
-
-            if self.energy_value >= 30 * global_config.chat.focus_value:
-                self.loop_mode = ChatMode.FOCUS
-                return True
+                if self.energy_value >= 30:
+                    self.loop_mode = ChatMode.FOCUS
+                    return True
 
             if new_messages_data:
                 earliest_messages_data = new_messages_data[0]
@@ -235,10 +235,10 @@ class HeartFChatting:
                 if_think = await self.normal_response(earliest_messages_data)
                 if if_think:
                     factor = max(global_config.chat.focus_value, 0.1)
-                    self.energy_value *= 1.1 / factor
+                    self.energy_value *= 1.1 * factor
                     logger.info(f"{self.log_prefix} 进行了思考，能量值按倍数增加，当前能量值：{self.energy_value:.1f}")
                 else:
-                    self.energy_value += 0.1 / global_config.chat.focus_value
+                    self.energy_value += 0.1 * global_config.chat.focus_value
                     logger.debug(f"{self.log_prefix} 没有进行思考，能量值线性增加，当前能量值：{self.energy_value:.1f}")
 
                 logger.debug(f"{self.log_prefix} 当前能量值：{self.energy_value:.1f}")
@@ -257,44 +257,69 @@ class HeartFChatting:
         person_name = await person_info_manager.get_value(person_id, "person_name")
         return f"{person_name}:{message_data.get('processed_plain_text')}"
 
-    async def send_typing(self):
-        group_info = GroupInfo(platform="amaidesu_default", group_id="114514", group_name="内心")
+    async def _send_and_store_reply(
+        self,
+        response_set,
+        reply_to_str,
+        loop_start_time,
+        action_message,
+        cycle_timers: Dict[str, float],
+        thinking_id,
+        plan_result,
+    ) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
+        with Timer("回复发送", cycle_timers):
+            reply_text = await self._send_response(response_set, reply_to_str, loop_start_time, action_message)
 
-        chat = await get_chat_manager().get_or_create_stream(
-            platform="amaidesu_default",
-            user_info=None,
-            group_info=group_info,
+            # 存储reply action信息
+        person_info_manager = get_person_info_manager()
+        person_id = person_info_manager.get_person_id(
+            action_message.get("chat_info_platform", ""),
+            action_message.get("user_id", ""),
+        )
+        person_name = await person_info_manager.get_value(person_id, "person_name")
+        action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
+
+        await database_api.store_action_info(
+            chat_stream=self.chat_stream,
+            action_build_into_prompt=False,
+            action_prompt_display=action_prompt_display,
+            action_done=True,
+            thinking_id=thinking_id,
+            action_data={"reply_text": reply_text, "reply_to": reply_to_str},
+            action_name="reply",
         )
 
-        await send_api.custom_to_stream(
-            message_type="state", content="typing", stream_id=chat.stream_id, storage_message=False
-        )
+        # 构建循环信息
+        loop_info: Dict[str, Any] = {
+            "loop_plan_info": {
+                "action_result": plan_result.get("action_result", {}),
+            },
+            "loop_action_info": {
+                "action_taken": True,
+                "reply_text": reply_text,
+                "command": "",
+                "taken_time": time.time(),
+            },
+        }
 
-    async def stop_typing(self):
-        group_info = GroupInfo(platform="amaidesu_default", group_id="114514", group_name="内心")
-
-        chat = await get_chat_manager().get_or_create_stream(
-            platform="amaidesu_default",
-            user_info=None,
-            group_info=group_info,
-        )
-
-        await send_api.custom_to_stream(
-            message_type="state", content="stop_typing", stream_id=chat.stream_id, storage_message=False
-        )
+        return loop_info, reply_text, cycle_timers
 
     async def _observe(self, message_data: Optional[Dict[str, Any]] = None):
         # sourcery skip: hoist-statement-from-if, merge-comparisons, reintroduce-else
         if not message_data:
             message_data = {}
         action_type = "no_action"
+        reply_text = ""  # 初始化reply_text变量，避免UnboundLocalError
+        gen_task = None  # 初始化gen_task变量，避免UnboundLocalError
+        reply_to_str = ""  # 初始化reply_to_str变量
+
         # 创建新的循环信息
         cycle_timers, thinking_id = self.start_cycle()
 
         logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考[模式：{self.loop_mode}]")
-        
+
         if ENABLE_S4U:
-            await self.send_typing()
+            await send_typing()
 
         async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
             loop_start_time = time.time()
@@ -310,95 +335,254 @@ class HeartFChatting:
                 except Exception as e:
                     logger.error(f"{self.log_prefix} 动作修改失败: {e}")
 
-            # 如果normal，开始一个回复生成进程，先准备好回复（其实是和planer同时进行的）
+            # 检查是否在normal模式下没有可用动作（除了reply相关动作）
+            skip_planner = False
             if self.loop_mode == ChatMode.NORMAL:
-                reply_to_str = await self.build_reply_to_str(message_data)
-                gen_task = asyncio.create_task(self._generate_response(message_data, available_actions, reply_to_str))
+                # 过滤掉reply相关的动作，检查是否还有其他动作
+                non_reply_actions = {
+                    k: v for k, v in available_actions.items() if k not in ["reply", "no_reply", "no_action"]
+                }
 
-            with Timer("规划器", cycle_timers):
-                plan_result, target_message = await self.action_planner.plan(mode=self.loop_mode)
+                if not non_reply_actions:
+                    skip_planner = True
+                    logger.info(f"{self.log_prefix} Normal模式下没有可用动作，直接回复")
 
-            action_result: dict = plan_result.get("action_result", {})  # type: ignore
-            action_type, action_data, reasoning, is_parallel = (
-                action_result.get("action_type", "error"),
-                action_result.get("action_data", {}),
-                action_result.get("reasoning", "未提供理由"),
-                action_result.get("is_parallel", True),
-            )
+                    # 直接设置为reply动作
+                    action_type = "reply"
+                    reasoning = ""
+                    action_data = {"loop_start_time": loop_start_time}
+                    is_parallel = False
 
-            action_data["loop_start_time"] = loop_start_time
+                    # 构建plan_result用于后续处理
+                    plan_result = {
+                        "action_result": {
+                            "action_type": action_type,
+                            "action_data": action_data,
+                            "reasoning": reasoning,
+                            "timestamp": time.time(),
+                            "is_parallel": is_parallel,
+                        },
+                        "action_prompt": "",
+                    }
+                    target_message = message_data
 
-            if self.loop_mode == ChatMode.NORMAL:
-                if action_type == "no_action":
-                    logger.info(f"[{self.log_prefix}] {global_config.bot.nickname} 决定进行回复")
-                elif is_parallel:
-                    logger.info(
-                        f"[{self.log_prefix}] {global_config.bot.nickname} 决定进行回复, 同时执行{action_type}动作"
+                # 如果normal模式且不跳过规划器，开始一个回复生成进程，先准备好回复（其实是和planer同时进行的）
+                if not skip_planner:
+                    reply_to_str = await self.build_reply_to_str(message_data)
+                    gen_task = asyncio.create_task(
+                        self._generate_response(
+                            message_data=message_data,
+                            available_actions=available_actions,
+                            reply_to=reply_to_str,
+                            request_type="chat.replyer.normal",
+                        )
                     )
-                else:
-                    logger.info(f"[{self.log_prefix}] {global_config.bot.nickname} 决定执行{action_type}动作")
 
-            if action_type == "no_action":
+            if not skip_planner:
+                with Timer("规划器", cycle_timers):
+                    plan_result, target_message = await self.action_planner.plan(mode=self.loop_mode)
+
+                action_result: Dict[str, Any] = plan_result.get("action_result", {})  # type: ignore
+                action_type, action_data, reasoning, is_parallel = (
+                    action_result.get("action_type", "error"),
+                    action_result.get("action_data", {}),
+                    action_result.get("reasoning", "未提供理由"),
+                    action_result.get("is_parallel", True),
+                )
+
+                action_data["loop_start_time"] = loop_start_time
+
+            if action_type == "reply":
+                logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复")
+            elif is_parallel:
+                logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复, 同时执行{action_type}动作")
+            else:
+                # 只有在gen_task存在时才进行相关操作
+                if gen_task:
+                    if not gen_task.done():
+                        gen_task.cancel()
+                        logger.debug(f"{self.log_prefix} 已取消预生成的回复任务")
+                        logger.info(
+                            f"{self.log_prefix}{global_config.bot.nickname} 原本想要回复，但选择执行{action_type}，不发表回复"
+                        )
+                    elif generation_result := gen_task.result():
+                        content = " ".join([item[1] for item in generation_result if item[0] == "text"])
+                        logger.debug(f"{self.log_prefix} 预生成的回复任务已完成")
+                        logger.info(
+                            f"{self.log_prefix}{global_config.bot.nickname} 原本想要回复：{content}，但选择执行{action_type}，不发表回复"
+                        )
+                    else:
+                        logger.warning(f"{self.log_prefix} 预生成的回复任务未生成有效内容")
+
+            action_message: Dict[str, Any] = message_data or target_message  # type: ignore
+            if action_type == "reply":
                 # 等待回复生成完毕
-                gather_timeout = global_config.chat.thinking_timeout
-                try:
-                    response_set = await asyncio.wait_for(gen_task, timeout=gather_timeout)
-                except asyncio.TimeoutError:
-                    response_set = None
+                if self.loop_mode == ChatMode.NORMAL:
+                    # 只有在gen_task存在时才等待
+                    if not gen_task:
+                        reply_to_str = await self.build_reply_to_str(message_data)
+                        gen_task = asyncio.create_task(
+                            self._generate_response(
+                                message_data=message_data,
+                                available_actions=available_actions,
+                                reply_to=reply_to_str,
+                                request_type="chat.replyer.normal",
+                            )
+                        )
 
-                if response_set:
-                    content = " ".join([item[1] for item in response_set if item[0] == "text"])
+                    gather_timeout = global_config.chat.thinking_timeout
+                    try:
+                        response_set = await asyncio.wait_for(gen_task, timeout=gather_timeout)
+                    except asyncio.TimeoutError:
+                        logger.warning(f"{self.log_prefix} 回复生成超时>{global_config.chat.thinking_timeout}s，已跳过")
+                        response_set = None
 
-                # 模型炸了，没有回复内容生成
-                if not response_set:
-                    logger.warning(f"[{self.log_prefix}] 模型未生成回复内容")
-                    return False
-                elif action_type not in ["no_action"] and not is_parallel:
-                    logger.info(
-                        f"[{self.log_prefix}] {global_config.bot.nickname} 原本想要回复：{content}，但选择执行{action_type}，不发表回复"
-                    )
-                    return False
+                    # 模型炸了或超时，没有回复内容生成
+                    if not response_set:
+                        logger.warning(f"{self.log_prefix}模型未生成回复内容")
+                        return False
+                else:
+                    logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复 (focus模式)")
 
-                logger.info(f"[{self.log_prefix}] {global_config.bot.nickname} 决定的回复内容: {content}")
+                    # 构建reply_to字符串
+                    reply_to_str = await self.build_reply_to_str(action_message)
 
-                # 发送回复 (不再需要传入 chat)
-                reply_text = await self._send_response(response_set, reply_to_str, loop_start_time,message_data)
-                
-                
-                
-                
-                
-                if ENABLE_S4U:
-                    await self.stop_typing()
-                    await mai_thinking_manager.get_mai_think(self.stream_id).do_think_after_response(reply_text)
+                    # 生成回复
+                    with Timer("回复生成", cycle_timers):
+                        response_set = await self._generate_response(
+                            message_data=action_message,
+                            available_actions=available_actions,
+                            reply_to=reply_to_str,
+                            request_type="chat.replyer.focus",
+                        )
+
+                    if not response_set:
+                        logger.warning(f"{self.log_prefix}模型未生成回复内容")
+                        return False
+
+                loop_info, reply_text, cycle_timers = await self._send_and_store_reply(
+                    response_set, reply_to_str, loop_start_time, action_message, cycle_timers, thinking_id, plan_result
+                )
 
                 return True
 
             else:
-                action_message: Dict[str, Any] = message_data or target_message  # type: ignore
+                # 并行执行：同时进行回复发送和动作执行
+                # 先置空防止未定义错误
+                background_reply_task = None
+                background_action_task = None
+                # 如果是并行执行且在normal模式下，需要等待预生成的回复任务完成并发送回复
+                if self.loop_mode == ChatMode.NORMAL and is_parallel and gen_task:
 
-                # 动作执行计时
-                with Timer("动作执行", cycle_timers):
-                    success, reply_text, command = await self._handle_action(
-                        action_type, reasoning, action_data, cycle_timers, thinking_id, action_message
+                    async def handle_reply_task() -> Tuple[Optional[Dict[str, Any]], str, Dict[str, float]]:
+                        # 等待预生成的回复任务完成
+                        gather_timeout = global_config.chat.thinking_timeout
+                        try:
+                            response_set = await asyncio.wait_for(gen_task, timeout=gather_timeout)
+
+                        except asyncio.TimeoutError:
+                            logger.warning(
+                                f"{self.log_prefix} 并行执行：回复生成超时>{global_config.chat.thinking_timeout}s，已跳过"
+                            )
+                            return None, "", {}
+                        except asyncio.CancelledError:
+                            logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消")
+                            return None, "", {}
+
+                        if not response_set:
+                            logger.warning(f"{self.log_prefix} 模型超时或生成回复内容为空")
+                            return None, "", {}
+
+                        reply_to_str = await self.build_reply_to_str(action_message)
+                        loop_info, reply_text, cycle_timers_reply = await self._send_and_store_reply(
+                            response_set,
+                            reply_to_str,
+                            loop_start_time,
+                            action_message,
+                            cycle_timers,
+                            thinking_id,
+                            plan_result,
+                        )
+                        return loop_info, reply_text, cycle_timers_reply
+
+                    # 执行回复任务并赋值到变量
+                    background_reply_task = asyncio.create_task(handle_reply_task())
+
+                # 动作执行任务
+                async def handle_action_task():
+                    with Timer("动作执行", cycle_timers):
+                        success, reply_text, command = await self._handle_action(
+                            action_type, reasoning, action_data, cycle_timers, thinking_id, action_message
+                        )
+                    return success, reply_text, command
+
+                # 执行动作任务并赋值到变量
+                background_action_task = asyncio.create_task(handle_action_task())
+
+                reply_loop_info = None
+                reply_text_from_reply = ""
+                action_success = False
+                action_reply_text = ""
+                action_command = ""
+
+                # 并行执行所有任务
+                if background_reply_task:
+                    results = await asyncio.gather(
+                        background_reply_task, background_action_task, return_exceptions=True
                     )
+                    # 处理回复任务结果
+                    reply_result = results[0]
+                    if isinstance(reply_result, BaseException):
+                        logger.error(f"{self.log_prefix} 回复任务执行异常: {reply_result}")
+                    elif reply_result and reply_result[0] is not None:
+                        reply_loop_info, reply_text_from_reply, _ = reply_result
 
-                loop_info = {
-                    "loop_plan_info": {
-                        "action_result": plan_result.get("action_result", {}),
-                    },
-                    "loop_action_info": {
-                        "action_taken": success,
-                        "reply_text": reply_text,
-                        "command": command,
-                        "taken_time": time.time(),
-                    },
-                }
+                    # 处理动作任务结果
+                    action_task_result = results[1]
+                    if isinstance(action_task_result, BaseException):
+                        logger.error(f"{self.log_prefix} 动作任务执行异常: {action_task_result}")
+                    else:
+                        action_success, action_reply_text, action_command = action_task_result
+                else:
+                    results = await asyncio.gather(background_action_task, return_exceptions=True)
+                    # 只有动作任务
+                    action_task_result = results[0]
+                    if isinstance(action_task_result, BaseException):
+                        logger.error(f"{self.log_prefix} 动作任务执行异常: {action_task_result}")
+                    else:
+                        action_success, action_reply_text, action_command = action_task_result
 
-                if loop_info["loop_action_info"]["command"] == "stop_focus_chat":
-                    logger.info(f"{self.log_prefix} 麦麦决定停止专注聊天")
-                    return False
-                    # 停止该聊天模式的循环
+                # 构建最终的循环信息
+                if reply_loop_info:
+                    # 如果有回复信息，使用回复的loop_info作为基础
+                    loop_info = reply_loop_info
+                    # 更新动作执行信息
+                    loop_info["loop_action_info"].update(
+                        {
+                            "action_taken": action_success,
+                            "command": action_command,
+                            "taken_time": time.time(),
+                        }
+                    )
+                    reply_text = reply_text_from_reply
+                else:
+                    # 没有回复信息，构建纯动作的loop_info
+                    loop_info = {
+                        "loop_plan_info": {
+                            "action_result": plan_result.get("action_result", {}),
+                        },
+                        "loop_action_info": {
+                            "action_taken": action_success,
+                            "reply_text": action_reply_text,
+                            "command": action_command,
+                            "taken_time": time.time(),
+                        },
+                    }
+                    reply_text = action_reply_text
+
+        if ENABLE_S4U:
+            await stop_typing()
+            await mai_thinking_manager.get_mai_think(self.stream_id).do_think_after_response(reply_text)
 
         self.end_cycle(loop_info, cycle_timers)
         self.print_cycle_info(cycle_timers)
@@ -406,8 +590,16 @@ class HeartFChatting:
         if self.loop_mode == ChatMode.NORMAL:
             await self.willing_manager.after_generate_reply_handle(message_data.get("message_id", ""))
 
+        # 管理no_reply计数器：当执行了非no_reply动作时，重置计数器
         if action_type != "no_reply" and action_type != "no_action":
+            # 导入NoReplyAction并重置计数器
+            NoReplyAction.reset_consecutive_count()
+            logger.info(f"{self.log_prefix} 执行了{action_type}动作，重置no_reply计数器")
             return True
+        elif action_type == "no_action":
+            # 当执行回复动作时，也重置no_reply计数器s
+            NoReplyAction.reset_consecutive_count()
+            logger.info(f"{self.log_prefix} 执行了回复动作，重置no_reply计数器")
 
         return True
 
@@ -435,7 +627,7 @@ class HeartFChatting:
         action: str,
         reasoning: str,
         action_data: dict,
-        cycle_timers: dict,
+        cycle_timers: Dict[str, float],
         thinking_id: str,
         action_message: dict,
     ) -> tuple[bool, str, str]:
@@ -501,7 +693,7 @@ class HeartFChatting:
         在"兴趣"模式下，判断是否回复并生成内容。
         """
 
-        interested_rate = (message_data.get("interest_value") or 0.0) * self.willing_amplifier
+        interested_rate = (message_data.get("interest_value") or 0.0) * global_config.chat.willing_amplifier
 
         self.willing_manager.setup(message_data, self.chat_stream)
 
@@ -515,8 +707,8 @@ class HeartFChatting:
                 reply_probability += additional_config["maimcore_reply_probability_gain"]
                 reply_probability = min(max(reply_probability, 0), 1)  # 确保概率在 0-1 之间
 
-            talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id)
-            reply_probability = talk_frequency * reply_probability
+        talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id)
+        reply_probability = talk_frequency * reply_probability
 
         # 处理表情包
         if message_data.get("is_emoji") or message_data.get("is_picid"):
@@ -544,7 +736,11 @@ class HeartFChatting:
         return False
 
     async def _generate_response(
-        self, message_data: dict, available_actions: Optional[Dict[str, ActionInfo]], reply_to: str
+        self,
+        message_data: dict,
+        available_actions: Optional[Dict[str, ActionInfo]],
+        reply_to: str,
+        request_type: str = "chat.replyer.normal",
     ) -> Optional[list]:
         """生成普通回复"""
         try:
@@ -552,8 +748,8 @@ class HeartFChatting:
                 chat_stream=self.chat_stream,
                 reply_to=reply_to,
                 available_actions=available_actions,
-                enable_tool=global_config.tool.enable_in_normal_chat,
-                request_type="chat.replyer.normal",
+                enable_tool=global_config.tool.enable_tool,
+                request_type=request_type,
             )
 
             if not success or not reply_set:
@@ -563,10 +759,10 @@ class HeartFChatting:
             return reply_set
 
         except Exception as e:
-            logger.error(f"[{self.log_prefix}] 回复生成出现错误：{str(e)} {traceback.format_exc()}")
+            logger.error(f"{self.log_prefix}回复生成出现错误：{str(e)} {traceback.format_exc()}")
             return None
 
-    async def _send_response(self, reply_set, reply_to, thinking_start_time, message_data):
+    async def _send_response(self, reply_set, reply_to, thinking_start_time, message_data) -> str:
         current_time = time.time()
         new_message_count = message_api.count_new_messages(
             chat_id=self.chat_stream.stream_id, start_time=thinking_start_time, end_time=current_time
@@ -578,13 +774,9 @@ class HeartFChatting:
         need_reply = new_message_count >= random.randint(2, 4)
 
         if need_reply:
-            logger.info(
-                f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，使用引用回复"
-            )
+            logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，使用引用回复")
         else:
-            logger.debug(
-                f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，不使用引用回复"
-            )
+            logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，不使用引用回复")
 
         reply_text = ""
         first_replied = False

src/chat/chat_loop/hfc_utils.py

@@ -1,10 +1,13 @@
 import time
-
 from typing import Optional, Dict, Any
 
 from src.config.config import global_config
-from src.common.message_repository import count_messages
 from src.common.logger import get_logger
+from src.chat.message_receive.chat_stream import get_chat_manager
+from src.plugin_system.apis import send_api
+from maim_message.message_base import GroupInfo
+
+from src.common.message_repository import count_messages
 
 logger = get_logger(__name__)
 
@@ -106,3 +109,30 @@ def get_recent_message_stats(minutes: float = 30, chat_id: Optional[str] = None)
     bot_reply_count = count_messages(bot_filter)
 
     return {"bot_reply_count": bot_reply_count, "total_message_count": total_message_count}
+
+
+async def send_typing():
+    group_info = GroupInfo(platform="amaidesu_default", group_id="114514", group_name="内心")
+
+    chat = await get_chat_manager().get_or_create_stream(
+        platform="amaidesu_default",
+        user_info=None,
+        group_info=group_info,
+    )
+
+    await send_api.custom_to_stream(
+        message_type="state", content="typing", stream_id=chat.stream_id, storage_message=False
+    )
+
+async def stop_typing():
+    group_info = GroupInfo(platform="amaidesu_default", group_id="114514", group_name="内心")
+
+    chat = await get_chat_manager().get_or_create_stream(
+        platform="amaidesu_default",
+        user_info=None,
+        group_info=group_info,
+    )
+
+    await send_api.custom_to_stream(
+        message_type="state", content="stop_typing", stream_id=chat.stream_id, storage_message=False
+    )

src/chat/emoji_system/emoji_manager.py

@@ -525,9 +525,9 @@ class EmojiManager:
         如果文件已被删除，则执行对象的删除方法并从列表中移除
         """
         try:
-            if not self.emoji_objects:
-                logger.warning("[检查] emoji_objects为空，跳过完整性检查")
-                return
+            # if not self.emoji_objects:
+            #     logger.warning("[检查] emoji_objects为空，跳过完整性检查")
+            #     return
 
             total_count = len(self.emoji_objects)
             self.emoji_num = total_count
@@ -707,6 +707,38 @@ class EmojiManager:
                 return emoji
         return None  # 如果循环结束还没找到，则返回 None
 
+    async def get_emoji_description_by_hash(self, emoji_hash: str) -> Optional[str]:
+        """根据哈希值获取已注册表情包的描述
+        
+        Args:
+            emoji_hash: 表情包的哈希值
+            
+        Returns:
+            Optional[str]: 表情包描述，如果未找到则返回None
+        """
+        try:
+            # 先从内存中查找
+            emoji = await self.get_emoji_from_manager(emoji_hash)
+            if emoji and emoji.description:
+                logger.info(f"[缓存命中] 从内存获取表情包描述: {emoji.description[:50]}...")
+                return emoji.description
+            
+            # 如果内存中没有，从数据库查找
+            self._ensure_db()
+            try:
+                emoji_record = Emoji.get_or_none(Emoji.emoji_hash == emoji_hash)
+                if emoji_record and emoji_record.description:
+                    logger.info(f"[缓存命中] 从数据库获取表情包描述: {emoji_record.description[:50]}...")
+                    return emoji_record.description
+            except Exception as e:
+                logger.error(f"从数据库查询表情包描述时出错: {e}")
+            
+            return None
+            
+        except Exception as e:
+            logger.error(f"获取表情包描述失败 (Hash: {emoji_hash}): {str(e)}")
+            return None
+
     async def delete_emoji(self, emoji_hash: str) -> bool:
         """根据哈希值删除表情包
 

src/chat/express/expression_learner.py

@@ -51,7 +51,7 @@ def init_prompt() -> None:
 当"想说明某个具体的事实观点，但懒得明说，或者不便明说，或表达一种默契"，使用"懂的都懂"
 当"当涉及游戏相关时，表示意外的夸赞，略带戏谑意味"时，使用"这么强！"
 
-注意不要总结你自己（SELF）的发言
+请注意：不要总结你自己（SELF）的发言
 现在请你概括
 """
     Prompt(learn_style_prompt, "learn_style_prompt")
@@ -330,48 +330,8 @@ class ExpressionLearner:
         """
         current_time = time.time()
 
-        # 全局衰减所有已存储的表达方式
-        for type in ["style", "grammar"]:
-            base_dir = os.path.join("data", "expression", f"learnt_{type}")
-            if not os.path.exists(base_dir):
-                logger.debug(f"目录不存在，跳过衰减: {base_dir}")
-                continue
-
-            try:
-                chat_ids = os.listdir(base_dir)
-                logger.debug(f"在 {base_dir} 中找到 {len(chat_ids)} 个聊天ID目录进行衰减")
-            except Exception as e:
-                logger.error(f"读取目录失败 {base_dir}: {e}")
-                continue
-
-            for chat_id in chat_ids:
-                file_path = os.path.join(base_dir, chat_id, "expressions.json")
-                if not os.path.exists(file_path):
-                    continue
-
-                try:
-                    with open(file_path, "r", encoding="utf-8") as f:
-                        expressions = json.load(f)
-
-                    if not isinstance(expressions, list):
-                        logger.warning(f"表达方式文件格式错误，跳过衰减: {file_path}")
-                        continue
-
-                    # 应用全局衰减
-                    decayed_expressions = self.apply_decay_to_expressions(expressions, current_time)
-
-                    # 保存衰减后的结果
-                    with open(file_path, "w", encoding="utf-8") as f:
-                        json.dump(decayed_expressions, f, ensure_ascii=False, indent=2)
-                        
-                    logger.debug(f"已对 {file_path} 应用衰减，剩余 {len(decayed_expressions)} 个表达方式")
-                except json.JSONDecodeError as e:
-                    logger.error(f"JSON解析失败，跳过衰减 {file_path}: {e}")
-                except PermissionError as e:
-                    logger.error(f"权限不足，无法更新 {file_path}: {e}")
-                except Exception as e:
-                    logger.error(f"全局衰减{type}表达方式失败 {file_path}: {e}")
-                    continue
+        # 全局衰减所有已存储的表达方式（直接操作数据库）
+        self._apply_global_decay_to_database(current_time)
 
         learnt_style: Optional[List[Tuple[str, str, str]]] = []
         learnt_grammar: Optional[List[Tuple[str, str, str]]] = []
@@ -388,6 +348,42 @@ class ExpressionLearner:
 
         return learnt_style, learnt_grammar
 
+    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
+        """
+        try:
+            # 获取所有表达方式
+            all_expressions = Expression.select()
+            
+            updated_count = 0
+            deleted_count = 0
+            
+            for expr in all_expressions:
+                # 计算时间差
+                last_active = expr.last_active_time
+                time_diff_days = (current_time - last_active) / (24 * 3600)  # 转换为天
+                
+                # 计算衰减值
+                decay_value = self.calculate_decay_factor(time_diff_days)
+                new_count = max(0.01, expr.count - decay_value)
+                
+                if new_count <= 0.01:
+                    # 如果count太小，删除这个表达方式
+                    expr.delete_instance()
+                    deleted_count += 1
+                else:
+                    # 更新count
+                    expr.count = new_count
+                    expr.save()
+                    updated_count += 1
+            
+            if updated_count > 0 or deleted_count > 0:
+                logger.info(f"全局衰减完成：更新了 {updated_count} 个表达方式，删除了 {deleted_count} 个表达方式")
+                
+        except Exception as e:
+            logger.error(f"数据库全局衰减失败: {e}")
+
     def calculate_decay_factor(self, time_diff_days: float) -> float:
         """
         计算衰减值
@@ -410,30 +406,6 @@ class ExpressionLearner:
 
         return min(0.01, decay)
 
-    def apply_decay_to_expressions(
-        self, expressions: List[Dict[str, Any]], current_time: float
-    ) -> List[Dict[str, Any]]:
-        """
-        对表达式列表应用衰减
-        返回衰减后的表达式列表，移除count小于0的项
-        """
-        result = []
-        for expr in expressions:
-            # 确保last_active_time存在，如果不存在则使用current_time
-            if "last_active_time" not in expr:
-                expr["last_active_time"] = current_time
-
-            last_active = expr["last_active_time"]
-            time_diff_days = (current_time - last_active) / (24 * 3600)  # 转换为天
-
-            decay_value = self.calculate_decay_factor(time_diff_days)
-            expr["count"] = max(0.01, expr.get("count", 1) - decay_value)
-
-            if expr["count"] > 0:
-                result.append(expr)
-
-        return result
-
     async def learn_and_store(self, type: str, num: int = 10) -> List[Tuple[str, str, str]]:
         # sourcery skip: use-join
         """

src/chat/express/expression_selector.py

@@ -2,7 +2,7 @@ import json
 import time
 import random
 
-from typing import List, Dict, Tuple, Optional
+from typing import List, Dict, Tuple, Optional, Any
 from json_repair import repair_json
 
 from src.llm_models.utils_model import LLMRequest
@@ -117,36 +117,42 @@ class ExpressionSelector:
 
     def get_random_expressions(
         self, chat_id: str, total_num: int, style_percentage: float, grammar_percentage: float
-    ) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
+    ) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
         # 支持多chat_id合并抽选
         related_chat_ids = self.get_related_chat_ids(chat_id)
-        style_exprs = []
-        grammar_exprs = []
-        for cid in related_chat_ids:
-            style_query = Expression.select().where((Expression.chat_id == cid) & (Expression.type == "style"))
-            grammar_query = Expression.select().where((Expression.chat_id == cid) & (Expression.type == "grammar"))
-            style_exprs.extend([
-                {
-                    "situation": expr.situation,
-                    "style": expr.style,
-                    "count": expr.count,
-                    "last_active_time": expr.last_active_time,
-                    "source_id": cid,
-                    "type": "style",
-                    "create_date": expr.create_date if expr.create_date is not None else expr.last_active_time,
-                } for expr in style_query
-            ])
-            grammar_exprs.extend([
-                {
-                    "situation": expr.situation,
-                    "style": expr.style,
-                    "count": expr.count,
-                    "last_active_time": expr.last_active_time,
-                    "source_id": cid,
-                    "type": "grammar",
-                    "create_date": expr.create_date if expr.create_date is not None else expr.last_active_time,
-                } for expr in grammar_query
-            ])
+        
+        # 优化：一次性查询所有相关chat_id的表达方式
+        style_query = Expression.select().where(
+            (Expression.chat_id.in_(related_chat_ids)) & (Expression.type == "style")
+        )
+        grammar_query = Expression.select().where(
+            (Expression.chat_id.in_(related_chat_ids)) & (Expression.type == "grammar")
+        )
+        
+        style_exprs = [
+            {
+                "situation": expr.situation,
+                "style": expr.style,
+                "count": expr.count,
+                "last_active_time": expr.last_active_time,
+                "source_id": expr.chat_id,
+                "type": "style",
+                "create_date": expr.create_date if expr.create_date is not None else expr.last_active_time,
+            } for expr in style_query
+        ]
+        
+        grammar_exprs = [
+            {
+                "situation": expr.situation,
+                "style": expr.style,
+                "count": expr.count,
+                "last_active_time": expr.last_active_time,
+                "source_id": expr.chat_id,
+                "type": "grammar",
+                "create_date": expr.create_date if expr.create_date is not None else expr.last_active_time,
+            } for expr in grammar_query
+        ]
+        
         style_num = int(total_num * style_percentage)
         grammar_num = int(total_num * grammar_percentage)
         # 按权重抽样（使用count作为权重）
@@ -162,7 +168,7 @@ class ExpressionSelector:
             selected_grammar = []
         return selected_style, selected_grammar
 
-    def update_expressions_count_batch(self, expressions_to_update: List[Dict[str, str]], increment: float = 0.1):
+    def update_expressions_count_batch(self, expressions_to_update: List[Dict[str, Any]], increment: float = 0.1):
         """对一批表达方式更新count值，按chat_id+type分组后一次性写入数据库"""
         if not expressions_to_update:
             return
@@ -203,7 +209,7 @@ class ExpressionSelector:
         max_num: int = 10,
         min_num: int = 5,
         target_message: Optional[str] = None,
-    ) -> List[Dict[str, str]]:
+    ) -> List[Dict[str, Any]]:
         # sourcery skip: inline-variable, list-comprehension
         """使用LLM选择适合的表达方式"""
 
@@ -273,6 +279,7 @@ class ExpressionSelector:
 
             if not isinstance(result, dict) or "selected_situations" not in result:
                 logger.error("LLM返回格式错误")
+                logger.info(f"LLM返回结果: \n{content}")
                 return []
 
             selected_indices = result["selected_situations"]

src/chat/heart_flow/heartflow_message_processor.py

@@ -12,6 +12,7 @@ from src.chat.message_receive.storage import MessageStorage
 from src.chat.heart_flow.heartflow import heartflow
 from src.chat.utils.utils import is_mentioned_bot_in_message
 from src.chat.utils.timer_calculator import Timer
+from src.chat.utils.chat_message_builder import replace_user_references_sync
 from src.common.logger import get_logger
 from src.person_info.relationship_manager import get_relationship_manager
 from src.mood.mood_manager import mood_manager
@@ -56,16 +57,41 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool]:
     with Timer("记忆激活"):
         interested_rate = await hippocampus_manager.get_activate_from_text(
             message.processed_plain_text,
+            max_depth= 5,
             fast_retrieval=False,
         )
         logger.debug(f"记忆激活率: {interested_rate:.2f}")
 
     text_len = len(message.processed_plain_text)
-    # 根据文本长度调整兴趣度，长度越大兴趣度越高，但增长率递减，最低0.01，最高0.05
-    # 采用对数函数实现递减增长
-
-    base_interest = 0.01 + (0.05 - 0.01) * (math.log10(text_len + 1) / math.log10(1000 + 1))
-    base_interest = min(max(base_interest, 0.01), 0.05)
+    # 根据文本长度分布调整兴趣度，采用分段函数实现更精确的兴趣度计算
+    # 基于实际分布：0-5字符(26.57%), 6-10字符(27.18%), 11-20字符(22.76%), 21-30字符(10.33%), 31+字符(13.86%)
+    
+    if text_len == 0:
+        base_interest = 0.01  # 空消息最低兴趣度
+    elif text_len <= 5:
+        # 1-5字符：线性增长 0.01 -> 0.03
+        base_interest = 0.01 + (text_len - 1) * (0.03 - 0.01) / 4
+    elif text_len <= 10:
+        # 6-10字符：线性增长 0.03 -> 0.06
+        base_interest = 0.03 + (text_len - 5) * (0.06 - 0.03) / 5
+    elif text_len <= 20:
+        # 11-20字符：线性增长 0.06 -> 0.12
+        base_interest = 0.06 + (text_len - 10) * (0.12 - 0.06) / 10
+    elif text_len <= 30:
+        # 21-30字符：线性增长 0.12 -> 0.18
+        base_interest = 0.12 + (text_len - 20) * (0.18 - 0.12) / 10
+    elif text_len <= 50:
+        # 31-50字符：线性增长 0.18 -> 0.22
+        base_interest = 0.18 + (text_len - 30) * (0.22 - 0.18) / 20
+    elif text_len <= 100:
+        # 51-100字符：线性增长 0.22 -> 0.26
+        base_interest = 0.22 + (text_len - 50) * (0.26 - 0.22) / 50
+    else:
+        # 100+字符：对数增长 0.26 -> 0.3，增长率递减
+        base_interest = 0.26 + (0.3 - 0.26) * (math.log10(text_len - 99) / math.log10(901))  # 1000-99=901
+    
+    # 确保在范围内
+    base_interest = min(max(base_interest, 0.01), 0.3)
 
     interested_rate += base_interest
 
@@ -123,8 +149,15 @@ class HeartFCMessageReceiver:
             # 如果消息中包含图片标识，则将 [picid:...] 替换为 [图片]
             picid_pattern = r"\[picid:([^\]]+)\]"
             processed_plain_text = re.sub(picid_pattern, "[图片]", message.processed_plain_text)
+            
+            # 应用用户引用格式替换，将回复<aaa:bbb>和@<aaa:bbb>格式转换为可读格式
+            processed_plain_text = replace_user_references_sync(
+                processed_plain_text,
+                message.message_info.platform, # type: ignore
+                replace_bot_name=True
+            )
 
-            logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}")  # type: ignore
+            logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[兴趣度：{interested_rate:.2f}]")  # type: ignore
 
             logger.debug(f"[{mes_name}][当前时段回复频率: {current_talk_frequency}]")
 

src/chat/memory_system/Hippocampus.py

@@ -224,10 +224,16 @@ class Hippocampus:
         return hash((source, target))
 
     @staticmethod
-    def find_topic_llm(text, topic_num):
+    def find_topic_llm(text: str, topic_num: int | list[int]):
         # sourcery skip: inline-immediately-returned-variable
+        topic_num_str = ""
+        if isinstance(topic_num, list):
+            topic_num_str = f"{topic_num[0]}-{topic_num[1]}"
+        else:
+            topic_num_str = topic_num
+
         prompt = (
-            f"这是一段文字：\n{text}\n\n请你从这段话中总结出最多{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，"
+            f"这是一段文字：\n{text}\n\n请你从这段话中总结出最多{topic_num_str}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，"
             f"将主题用逗号隔开，并加上<>,例如<主题1>,<主题2>......尽可能精简。只需要列举最多{topic_num}个话题就好，不要有序号，不要告诉我其他内容。"
             f"如果确定找不出主题或者没有明显主题，返回<none>。"
         )
@@ -300,6 +306,60 @@ class Hippocampus:
         memories.sort(key=lambda x: x[2], reverse=True)
         return memories
 
+    async def get_keywords_from_text(self, text: str) -> list:
+        """从文本中提取关键词。
+
+        Args:
+            text (str): 输入文本
+            fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
+                如果为True，使用jieba分词提取关键词，速度更快但可能不够准确。
+                如果为False，使用LLM提取关键词，速度较慢但更准确。
+        """
+        if not text:
+            return []
+
+        # 使用LLM提取关键词 - 根据详细文本长度分布优化topic_num计算
+        text_length = len(text)
+        topic_num: int | list[int] = 0
+        if text_length <= 5:
+            words = jieba.cut(text)
+            keywords = [word for word in words if len(word) > 1]
+            keywords = list(set(keywords))[:3]  # 限制最多3个关键词
+            if keywords:
+                logger.info(f"提取关键词: {keywords}")
+            return keywords
+        elif text_length <= 10:
+            topic_num = [1, 3]  # 6-10字符: 1个关键词 (27.18%的文本)
+        elif text_length <= 20:
+            topic_num = [2, 4]  # 11-20字符: 2个关键词 (22.76%的文本)
+        elif text_length <= 30:
+            topic_num = [3, 5]  # 21-30字符: 3个关键词 (10.33%的文本)
+        elif text_length <= 50:
+            topic_num = [4, 5]  # 31-50字符: 4个关键词 (9.79%的文本)
+        else:
+            topic_num = 5  # 51+字符: 5个关键词 (其余长文本)
+
+        topics_response, (reasoning_content, model_name) = await self.model_summary.generate_response_async(
+            self.find_topic_llm(text, topic_num)
+        )
+
+        # 提取关键词
+        keywords = re.findall(r"<([^>]+)>", topics_response)
+        if not keywords:
+            keywords = []
+        else:
+            keywords = [
+                keyword.strip()
+                for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
+                if keyword.strip()
+            ]
+        
+        if keywords:
+            logger.info(f"提取关键词: {keywords}")
+        
+        return keywords 
+        
+
     async def get_memory_from_text(
         self,
         text: str,
@@ -325,39 +385,7 @@ class Hippocampus:
                 - memory_items: list, 该主题下的记忆项列表
                 - similarity: float, 与文本的相似度
         """
-        if not text:
-            return []
-
-        if fast_retrieval:
-            # 使用jieba分词提取关键词
-            words = jieba.cut(text)
-            # 过滤掉停用词和单字词
-            keywords = [word for word in words if len(word) > 1]
-            # 去重
-            keywords = list(set(keywords))
-            # 限制关键词数量
-            logger.debug(f"提取关键词: {keywords}")
-
-        else:
-            # 使用LLM提取关键词
-            topic_num = min(5, max(1, int(len(text) * 0.1)))  # 根据文本长度动态调整关键词数量
-            # logger.info(f"提取关键词数量: {topic_num}")
-            topics_response, (reasoning_content, model_name) = await self.model_summary.generate_response_async(
-                self.find_topic_llm(text, topic_num)
-            )
-
-            # 提取关键词
-            keywords = re.findall(r"<([^>]+)>", topics_response)
-            if not keywords:
-                keywords = []
-            else:
-                keywords = [
-                    keyword.strip()
-                    for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
-                    if keyword.strip()
-                ]
-
-        # logger.info(f"提取的关键词: {', '.join(keywords)}")
+        keywords = await self.get_keywords_from_text(text)
 
         # 过滤掉不存在于记忆图中的关键词
         valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
@@ -679,38 +707,7 @@ class Hippocampus:
         Returns:
             float: 激活节点数与总节点数的比值
         """
-        if not text:
-            return 0
-
-        if fast_retrieval:
-            # 使用jieba分词提取关键词
-            words = jieba.cut(text)
-            # 过滤掉停用词和单字词
-            keywords = [word for word in words if len(word) > 1]
-            # 去重
-            keywords = list(set(keywords))
-            # 限制关键词数量
-            keywords = keywords[:5]
-        else:
-            # 使用LLM提取关键词
-            topic_num = min(5, max(1, int(len(text) * 0.1)))  # 根据文本长度动态调整关键词数量
-            # logger.info(f"提取关键词数量: {topic_num}")
-            topics_response, (reasoning_content, model_name) = await self.model_summary.generate_response_async(
-                self.find_topic_llm(text, topic_num)
-            )
-
-            # 提取关键词
-            keywords = re.findall(r"<([^>]+)>", topics_response)
-            if not keywords:
-                keywords = []
-            else:
-                keywords = [
-                    keyword.strip()
-                    for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
-                    if keyword.strip()
-                ]
-
-        # logger.info(f"提取的关键词: {', '.join(keywords)}")
+        keywords = await self.get_keywords_from_text(text)
 
         # 过滤掉不存在于记忆图中的关键词
         valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
@@ -727,7 +724,7 @@ class Hippocampus:
         for keyword in valid_keywords:
             logger.debug(f"开始以关键词 '{keyword}' 为中心进行扩散检索 (最大深度: {max_depth}):")
             # 初始化激活值
-            activation_values = {keyword: 1.0}
+            activation_values = {keyword: 1.5}
             # 记录已访问的节点
             visited_nodes = {keyword}
             # 待处理的节点队列，每个元素是(节点, 激活值, 当前深度)
@@ -1315,6 +1312,7 @@ class ParahippocampalGyrus:
         return compressed_memory, similar_topics_dict
 
     async def operation_build_memory(self):
+        # sourcery skip: merge-list-appends-into-extend
         logger.info("------------------------------------开始构建记忆--------------------------------------")
         start_time = time.time()
         memory_samples = self.hippocampus.entorhinal_cortex.get_memory_sample()

src/chat/message_receive/message.py

@@ -444,7 +444,7 @@ class MessageSending(MessageProcessBase):
         is_emoji: bool = False,
         thinking_start_time: float = 0,
         apply_set_reply_logic: bool = False,
-        reply_to: str = None,  # type: ignore
+        reply_to: Optional[str] = None,
     ):
         # 调用父类初始化
         super().__init__(

src/chat/planner_actions/action_manager.py

@@ -3,7 +3,7 @@ from src.plugin_system.base.base_action import BaseAction
 from src.chat.message_receive.chat_stream import ChatStream
 from src.common.logger import get_logger
 from src.plugin_system.core.component_registry import component_registry
-from src.plugin_system.base.component_types import ComponentType, ActionActivationType, ChatMode, ActionInfo
+from src.plugin_system.base.component_types import ComponentType, ActionInfo
 
 logger = get_logger("action_manager")
 
@@ -15,11 +15,6 @@ class ActionManager:
     现在统一使用新插件系统，简化了原有的新旧兼容逻辑。
     """
 
-    # 类常量
-    DEFAULT_RANDOM_PROBABILITY = 0.3
-    DEFAULT_MODE = ChatMode.ALL
-    DEFAULT_ACTIVATION_TYPE = ActionActivationType.ALWAYS
-
     def __init__(self):
         """初始化动作管理器"""
 

src/chat/planner_actions/action_modifier.py

@@ -174,7 +174,7 @@ class ActionModifier:
                 continue  # 总是激活，无需处理
 
             elif activation_type == ActionActivationType.RANDOM:
-                probability = action_info.random_activation_probability or ActionManager.DEFAULT_RANDOM_PROBABILITY
+                probability = action_info.random_activation_probability
                 if random.random() >= probability:
                     reason = f"RANDOM类型未触发（概率{probability}）"
                     deactivated_actions.append((action_name, reason))

src/chat/planner_actions/planner.py

@@ -33,10 +33,11 @@ def init_prompt():
 {time_block}
 {identity_block}
 你现在需要根据聊天内容，选择的合适的action来参与聊天。
-{chat_context_description}，以下是具体的聊天内容：
+{chat_context_description}，以下是具体的聊天内容
 {chat_content_block}
 
 
+
 {moderation_prompt}
 
 现在请你根据{by_what}选择合适的action和触发action的消息:
@@ -45,7 +46,7 @@ def init_prompt():
 {no_action_block}
 {action_options_text}
 
-你必须从上面列出的可用action中选择一个，并说明触发action的消息id和原因。
+你必须从上面列出的可用action中选择一个，并说明触发action的消息id（不是消息原文）和选择该action的原因。
 
 请根据动作示例，以严格的 JSON 格式输出，且仅包含 JSON 内容：
 """,
@@ -128,20 +129,6 @@ class ActionPlanner:
                 else:
                     logger.warning(f"{self.log_prefix}使用中的动作 {action_name} 未在已注册动作中找到")
 
-            # 如果没有可用动作或只有no_reply动作，直接返回no_reply
-            # 因为现在reply是永远激活，所以不需要空跳判定
-            # if not current_available_actions:
-            #     action = "no_reply" if mode == ChatMode.FOCUS else "no_action"
-            #     reasoning = "没有可用的动作"
-            #     logger.info(f"{self.log_prefix}{reasoning}")
-            #     return {
-            #         "action_result": {
-            #             "action_type": action,
-            #             "action_data": action_data,
-            #             "reasoning": reasoning,
-            #         },
-            #     }, None
-
             # --- 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
@@ -224,7 +211,7 @@ class ActionPlanner:
             reasoning = f"Planner 内部处理错误: {outer_e}"
 
         is_parallel = False
-        if action in current_available_actions:
+        if mode == ChatMode.NORMAL and action in current_available_actions:
             is_parallel = current_available_actions[action].parallel_action
 
         action_result = {
@@ -268,7 +255,7 @@ class ActionPlanner:
 
             actions_before_now = get_actions_by_timestamp_with_chat(
                 chat_id=self.chat_id,
-                timestamp_start=time.time()-3600,
+                timestamp_start=time.time() - 3600,
                 timestamp_end=time.time(),
                 limit=5,
             )
@@ -276,7 +263,7 @@ class ActionPlanner:
             actions_before_now_block = build_readable_actions(
                 actions=actions_before_now,
             )
-            
+
             actions_before_now_block = f"你刚刚选择并执行过的action是：\n{actions_before_now_block}"
 
             self.last_obs_time_mark = time.time()
@@ -288,7 +275,6 @@ class ActionPlanner:
                 if global_config.chat.at_bot_inevitable_reply:
                     mentioned_bonus = "\n- 有人提到你，或者at你"
 
-                
                 by_what = "聊天内容"
                 target_prompt = '\n    "target_message_id":"触发action的消息id"'
                 no_action_block = f"""重要说明：
@@ -311,7 +297,7 @@ class ActionPlanner:
                 by_what = "聊天内容和用户的最新消息"
                 target_prompt = ""
                 no_action_block = """重要说明：
-- 'no_action' 表示只进行普通聊天回复，不执行任何额外动作
+- 'reply' 表示只进行普通聊天回复，不执行任何额外动作
 - 其他action表示在普通回复的基础上，执行相应的额外动作"""
 
             chat_context_description = "你现在正在一个群聊中"

src/chat/replyer/default_generator.py

@@ -17,7 +17,11 @@ from src.chat.message_receive.uni_message_sender import HeartFCSender
 from src.chat.utils.timer_calculator import Timer  # <--- Import Timer
 from src.chat.utils.utils import get_chat_type_and_target_info
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
-from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
+from src.chat.utils.chat_message_builder import (
+    build_readable_messages,
+    get_raw_msg_before_timestamp_with_chat,
+    replace_user_references_sync,
+)
 from src.chat.express.expression_selector import expression_selector
 from src.chat.knowledge.knowledge_lib import qa_manager
 from src.chat.memory_system.memory_activator import MemoryActivator
@@ -25,42 +29,16 @@ from src.chat.memory_system.instant_memory import InstantMemory
 from src.mood.mood_manager import mood_manager
 from src.person_info.relationship_fetcher import relationship_fetcher_manager
 from src.person_info.person_info import get_person_info_manager
-from src.tools.tool_executor import ToolExecutor
 from src.plugin_system.base.component_types import ActionInfo
 
 logger = get_logger("replyer")
 
+
 def init_prompt():
     Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1")
     Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
     Prompt("在群里聊天", "chat_target_group2")
     Prompt("和{sender_name}聊天", "chat_target_private2")
-    Prompt("\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n", "knowledge_prompt")
-
-    Prompt(
-        """
-{expression_habits_block}
-{tool_info_block}
-{knowledge_prompt}
-{memory_block}
-{relation_info_block}
-{extra_info_block}
-
-{chat_target}
-{time_block}
-{chat_info}
-{reply_target_block}
-{identity}
-
-{action_descriptions}
-你正在{chat_target_2},你现在的心情是：{mood_state}
-现在请你读读之前的聊天记录，并给出回复
-{config_expression_style}。注意不要复读你说过的话
-{keywords_reaction_prompt}
-{moderation_prompt}
-不要浮夸，不要夸张修辞，不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容""",
-        "default_generator_prompt",
-    )
 
     Prompt(
         """
@@ -109,7 +87,8 @@ def init_prompt():
 {core_dialogue_prompt}
 
 {reply_target_block}
-对方最新发送的内容：{message_txt}
+
+
 你现在的心情是：{mood_state}
 {config_expression_style}
 注意不要复读你说过的话
@@ -159,6 +138,8 @@ class DefaultReplyer:
         self.heart_fc_sender = HeartFCSender()
         self.memory_activator = MemoryActivator()
         self.instant_memory = InstantMemory(chat_id=self.chat_stream.stream_id)
+
+        from src.plugin_system.core.tool_use import ToolExecutor # 延迟导入ToolExecutor，不然会循环依赖
         self.tool_executor = ToolExecutor(chat_id=self.chat_stream.stream_id, enable_cache=True, cache_ttl=3)
 
     def _select_weighted_model_config(self) -> Dict[str, Any]:
@@ -171,67 +152,49 @@ class DefaultReplyer:
 
     async def generate_reply_with_context(
         self,
-        reply_data: Optional[Dict[str, Any]] = None,
         reply_to: str = "",
         extra_info: str = "",
         available_actions: Optional[Dict[str, ActionInfo]] = None,
         enable_tool: bool = True,
-        enable_timeout: bool = False,
     ) -> Tuple[bool, Optional[str], Optional[str]]:
         """
-        回复器 (Replier): 核心逻辑，负责生成回复文本。
-        (已整合原 HeartFCGenerator 的功能)
+        回复器 (Replier): 负责生成回复文本的核心逻辑。
+
+        Args:
+            reply_to: 回复对象，格式为 "发送者:消息内容"
+            extra_info: 额外信息，用于补充上下文
+            available_actions: 可用的动作信息字典
+            enable_tool: 是否启用工具调用
+
+        Returns:
+            Tuple[bool, Optional[str], Optional[str]]: (是否成功, 生成的回复内容, 使用的prompt)
         """
         prompt = None
         if available_actions is None:
             available_actions = {}
         try:
-            if not reply_data:
-                reply_data = {
-                    "reply_to": reply_to,
-                    "extra_info": extra_info,
-                }
-                for key, value in reply_data.items():
-                    if not value:
-                        logger.debug(f"回复数据跳过{key}，生成回复时将忽略。")
-
             # 3. 构建 Prompt
             with Timer("构建Prompt", {}):  # 内部计时器，可选保留
                 prompt = await self.build_prompt_reply_context(
-                    reply_data=reply_data,  # 传递action_data
+                    reply_to=reply_to,
+                    extra_info=extra_info,
                     available_actions=available_actions,
-                    enable_timeout=enable_timeout,
                     enable_tool=enable_tool,
                 )
 
+            if not prompt:
+                logger.warning("构建prompt失败，跳过回复生成")
+                return False, None, None
+
             # 4. 调用 LLM 生成回复
             content = None
-            reasoning_content = None
-            model_name = "unknown_model"
+            # TODO: 复活这里
+            # reasoning_content = None
+            # model_name = "unknown_model"
 
             try:
-                with Timer("LLM生成", {}):  # 内部计时器，可选保留
-                    # 加权随机选择一个模型配置
-                    selected_model_config = self._select_weighted_model_config()
-                    # 兼容新旧格式的模型名称获取
-                    model_display_name = selected_model_config.get('model_name', selected_model_config.get('name', 'N/A'))
-                    logger.info(
-                        f"使用模型生成回复: {model_display_name} (选中概率: {selected_model_config.get('weight', 1.0)})"
-                    )
-
-                    express_model = LLMRequest(
-                        model=selected_model_config,
-                        request_type=self.request_type,
-                    )
-
-                    if global_config.debug.show_prompt:
-                        logger.info(f"\n{prompt}\n")
-                    else:
-                        logger.debug(f"\n{prompt}\n")
-
-                    content, (reasoning_content, model_name) = await express_model.generate_response_async(prompt)
-
-                    logger.debug(f"replyer生成内容: {content}")
+                content = await self.llm_generate_content(prompt)
+                logger.debug(f"replyer生成内容: {content}")
 
             except Exception as llm_e:
                 # 精简报错信息
@@ -247,73 +210,62 @@ class DefaultReplyer:
 
     async def rewrite_reply_with_context(
         self,
-        reply_data: Dict[str, Any],
         raw_reply: str = "",
         reason: str = "",
         reply_to: str = "",
-        relation_info: str = "",
-    ) -> Tuple[bool, Optional[str]]:
+        return_prompt: bool = False,
+    ) -> Tuple[bool, Optional[str], Optional[str]]:
         """
-        表达器 (Expressor): 核心逻辑，负责生成回复文本。
+        表达器 (Expressor): 负责重写和优化回复文本。
+
+        Args:
+            raw_reply: 原始回复内容
+            reason: 回复原因
+            reply_to: 回复对象，格式为 "发送者:消息内容"
+            relation_info: 关系信息
+
+        Returns:
+            Tuple[bool, Optional[str]]: (是否成功, 重写后的回复内容)
         """
         try:
-            if not reply_data:
-                reply_data = {
-                    "reply_to": reply_to,
-                    "relation_info": relation_info,
-                }
-
             with Timer("构建Prompt", {}):  # 内部计时器，可选保留
                 prompt = await self.build_prompt_rewrite_context(
-                    reply_data=reply_data,
+                    raw_reply=raw_reply,
+                    reason=reason,
+                    reply_to=reply_to,
                 )
 
             content = None
-            reasoning_content = None
-            model_name = "unknown_model"
+            # TODO: 复活这里
+            # 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
+                return False, None, None
 
             try:
-                with Timer("LLM生成", {}):  # 内部计时器，可选保留
-                    # 加权随机选择一个模型配置
-                    selected_model_config = self._select_weighted_model_config()
-                    # 兼容新旧格式的模型名称获取
-                    model_display_name = selected_model_config.get('model_name', selected_model_config.get('name', 'N/A'))
-                    logger.info(
-                        f"使用模型重写回复: {model_display_name} (选中概率: {selected_model_config.get('weight', 1.0)})"
-                    )
-
-                    express_model = LLMRequest(
-                        model=selected_model_config,
-                        request_type=self.request_type,
-                    )
-
-                    content, (reasoning_content, model_name) = await express_model.generate_response_async(prompt)
-
-                    logger.info(f"想要表达：{raw_reply}||理由：{reason}||生成回复: {content}\n")
+                content = await self.llm_generate_content(prompt)
+                logger.info(f"想要表达：{raw_reply}||理由：{reason}||生成回复: {content}\n")
 
             except Exception as llm_e:
                 # 精简报错信息
                 logger.error(f"LLM 生成失败: {llm_e}")
-                return False, None  # LLM 调用失败则无法生成回复
+                return False, None, prompt if return_prompt else None  # LLM 调用失败则无法生成回复
 
-            return True, content
+            return True, content, prompt if return_prompt else None
 
         except Exception as e:
             logger.error(f"回复生成意外失败: {e}")
             traceback.print_exc()
-            return False, None
+            return False, None, prompt if return_prompt else None
 
-    async def build_relation_info(self, reply_data=None):
+    async def build_relation_info(self, reply_to: str = ""):
         if not global_config.relationship.enable_relationship:
             return ""
 
         relationship_fetcher = relationship_fetcher_manager.get_fetcher(self.chat_stream.stream_id)
-        if not reply_data:
+        if not reply_to:
             return ""
-        reply_to = reply_data.get("reply_to", "")
         sender, text = self._parse_reply_target(reply_to)
         if not sender or not text:
             return ""
@@ -327,7 +279,16 @@ class DefaultReplyer:
 
         return await relationship_fetcher.build_relation_info(person_id, points_num=5)
 
-    async def build_expression_habits(self, chat_history, target):
+    async def build_expression_habits(self, chat_history: str, target: str) -> str:
+        """构建表达习惯块
+
+        Args:
+            chat_history: 聊天历史记录
+            target: 目标消息内容
+
+        Returns:
+            str: 表达习惯信息字符串
+        """
         if not global_config.expression.enable_expression:
             return ""
 
@@ -360,54 +321,65 @@ class DefaultReplyer:
         expression_habits_block = ""
         expression_habits_title = ""
         if style_habits_str.strip():
-            expression_habits_title = "你可以参考以下的语言习惯，当情景合适就使用，但不要生硬使用，以合理的方式结合到你的回复中："
+            expression_habits_title = (
+                "你可以参考以下的语言习惯，当情景合适就使用，但不要生硬使用，以合理的方式结合到你的回复中："
+            )
             expression_habits_block += f"{style_habits_str}\n"
         if grammar_habits_str.strip():
-            expression_habits_title = "你可以选择下面的句法进行回复，如果情景合适就使用，不要盲目使用,不要生硬使用，以合理的方式使用："
+            expression_habits_title = (
+                "你可以选择下面的句法进行回复，如果情景合适就使用，不要盲目使用,不要生硬使用，以合理的方式使用："
+            )
             expression_habits_block += f"{grammar_habits_str}\n"
-            
+
         if style_habits_str.strip() and grammar_habits_str.strip():
             expression_habits_title = "你可以参考以下的语言习惯和句法，如果情景合适就使用，不要盲目使用,不要生硬使用，以合理的方式结合到你的回复中："
-            
-        expression_habits_block = f"{expression_habits_title}\n{expression_habits_block}"
-        
 
-        return expression_habits_block
+        return f"{expression_habits_title}\n{expression_habits_block}"
 
-    async def build_memory_block(self, chat_history, target):
+    async def build_memory_block(self, chat_history: str, target: str) -> str:
+        """构建记忆块
+
+        Args:
+            chat_history: 聊天历史记录
+            target: 目标消息内容
+
+        Returns:
+            str: 记忆信息字符串
+        """
         if not global_config.memory.enable_memory:
             return ""
 
         instant_memory = None
-        
+
         running_memories = await self.memory_activator.activate_memory_with_chat_history(
             target_message=target, chat_history_prompt=chat_history
         )
-        
+
         if global_config.memory.enable_instant_memory:
             asyncio.create_task(self.instant_memory.create_and_store_memory(chat_history))
 
             instant_memory = await self.instant_memory.get_memory(target)
             logger.info(f"即时记忆：{instant_memory}")
-            
+
         if not running_memories:
             return ""
 
         memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
         for running_memory in running_memories:
             memory_str += f"- {running_memory['content']}\n"
-        
+
         if instant_memory:
             memory_str += f"- {instant_memory}\n"
-            
+
         return memory_str
 
-    async def build_tool_info(self, chat_history, reply_data: Optional[Dict], enable_tool: bool = True):
+    async def build_tool_info(self, chat_history: str, reply_to: str = "", enable_tool: bool = True) -> str:
         """构建工具信息块
 
         Args:
-            reply_data: 回复数据，包含要回复的消息内容
-            chat_history: 聊天历史
+            chat_history: 聊天历史记录
+            reply_to: 回复对象，格式为 "发送者:消息内容"
+            enable_tool: 是否启用工具调用
 
         Returns:
             str: 工具信息字符串
@@ -416,10 +388,9 @@ class DefaultReplyer:
         if not enable_tool:
             return ""
 
-        if not reply_data:
+        if not reply_to:
             return ""
 
-        reply_to = reply_data.get("reply_to", "")
         sender, text = self._parse_reply_target(reply_to)
 
         if not text:
@@ -442,7 +413,7 @@ class DefaultReplyer:
 
                 tool_info_str += "以上是你获取到的实时信息，请在回复时参考这些信息。"
                 logger.info(f"获取到 {len(tool_results)} 个工具结果")
-                
+
                 return tool_info_str
             else:
                 logger.debug("未获取到任何工具结果")
@@ -452,7 +423,15 @@ class DefaultReplyer:
             logger.error(f"工具信息获取失败: {e}")
             return ""
 
-    def _parse_reply_target(self, target_message: str) -> tuple:
+    def _parse_reply_target(self, target_message: str) -> Tuple[str, str]:
+        """解析回复目标消息
+
+        Args:
+            target_message: 目标消息，格式为 "发送者:消息内容" 或 "发送者：消息内容"
+
+        Returns:
+            Tuple[str, str]: (发送者名称, 消息内容)
+        """
         sender = ""
         target = ""
         # 添加None检查，防止NoneType错误
@@ -466,14 +445,22 @@ class DefaultReplyer:
                 target = parts[1].strip()
         return sender, target
 
-    async def build_keywords_reaction_prompt(self, target):
+    async def build_keywords_reaction_prompt(self, target: Optional[str]) -> str:
+        """构建关键词反应提示
+
+        Args:
+            target: 目标消息内容
+
+        Returns:
+            str: 关键词反应提示字符串
+        """
         # 关键词检测与反应
         keywords_reaction_prompt = ""
         try:
             # 添加None检查，防止NoneType错误
             if target is None:
                 return keywords_reaction_prompt
-                
+
             # 处理关键词规则
             for rule in global_config.keyword_reaction.keyword_rules:
                 if any(keyword in target for keyword in rule.keywords):
@@ -500,15 +487,25 @@ class DefaultReplyer:
 
         return keywords_reaction_prompt
 
-    async def _time_and_run_task(self, coroutine, name: str):
-        """一个简单的帮助函数，用于计时和运行异步任务，返回任务名、结果和耗时"""
+    async def _time_and_run_task(self, coroutine, name: str) -> Tuple[str, Any, float]:
+        """计时并运行异步任务的辅助函数
+
+        Args:
+            coroutine: 要执行的协程
+            name: 任务名称
+
+        Returns:
+            Tuple[str, Any, float]: (任务名称, 任务结果, 执行耗时)
+        """
         start_time = time.time()
         result = await coroutine
         end_time = time.time()
         duration = end_time - start_time
         return name, result, duration
 
-    def build_s4u_chat_history_prompts(self, message_list_before_now: list, target_user_id: str) -> tuple[str, str]:
+    def build_s4u_chat_history_prompts(
+        self, message_list_before_now: List[Dict[str, Any]], target_user_id: str
+    ) -> Tuple[str, str]:
         """
         构建 s4u 风格的分离对话 prompt
 
@@ -517,7 +514,7 @@ class DefaultReplyer:
             target_user_id: 目标用户ID（当前对话对象）
 
         Returns:
-            tuple: (核心对话prompt, 背景对话prompt)
+            Tuple[str, str]: (核心对话prompt, 背景对话prompt)
         """
         core_dialogue_list = []
         background_dialogue_list = []
@@ -536,7 +533,7 @@ class DefaultReplyer:
                     # 其他用户的对话
                     background_dialogue_list.append(msg_dict)
             except Exception as e:
-                logger.error(f"![1753364551656](image/default_generator/1753364551656.png)记录: {msg_dict}, 错误: {e}")
+                logger.error(f"处理消息记录时出错: {msg_dict}, 错误: {e}")
 
         # 构建背景对话 prompt
         background_dialogue_prompt = ""
@@ -581,8 +578,25 @@ class DefaultReplyer:
         sender: str,
         target: str,
         chat_info: str,
-    ):
-        """构建 mai_think 上下文信息"""
+    ) -> Any:
+        """构建 mai_think 上下文信息
+
+        Args:
+            chat_id: 聊天ID
+            memory_block: 记忆块内容
+            relation_info: 关系信息
+            time_block: 时间块内容
+            chat_target_1: 聊天目标1
+            chat_target_2: 聊天目标2
+            mood_prompt: 情绪提示
+            identity_block: 身份块内容
+            sender: 发送者名称
+            target: 目标消息内容
+            chat_info: 聊天信息
+
+        Returns:
+            Any: mai_think 实例
+        """
         mai_think = mai_thinking_manager.get_mai_think(chat_id)
         mai_think.memory_block = memory_block
         mai_think.relation_info_block = relation_info
@@ -598,21 +612,20 @@ class DefaultReplyer:
 
     async def build_prompt_reply_context(
         self,
-        reply_data: Dict[str, Any],
+        reply_to: str,
+        extra_info: str = "",
         available_actions: Optional[Dict[str, ActionInfo]] = None,
-        enable_timeout: bool = False,
         enable_tool: bool = True,
     ) -> str:  # sourcery skip: merge-else-if-into-elif, remove-redundant-if
         """
         构建回复器上下文
 
         Args:
-            reply_data: 回复数据
-                replay_data 包含以下字段：
-                    structured_info: 结构化信息，一般是工具调用获得的信息
-                    reply_to: 回复对象
-                    extra_info/extra_info_block: 额外信息
+            reply_to: 回复对象，格式为 "发送者:消息内容"
+            extra_info: 额外信息，用于补充上下文
             available_actions: 可用动作
+            enable_timeout: 是否启用超时处理
+            enable_tool: 是否启用工具调用
 
         Returns:
             str: 构建好的上下文
@@ -623,9 +636,7 @@ class DefaultReplyer:
         chat_id = chat_stream.stream_id
         person_info_manager = get_person_info_manager()
         is_group_chat = bool(chat_stream.group_info)
-        reply_to = reply_data.get("reply_to", "none")
-        extra_info_block = reply_data.get("extra_info", "") or reply_data.get("extra_info_block", "")
-            
+
         if global_config.mood.enable_mood:
             chat_mood = mood_manager.get_mood_by_chat_id(chat_id)
             mood_prompt = chat_mood.mood_state
@@ -633,6 +644,15 @@ class DefaultReplyer:
             mood_prompt = ""
 
         sender, target = self._parse_reply_target(reply_to)
+        person_info_manager = get_person_info_manager()
+        person_id = person_info_manager.get_person_id_by_person_name(sender)
+        user_id = person_info_manager.get_value_sync(person_id, "user_id")
+        platform = chat_stream.platform
+        if user_id == global_config.bot.qq_account and platform == global_config.bot.platform:
+            logger.warning("选取了自身作为回复对象，跳过构建prompt")
+            return ""
+
+        target = replace_user_references_sync(target, chat_stream.platform, replace_bot_name=True)
 
         # 构建action描述 (如果启用planner)
         action_descriptions = ""
@@ -649,21 +669,6 @@ class DefaultReplyer:
             limit=global_config.chat.max_context_size * 2,
         )
 
-        message_list_before_now = get_raw_msg_before_timestamp_with_chat(
-            chat_id=chat_id,
-            timestamp=time.time(),
-            limit=global_config.chat.max_context_size,
-        )
-        chat_talking_prompt = build_readable_messages(
-            message_list_before_now,
-            replace_bot_name=True,
-            merge_messages=False,
-            timestamp_mode="normal_no_YMD",
-            read_mark=0.0,
-            truncate=True,
-            show_actions=True,
-        )
-
         message_list_before_short = get_raw_msg_before_timestamp_with_chat(
             chat_id=chat_id,
             timestamp=time.time(),
@@ -683,25 +688,21 @@ class DefaultReplyer:
             self._time_and_run_task(
                 self.build_expression_habits(chat_talking_prompt_short, target), "expression_habits"
             ),
-            self._time_and_run_task(
-                self.build_relation_info(reply_data), "relation_info"
-            ),
+            self._time_and_run_task(self.build_relation_info(reply_to), "relation_info"),
             self._time_and_run_task(self.build_memory_block(chat_talking_prompt_short, target), "memory_block"),
             self._time_and_run_task(
-                self.build_tool_info(chat_talking_prompt_short, reply_data, enable_tool=enable_tool), "tool_info"
-            ),
-            self._time_and_run_task(
-                get_prompt_info(target, threshold=0.38), "prompt_info"
+                self.build_tool_info(chat_talking_prompt_short, reply_to, enable_tool=enable_tool), "tool_info"
             ),
+            self._time_and_run_task(get_prompt_info(target, threshold=0.38), "prompt_info"),
         )
 
         # 任务名称中英文映射
         task_name_mapping = {
             "expression_habits": "选取表达方式",
-            "relation_info": "感受关系", 
+            "relation_info": "感受关系",
             "memory_block": "回忆",
             "tool_info": "使用工具",
-            "prompt_info": "获取知识"
+            "prompt_info": "获取知识",
         }
 
         # 处理结果
@@ -723,8 +724,8 @@ class DefaultReplyer:
 
         keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
 
-        if extra_info_block:
-            extra_info_block = f"以下是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策\n{extra_info_block}\n以上是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策"
+        if extra_info:
+            extra_info_block = f"以下是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策\n{extra_info}\n以上是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策"
         else:
             extra_info_block = ""
 
@@ -779,116 +780,74 @@ class DefaultReplyer:
             # 根据sender通过person_info_manager反向查找person_id，再获取user_id
             person_id = person_info_manager.get_person_id_by_person_name(sender)
 
-        # 根据配置选择使用哪种 prompt 构建模式
-        if global_config.chat.use_s4u_prompt_mode and person_id:
-            # 使用 s4u 对话构建模式：分离当前对话对象和其他对话
-            try:
-                user_id_value = await person_info_manager.get_value(person_id, "user_id")
-                if user_id_value:
-                    target_user_id = str(user_id_value)
-            except Exception as e:
-                logger.warning(f"无法从person_id {person_id} 获取user_id: {e}")
-                target_user_id = ""
+        # 使用 s4u 对话构建模式：分离当前对话对象和其他对话
+        try:
+            user_id_value = await person_info_manager.get_value(person_id, "user_id")
+            if user_id_value:
+                target_user_id = str(user_id_value)
+        except Exception as e:
+            logger.warning(f"无法从person_id {person_id} 获取user_id: {e}")
+            target_user_id = ""
 
-            # 构建分离的对话 prompt
-            core_dialogue_prompt, background_dialogue_prompt = self.build_s4u_chat_history_prompts(
-                message_list_before_now_long, target_user_id
-            )
-            
-            self.build_mai_think_context(
-                chat_id=chat_id,
-                memory_block=memory_block,
-                relation_info=relation_info,
-                time_block=time_block,
-                chat_target_1=chat_target_1,
-                chat_target_2=chat_target_2,
-                mood_prompt=mood_prompt,
-                identity_block=identity_block,
-                sender=sender,
-                target=target,
-                chat_info=f"""
+        # 构建分离的对话 prompt
+        core_dialogue_prompt, background_dialogue_prompt = self.build_s4u_chat_history_prompts(
+            message_list_before_now_long, target_user_id
+        )
+
+        self.build_mai_think_context(
+            chat_id=chat_id,
+            memory_block=memory_block,
+            relation_info=relation_info,
+            time_block=time_block,
+            chat_target_1=chat_target_1,
+            chat_target_2=chat_target_2,
+            mood_prompt=mood_prompt,
+            identity_block=identity_block,
+            sender=sender,
+            target=target,
+            chat_info=f"""
 {background_dialogue_prompt}
 --------------------------------
 {time_block}
 这是你和{sender}的对话，你们正在交流中：
-{core_dialogue_prompt}"""
-            )
-            
+{core_dialogue_prompt}""",
+        )
 
-            # 使用 s4u 风格的模板
-            template_name = "s4u_style_prompt"
+        # 使用 s4u 风格的模板
+        template_name = "s4u_style_prompt"
 
-            return await global_prompt_manager.format_prompt(
-                template_name,
-                expression_habits_block=expression_habits_block,
-                tool_info_block=tool_info,
-                knowledge_prompt=prompt_info,
-                memory_block=memory_block,
-                relation_info_block=relation_info,
-                extra_info_block=extra_info_block,
-                identity=identity_block,
-                action_descriptions=action_descriptions,
-                sender_name=sender,
-                mood_state=mood_prompt,
-                background_dialogue_prompt=background_dialogue_prompt,
-                time_block=time_block,
-                core_dialogue_prompt=core_dialogue_prompt,
-                reply_target_block=reply_target_block,
-                message_txt=target,
-                config_expression_style=global_config.expression.expression_style,
-                keywords_reaction_prompt=keywords_reaction_prompt,
-                moderation_prompt=moderation_prompt_block,
-            )
-        else:
-            self.build_mai_think_context(
-                chat_id=chat_id,
-                memory_block=memory_block,
-                relation_info=relation_info,
-                time_block=time_block,
-                chat_target_1=chat_target_1,
-                chat_target_2=chat_target_2,
-                mood_prompt=mood_prompt,
-                identity_block=identity_block,
-                sender=sender,
-                target=target,
-                chat_info=chat_talking_prompt
-            )
-            
-            # 使用原有的模式
-            return await global_prompt_manager.format_prompt(
-                template_name,
-                expression_habits_block=expression_habits_block,
-                chat_target=chat_target_1,
-                chat_info=chat_talking_prompt,
-                memory_block=memory_block,
-                tool_info_block=tool_info,
-                knowledge_prompt=prompt_info,
-                extra_info_block=extra_info_block,
-                relation_info_block=relation_info,
-                time_block=time_block,
-                reply_target_block=reply_target_block,
-                moderation_prompt=moderation_prompt_block,
-                keywords_reaction_prompt=keywords_reaction_prompt,
-                identity=identity_block,
-                target_message=target,
-                sender_name=sender,
-                config_expression_style=global_config.expression.expression_style,
-                action_descriptions=action_descriptions,
-                chat_target_2=chat_target_2,
-                mood_state=mood_prompt,
-            )
+        return await global_prompt_manager.format_prompt(
+            template_name,
+            expression_habits_block=expression_habits_block,
+            tool_info_block=tool_info,
+            knowledge_prompt=prompt_info,
+            memory_block=memory_block,
+            relation_info_block=relation_info,
+            extra_info_block=extra_info_block,
+            identity=identity_block,
+            action_descriptions=action_descriptions,
+            sender_name=sender,
+            mood_state=mood_prompt,
+            background_dialogue_prompt=background_dialogue_prompt,
+            time_block=time_block,
+            core_dialogue_prompt=core_dialogue_prompt,
+            reply_target_block=reply_target_block,
+            message_txt=target,
+            config_expression_style=global_config.expression.expression_style,
+            keywords_reaction_prompt=keywords_reaction_prompt,
+            moderation_prompt=moderation_prompt_block,
+        )
 
     async def build_prompt_rewrite_context(
         self,
-        reply_data: Dict[str, Any],
+        raw_reply: str,
+        reason: str,
+        reply_to: str,
     ) -> str:
         chat_stream = self.chat_stream
         chat_id = chat_stream.stream_id
         is_group_chat = bool(chat_stream.group_info)
 
-        reply_to = reply_data.get("reply_to", "none")
-        raw_reply = reply_data.get("raw_reply", "")
-        reason = reply_data.get("reason", "")
         sender, target = self._parse_reply_target(reply_to)
 
         # 添加情绪状态获取
@@ -915,7 +874,7 @@ class DefaultReplyer:
         # 并行执行2个构建任务
         expression_habits_block, relation_info = await asyncio.gather(
             self.build_expression_habits(chat_talking_prompt_half, target),
-            self.build_relation_info(reply_data),
+            self.build_relation_info(reply_to),
         )
 
         keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
@@ -1018,6 +977,31 @@ class DefaultReplyer:
             display_message=display_message,
         )
 
+    async def llm_generate_content(self, prompt: str) -> str:
+        with Timer("LLM生成", {}):  # 内部计时器，可选保留
+            # 加权随机选择一个模型配置
+            selected_model_config = self._select_weighted_model_config()
+            model_display_name = selected_model_config.get('model_name') or selected_model_config.get('name', 'N/A')
+            logger.info(
+                f"使用模型生成回复: {model_display_name} (选中概率: {selected_model_config.get('weight', 1.0)})"
+            )
+
+            express_model = LLMRequest(
+                model=selected_model_config,
+                request_type=self.request_type,
+            )
+
+            if global_config.debug.show_prompt:
+                logger.info(f"\n{prompt}\n")
+            else:
+                logger.debug(f"\n{prompt}\n")
+
+            # TODO: 这里的_应该做出替换
+            content, _ = await express_model.generate_response_async(prompt)
+
+            logger.debug(f"replyer生成内容: {content}")
+        return content
+
 
 def weighted_sample_no_replacement(items, weights, k) -> list:
     """
@@ -1075,10 +1059,8 @@ async def get_prompt_info(message: str, threshold: float):
             related_info += found_knowledge_from_lpmm
             logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒")
             logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
-            
-            # 格式化知识信息
-            formatted_prompt_info = await global_prompt_manager.format_prompt("knowledge_prompt", prompt_info=related_info)
-            return formatted_prompt_info
+
+            return f"你有以下这些**知识**：\n{related_info}\n请你**记住上面的知识**，之后可能会用到。\n"
         else:
             logger.debug("从LPMM知识库获取知识失败，可能是从未导入过知识，返回空知识...")
             return ""

src/chat/utils/chat_message_builder.py

@@ -2,7 +2,7 @@ import time  # 导入 time 模块以获取当前时间
 import random
 import re
 
-from typing import List, Dict, Any, Tuple, Optional
+from typing import List, Dict, Any, Tuple, Optional, Callable
 from rich.traceback import install
 
 from src.config.config import global_config
@@ -10,11 +10,161 @@ from src.common.message_repository import find_messages, count_messages
 from src.common.database.database_model import ActionRecords
 from src.common.database.database_model import Images
 from src.person_info.person_info import PersonInfoManager, get_person_info_manager
-from src.chat.utils.utils import translate_timestamp_to_human_readable,assign_message_ids
+from src.chat.utils.utils import translate_timestamp_to_human_readable, assign_message_ids
 
 install(extra_lines=3)
 
 
+def replace_user_references_sync(
+    content: str,
+    platform: str,
+    name_resolver: Optional[Callable[[str, str], str]] = None,
+    replace_bot_name: bool = True,
+) -> str:
+    """
+    替换内容中的用户引用格式，包括回复<aaa:bbb>和@<aaa:bbb>格式
+
+    Args:
+        content: 要处理的内容字符串
+        platform: 平台标识
+        name_resolver: 名称解析函数，接收(platform, user_id)参数，返回用户名称
+                       如果为None，则使用默认的person_info_manager
+        replace_bot_name: 是否将机器人的user_id替换为"机器人昵称(你)"
+
+    Returns:
+        str: 处理后的内容字符串
+    """
+    if name_resolver is None:
+        person_info_manager = get_person_info_manager()
+
+        def default_resolver(platform: str, user_id: str) -> str:
+            # 检查是否是机器人自己
+            if replace_bot_name and user_id == global_config.bot.qq_account:
+                return f"{global_config.bot.nickname}(你)"
+            person_id = PersonInfoManager.get_person_id(platform, user_id)
+            return person_info_manager.get_value_sync(person_id, "person_name") or user_id  # type: ignore
+
+        name_resolver = default_resolver
+
+    # 处理回复<aaa:bbb>格式
+    reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
+    match = re.search(reply_pattern, content)
+    if match:
+        aaa = match[1]
+        bbb = match[2]
+        try:
+            # 检查是否是机器人自己
+            if replace_bot_name and bbb == global_config.bot.qq_account:
+                reply_person_name = f"{global_config.bot.nickname}(你)"
+            else:
+                reply_person_name = name_resolver(platform, bbb) or aaa
+            content = re.sub(reply_pattern, f"回复 {reply_person_name}", content, count=1)
+        except Exception:
+            # 如果解析失败，使用原始昵称
+            content = re.sub(reply_pattern, f"回复 {aaa}", content, count=1)
+
+    # 处理@<aaa:bbb>格式
+    at_pattern = r"@<([^:<>]+):([^:<>]+)>"
+    at_matches = list(re.finditer(at_pattern, content))
+    if at_matches:
+        new_content = ""
+        last_end = 0
+        for m in at_matches:
+            new_content += content[last_end : m.start()]
+            aaa = m.group(1)
+            bbb = m.group(2)
+            try:
+                # 检查是否是机器人自己
+                if replace_bot_name and bbb == global_config.bot.qq_account:
+                    at_person_name = f"{global_config.bot.nickname}(你)"
+                else:
+                    at_person_name = name_resolver(platform, bbb) or aaa
+                new_content += f"@{at_person_name}"
+            except Exception:
+                # 如果解析失败，使用原始昵称
+                new_content += f"@{aaa}"
+            last_end = m.end()
+        new_content += content[last_end:]
+        content = new_content
+
+    return content
+
+
+async def replace_user_references_async(
+    content: str,
+    platform: str,
+    name_resolver: Optional[Callable[[str, str], Any]] = None,
+    replace_bot_name: bool = True,
+) -> str:
+    """
+    替换内容中的用户引用格式，包括回复<aaa:bbb>和@<aaa:bbb>格式
+
+    Args:
+        content: 要处理的内容字符串
+        platform: 平台标识
+        name_resolver: 名称解析函数，接收(platform, user_id)参数，返回用户名称
+                       如果为None，则使用默认的person_info_manager
+        replace_bot_name: 是否将机器人的user_id替换为"机器人昵称(你)"
+
+    Returns:
+        str: 处理后的内容字符串
+    """
+    if name_resolver is None:
+        person_info_manager = get_person_info_manager()
+
+        async def default_resolver(platform: str, user_id: str) -> str:
+            # 检查是否是机器人自己
+            if replace_bot_name and user_id == global_config.bot.qq_account:
+                return f"{global_config.bot.nickname}(你)"
+            person_id = PersonInfoManager.get_person_id(platform, user_id)
+            return await person_info_manager.get_value(person_id, "person_name") or user_id  # type: ignore
+
+        name_resolver = default_resolver
+
+    # 处理回复<aaa:bbb>格式
+    reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
+    match = re.search(reply_pattern, content)
+    if match:
+        aaa = match.group(1)
+        bbb = match.group(2)
+        try:
+            # 检查是否是机器人自己
+            if replace_bot_name and bbb == global_config.bot.qq_account:
+                reply_person_name = f"{global_config.bot.nickname}(你)"
+            else:
+                reply_person_name = await name_resolver(platform, bbb) or aaa
+            content = re.sub(reply_pattern, f"回复 {reply_person_name}", content, count=1)
+        except Exception:
+            # 如果解析失败，使用原始昵称
+            content = re.sub(reply_pattern, f"回复 {aaa}", content, count=1)
+
+    # 处理@<aaa:bbb>格式
+    at_pattern = r"@<([^:<>]+):([^:<>]+)>"
+    at_matches = list(re.finditer(at_pattern, content))
+    if at_matches:
+        new_content = ""
+        last_end = 0
+        for m in at_matches:
+            new_content += content[last_end : m.start()]
+            aaa = m.group(1)
+            bbb = m.group(2)
+            try:
+                # 检查是否是机器人自己
+                if replace_bot_name and bbb == global_config.bot.qq_account:
+                    at_person_name = f"{global_config.bot.nickname}(你)"
+                else:
+                    at_person_name = await name_resolver(platform, bbb) or aaa
+                new_content += f"@{at_person_name}"
+            except Exception:
+                # 如果解析失败，使用原始昵称
+                new_content += f"@{aaa}"
+            last_end = m.end()
+        new_content += content[last_end:]
+        content = new_content
+
+    return content
+
+
 def get_raw_msg_by_timestamp(
     timestamp_start: float, timestamp_end: float, limit: int = 0, limit_mode: str = "latest"
 ) -> List[Dict[str, Any]]:
@@ -374,33 +524,8 @@ def _build_readable_messages_internal(
             else:
                 person_name = "某人"
 
-        # 检查是否有 回复<aaa:bbb> 字段
-        reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
-        match = re.search(reply_pattern, content)
-        if match:
-            aaa: str = match[1]
-            bbb: str = match[2]
-            reply_person_id = PersonInfoManager.get_person_id(platform, bbb)
-            reply_person_name = person_info_manager.get_value_sync(reply_person_id, "person_name") or aaa
-            # 在内容前加上回复信息
-            content = re.sub(reply_pattern, lambda m, name=reply_person_name: f"回复 {name}", content, count=1)
-
-        # 检查是否有 @<aaa:bbb> 字段 @<{member_info.get('nickname')}:{member_info.get('user_id')}>
-        at_pattern = r"@<([^:<>]+):([^:<>]+)>"
-        at_matches = list(re.finditer(at_pattern, content))
-        if at_matches:
-            new_content = ""
-            last_end = 0
-            for m in at_matches:
-                new_content += content[last_end : m.start()]
-                aaa = m.group(1)
-                bbb = m.group(2)
-                at_person_id = PersonInfoManager.get_person_id(platform, bbb)
-                at_person_name = person_info_manager.get_value_sync(at_person_id, "person_name") or aaa
-                new_content += f"@{at_person_name}"
-                last_end = m.end()
-            new_content += content[last_end:]
-            content = new_content
+        # 使用独立函数处理用户引用格式
+        content = replace_user_references_sync(content, platform, replace_bot_name=replace_bot_name)
 
         target_str = "这是QQ的一个功能，用于提及某人，但没那么明显"
         if target_str in content and random.random() < 0.6:
@@ -654,6 +779,7 @@ async def build_readable_messages_with_list(
 
     return formatted_string, details_list
 
+
 def build_readable_messages_with_id(
     messages: List[Dict[str, Any]],
     replace_bot_name: bool = True,
@@ -669,9 +795,9 @@ def build_readable_messages_with_id(
     允许通过参数控制格式化行为。
     """
     message_id_list = assign_message_ids(messages)
-    
+
     formatted_string = build_readable_messages(
-        messages = messages,
+        messages=messages,
         replace_bot_name=replace_bot_name,
         merge_messages=merge_messages,
         timestamp_mode=timestamp_mode,
@@ -682,10 +808,7 @@ def build_readable_messages_with_id(
         message_id_list=message_id_list,
     )
 
-    
-    
-    
-    return formatted_string , message_id_list
+    return formatted_string, message_id_list
 
 
 def build_readable_messages(
@@ -770,7 +893,13 @@ def build_readable_messages(
     if read_mark <= 0:
         # 没有有效的 read_mark，直接格式化所有消息
         formatted_string, _, pic_id_mapping, _ = _build_readable_messages_internal(
-            copy_messages, replace_bot_name, merge_messages, timestamp_mode, truncate, show_pic=show_pic, message_id_list=message_id_list
+            copy_messages,
+            replace_bot_name,
+            merge_messages,
+            timestamp_mode,
+            truncate,
+            show_pic=show_pic,
+            message_id_list=message_id_list,
         )
 
         # 生成图片映射信息并添加到最前面
@@ -893,7 +1022,7 @@ async def build_anonymous_messages(messages: List[Dict[str, Any]]) -> str:
 
     for msg in messages:
         try:
-            platform = msg.get("chat_info_platform")
+            platform: str = msg.get("chat_info_platform")  # type: ignore
             user_id = msg.get("user_id")
             _timestamp = msg.get("time")
             content: str = ""
@@ -916,38 +1045,14 @@ async def build_anonymous_messages(messages: List[Dict[str, Any]]) -> str:
             anon_name = get_anon_name(platform, user_id)
             # print(f"anon_name:{anon_name}")
 
-            # 处理 回复<aaa:bbb>
-            reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
-            match = re.search(reply_pattern, content)
-            if match:
-                # print(f"发现回复match:{match}")
-                bbb = match.group(2)
+            # 使用独立函数处理用户引用格式，传入自定义的匿名名称解析器
+            def anon_name_resolver(platform: str, user_id: str) -> str:
                 try:
-                    anon_reply = get_anon_name(platform, bbb)
-                    # print(f"anon_reply:{anon_reply}")
+                    return get_anon_name(platform, user_id)
                 except Exception:
-                    anon_reply = "?"
-                content = re.sub(reply_pattern, f"回复 {anon_reply}", content, count=1)
+                    return "?"
 
-            # 处理 @<aaa:bbb>，无嵌套def
-            at_pattern = r"@<([^:<>]+):([^:<>]+)>"
-            at_matches = list(re.finditer(at_pattern, content))
-            if at_matches:
-                # print(f"发现@match:{at_matches}")
-                new_content = ""
-                last_end = 0
-                for m in at_matches:
-                    new_content += content[last_end : m.start()]
-                    bbb = m.group(2)
-                    try:
-                        anon_at = get_anon_name(platform, bbb)
-                        # print(f"anon_at:{anon_at}")
-                    except Exception:
-                        anon_at = "?"
-                    new_content += f"@{anon_at}"
-                    last_end = m.end()
-                new_content += content[last_end:]
-                content = new_content
+            content = replace_user_references_sync(content, platform, anon_name_resolver, replace_bot_name=False)
 
             header = f"{anon_name}说 "
             output_lines.append(header)

src/chat/utils/utils_image.py

@@ -37,7 +37,7 @@ class ImageManager:
             self._ensure_image_dir()
 
             self._initialized = True
-            self._llm = LLMRequest(model=global_config.model.vlm, temperature=0.4, max_tokens=300, request_type="image")
+            self.vlm = LLMRequest(model=global_config.model.vlm, temperature=0.4, max_tokens=300, request_type="image")
 
             try:
                 db.connect(reuse_if_open=True)
@@ -94,7 +94,7 @@ class ImageManager:
             logger.error(f"保存描述到数据库失败 (Peewee): {str(e)}")
 
     async def get_emoji_description(self, image_base64: str) -> str:
-        """获取表情包描述，使用二步走识别并带缓存优化"""
+        """获取表情包描述，优先使用Emoji表中的缓存数据"""
         try:
             # 计算图片哈希
             # 确保base64字符串只包含ASCII字符
@@ -104,9 +104,21 @@ class ImageManager:
             image_hash = hashlib.md5(image_bytes).hexdigest()
             image_format = Image.open(io.BytesIO(image_bytes)).format.lower()  # type: ignore
 
-            # 查询缓存的描述
+            # 优先使用EmojiManager查询已注册表情包的描述
+            try:
+                from src.chat.emoji_system.emoji_manager import get_emoji_manager
+                emoji_manager = get_emoji_manager()
+                cached_emoji_description = await emoji_manager.get_emoji_description_by_hash(image_hash)
+                if cached_emoji_description:
+                    logger.info(f"[缓存命中] 使用已注册表情包描述: {cached_emoji_description[:50]}...")
+                    return cached_emoji_description
+            except Exception as e:
+                logger.debug(f"查询EmojiManager时出错: {e}")
+
+            # 查询ImageDescriptions表的缓存描述
             cached_description = self._get_description_from_db(image_hash, "emoji")
             if cached_description:
+                logger.info(f"[缓存命中] 使用ImageDescriptions表中的描述: {cached_description[:50]}...")
                 return f"[表情包：{cached_description}]"
 
             # === 二步走识别流程 ===
@@ -118,10 +130,10 @@ class ImageManager:
                     logger.warning("GIF转换失败，无法获取描述")
                     return "[表情包(GIF处理失败)]"
                 vlm_prompt = "这是一个动态图表情包，每一张图代表了动态图的某一帧，黑色背景代表透明，描述一下表情包表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
-                detailed_description, _ = await self._llm.generate_response_for_image(vlm_prompt, image_base64_processed, "jpg")
+                detailed_description, _ = await self.vlm.generate_response_for_image(vlm_prompt, image_base64_processed, "jpg")
             else:
                 vlm_prompt = "这是一个表情包，请详细描述一下表情包所表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
-                detailed_description, _ = await self._llm.generate_response_for_image(vlm_prompt, image_base64, image_format)
+                detailed_description, _ = await self.vlm.generate_response_for_image(vlm_prompt, image_base64, image_format)
 
             if detailed_description is None:
                 logger.warning("VLM未能生成表情包详细描述")
@@ -158,7 +170,7 @@ class ImageManager:
             if len(emotions) > 1 and emotions[1] != emotions[0]:
                 final_emotion = f"{emotions[0]}，{emotions[1]}"
 
-            logger.info(f"[二步走识别] 详细描述: {detailed_description[:50]}... -> 情感标签: {final_emotion}")
+            logger.info(f"[emoji识别] 详细描述: {detailed_description[:50]}... -> 情感标签: {final_emotion}")
 
             # 再次检查缓存，防止并发写入时重复生成
             cached_description = self._get_description_from_db(image_hash, "emoji")
@@ -201,13 +213,13 @@ class ImageManager:
             self._save_description_to_db(image_hash, final_emotion, "emoji")
 
             return f"[表情包：{final_emotion}]"
-            
+
         except Exception as e:
             logger.error(f"获取表情包描述失败: {str(e)}")
-            return "[表情包]"
+            return "[表情包(处理失败)]"
 
     async def get_image_description(self, image_base64: str) -> str:
-        """获取普通图片描述，带查重和保存功能"""
+        """获取普通图片描述，优先使用Images表中的缓存数据"""
         try:
             # 计算图片哈希
             if isinstance(image_base64, str):
@@ -215,7 +227,7 @@ class ImageManager:
             image_bytes = base64.b64decode(image_base64)
             image_hash = hashlib.md5(image_bytes).hexdigest()
 
-            # 检查图片是否已存在
+            # 优先检查Images表中是否已有完整的描述
             existing_image = Images.get_or_none(Images.emoji_hash == image_hash)
             if existing_image:
                 # 更新计数
@@ -227,18 +239,20 @@ class ImageManager:
 
                 # 如果已有描述，直接返回
                 if existing_image.description:
+                    logger.debug(f"[缓存命中] 使用Images表中的图片描述: {existing_image.description[:50]}...")
                     return f"[图片：{existing_image.description}]"
 
-            # 查询缓存的描述
+            # 查询ImageDescriptions表的缓存描述
             cached_description = self._get_description_from_db(image_hash, "image")
             if cached_description:
-                logger.debug(f"图片描述缓存中 {cached_description}")
+                logger.debug(f"[缓存命中] 使用ImageDescriptions表中的描述: {cached_description[:50]}...")
                 return f"[图片：{cached_description}]"
 
             # 调用AI获取描述
             image_format = Image.open(io.BytesIO(image_bytes)).format.lower()  # type: ignore
             prompt = global_config.custom_prompt.image_prompt
-            description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
+            logger.info(f"[VLM调用] 为图片生成新描述 (Hash: {image_hash[:8]}...)")
+            description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
 
             if description is None:
                 logger.warning("AI未能生成图片描述")
@@ -266,6 +280,7 @@ class ImageManager:
                     if not hasattr(existing_image, "vlm_processed") or existing_image.vlm_processed is None:
                         existing_image.vlm_processed = True
                     existing_image.save()
+                    logger.debug(f"[数据库] 更新已有图片记录: {image_hash[:8]}...")
                 else:
                     Images.create(
                         image_id=str(uuid.uuid4()),
@@ -277,16 +292,18 @@ class ImageManager:
                         vlm_processed=True,
                         count=1,
                     )
+                    logger.debug(f"[数据库] 创建新图片记录: {image_hash[:8]}...")
             except Exception as e:
                 logger.error(f"保存图片文件或元数据失败: {str(e)}")
 
-            # 保存描述到ImageDescriptions表
+            # 保存描述到ImageDescriptions表作为备用缓存
             self._save_description_to_db(image_hash, description, "image")
 
+            logger.info(f"[VLM完成] 图片描述生成: {description[:50]}...")
             return f"[图片：{description}]"
         except Exception as e:
             logger.error(f"获取图片描述失败: {str(e)}")
-            return "[图片]"
+            return "[图片(处理失败)]"
 
     @staticmethod
     def transform_gif(gif_base64: str, similarity_threshold: float = 1000.0, max_frames: int = 15) -> Optional[str]:
@@ -502,12 +519,28 @@ class ImageManager:
             image_bytes = base64.b64decode(image_base64)
             image_hash = hashlib.md5(image_bytes).hexdigest()
 
-            # 先检查缓存的描述
+            # 获取当前图片记录
+            image = Images.get(Images.image_id == image_id)
+
+            # 优先检查是否已有其他相同哈希的图片记录包含描述
+            existing_with_description = Images.get_or_none(
+                (Images.emoji_hash == image_hash) & 
+                (Images.description.is_null(False)) & 
+                (Images.description != "")
+            )
+            if existing_with_description and existing_with_description.id != image.id:
+                logger.debug(f"[缓存复用] 从其他相同图片记录复用描述: {existing_with_description.description[:50]}...")
+                image.description = existing_with_description.description
+                image.vlm_processed = True
+                image.save()
+                # 同时保存到ImageDescriptions表作为备用缓存
+                self._save_description_to_db(image_hash, existing_with_description.description, "image")
+                return
+
+            # 检查ImageDescriptions表的缓存描述
             cached_description = self._get_description_from_db(image_hash, "image")
             if cached_description:
-                logger.debug(f"VLM处理时发现缓存描述: {cached_description}")
-                # 更新数据库
-                image = Images.get(Images.image_id == image_id)
+                logger.debug(f"[缓存复用] 从ImageDescriptions表复用描述: {cached_description[:50]}...")
                 image.description = cached_description
                 image.vlm_processed = True
                 image.save()
@@ -520,7 +553,8 @@ class ImageManager:
             prompt = global_config.custom_prompt.image_prompt
 
             # 获取VLM描述
-            description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
+            logger.info(f"[VLM异步调用] 为图片生成描述 (ID: {image_id}, Hash: {image_hash[:8]}...)")
+            description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
 
             if description is None:
                 logger.warning("VLM未能生成图片描述")
@@ -533,14 +567,15 @@ class ImageManager:
                 description = cached_description
 
             # 更新数据库
-            image = Images.get(Images.image_id == image_id)
             image.description = description
             image.vlm_processed = True
             image.save()
 
-            # 保存描述到ImageDescriptions表
+            # 保存描述到ImageDescriptions表作为备用缓存
             self._save_description_to_db(image_hash, description, "image")
 
+            logger.info(f"[VLM异步完成] 图片描述生成: {description[:50]}...")
+
         except Exception as e:
             logger.error(f"VLM处理图片失败: {str(e)}")
 

src/chat/willing/mode_classical.py

@@ -28,7 +28,7 @@ class ClassicalWillingManager(BaseWillingManager):
         
         # print(f"[{chat_id}] 回复意愿: {current_willing}")
 
-        interested_rate = willing_info.interested_rate * global_config.normal_chat.response_interested_rate_amplifier
+        interested_rate = willing_info.interested_rate
         
         # print(f"[{chat_id}] 兴趣值: {interested_rate}")
 
@@ -36,20 +36,18 @@ class ClassicalWillingManager(BaseWillingManager):
             current_willing += interested_rate - 0.2
 
         if willing_info.is_mentioned_bot and global_config.chat.mentioned_bot_inevitable_reply and current_willing < 2:
-            current_willing += 1 if current_willing < 1.0 else 0.05
+            current_willing += 1 if current_willing < 1.0 else 0.2
 
         self.chat_reply_willing[chat_id] = min(current_willing, 1.0)
         
-        reply_probability = min(max((current_willing - 0.5), 0.01) * 2, 1)
+        reply_probability = min(max((current_willing - 0.5), 0.01) * 2, 1.5)
         
         # print(f"[{chat_id}] 回复概率: {reply_probability}")
         
         return reply_probability
 
     async def before_generate_reply_handle(self, message_id):
-        chat_id = self.ongoing_messages[message_id].chat_id
-        current_willing = self.chat_reply_willing.get(chat_id, 0)
-        self.chat_reply_willing[chat_id] = max(0.0, current_willing - 1.8)
+        pass
 
     async def after_generate_reply_handle(self, message_id):
         if message_id not in self.ongoing_messages:
@@ -58,7 +56,7 @@ class ClassicalWillingManager(BaseWillingManager):
         chat_id = self.ongoing_messages[message_id].chat_id
         current_willing = self.chat_reply_willing.get(chat_id, 0)
         if current_willing < 1:
-            self.chat_reply_willing[chat_id] = min(1.0, current_willing + 0.4)
+            self.chat_reply_willing[chat_id] = min(1.0, current_willing + 0.3)
 
     async def not_reply_handle(self, message_id):
         return await super().not_reply_handle(message_id)

src/common/logger.py

@@ -390,7 +390,7 @@ MODULE_COLORS = {
     "tts_action": "\033[38;5;58m",  # 深黄色
     "doubao_pic_plugin": "\033[38;5;64m",  # 深绿色
     # Action组件
-    "no_reply_action": "\033[38;5;196m",  # 亮红色，更显眼
+    "no_reply_action": "\033[38;5;214m",  # 亮橙色，显眼但不像警告
     "reply_action": "\033[38;5;46m",  # 亮绿色
     "base_action": "\033[38;5;250m",  # 浅灰色
     # 数据库和消息

src/config/official_configs.py

@@ -69,6 +69,8 @@ class ChatConfig(ConfigBase):
 
     max_context_size: int = 18
     """上下文长度"""
+    
+    willing_amplifier: float = 1.0
 
     replyer_random_probability: float = 0.5
     """
@@ -76,15 +78,12 @@ class ChatConfig(ConfigBase):
     选择普通模型的概率为 1 - reasoning_normal_model_probability
     """
 
-    thinking_timeout: int = 30
+    thinking_timeout: int = 40
     """麦麦最长思考规划时间，超过这个时间的思考会放弃（往往是api反应太慢）"""
 
     talk_frequency: float = 1
     """回复频率阈值"""
 
-    use_s4u_prompt_mode: bool = False
-    """是否使用 s4u 对话构建模式，该模式会分开处理当前对话对象和其他所有对话的内容进行 prompt 构建"""
-
     mentioned_bot_inevitable_reply: bool = False
     """提及 bot 必然回复"""
 
@@ -274,12 +273,6 @@ class NormalChatConfig(ConfigBase):
     willing_mode: str = "classical"
     """意愿模式"""
 
-    response_interested_rate_amplifier: float = 1.0
-    """回复兴趣度放大系数"""
-
-
-
-
 @dataclass
 class ExpressionConfig(ConfigBase):
     """表达配置类"""
@@ -307,11 +300,8 @@ class ExpressionConfig(ConfigBase):
 class ToolConfig(ConfigBase):
     """工具配置类"""
 
-    enable_in_normal_chat: bool = False
-    """是否在普通聊天中启用工具"""
-
-    enable_in_focus_chat: bool = True
-    """是否在专注聊天中启用工具"""
+    enable_tool: bool = False
+    """是否在聊天中启用工具"""
     
 @dataclass
 class VoiceConfig(ConfigBase):

src/individuality/individuality.py

@@ -273,15 +273,19 @@ class Individuality:
                 prompt=prompt,
             )
 
-            if response.strip():
+            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
+                personality_result = personality_core or "友好活泼"
         else:
             personality_result = personality_core
             if personality_side:
@@ -308,13 +312,14 @@ class Individuality:
                 prompt=prompt,
             )
 
-            if response.strip():
+            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 = "。".join(identity)
+            identity_result = identity
 
         return identity_result
 

src/mais4u/mais4u_chat/s4u_msg_processor.py

@@ -47,11 +47,35 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool]:
             logger.debug(f"记忆激活率: {interested_rate:.2f}")
 
     text_len = len(message.processed_plain_text)
-    # 根据文本长度调整兴趣度，长度越大兴趣度越高，但增长率递减，最低0.01，最高0.05
-    # 采用对数函数实现递减增长
-
-    base_interest = 0.01 + (0.05 - 0.01) * (math.log10(text_len + 1) / math.log10(1000 + 1))
-    base_interest = min(max(base_interest, 0.01), 0.05)
+    # 根据文本长度分布调整兴趣度，采用分段函数实现更精确的兴趣度计算
+    # 基于实际分布：0-5字符(26.57%), 6-10字符(27.18%), 11-20字符(22.76%), 21-30字符(10.33%), 31+字符(13.86%)
+    
+    if text_len == 0:
+        base_interest = 0.01  # 空消息最低兴趣度
+    elif text_len <= 5:
+        # 1-5字符：线性增长 0.01 -> 0.03
+        base_interest = 0.01 + (text_len - 1) * (0.03 - 0.01) / 4
+    elif text_len <= 10:
+        # 6-10字符：线性增长 0.03 -> 0.06
+        base_interest = 0.03 + (text_len - 5) * (0.06 - 0.03) / 5
+    elif text_len <= 20:
+        # 11-20字符：线性增长 0.06 -> 0.12
+        base_interest = 0.06 + (text_len - 10) * (0.12 - 0.06) / 10
+    elif text_len <= 30:
+        # 21-30字符：线性增长 0.12 -> 0.18
+        base_interest = 0.12 + (text_len - 20) * (0.18 - 0.12) / 10
+    elif text_len <= 50:
+        # 31-50字符：线性增长 0.18 -> 0.22
+        base_interest = 0.18 + (text_len - 30) * (0.22 - 0.18) / 20
+    elif text_len <= 100:
+        # 51-100字符：线性增长 0.22 -> 0.26
+        base_interest = 0.22 + (text_len - 50) * (0.26 - 0.22) / 50
+    else:
+        # 100+字符：对数增长 0.26 -> 0.3，增长率递减
+        base_interest = 0.26 + (0.3 - 0.26) * (math.log10(text_len - 99) / math.log10(901))  # 1000-99=901
+    
+    # 确保在范围内
+    base_interest = min(max(base_interest, 0.01), 0.3)
 
     interested_rate += base_interest
 

src/mood/mood_manager.py

@@ -78,7 +78,7 @@ class ChatMood:
         if interested_rate <= 0:
             interest_multiplier = 0
         else:
-            interest_multiplier = 3 * math.pow(interested_rate, 0.25)
+            interest_multiplier = 2 * math.pow(interested_rate, 0.25)
 
         logger.debug(
             f"base_probability: {base_probability}, time_multiplier: {time_multiplier}, interest_multiplier: {interest_multiplier}"

src/person_info/relationship_manager.py

@@ -139,7 +139,7 @@ class RelationshipManager:
 请用json格式输出，引起了你的兴趣，或者有什么需要你记忆的点。
 并为每个点赋予1-10的权重，权重越高，表示越重要。
 格式如下:
-{{
+[
     {{
         "point": "{person_name}想让我记住他的生日，我回答确认了，他的生日是11月23日",
         "weight": 10
@@ -156,13 +156,10 @@ class RelationshipManager:
         "point": "{person_name}喜欢吃辣，具体来说，没有辣的食物ta都不喜欢吃，可能是因为ta是湖南人。",
         "weight": 7
     }}
-}}
+]
 
-如果没有，就输出none,或points为空：
-{{
-    "point": "none",
-    "weight": 0
-}}
+如果没有，就输出none,或返回空数组：
+[]
 """
 
         # 调用LLM生成印象
@@ -184,17 +181,25 @@ class RelationshipManager:
         try:
             points = repair_json(points)
             points_data = json.loads(points)
-            if points_data == "none" or not points_data or points_data.get("point") == "none":
+            
+            # 只处理正确的格式，错误格式直接跳过
+            if points_data == "none" or not points_data:
                 points_list = []
+            elif isinstance(points_data, str) and points_data.lower() == "none":
+                points_list = []
+            elif isinstance(points_data, list):
+                # 正确格式：数组格式 [{"point": "...", "weight": 10}, ...]
+                if not points_data:  # 空数组
+                    points_list = []
+                else:
+                    points_list = [(item["point"], float(item["weight"]), current_time) for item in points_data]
             else:
-                # logger.info(f"points_data: {points_data}")
-                if isinstance(points_data, dict) and "points" in points_data:
-                    points_data = points_data["points"]
-                if not isinstance(points_data, list):
-                    points_data = [points_data]
-                # 添加可读时间到每个point
-                points_list = [(item["point"], float(item["weight"]), current_time) for item in points_data]
+                # 错误格式，直接跳过不解析
+                logger.warning(f"LLM返回了错误的JSON格式，跳过解析: {type(points_data)}, 内容: {points_data}")
+                points_list = []
 
+            # 权重过滤逻辑
+            if points_list:
                 original_points_list = list(points_list)
                 points_list.clear()
                 discarded_count = 0

src/plugin_system/__init__.py

@@ -9,6 +9,7 @@ from .base import (
     BasePlugin,
     BaseAction,
     BaseCommand,
+    BaseTool,
     ConfigField,
     ComponentType,
     ActionActivationType,
@@ -34,6 +35,7 @@ from .utils import (
 
 from .apis import (
     chat_api,
+    tool_api,  
     component_manage_api,
     config_api,
     database_api,
@@ -44,17 +46,17 @@ from .apis import (
     person_api,
     plugin_manage_api,
     send_api,
-    utils_api,
     register_plugin,
     get_logger,
 )
 
 
-__version__ = "1.0.0"
+__version__ = "2.0.0"
 
 __all__ = [
     # API 模块
     "chat_api",
+    "tool_api",
     "component_manage_api",
     "config_api",
     "database_api",
@@ -65,13 +67,13 @@ __all__ = [
     "person_api",
     "plugin_manage_api",
     "send_api",
-    "utils_api",
     "register_plugin",
     "get_logger",
     # 基础类
     "BasePlugin",
     "BaseAction",
     "BaseCommand",
+    "BaseTool",
     "BaseEventHandler",
     # 类型定义
     "ComponentType",

src/plugin_system/apis/__init__.py

@@ -17,7 +17,7 @@ from src.plugin_system.apis import (
     person_api,
     plugin_manage_api,
     send_api,
-    utils_api,
+    tool_api,
 )
 from .logging_api import get_logger
 from .plugin_register_api import register_plugin
@@ -35,7 +35,7 @@ __all__ = [
     "person_api",
     "plugin_manage_api",
     "send_api",
-    "utils_api",
     "get_logger",
     "register_plugin",
+    "tool_api",
 ]

src/plugin_system/apis/chat_api.py

@@ -32,6 +32,7 @@ class ChatManager:
 
     @staticmethod
     def get_all_streams(platform: Optional[str] | SpecialTypes = "qq") -> List[ChatStream]:
+        # sourcery skip: for-append-to-extend
         """获取所有聊天流
 
         Args:
@@ -57,6 +58,7 @@ class ChatManager:
 
     @staticmethod
     def get_group_streams(platform: Optional[str] | SpecialTypes = "qq") -> List[ChatStream]:
+        # sourcery skip: for-append-to-extend
         """获取所有群聊聊天流
 
         Args:
@@ -79,6 +81,7 @@ class ChatManager:
 
     @staticmethod
     def get_private_streams(platform: Optional[str] | SpecialTypes = "qq") -> List[ChatStream]:
+        # sourcery skip: for-append-to-extend
         """获取所有私聊聊天流
 
         Args:
@@ -105,7 +108,7 @@ class ChatManager:
     @staticmethod
     def get_group_stream_by_group_id(
         group_id: str, platform: Optional[str] | SpecialTypes = "qq"
-    ) -> Optional[ChatStream]:
+    ) -> Optional[ChatStream]:  # sourcery skip: remove-unnecessary-cast
         """根据群ID获取聊天流
 
         Args:
@@ -142,7 +145,7 @@ class ChatManager:
     @staticmethod
     def get_private_stream_by_user_id(
         user_id: str, platform: Optional[str] | SpecialTypes = "qq"
-    ) -> Optional[ChatStream]:
+    ) -> Optional[ChatStream]:  # sourcery skip: remove-unnecessary-cast
         """根据用户ID获取私聊流
 
         Args:
@@ -207,7 +210,7 @@ class ChatManager:
             chat_stream: 聊天流对象
 
         Returns:
-            Dict[str, Any]: 聊天流信息字典
+            Dict ({str: Any}): 聊天流信息字典
 
         Raises:
             TypeError: 如果 chat_stream 不是 ChatStream 类型
@@ -282,41 +285,41 @@ class ChatManager:
 # =============================================================================
 
 
-def get_all_streams(platform: Optional[str] | SpecialTypes = "qq"):
+def get_all_streams(platform: Optional[str] | SpecialTypes = "qq") -> List[ChatStream]:
     """获取所有聊天流的便捷函数"""
     return ChatManager.get_all_streams(platform)
 
 
-def get_group_streams(platform: Optional[str] | SpecialTypes = "qq"):
+def get_group_streams(platform: Optional[str] | SpecialTypes = "qq") -> List[ChatStream]:
     """获取群聊聊天流的便捷函数"""
     return ChatManager.get_group_streams(platform)
 
 
-def get_private_streams(platform: Optional[str] | SpecialTypes = "qq"):
+def get_private_streams(platform: Optional[str] | SpecialTypes = "qq") -> List[ChatStream]:
     """获取私聊聊天流的便捷函数"""
     return ChatManager.get_private_streams(platform)
 
 
-def get_stream_by_group_id(group_id: str, platform: Optional[str] | SpecialTypes = "qq"):
+def get_stream_by_group_id(group_id: str, platform: Optional[str] | SpecialTypes = "qq") -> Optional[ChatStream]:
     """根据群ID获取聊天流的便捷函数"""
     return ChatManager.get_group_stream_by_group_id(group_id, platform)
 
 
-def get_stream_by_user_id(user_id: str, platform: Optional[str] | SpecialTypes = "qq"):
+def get_stream_by_user_id(user_id: str, platform: Optional[str] | SpecialTypes = "qq") -> Optional[ChatStream]:
     """根据用户ID获取私聊流的便捷函数"""
     return ChatManager.get_private_stream_by_user_id(user_id, platform)
 
 
-def get_stream_type(chat_stream: ChatStream):
+def get_stream_type(chat_stream: ChatStream) -> str:
     """获取聊天流类型的便捷函数"""
     return ChatManager.get_stream_type(chat_stream)
 
 
-def get_stream_info(chat_stream: ChatStream):
+def get_stream_info(chat_stream: ChatStream) -> Dict[str, Any]:
     """获取聊天流信息的便捷函数"""
     return ChatManager.get_stream_info(chat_stream)
 
 
-def get_streams_summary():
+def get_streams_summary() -> Dict[str, int]:
     """获取聊天流统计摘要的便捷函数"""
     return ChatManager.get_streams_summary()

src/plugin_system/apis/component_manage_api.py

@@ -5,6 +5,7 @@ from src.plugin_system.base.component_types import (
     EventHandlerInfo,
     PluginInfo,
     ComponentType,
+    ToolInfo,
 )
 
 
@@ -119,6 +120,21 @@ def get_registered_command_info(command_name: str) -> Optional[CommandInfo]:
     return component_registry.get_registered_command_info(command_name)
 
 
+def get_registered_tool_info(tool_name: str) -> Optional[ToolInfo]:
+    """
+    获取指定 Tool 的注册信息。
+
+    Args:
+        tool_name (str): Tool 名称。
+
+    Returns:
+        ToolInfo: Tool 信息对象，如果 Tool 不存在则返回 None。
+    """
+    from src.plugin_system.core.component_registry import component_registry
+
+    return component_registry.get_registered_tool_info(tool_name)
+
+
 # === EventHandler 特定查询方法 ===
 def get_registered_event_handler_info(
     event_handler_name: str,
@@ -191,6 +207,8 @@ def locally_enable_component(component_name: str, component_type: ComponentType,
             return global_announcement_manager.enable_specific_chat_action(stream_id, component_name)
         case ComponentType.COMMAND:
             return global_announcement_manager.enable_specific_chat_command(stream_id, component_name)
+        case ComponentType.TOOL:
+            return global_announcement_manager.enable_specific_chat_tool(stream_id, component_name)
         case ComponentType.EVENT_HANDLER:
             return global_announcement_manager.enable_specific_chat_event_handler(stream_id, component_name)
         case _:
@@ -216,11 +234,14 @@ def locally_disable_component(component_name: str, component_type: ComponentType
             return global_announcement_manager.disable_specific_chat_action(stream_id, component_name)
         case ComponentType.COMMAND:
             return global_announcement_manager.disable_specific_chat_command(stream_id, component_name)
+        case ComponentType.TOOL:
+            return global_announcement_manager.disable_specific_chat_tool(stream_id, component_name)
         case ComponentType.EVENT_HANDLER:
             return global_announcement_manager.disable_specific_chat_event_handler(stream_id, component_name)
         case _:
             raise ValueError(f"未知 component type: {component_type}")
 
+
 def get_locally_disabled_components(stream_id: str, component_type: ComponentType) -> list[str]:
     """
     获取指定消息流中禁用的组件列表。
@@ -239,7 +260,9 @@ def get_locally_disabled_components(stream_id: str, component_type: ComponentTyp
             return global_announcement_manager.get_disabled_chat_actions(stream_id)
         case ComponentType.COMMAND:
             return global_announcement_manager.get_disabled_chat_commands(stream_id)
+        case ComponentType.TOOL:
+            return global_announcement_manager.get_disabled_chat_tools(stream_id)
         case ComponentType.EVENT_HANDLER:
             return global_announcement_manager.get_disabled_chat_event_handlers(stream_id)
         case _:
-            raise ValueError(f"未知 component type: {component_type}")
+            raise ValueError(f"未知 component type: {component_type}")

src/plugin_system/apis/config_api.py

@@ -10,7 +10,6 @@
 from typing import Any
 from src.common.logger import get_logger
 from src.config.config import global_config
-from src.person_info.person_info import get_person_info_manager
 
 logger = get_logger("config_api")
 
@@ -26,7 +25,7 @@ def get_global_config(key: str, default: Any = None) -> Any:
     插件应使用此方法读取全局配置，以保证只读和隔离性。
 
     Args:
-        key: 命名空间式配置键名，支持嵌套访问，如 "section.subsection.key"，大小写敏感
+        key: 命名空间式配置键名，使用嵌套访问，如 "section.subsection.key"，大小写敏感
         default: 如果配置不存在时返回的默认值
 
     Returns:
@@ -76,50 +75,3 @@ def get_plugin_config(plugin_config: dict, key: str, default: Any = None) -> Any
     except Exception as e:
         logger.warning(f"[ConfigAPI] 获取插件配置 {key} 失败: {e}")
         return default
-
-
-# =============================================================================
-# 用户信息API函数
-# =============================================================================
-
-
-async def get_user_id_by_person_name(person_name: str) -> tuple[str, str]:
-    """根据内部用户名获取用户ID
-
-    Args:
-        person_name: 用户名
-
-    Returns:
-        tuple[str, str]: (平台, 用户ID)
-    """
-    try:
-        person_info_manager = get_person_info_manager()
-        person_id = person_info_manager.get_person_id_by_person_name(person_name)
-        user_id: str = await person_info_manager.get_value(person_id, "user_id")  # type: ignore
-        platform: str = await person_info_manager.get_value(person_id, "platform")  # type: ignore
-        return platform, user_id
-    except Exception as e:
-        logger.error(f"[ConfigAPI] 根据用户名获取用户ID失败: {e}")
-        return "", ""
-
-
-async def get_person_info(person_id: str, key: str, default: Any = None) -> Any:
-    """获取用户信息
-
-    Args:
-        person_id: 用户ID
-        key: 信息键名
-        default: 默认值
-
-    Returns:
-        Any: 用户信息值或默认值
-    """
-    try:
-        person_info_manager = get_person_info_manager()
-        response = await person_info_manager.get_value(person_id, key)
-        if not response:
-            raise ValueError(f"[ConfigAPI] 获取用户 {person_id} 的信息 '{key}' 失败，返回默认值")
-        return response
-    except Exception as e:
-        logger.error(f"[ConfigAPI] 获取用户信息失败: {e}")
-        return default

src/plugin_system/apis/database_api.py

@@ -152,10 +152,7 @@ async def db_query(
 
     except DoesNotExist:
         # 记录不存在
-        if query_type == "get" and single_result:
-            return None
-        return []
-
+        return None if query_type == "get" and single_result else []
     except Exception as e:
         logger.error(f"[DatabaseAPI] 数据库操作出错: {e}")
         traceback.print_exc()
@@ -170,7 +167,8 @@ async def db_query(
 
 async def db_save(
     model_class: Type[Model], data: Dict[str, Any], key_field: Optional[str] = None, key_value: Optional[Any] = None
-) -> Union[Dict[str, Any], None]:
+) -> Optional[Dict[str, Any]]:
+    # sourcery skip: inline-immediately-returned-variable
     """保存数据到数据库（创建或更新）
 
     如果提供了key_field和key_value，会先尝试查找匹配的记录进行更新；
@@ -203,10 +201,9 @@ async def db_save(
     try:
         # 如果提供了key_field和key_value，尝试更新现有记录
         if key_field and key_value is not None:
-            # 查找现有记录
-            existing_records = list(model_class.select().where(getattr(model_class, key_field) == key_value).limit(1))
-
-            if existing_records:
+            if existing_records := list(
+                model_class.select().where(getattr(model_class, key_field) == key_value).limit(1)
+            ):
                 # 更新现有记录
                 existing_record = existing_records[0]
                 for field, value in data.items():
@@ -244,8 +241,8 @@ async def db_get(
     Args:
         model_class: Peewee模型类
         filters: 过滤条件，字段名和值的字典
-        order_by: 排序字段，前缀'-'表示降序，例如'-time'表示按时间字段（即time字段）降序
         limit: 结果数量限制
+        order_by: 排序字段，前缀'-'表示降序，例如'-time'表示按时间字段（即time字段）降序
         single_result: 是否只返回单个结果，如果为True，则返回单个记录字典或None；否则返回记录字典列表或空列表
 
     Returns:
@@ -310,7 +307,7 @@ async def store_action_info(
     thinking_id: str = "",
     action_data: Optional[dict] = None,
     action_name: str = "",
-) -> Union[Dict[str, Any], None]:
+) -> Optional[Dict[str, Any]]:
     """存储动作信息到数据库
 
     将Action执行的相关信息保存到ActionRecords表中，用于后续的记忆和上下文构建。

src/plugin_system/apis/emoji_api.py

@@ -65,14 +65,14 @@ async def get_by_description(description: str) -> Optional[Tuple[str, str, str]]
         return None
 
 
-async def get_random(count: Optional[int] = 1) -> Optional[List[Tuple[str, str, str]]]:
+async def get_random(count: Optional[int] = 1) -> List[Tuple[str, str, str]]:
     """随机获取指定数量的表情包
 
     Args:
         count: 要获取的表情包数量，默认为1
 
     Returns:
-        Optional[List[Tuple[str, str, str]]]: 包含(base64编码, 表情包描述, 随机情感标签)的元组列表，如果失败则为None
+        List[Tuple[str, str, str]]: 包含(base64编码, 表情包描述, 随机情感标签)的元组列表，失败则返回空列表
 
     Raises:
         TypeError: 如果count不是整数类型
@@ -94,13 +94,13 @@ async def get_random(count: Optional[int] = 1) -> Optional[List[Tuple[str, str,
 
         if not all_emojis:
             logger.warning("[EmojiAPI] 没有可用的表情包")
-            return None
+            return []
 
         # 过滤有效表情包
         valid_emojis = [emoji for emoji in all_emojis if not emoji.is_deleted]
         if not valid_emojis:
             logger.warning("[EmojiAPI] 没有有效的表情包")
-            return None
+            return []
 
         if len(valid_emojis) < count:
             logger.warning(
@@ -127,14 +127,14 @@ async def get_random(count: Optional[int] = 1) -> Optional[List[Tuple[str, str,
 
         if not results and count > 0:
             logger.warning("[EmojiAPI] 随机获取表情包失败，没有一个可以成功处理")
-            return None
+            return []
 
         logger.info(f"[EmojiAPI] 成功获取 {len(results)} 个随机表情包")
         return results
 
     except Exception as e:
         logger.error(f"[EmojiAPI] 获取随机表情包失败: {e}")
-        return None
+        return []
 
 
 async def get_by_emotion(emotion: str) -> Optional[Tuple[str, str, str]]:
@@ -162,10 +162,11 @@ async def get_by_emotion(emotion: str) -> Optional[Tuple[str, str, str]]:
 
         # 筛选匹配情感的表情包
         matching_emojis = []
-        for emoji_obj in all_emojis:
-            if not emoji_obj.is_deleted and emotion.lower() in [e.lower() for e in emoji_obj.emotion]:
-                matching_emojis.append(emoji_obj)
-
+        matching_emojis.extend(
+            emoji_obj
+            for emoji_obj in all_emojis
+            if not emoji_obj.is_deleted and emotion.lower() in [e.lower() for e in emoji_obj.emotion]
+        )
         if not matching_emojis:
             logger.warning(f"[EmojiAPI] 未找到匹配情感 '{emotion}' 的表情包")
             return None
@@ -256,10 +257,11 @@ def get_descriptions() -> List[str]:
         emoji_manager = get_emoji_manager()
         descriptions = []
 
-        for emoji_obj in emoji_manager.emoji_objects:
-            if not emoji_obj.is_deleted and emoji_obj.description:
-                descriptions.append(emoji_obj.description)
-
+        descriptions.extend(
+            emoji_obj.description
+            for emoji_obj in emoji_manager.emoji_objects
+            if not emoji_obj.is_deleted and emoji_obj.description
+        )
         return descriptions
     except Exception as e:
         logger.error(f"[EmojiAPI] 获取表情包描述失败: {e}")

src/plugin_system/apis/generator_api.py

@@ -84,18 +84,23 @@ async def generate_reply(
     enable_chinese_typo: bool = True,
     return_prompt: bool = False,
     model_configs: Optional[List[Dict[str, Any]]] = None,
-    request_type: str = "",
-    enable_timeout: bool = False,
+    request_type: str = "generator_api",
 ) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]:
     """生成回复
 
     Args:
         chat_stream: 聊天流对象（优先）
         chat_id: 聊天ID（备用）
-        action_data: 动作数据
+        action_data: 动作数据（向下兼容，包含reply_to和extra_info）
+        reply_to: 回复对象，格式为 "发送者:消息内容"
+        extra_info: 额外信息，用于补充上下文
+        available_actions: 可用动作
+        enable_tool: 是否启用工具调用
         enable_splitter: 是否启用消息分割器
         enable_chinese_typo: 是否启用错字生成器
         return_prompt: 是否返回提示词
+        model_configs: 模型配置列表
+        request_type: 请求类型（可选，记录LLM使用）
     Returns:
         Tuple[bool, List[Tuple[str, Any]], Optional[str]]: (是否成功, 回复集合, 提示词)
     """
@@ -108,13 +113,16 @@ async def generate_reply(
 
         logger.debug("[GeneratorAPI] 开始生成回复")
 
+        if not reply_to and action_data:
+            reply_to = action_data.get("reply_to", "")
+        if not extra_info and action_data:
+            extra_info = action_data.get("extra_info", "")
+
         # 调用回复器生成回复
         success, content, prompt = await replyer.generate_reply_with_context(
-            reply_data=action_data or {},
             reply_to=reply_to,
             extra_info=extra_info,
             available_actions=available_actions,
-            enable_timeout=enable_timeout,
             enable_tool=enable_tool,
         )
         reply_set = []
@@ -136,6 +144,7 @@ async def generate_reply(
 
     except Exception as e:
         logger.error(f"[GeneratorAPI] 生成回复时出错: {e}")
+        logger.error(traceback.format_exc())
         return False, [], None
 
 
@@ -146,15 +155,24 @@ async def rewrite_reply(
     enable_splitter: bool = True,
     enable_chinese_typo: bool = True,
     model_configs: Optional[List[Dict[str, Any]]] = None,
-) -> Tuple[bool, List[Tuple[str, Any]]]:
+    raw_reply: str = "",
+    reason: str = "",
+    reply_to: str = "",
+    return_prompt: bool = False,
+) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]:
     """重写回复
 
     Args:
         chat_stream: 聊天流对象（优先）
-        reply_data: 回复数据
+        reply_data: 回复数据字典（向下兼容备用，当其他参数缺失时从此获取）
         chat_id: 聊天ID（备用）
         enable_splitter: 是否启用消息分割器
         enable_chinese_typo: 是否启用错字生成器
+        model_configs: 模型配置列表
+        raw_reply: 原始回复内容
+        reason: 回复原因
+        reply_to: 回复对象
+        return_prompt: 是否返回提示词
 
     Returns:
         Tuple[bool, List[Tuple[str, Any]]]: (是否成功, 回复集合)
@@ -164,12 +182,23 @@ async def rewrite_reply(
         replyer = get_replyer(chat_stream, chat_id, model_configs=model_configs)
         if not replyer:
             logger.error("[GeneratorAPI] 无法获取回复器")
-            return False, []
+            return False, [], None
 
         logger.info("[GeneratorAPI] 开始重写回复")
 
+        # 如果参数缺失，从reply_data中获取
+        if reply_data:
+            raw_reply = raw_reply or reply_data.get("raw_reply", "")
+            reason = reason or reply_data.get("reason", "")
+            reply_to = reply_to or reply_data.get("reply_to", "")
+
         # 调用回复器重写回复
-        success, content = await replyer.rewrite_reply_with_context(reply_data=reply_data or {})
+        success, content, prompt = await replyer.rewrite_reply_with_context(
+            raw_reply=raw_reply,
+            reason=reason,
+            reply_to=reply_to,
+            return_prompt=return_prompt,
+        )
         reply_set = []
         if content:
             reply_set = await process_human_text(content, enable_splitter, enable_chinese_typo)
@@ -179,14 +208,14 @@ async def rewrite_reply(
         else:
             logger.warning("[GeneratorAPI] 重写回复失败")
 
-        return success, reply_set
+        return success, reply_set, prompt if return_prompt else None
 
     except ValueError as ve:
         raise ve
 
     except Exception as e:
         logger.error(f"[GeneratorAPI] 重写回复时出错: {e}")
-        return False, []
+        return False, [], None
 
 
 async def process_human_text(content: str, enable_splitter: bool, enable_chinese_typo: bool) -> List[Tuple[str, Any]]:
@@ -212,3 +241,27 @@ async def process_human_text(content: str, enable_splitter: bool, enable_chinese
     except Exception as e:
         logger.error(f"[GeneratorAPI] 处理人形文本时出错: {e}")
         return []
+
+async def generate_response_custom(
+    chat_stream: Optional[ChatStream] = None,
+    chat_id: Optional[str] = None,
+    model_configs: Optional[List[Dict[str, Any]]] = None,
+    prompt: str = "",
+) -> Optional[str]:
+    replyer = get_replyer(chat_stream, chat_id, model_configs=model_configs)
+    if not replyer:
+        logger.error("[GeneratorAPI] 无法获取回复器")
+        return None
+
+    try:
+        logger.debug("[GeneratorAPI] 开始生成自定义回复")
+        response = await replyer.llm_generate_content(prompt)
+        if response:
+            logger.debug("[GeneratorAPI] 自定义回复生成成功")
+            return response
+        else:
+            logger.warning("[GeneratorAPI] 自定义回复生成失败")
+            return None
+    except Exception as e:
+        logger.error(f"[GeneratorAPI] 生成自定义回复时出错: {e}")
+        return None

src/plugin_system/apis/llm_api.py

@@ -54,7 +54,7 @@ def get_available_models() -> Dict[str, Any]:
 
 async def generate_with_model(
     prompt: str, model_config: Dict[str, Any], request_type: str = "plugin.generate", **kwargs
-) -> Tuple[bool, str, str, str]:
+) -> Tuple[bool, str]:
     """使用指定模型生成内容
 
     Args:
@@ -73,10 +73,11 @@ async def generate_with_model(
 
         llm_request = LLMRequest(model=model_config, request_type=request_type, **kwargs)
 
-        response, (reasoning, model_name) = await llm_request.generate_response_async(prompt)
-        return True, response, reasoning, model_name
+        # TODO: 复活这个_
+        response, _ = await llm_request.generate_response_async(prompt)
+        return True, response
 
     except Exception as e:
         error_msg = f"生成内容时出错: {str(e)}"
         logger.error(f"[LLMAPI] {error_msg}")
-        return False, error_msg, "", ""
+        return False, error_msg

src/plugin_system/apis/message_api.py

@@ -207,7 +207,7 @@ def get_random_chat_messages(
 
 
 def get_messages_by_time_for_users(
-    start_time: float, end_time: float, person_ids: list, limit: int = 0, limit_mode: str = "latest"
+    start_time: float, end_time: float, person_ids: List[str], limit: int = 0, limit_mode: str = "latest"
 ) -> List[Dict[str, Any]]:
     """
     获取指定用户在所有聊天中指定时间范围内的消息
@@ -287,7 +287,7 @@ def get_messages_before_time_in_chat(
     return get_raw_msg_before_timestamp_with_chat(chat_id, timestamp, limit)
 
 
-def get_messages_before_time_for_users(timestamp: float, person_ids: list, limit: int = 0) -> List[Dict[str, Any]]:
+def get_messages_before_time_for_users(timestamp: float, person_ids: List[str], limit: int = 0) -> List[Dict[str, Any]]:
     """
     获取指定用户在指定时间戳之前的消息
 
@@ -372,7 +372,7 @@ def count_new_messages(chat_id: str, start_time: float = 0.0, end_time: Optional
     return num_new_messages_since(chat_id, start_time, end_time)
 
 
-def count_new_messages_for_users(chat_id: str, start_time: float, end_time: float, person_ids: list) -> int:
+def count_new_messages_for_users(chat_id: str, start_time: float, end_time: float, person_ids: List[str]) -> int:
     """
     计算指定聊天中指定用户从开始时间到结束时间的新消息数量
 

src/plugin_system/apis/plugin_manage_api.py

@@ -1,10 +1,12 @@
 from typing import Tuple, List
+
+
 def list_loaded_plugins() -> List[str]:
     """
     列出所有当前加载的插件。
 
     Returns:
-        list: 当前加载的插件名称列表。
+        List[str]: 当前加载的插件名称列表。
     """
     from src.plugin_system.core.plugin_manager import plugin_manager
 
@@ -16,17 +18,38 @@ def list_registered_plugins() -> List[str]:
     列出所有已注册的插件。
 
     Returns:
-        list: 已注册的插件名称列表。
+        List[str]: 已注册的插件名称列表。
     """
     from src.plugin_system.core.plugin_manager import plugin_manager
 
     return plugin_manager.list_registered_plugins()
 
 
+def get_plugin_path(plugin_name: str) -> str:
+    """
+    获取指定插件的路径。
+
+    Args:
+        plugin_name (str): 插件名称。
+
+    Returns:
+        str: 插件目录的绝对路径。
+        
+    Raises:
+        ValueError: 如果插件不存在。
+    """
+    from src.plugin_system.core.plugin_manager import plugin_manager
+
+    if plugin_path := plugin_manager.get_plugin_path(plugin_name):
+        return plugin_path
+    else:
+        raise ValueError(f"插件 '{plugin_name}' 不存在。")
+
+
 async def remove_plugin(plugin_name: str) -> bool:
     """
     卸载指定的插件。
-    
+
     **此函数是异步的，确保在异步环境中调用。**
 
     Args:
@@ -43,7 +66,7 @@ async def remove_plugin(plugin_name: str) -> bool:
 async def reload_plugin(plugin_name: str) -> bool:
     """
     重新加载指定的插件。
-    
+
     **此函数是异步的，确保在异步环境中调用。**
 
     Args:
@@ -71,6 +94,7 @@ def load_plugin(plugin_name: str) -> Tuple[bool, int]:
 
     return plugin_manager.load_registered_plugin_classes(plugin_name)
 
+
 def add_plugin_directory(plugin_directory: str) -> bool:
     """
     添加插件目录。
@@ -84,6 +108,7 @@ def add_plugin_directory(plugin_directory: str) -> bool:
 
     return plugin_manager.add_plugin_directory(plugin_directory)
 
+
 def rescan_plugin_directory() -> Tuple[int, int]:
     """
     重新扫描插件目录，加载新插件。
@@ -92,4 +117,4 @@ def rescan_plugin_directory() -> Tuple[int, int]:
     """
     from src.plugin_system.core.plugin_manager import plugin_manager
 
-    return plugin_manager.rescan_plugin_directory()
+    return plugin_manager.rescan_plugin_directory()

src/plugin_system/apis/send_api.py

@@ -22,7 +22,6 @@
 import traceback
 import time
 import difflib
-import re
 from typing import Optional, Union
 from src.common.logger import get_logger
 
@@ -30,7 +29,7 @@ from src.common.logger import get_logger
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.chat.message_receive.uni_message_sender import HeartFCSender
 from src.chat.message_receive.message import MessageSending, MessageRecv
-from src.chat.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat
+from src.chat.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat, replace_user_references_async
 from src.person_info.person_info import get_person_info_manager
 from maim_message import Seg, UserInfo
 from src.config.config import global_config
@@ -50,7 +49,7 @@ async def _send_to_target(
     display_message: str = "",
     typing: bool = False,
     reply_to: str = "",
-    reply_to_platform_id: str = "",
+    reply_to_platform_id: Optional[str] = None,
     storage_message: bool = True,
     show_log: bool = True,
 ) -> bool:
@@ -61,8 +60,11 @@ async def _send_to_target(
         content: 消息内容
         stream_id: 目标流ID
         display_message: 显示消息
-        typing: 是否显示正在输入
-        reply_to: 回复消息的格式，如"发送者:消息内容"
+        typing: 是否模拟打字等待。
+        reply_to: 回复消息，格式为"发送者:消息内容"
+        reply_to_platform_id: 回复消息，格式为"平台:用户ID"，如果不提供则自动查找（插件开发者禁用！）
+        storage_message: 是否存储消息到数据库
+        show_log: 发送是否显示日志
 
     Returns:
         bool: 是否发送成功
@@ -98,6 +100,10 @@ async def _send_to_target(
         anchor_message = None
         if reply_to:
             anchor_message = await _find_reply_message(target_stream, reply_to)
+            if anchor_message and anchor_message.message_info.user_info and not reply_to_platform_id:
+                reply_to_platform_id = (
+                    f"{anchor_message.message_info.platform}:{anchor_message.message_info.user_info.user_id}"
+                )
 
         # 构建发送消息对象
         bot_message = MessageSending(
@@ -183,32 +189,8 @@ async def _find_reply_message(target_stream, reply_to: str) -> Optional[MessageR
             if person_name == sender:
                 translate_text = message["processed_plain_text"]
 
-                # 检查是否有 回复<aaa:bbb> 字段
-                reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
-                if match := re.search(reply_pattern, translate_text):
-                    aaa = match.group(1)
-                    bbb = match.group(2)
-                    reply_person_id = get_person_info_manager().get_person_id(platform, bbb)
-                    reply_person_name = await get_person_info_manager().get_value(reply_person_id, "person_name") or aaa
-                    # 在内容前加上回复信息
-                    translate_text = re.sub(reply_pattern, f"回复 {reply_person_name}", translate_text, count=1)
-
-                # 检查是否有 @<aaa:bbb> 字段
-                at_pattern = r"@<([^:<>]+):([^:<>]+)>"
-                at_matches = list(re.finditer(at_pattern, translate_text))
-                if at_matches:
-                    new_content = ""
-                    last_end = 0
-                    for m in at_matches:
-                        new_content += translate_text[last_end : m.start()]
-                        aaa = m.group(1)
-                        bbb = m.group(2)
-                        at_person_id = get_person_info_manager().get_person_id(platform, bbb)
-                        at_person_name = await get_person_info_manager().get_value(at_person_id, "person_name") or aaa
-                        new_content += f"@{at_person_name}"
-                        last_end = m.end()
-                    new_content += translate_text[last_end:]
-                    translate_text = new_content
+                # 使用独立函数处理用户引用格式
+                translate_text = await replace_user_references_async(translate_text, platform)
 
                 similarity = difflib.SequenceMatcher(None, text, translate_text).ratio()
                 if similarity >= 0.9:
@@ -287,12 +269,22 @@ async def text_to_stream(
         stream_id: 聊天流ID
         typing: 是否显示正在输入
         reply_to: 回复消息，格式为"发送者:消息内容"
+        reply_to_platform_id: 回复消息，格式为"平台:用户ID"，如果不提供则自动查找（插件开发者禁用！）
         storage_message: 是否存储消息到数据库
 
     Returns:
         bool: 是否发送成功
     """
-    return await _send_to_target("text", text, stream_id, "", typing, reply_to, reply_to_platform_id, storage_message)
+    return await _send_to_target(
+        "text",
+        text,
+        stream_id,
+        "",
+        typing,
+        reply_to,
+        reply_to_platform_id=reply_to_platform_id,
+        storage_message=storage_message,
+    )
 
 
 async def emoji_to_stream(emoji_base64: str, stream_id: str, storage_message: bool = True) -> bool:
@@ -375,249 +367,3 @@ async def custom_to_stream(
         storage_message=storage_message,
         show_log=show_log,
     )
-
-
-async def text_to_group(
-    text: str,
-    group_id: str,
-    platform: str = "qq",
-    typing: bool = False,
-    reply_to: str = "",
-    storage_message: bool = True,
-) -> bool:
-    """向群聊发送文本消息
-
-    Args:
-        text: 要发送的文本内容
-        group_id: 群聊ID
-        platform: 平台，默认为"qq"
-        typing: 是否显示正在输入
-        reply_to: 回复消息，格式为"发送者:消息内容"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, group_id, True)
-
-    return await _send_to_target("text", text, stream_id, "", typing, reply_to, storage_message=storage_message)
-
-
-async def text_to_user(
-    text: str,
-    user_id: str,
-    platform: str = "qq",
-    typing: bool = False,
-    reply_to: str = "",
-    storage_message: bool = True,
-) -> bool:
-    """向用户发送私聊文本消息
-
-    Args:
-        text: 要发送的文本内容
-        user_id: 用户ID
-        platform: 平台，默认为"qq"
-        typing: 是否显示正在输入
-        reply_to: 回复消息，格式为"发送者:消息内容"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, user_id, False)
-    return await _send_to_target("text", text, stream_id, "", typing, reply_to, storage_message=storage_message)
-
-
-async def emoji_to_group(emoji_base64: str, group_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向群聊发送表情包
-
-    Args:
-        emoji_base64: 表情包的base64编码
-        group_id: 群聊ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, group_id, True)
-    return await _send_to_target("emoji", emoji_base64, stream_id, "", typing=False, storage_message=storage_message)
-
-
-async def emoji_to_user(emoji_base64: str, user_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向用户发送表情包
-
-    Args:
-        emoji_base64: 表情包的base64编码
-        user_id: 用户ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, user_id, False)
-    return await _send_to_target("emoji", emoji_base64, stream_id, "", typing=False, storage_message=storage_message)
-
-
-async def image_to_group(image_base64: str, group_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向群聊发送图片
-
-    Args:
-        image_base64: 图片的base64编码
-        group_id: 群聊ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, group_id, True)
-    return await _send_to_target("image", image_base64, stream_id, "", typing=False, storage_message=storage_message)
-
-
-async def image_to_user(image_base64: str, user_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向用户发送图片
-
-    Args:
-        image_base64: 图片的base64编码
-        user_id: 用户ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, user_id, False)
-    return await _send_to_target("image", image_base64, stream_id, "", typing=False)
-
-
-async def command_to_group(command: str, group_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向群聊发送命令
-
-    Args:
-        command: 命令
-        group_id: 群聊ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, group_id, True)
-    return await _send_to_target("command", command, stream_id, "", typing=False, storage_message=storage_message)
-
-
-async def command_to_user(command: str, user_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向用户发送命令
-
-    Args:
-        command: 命令
-        user_id: 用户ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, user_id, False)
-    return await _send_to_target("command", command, stream_id, "", typing=False, storage_message=storage_message)
-
-
-# =============================================================================
-# 通用发送函数 - 支持任意消息类型
-# =============================================================================
-
-
-async def custom_to_group(
-    message_type: str,
-    content: str,
-    group_id: str,
-    platform: str = "qq",
-    display_message: str = "",
-    typing: bool = False,
-    reply_to: str = "",
-    storage_message: bool = True,
-) -> bool:
-    """向群聊发送自定义类型消息
-
-    Args:
-        message_type: 消息类型，如"text"、"image"、"emoji"、"video"、"file"等
-        content: 消息内容（通常是base64编码或文本）
-        group_id: 群聊ID
-        platform: 平台，默认为"qq"
-        display_message: 显示消息
-        typing: 是否显示正在输入
-        reply_to: 回复消息，格式为"发送者:消息内容"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, group_id, True)
-    return await _send_to_target(
-        message_type, content, stream_id, display_message, typing, reply_to, storage_message=storage_message
-    )
-
-
-async def custom_to_user(
-    message_type: str,
-    content: str,
-    user_id: str,
-    platform: str = "qq",
-    display_message: str = "",
-    typing: bool = False,
-    reply_to: str = "",
-    storage_message: bool = True,
-) -> bool:
-    """向用户发送自定义类型消息
-
-    Args:
-        message_type: 消息类型，如"text"、"image"、"emoji"、"video"、"file"等
-        content: 消息内容（通常是base64编码或文本）
-        user_id: 用户ID
-        platform: 平台，默认为"qq"
-        display_message: 显示消息
-        typing: 是否显示正在输入
-        reply_to: 回复消息，格式为"发送者:消息内容"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, user_id, False)
-    return await _send_to_target(
-        message_type, content, stream_id, display_message, typing, reply_to, storage_message=storage_message
-    )
-
-
-async def custom_message(
-    message_type: str,
-    content: str,
-    target_id: str,
-    is_group: bool = True,
-    platform: str = "qq",
-    display_message: str = "",
-    typing: bool = False,
-    reply_to: str = "",
-    storage_message: bool = True,
-) -> bool:
-    """发送自定义消息的通用接口
-
-    Args:
-        message_type: 消息类型，如"text"、"image"、"emoji"、"video"、"file"、"audio"等
-        content: 消息内容
-        target_id: 目标ID（群ID或用户ID）
-        is_group: 是否为群聊，True为群聊，False为私聊
-        platform: 平台，默认为"qq"
-        display_message: 显示消息
-        typing: 是否显示正在输入
-        reply_to: 回复消息，格式为"发送者:消息内容"
-
-    Returns:
-        bool: 是否发送成功
-
-    示例:
-        # 发送视频到群聊
-        await send_api.custom_message("video", video_base64, "123456", True)
-
-        # 发送文件到用户
-        await send_api.custom_message("file", file_base64, "987654", False)
-
-        # 发送音频到群聊并回复特定消息
-        await send_api.custom_message("audio", audio_base64, "123456", True, reply_to="张三:你好")
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, target_id, is_group)
-    return await _send_to_target(
-        message_type, content, stream_id, display_message, typing, reply_to, storage_message=storage_message
-    )

src/plugin_system/apis/tool_api.py

@@ -0,0 +1,27 @@
+from typing import Optional, Type
+from src.plugin_system.base.base_tool import BaseTool
+from src.plugin_system.base.component_types import ComponentType
+
+from src.common.logger import get_logger
+
+logger = get_logger("tool_api")
+
+
+def get_tool_instance(tool_name: str) -> Optional[BaseTool]:
+    """获取公开工具实例"""
+    from src.plugin_system.core import component_registry
+
+    tool_class: Type[BaseTool] = component_registry.get_component_class(tool_name, ComponentType.TOOL)  # type: ignore
+    return tool_class() if tool_class else None
+
+
+def get_llm_available_tool_definitions():
+    """获取LLM可用的工具定义列表
+    
+    Returns:
+        List[Tuple[str, Dict[str, Any]]]: 工具定义列表，为[("tool_name", 定义)]
+    """
+    from src.plugin_system.core import component_registry
+
+    llm_available_tools = component_registry.get_llm_available_tools()
+    return [(name, tool_class.get_tool_definition()) for name, tool_class in llm_available_tools.items()]

src/plugin_system/apis/utils_api.py

@@ -1,168 +0,0 @@
-"""工具类API模块
-
-提供了各种辅助功能
-使用方式：
-    from src.plugin_system.apis import utils_api
-    plugin_path = utils_api.get_plugin_path()
-    data = utils_api.read_json_file("data.json")
-    timestamp = utils_api.get_timestamp()
-"""
-
-import os
-import json
-import time
-import inspect
-import datetime
-import uuid
-from typing import Any, Optional
-from src.common.logger import get_logger
-
-logger = get_logger("utils_api")
-
-
-# =============================================================================
-# 文件操作API函数
-# =============================================================================
-
-
-def get_plugin_path(caller_frame=None) -> str:
-    """获取调用者插件的路径
-
-    Args:
-        caller_frame: 调用者的栈帧，默认为None（自动获取）
-
-    Returns:
-        str: 插件目录的绝对路径
-    """
-    try:
-        if caller_frame is None:
-            caller_frame = inspect.currentframe().f_back  # type: ignore
-
-        plugin_module_path = inspect.getfile(caller_frame)  # type: ignore
-        plugin_dir = os.path.dirname(plugin_module_path)
-        return plugin_dir
-    except Exception as e:
-        logger.error(f"[UtilsAPI] 获取插件路径失败: {e}")
-        return ""
-
-
-def read_json_file(file_path: str, default: Any = None) -> Any:
-    """读取JSON文件
-
-    Args:
-        file_path: 文件路径，可以是相对于插件目录的路径
-        default: 如果文件不存在或读取失败时返回的默认值
-
-    Returns:
-        Any: JSON数据或默认值
-    """
-    try:
-        # 如果是相对路径，则相对于调用者的插件目录
-        if not os.path.isabs(file_path):
-            caller_frame = inspect.currentframe().f_back  # type: ignore
-            plugin_dir = get_plugin_path(caller_frame)
-            file_path = os.path.join(plugin_dir, file_path)
-
-        if not os.path.exists(file_path):
-            logger.warning(f"[UtilsAPI] 文件不存在: {file_path}")
-            return default
-
-        with open(file_path, "r", encoding="utf-8") as f:
-            return json.load(f)
-    except Exception as e:
-        logger.error(f"[UtilsAPI] 读取JSON文件出错: {e}")
-        return default
-
-
-def write_json_file(file_path: str, data: Any, indent: int = 2) -> bool:
-    """写入JSON文件
-
-    Args:
-        file_path: 文件路径，可以是相对于插件目录的路径
-        data: 要写入的数据
-        indent: JSON缩进
-
-    Returns:
-        bool: 是否写入成功
-    """
-    try:
-        # 如果是相对路径，则相对于调用者的插件目录
-        if not os.path.isabs(file_path):
-            caller_frame = inspect.currentframe().f_back  # type: ignore
-            plugin_dir = get_plugin_path(caller_frame)
-            file_path = os.path.join(plugin_dir, file_path)
-
-        # 确保目录存在
-        os.makedirs(os.path.dirname(file_path), exist_ok=True)
-
-        with open(file_path, "w", encoding="utf-8") as f:
-            json.dump(data, f, ensure_ascii=False, indent=indent)
-        return True
-    except Exception as e:
-        logger.error(f"[UtilsAPI] 写入JSON文件出错: {e}")
-        return False
-
-
-# =============================================================================
-# 时间相关API函数
-# =============================================================================
-
-
-def get_timestamp() -> int:
-    """获取当前时间戳
-
-    Returns:
-        int: 当前时间戳（秒）
-    """
-    return int(time.time())
-
-
-def format_time(timestamp: Optional[int | float] = None, format_str: str = "%Y-%m-%d %H:%M:%S") -> str:
-    """格式化时间
-
-    Args:
-        timestamp: 时间戳，如果为None则使用当前时间
-        format_str: 时间格式字符串
-
-    Returns:
-        str: 格式化后的时间字符串
-    """
-    try:
-        if timestamp is None:
-            timestamp = time.time()
-        return datetime.datetime.fromtimestamp(timestamp).strftime(format_str)
-    except Exception as e:
-        logger.error(f"[UtilsAPI] 格式化时间失败: {e}")
-        return ""
-
-
-def parse_time(time_str: str, format_str: str = "%Y-%m-%d %H:%M:%S") -> int:
-    """解析时间字符串为时间戳
-
-    Args:
-        time_str: 时间字符串
-        format_str: 时间格式字符串
-
-    Returns:
-        int: 时间戳（秒）
-    """
-    try:
-        dt = datetime.datetime.strptime(time_str, format_str)
-        return int(dt.timestamp())
-    except Exception as e:
-        logger.error(f"[UtilsAPI] 解析时间失败: {e}")
-        return 0
-
-
-# =============================================================================
-# 其他工具函数
-# =============================================================================
-
-
-def generate_unique_id() -> str:
-    """生成唯一ID
-
-    Returns:
-        str: 唯一ID
-    """
-    return str(uuid.uuid4())

src/plugin_system/base/__init__.py

@@ -6,6 +6,7 @@
 
 from .base_plugin import BasePlugin
 from .base_action import BaseAction
+from .base_tool import BaseTool
 from .base_command import BaseCommand
 from .base_events_handler import BaseEventHandler
 from .component_types import (
@@ -15,6 +16,7 @@ from .component_types import (
     ComponentInfo,
     ActionInfo,
     CommandInfo,
+    ToolInfo,
     PluginInfo,
     PythonDependency,
     EventHandlerInfo,
@@ -27,12 +29,14 @@ __all__ = [
     "BasePlugin",
     "BaseAction",
     "BaseCommand",
+    "BaseTool",
     "ComponentType",
     "ActionActivationType",
     "ChatMode",
     "ComponentInfo",
     "ActionInfo",
     "CommandInfo",
+    "ToolInfo",
     "PluginInfo",
     "PythonDependency",
     "ConfigField",

src/plugin_system/base/base_action.py

@@ -208,7 +208,7 @@ class BaseAction(ABC):
             return False, f"等待新消息失败: {str(e)}"
 
     async def send_text(
-        self, content: str, reply_to: str = "", reply_to_platform_id: str = "", typing: bool = False
+        self, content: str, reply_to: str = "", typing: bool = False
     ) -> bool:
         """发送文本消息
 
@@ -227,7 +227,6 @@ class BaseAction(ABC):
             text=content,
             stream_id=self.chat_id,
             reply_to=reply_to,
-            reply_to_platform_id=reply_to_platform_id,
             typing=typing,
         )
 
@@ -384,7 +383,7 @@ class BaseAction(ABC):
             keyword_case_sensitive=getattr(cls, "keyword_case_sensitive", False),
             mode_enable=getattr(cls, "mode_enable", ChatMode.ALL),
             parallel_action=getattr(cls, "parallel_action", True),
-            random_activation_probability=getattr(cls, "random_activation_probability", 0.3),
+            random_activation_probability=getattr(cls, "random_activation_probability", 0.0),
             llm_judge_prompt=getattr(cls, "llm_judge_prompt", ""),
             # 使用正确的字段名
             action_parameters=getattr(cls, "action_parameters", {}).copy(),

src/plugin_system/base/base_command.py

@@ -60,10 +60,10 @@ class BaseCommand(ABC):
         pass
 
     def get_config(self, key: str, default=None):
-        """获取插件配置值，支持嵌套键访问
+        """获取插件配置值，使用嵌套键访问
 
         Args:
-            key: 配置键名，支持嵌套访问如 "section.subsection.key"
+            key: 配置键名，使用嵌套访问如 "section.subsection.key"
             default: 默认值
 
         Returns:

src/plugin_system/base/base_tool.py

@@ -0,0 +1,85 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict
+from rich.traceback import install
+
+from src.common.logger import get_logger
+from src.plugin_system.base.component_types import ComponentType, ToolInfo
+
+install(extra_lines=3)
+
+logger = get_logger("base_tool")
+
+
+class BaseTool(ABC):
+    """所有工具的基类"""
+
+    name: str = ""
+    """工具的名称"""
+    description: str = ""
+    """工具的描述"""
+    parameters: Dict[str, Any] = {}
+    """工具的参数定义"""
+    available_for_llm: bool = False
+    """是否可供LLM使用"""
+
+    @classmethod
+    def get_tool_definition(cls) -> dict[str, Any]:
+        """获取工具定义，用于LLM工具调用
+
+        Returns:
+            dict: 工具定义字典
+        """
+        if not cls.name or not cls.description or not cls.parameters:
+            raise NotImplementedError(f"工具类 {cls.__name__} 必须定义 name, description 和 parameters 属性")
+
+        return {
+            "type": "function",
+            "function": {"name": cls.name, "description": cls.description, "parameters": cls.parameters},
+        }
+
+    @classmethod
+    def get_tool_info(cls) -> ToolInfo:
+        """获取工具信息"""
+        if not cls.name or not cls.description or not cls.parameters:
+            raise NotImplementedError(f"工具类 {cls.__name__} 必须定义 name, description 和 parameters 属性")
+
+        return ToolInfo(
+            name=cls.name,
+            tool_description=cls.description,
+            enabled=cls.available_for_llm,
+            tool_parameters=cls.parameters,
+            component_type=ComponentType.TOOL,
+        )
+
+    @abstractmethod
+    async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
+        """执行工具函数(供llm调用)
+           通过该方法，maicore会通过llm的tool call来调用工具
+           传入的是json格式的参数，符合parameters定义的格式
+
+        Args:
+            function_args: 工具调用参数
+
+        Returns:
+            dict: 工具执行结果
+        """
+        raise NotImplementedError("子类必须实现execute方法")
+
+    async def direct_execute(self, **function_args: dict[str, Any]) -> dict[str, Any]:
+        """直接执行工具函数(供插件调用)
+           通过该方法，插件可以直接调用工具，而不需要传入字典格式的参数
+           插件可以直接调用此方法，用更加明了的方式传入参数
+           示例: result = await tool.direct_execute(arg1="参数",arg2="参数2")
+
+           工具开发者可以重写此方法以实现与llm调用差异化的执行逻辑
+
+        Args:
+            **function_args: 工具调用参数
+
+        Returns:
+            dict: 工具执行结果
+        """
+        if self.parameters and (missing := [p for p in self.parameters.get("required", []) if p not in function_args]):
+            raise ValueError(f"工具类 {self.__class__.__name__} 缺少必要参数: {', '.join(missing)}")
+
+        return await self.execute(function_args)

src/plugin_system/base/component_types.py

@@ -10,6 +10,7 @@ class ComponentType(Enum):
 
     ACTION = "action"  # 动作组件
     COMMAND = "command"  # 命令组件
+    TOOL = "tool"  # 服务组件（预留）
     SCHEDULER = "scheduler"  # 定时任务组件（预留）
     EVENT_HANDLER = "event_handler"  # 事件处理组件（预留）
 
@@ -144,7 +145,17 @@ class CommandInfo(ComponentInfo):
     def __post_init__(self):
         super().__post_init__()
         self.component_type = ComponentType.COMMAND
+        
+@dataclass
+class ToolInfo(ComponentInfo):
+    """工具组件信息"""
 
+    tool_parameters: Dict[str, Any] = field(default_factory=dict)  # 工具参数定义
+    tool_description: str = ""  # 工具描述
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.component_type = ComponentType.TOOL   
 
 @dataclass
 class EventHandlerInfo(ComponentInfo):

src/plugin_system/core/__init__.py

@@ -6,14 +6,12 @@
 
 from src.plugin_system.core.plugin_manager import plugin_manager
 from src.plugin_system.core.component_registry import component_registry
-from src.plugin_system.core.dependency_manager import dependency_manager
 from src.plugin_system.core.events_manager import events_manager
 from src.plugin_system.core.global_announcement_manager import global_announcement_manager
 
 __all__ = [
     "plugin_manager",
     "component_registry",
-    "dependency_manager",
     "events_manager",
     "global_announcement_manager",
 ]

src/plugin_system/core/component_registry.py

@@ -6,6 +6,7 @@ from src.common.logger import get_logger
 from src.plugin_system.base.component_types import (
     ComponentInfo,
     ActionInfo,
+    ToolInfo,
     CommandInfo,
     EventHandlerInfo,
     PluginInfo,
@@ -13,6 +14,7 @@ from src.plugin_system.base.component_types import (
 )
 from src.plugin_system.base.base_command import BaseCommand
 from src.plugin_system.base.base_action import BaseAction
+from src.plugin_system.base.base_tool import BaseTool
 from src.plugin_system.base.base_events_handler import BaseEventHandler
 
 logger = get_logger("component_registry")
@@ -30,7 +32,7 @@ class ComponentRegistry:
         """组件注册表 命名空间式组件名 -> 组件信息"""
         self._components_by_type: Dict[ComponentType, Dict[str, ComponentInfo]] = {types: {} for types in ComponentType}
         """类型 -> 组件原名称 -> 组件信息"""
-        self._components_classes: Dict[str, Type[Union[BaseCommand, BaseAction, BaseEventHandler]]] = {}
+        self._components_classes: Dict[str, Type[Union[BaseCommand, BaseAction, BaseTool, BaseEventHandler]]] = {}
         """命名空间式组件名 -> 组件类"""
 
         # 插件注册表
@@ -49,6 +51,10 @@ class ComponentRegistry:
         self._command_patterns: Dict[Pattern, str] = {}
         """编译后的正则 -> command名"""
 
+        # 工具特定注册表
+        self._tool_registry: Dict[str, Type[BaseTool]] = {}  # 工具名 -> 工具类
+        self._llm_available_tools: Dict[str, Type[BaseTool]] = {}  # llm可用的工具名 -> 工具类
+
         # EventHandler特定注册表
         self._event_handler_registry: Dict[str, Type[BaseEventHandler]] = {}
         """event_handler名 -> event_handler类"""
@@ -79,7 +85,9 @@ class ComponentRegistry:
         return True
 
     def register_component(
-        self, component_info: ComponentInfo, component_class: Type[Union[BaseCommand, BaseAction, BaseEventHandler]]
+        self,
+        component_info: ComponentInfo,
+        component_class: Type[Union[BaseCommand, BaseAction, BaseEventHandler, BaseTool]],
     ) -> bool:
         """注册组件
 
@@ -125,6 +133,10 @@ class ComponentRegistry:
                 assert isinstance(component_info, CommandInfo)
                 assert issubclass(component_class, BaseCommand)
                 ret = self._register_command_component(component_info, component_class)
+            case ComponentType.TOOL:
+                assert isinstance(component_info, ToolInfo)
+                assert issubclass(component_class, BaseTool)
+                ret = self._register_tool_component(component_info, component_class)
             case ComponentType.EVENT_HANDLER:
                 assert isinstance(component_info, EventHandlerInfo)
                 assert issubclass(component_class, BaseEventHandler)
@@ -180,6 +192,18 @@ class ComponentRegistry:
 
         return True
 
+    def _register_tool_component(self, tool_info: ToolInfo, tool_class: Type[BaseTool]) -> bool:
+        """注册Tool组件到Tool特定注册表"""
+        tool_name = tool_info.name
+
+        self._tool_registry[tool_name] = tool_class
+
+        # 如果是llm可用的且启用的工具,添加到 llm可用工具列表
+        if tool_info.enabled:
+            self._llm_available_tools[tool_name] = tool_class
+
+        return True
+
     def _register_event_handler_component(
         self, handler_info: EventHandlerInfo, handler_class: Type[BaseEventHandler]
     ) -> bool:
@@ -222,6 +246,9 @@ class ComponentRegistry:
                     keys_to_remove = [k for k, v in self._command_patterns.items() if v == component_name]
                     for key in keys_to_remove:
                         self._command_patterns.pop(key)
+                case ComponentType.TOOL:
+                    self._tool_registry.pop(component_name)
+                    self._llm_available_tools.pop(component_name)
                 case ComponentType.EVENT_HANDLER:
                     from .events_manager import events_manager  # 延迟导入防止循环导入问题
 
@@ -234,13 +261,13 @@ class ComponentRegistry:
             self._components_classes.pop(namespaced_name)
             logger.info(f"组件 {component_name} 已移除")
             return True
-        except KeyError:
-            logger.warning(f"移除组件时未找到组件: {component_name}")
+        except KeyError as e:
+            logger.warning(f"移除组件时未找到组件: {component_name}, 发生错误: {e}")
             return False
         except Exception as e:
             logger.error(f"移除组件 {component_name} 时发生错误: {e}")
             return False
-    
+
     def remove_plugin_registry(self, plugin_name: str) -> bool:
         """移除插件注册信息
 
@@ -281,6 +308,10 @@ class ComponentRegistry:
                 assert isinstance(target_component_info, CommandInfo)
                 pattern = target_component_info.command_pattern
                 self._command_patterns[re.compile(pattern)] = component_name
+            case ComponentType.TOOL:
+                assert isinstance(target_component_info, ToolInfo)
+                assert issubclass(target_component_class, BaseTool)
+                self._llm_available_tools[component_name] = target_component_class
             case ComponentType.EVENT_HANDLER:
                 assert isinstance(target_component_info, EventHandlerInfo)
                 assert issubclass(target_component_class, BaseEventHandler)
@@ -308,20 +339,29 @@ class ComponentRegistry:
             logger.warning(f"组件 {component_name} 未注册，无法禁用")
             return False
         target_component_info.enabled = False
-        match component_type:
-            case ComponentType.ACTION:
-                self._default_actions.pop(component_name, None)
-            case ComponentType.COMMAND:
-                self._command_patterns = {k: v for k, v in self._command_patterns.items() if v != component_name}
-            case ComponentType.EVENT_HANDLER:
-                self._enabled_event_handlers.pop(component_name, None)
-                from .events_manager import events_manager  # 延迟导入防止循环导入问题
+        try:
+            match component_type:
+                case ComponentType.ACTION:
+                    self._default_actions.pop(component_name)
+                case ComponentType.COMMAND:
+                    self._command_patterns = {k: v for k, v in self._command_patterns.items() if v != component_name}
+                case ComponentType.TOOL:
+                    self._llm_available_tools.pop(component_name)
+                case ComponentType.EVENT_HANDLER:
+                    self._enabled_event_handlers.pop(component_name)
+                    from .events_manager import events_manager  # 延迟导入防止循环导入问题
 
-                await events_manager.unregister_event_subscriber(component_name)
-        self._components[component_name].enabled = False
-        self._components_by_type[component_type][component_name].enabled = False
-        logger.info(f"组件 {component_name} 已禁用")
-        return True
+                    await events_manager.unregister_event_subscriber(component_name)
+            self._components[component_name].enabled = False
+            self._components_by_type[component_type][component_name].enabled = False
+            logger.info(f"组件 {component_name} 已禁用")
+            return True
+        except KeyError as e:
+            logger.warning(f"禁用组件时未找到组件或已禁用: {component_name}, 发生错误: {e}")
+            return False
+        except Exception as e:
+            logger.error(f"禁用组件 {component_name} 时发生错误: {e}")
+            return False
 
     # === 组件查询方法 ===
     def get_component_info(
@@ -371,7 +411,7 @@ class ComponentRegistry:
         self,
         component_name: str,
         component_type: Optional[ComponentType] = None,
-    ) -> Optional[Union[Type[BaseCommand], Type[BaseAction], Type[BaseEventHandler]]]:
+    ) -> Optional[Union[Type[BaseCommand], Type[BaseAction], Type[BaseEventHandler], Type[BaseTool]]]:
         """获取组件类，支持自动命名空间解析
 
         Args:
@@ -476,6 +516,27 @@ class ComponentRegistry:
             command_info,
         )
 
+    # === Tool 特定查询方法 ===
+    def get_tool_registry(self) -> Dict[str, Type[BaseTool]]:
+        """获取Tool注册表"""
+        return self._tool_registry.copy()
+
+    def get_llm_available_tools(self) -> Dict[str, Type[BaseTool]]:
+        """获取LLM可用的Tool列表"""
+        return self._llm_available_tools.copy()
+
+    def get_registered_tool_info(self, tool_name: str) -> Optional[ToolInfo]:
+        """获取Tool信息
+
+        Args:
+            tool_name: 工具名称
+
+        Returns:
+            ToolInfo: 工具信息对象，如果工具不存在则返回 None
+        """
+        info = self.get_component_info(tool_name, ComponentType.TOOL)
+        return info if isinstance(info, ToolInfo) else None
+
     # === EventHandler 特定查询方法 ===
 
     def get_event_handler_registry(self) -> Dict[str, Type[BaseEventHandler]]:
@@ -529,17 +590,21 @@ class ComponentRegistry:
         """获取注册中心统计信息"""
         action_components: int = 0
         command_components: int = 0
+        tool_components: int = 0
         events_handlers: int = 0
         for component in self._components.values():
             if component.component_type == ComponentType.ACTION:
                 action_components += 1
             elif component.component_type == ComponentType.COMMAND:
                 command_components += 1
+            elif component.component_type == ComponentType.TOOL:
+                tool_components += 1
             elif component.component_type == ComponentType.EVENT_HANDLER:
                 events_handlers += 1
         return {
             "action_components": action_components,
             "command_components": command_components,
+            "tool_components": tool_components,
             "event_handlers": events_handlers,
             "total_components": len(self._components),
             "total_plugins": len(self._plugins),

src/plugin_system/core/dependency_manager.py

@@ -1,190 +0,0 @@
-"""
-插件依赖管理器
-
-负责检查和安装插件的Python包依赖
-"""
-
-import subprocess
-import sys
-import importlib
-from typing import List, Dict, Tuple, Any
-
-from src.common.logger import get_logger
-from src.plugin_system.base.component_types import PythonDependency
-
-logger = get_logger("dependency_manager")
-
-
-class DependencyManager:
-    """依赖管理器"""
-
-    def __init__(self):
-        self.install_log: List[str] = []
-        self.failed_installs: Dict[str, str] = {}
-
-    def check_dependencies(
-        self, dependencies: List[PythonDependency]
-    ) -> Tuple[List[PythonDependency], List[PythonDependency]]:
-        """检查依赖包状态
-
-        Args:
-            dependencies: 依赖包列表
-
-        Returns:
-            Tuple[List[PythonDependency], List[PythonDependency]]: (缺失的依赖, 可选缺失的依赖)
-        """
-        missing_required = []
-        missing_optional = []
-
-        for dep in dependencies:
-            if self._is_package_available(dep.package_name):
-                logger.debug(f"依赖包已存在: {dep.package_name}")
-            elif dep.optional:
-                missing_optional.append(dep)
-                logger.warning(f"可选依赖包缺失: {dep.package_name} - {dep.description}")
-            else:
-                missing_required.append(dep)
-                logger.error(f"必需依赖包缺失: {dep.package_name} - {dep.description}")
-        return missing_required, missing_optional
-
-    def _is_package_available(self, package_name: str) -> bool:
-        """检查包是否可用"""
-        try:
-            importlib.import_module(package_name)
-            return True
-        except ImportError:
-            return False
-
-    def install_dependencies(self, dependencies: List[PythonDependency], auto_install: bool = False) -> bool:
-        """安装依赖包
-
-        Args:
-            dependencies: 需要安装的依赖包列表
-            auto_install: 是否自动安装（True时不询问用户）
-
-        Returns:
-            bool: 安装是否成功
-        """
-        if not dependencies:
-            return True
-
-        logger.info(f"需要安装 {len(dependencies)} 个依赖包")
-
-        # 显示将要安装的包
-        for dep in dependencies:
-            install_cmd = dep.get_pip_requirement()
-            logger.info(f"  - {install_cmd} {'(可选)' if dep.optional else '(必需)'}")
-            if dep.description:
-                logger.info(f"    说明: {dep.description}")
-
-        if not auto_install:
-            # 这里可以添加用户确认逻辑
-            logger.warning("手动安装模式：请手动运行 pip install 命令安装依赖包")
-            return False
-
-        # 执行安装
-        success_count = 0
-        for dep in dependencies:
-            if self._install_single_package(dep):
-                success_count += 1
-            else:
-                self.failed_installs[dep.package_name] = f"安装失败: {dep.get_pip_requirement()}"
-
-        logger.info(f"依赖安装完成: {success_count}/{len(dependencies)} 个成功")
-        return success_count == len(dependencies)
-
-    def _install_single_package(self, dependency: PythonDependency) -> bool:
-        """安装单个包"""
-        pip_requirement = dependency.get_pip_requirement()
-
-        try:
-            logger.info(f"正在安装: {pip_requirement}")
-
-            # 使用subprocess安装包
-            cmd = [sys.executable, "-m", "pip", "install", pip_requirement]
-            result = subprocess.run(
-                cmd,
-                capture_output=True,
-                text=True,
-                timeout=300,  # 5分钟超时
-            )
-
-            if result.returncode == 0:
-                logger.info(f"✅ 成功安装: {pip_requirement}")
-                self.install_log.append(f"成功安装: {pip_requirement}")
-                return True
-            else:
-                logger.error(f"❌ 安装失败: {pip_requirement}")
-                logger.error(f"错误输出: {result.stderr}")
-                self.install_log.append(f"安装失败: {pip_requirement} - {result.stderr}")
-                return False
-
-        except subprocess.TimeoutExpired:
-            logger.error(f"❌ 安装超时: {pip_requirement}")
-            return False
-        except Exception as e:
-            logger.error(f"❌ 安装异常: {pip_requirement} - {str(e)}")
-            return False
-
-    def generate_requirements_file(
-        self, plugins_dependencies: List[List[PythonDependency]], output_path: str = "plugin_requirements.txt"
-    ) -> bool:
-        """生成插件依赖的requirements文件
-
-        Args:
-            plugins_dependencies: 所有插件的依赖列表
-            output_path: 输出文件路径
-
-        Returns:
-            bool: 生成是否成功
-        """
-        try:
-            all_deps = {}
-
-            # 合并所有插件的依赖
-            for plugin_deps in plugins_dependencies:
-                for dep in plugin_deps:
-                    key = dep.install_name
-                    if key in all_deps:
-                        # 如果已存在，可以添加版本兼容性检查逻辑
-                        existing = all_deps[key]
-                        if dep.version and existing.version != dep.version:
-                            logger.warning(f"依赖版本冲突: {key} ({existing.version} vs {dep.version})")
-                    else:
-                        all_deps[key] = dep
-
-            # 写入requirements文件
-            with open(output_path, "w", encoding="utf-8") as f:
-                f.write("# 插件依赖包自动生成\n")
-                f.write("# Auto-generated plugin dependencies\n\n")
-
-                # 按包名排序
-                sorted_deps = sorted(all_deps.values(), key=lambda x: x.install_name)
-
-                for dep in sorted_deps:
-                    requirement = dep.get_pip_requirement()
-                    if dep.description:
-                        f.write(f"# {dep.description}\n")
-                    if dep.optional:
-                        f.write("# Optional dependency\n")
-                    f.write(f"{requirement}\n\n")
-
-            logger.info(f"已生成插件依赖文件: {output_path} ({len(all_deps)} 个包)")
-            return True
-
-        except Exception as e:
-            logger.error(f"生成requirements文件失败: {str(e)}")
-            return False
-
-    def get_install_summary(self) -> Dict[str, Any]:
-        """获取安装摘要"""
-        return {
-            "install_log": self.install_log.copy(),
-            "failed_installs": self.failed_installs.copy(),
-            "total_attempts": len(self.install_log),
-            "failed_count": len(self.failed_installs),
-        }
-
-
-# 全局依赖管理器实例
-dependency_manager = DependencyManager()

src/plugin_system/core/global_announcement_manager.py

@@ -13,6 +13,8 @@ class GlobalAnnouncementManager:
         self._user_disabled_commands: Dict[str, List[str]] = {}
         # 用户禁用的事件处理器，chat_id -> [handler_name]
         self._user_disabled_event_handlers: Dict[str, List[str]] = {}
+        # 用户禁用的工具，chat_id -> [tool_name]
+        self._user_disabled_tools: Dict[str, List[str]] = {}
 
     def disable_specific_chat_action(self, chat_id: str, action_name: str) -> bool:
         """禁用特定聊天的某个动作"""
@@ -77,6 +79,27 @@ class GlobalAnnouncementManager:
                 return False
         return False
 
+    def disable_specific_chat_tool(self, chat_id: str, tool_name: str) -> bool:
+        """禁用特定聊天的某个工具"""
+        if chat_id not in self._user_disabled_tools:
+            self._user_disabled_tools[chat_id] = []
+        if tool_name in self._user_disabled_tools[chat_id]:
+            logger.warning(f"工具 {tool_name} 已经被禁用")
+            return False
+        self._user_disabled_tools[chat_id].append(tool_name)
+        return True
+    
+    def enable_specific_chat_tool(self, chat_id: str, tool_name: str) -> bool:
+        """启用特定聊天的某个工具"""
+        if chat_id in self._user_disabled_tools:
+            try:
+                self._user_disabled_tools[chat_id].remove(tool_name)
+                return True
+            except ValueError:
+                logger.warning(f"工具 {tool_name} 不在禁用列表中")
+                return False
+        return False
+
     def get_disabled_chat_actions(self, chat_id: str) -> List[str]:
         """获取特定聊天禁用的所有动作"""
         return self._user_disabled_actions.get(chat_id, []).copy()
@@ -88,6 +111,10 @@ class GlobalAnnouncementManager:
     def get_disabled_chat_event_handlers(self, chat_id: str) -> List[str]:
         """获取特定聊天禁用的所有事件处理器"""
         return self._user_disabled_event_handlers.get(chat_id, []).copy()
+    
+    def get_disabled_chat_tools(self, chat_id: str) -> List[str]:
+        """获取特定聊天禁用的所有工具"""
+        return self._user_disabled_tools.get(chat_id, []).copy()
 
 
 global_announcement_manager = GlobalAnnouncementManager()

src/plugin_system/core/plugin_manager.py

@@ -8,10 +8,9 @@ from pathlib import Path
 
 from src.common.logger import get_logger
 from src.plugin_system.base.plugin_base import PluginBase
-from src.plugin_system.base.component_types import ComponentType, PythonDependency
+from src.plugin_system.base.component_types import ComponentType
 from src.plugin_system.utils.manifest_utils import VersionComparator
 from .component_registry import component_registry
-from .dependency_manager import dependency_manager
 
 logger = get_logger("plugin_manager")
 
@@ -207,104 +206,6 @@ class PluginManager:
         """
         return self.loaded_plugins.get(plugin_name)
 
-    def check_all_dependencies(self, auto_install: bool = False) -> Dict[str, Any]:
-        """检查所有插件的Python依赖包
-
-        Args:
-            auto_install: 是否自动安装缺失的依赖包
-
-        Returns:
-            Dict[str, any]: 检查结果摘要
-        """
-        logger.info("开始检查所有插件的Python依赖包...")
-
-        all_required_missing: List[PythonDependency] = []
-        all_optional_missing: List[PythonDependency] = []
-        plugin_status = {}
-
-        for plugin_name in self.loaded_plugins:
-            plugin_info = component_registry.get_plugin_info(plugin_name)
-            if not plugin_info or not plugin_info.python_dependencies:
-                plugin_status[plugin_name] = {"status": "no_dependencies", "missing": []}
-                continue
-
-            logger.info(f"检查插件 {plugin_name} 的依赖...")
-
-            missing_required, missing_optional = dependency_manager.check_dependencies(plugin_info.python_dependencies)
-
-            if missing_required:
-                all_required_missing.extend(missing_required)
-                plugin_status[plugin_name] = {
-                    "status": "missing_required",
-                    "missing": [dep.package_name for dep in missing_required],
-                    "optional_missing": [dep.package_name for dep in missing_optional],
-                }
-                logger.error(f"插件 {plugin_name} 缺少必需依赖: {[dep.package_name for dep in missing_required]}")
-            elif missing_optional:
-                all_optional_missing.extend(missing_optional)
-                plugin_status[plugin_name] = {
-                    "status": "missing_optional",
-                    "missing": [],
-                    "optional_missing": [dep.package_name for dep in missing_optional],
-                }
-                logger.warning(f"插件 {plugin_name} 缺少可选依赖: {[dep.package_name for dep in missing_optional]}")
-            else:
-                plugin_status[plugin_name] = {"status": "ok", "missing": []}
-                logger.info(f"插件 {plugin_name} 依赖检查通过")
-
-        # 汇总结果
-        total_missing = len({dep.package_name for dep in all_required_missing})
-        total_optional_missing = len({dep.package_name for dep in all_optional_missing})
-
-        logger.info(f"依赖检查完成 - 缺少必需包: {total_missing}个, 缺少可选包: {total_optional_missing}个")
-
-        # 如果需要自动安装
-        install_success = True
-        if auto_install and all_required_missing:
-            unique_required = {dep.package_name: dep for dep in all_required_missing}
-            logger.info(f"开始自动安装 {len(unique_required)} 个必需依赖包...")
-            install_success = dependency_manager.install_dependencies(list(unique_required.values()), auto_install=True)
-
-        return {
-            "total_plugins_checked": len(plugin_status),
-            "plugins_with_missing_required": len(
-                [p for p in plugin_status.values() if p["status"] == "missing_required"]
-            ),
-            "plugins_with_missing_optional": len(
-                [p for p in plugin_status.values() if p["status"] == "missing_optional"]
-            ),
-            "total_missing_required": total_missing,
-            "total_missing_optional": total_optional_missing,
-            "plugin_status": plugin_status,
-            "auto_install_attempted": auto_install and bool(all_required_missing),
-            "auto_install_success": install_success,
-            "install_summary": dependency_manager.get_install_summary(),
-        }
-
-    def generate_plugin_requirements(self, output_path: str = "plugin_requirements.txt") -> bool:
-        """生成所有插件依赖的requirements文件
-
-        Args:
-            output_path: 输出文件路径
-
-        Returns:
-            bool: 生成是否成功
-        """
-        logger.info("开始生成插件依赖requirements文件...")
-
-        all_dependencies = []
-
-        for plugin_name in self.loaded_plugins:
-            plugin_info = component_registry.get_plugin_info(plugin_name)
-            if plugin_info and plugin_info.python_dependencies:
-                all_dependencies.append(plugin_info.python_dependencies)
-
-        if not all_dependencies:
-            logger.info("没有找到任何插件依赖")
-            return False
-
-        return dependency_manager.generate_requirements_file(all_dependencies, output_path)
-
     # === 查询方法 ===
     def list_loaded_plugins(self) -> List[str]:
         """
@@ -323,6 +224,18 @@ class PluginManager:
             list: 已注册的插件类名称列表。
         """
         return list(self.plugin_classes.keys())
+    
+    def get_plugin_path(self, plugin_name: str) -> Optional[str]:
+        """
+        获取指定插件的路径。
+
+        Args:
+            plugin_name: 插件名称
+
+        Returns:
+            Optional[str]: 插件目录的绝对路径，如果插件不存在则返回None。
+        """
+        return self.plugin_paths.get(plugin_name)
 
     # === 私有方法 ===
     # == 目录管理 ==
@@ -388,6 +301,7 @@ class PluginManager:
                 return False
 
             module = module_from_spec(spec)
+            module.__package__ = module_name  # 设置模块包名
             spec.loader.exec_module(module)
 
             logger.debug(f"插件模块加载成功: {plugin_file}")
@@ -444,6 +358,7 @@ class PluginManager:
         stats = component_registry.get_registry_stats()
         action_count = stats.get("action_components", 0)
         command_count = stats.get("command_components", 0)
+        tool_count = stats.get("tool_components", 0)
         event_handler_count = stats.get("event_handlers", 0)
         total_components = stats.get("total_components", 0)
 
@@ -451,7 +366,7 @@ class PluginManager:
         if total_registered > 0:
             logger.info("🎉 插件系统加载完成!")
             logger.info(
-                f"📊 总览: {total_registered}个插件, {total_components}个组件 (Action: {action_count}, Command: {command_count}, EventHandler: {event_handler_count})"
+                f"📊 总览: {total_registered}个插件, {total_components}个组件 (Action: {action_count}, Command: {command_count}, Tool: {tool_count}, EventHandler: {event_handler_count})"
             )
 
             # 显示详细的插件列表
@@ -486,6 +401,9 @@ class PluginManager:
                         command_components = [
                             c for c in plugin_info.components if c.component_type == ComponentType.COMMAND
                         ]
+                        tool_components = [
+                            c for c in plugin_info.components if c.component_type == ComponentType.TOOL
+                        ]
                         event_handler_components = [
                             c for c in plugin_info.components if c.component_type == ComponentType.EVENT_HANDLER
                         ]
@@ -497,7 +415,9 @@ class PluginManager:
                         if command_components:
                             command_names = [c.name for c in command_components]
                             logger.info(f"    ⚡ Command组件: {', '.join(command_names)}")
-
+                        if tool_components:
+                            tool_names = [c.name for c in tool_components]
+                            logger.info(f"    🛠️ Tool组件: {', '.join(tool_names)}")
                         if event_handler_components:
                             event_handler_names = [c.name for c in event_handler_components]
                             logger.info(f"    📢 EventHandler组件: {', '.join(event_handler_names)}")

src/plugin_system/core/tool_use.py

@@ -1,14 +1,16 @@
+import json
+import time
+from typing import List, Dict, Tuple, Optional, Any
+from src.plugin_system.apis.tool_api import get_llm_available_tool_definitions, get_tool_instance
+from src.plugin_system.core.global_announcement_manager import global_announcement_manager
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config
-import time
-from src.common.logger import get_logger
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
-from src.tools.tool_use import ToolUser
 from src.chat.utils.json_utils import process_llm_tool_calls
-from typing import List, Dict, Tuple, Optional
 from src.chat.message_receive.chat_stream import get_chat_manager
+from src.common.logger import get_logger
 
-logger = get_logger("tool_executor")
+logger = get_logger("tool_use")
 
 
 def init_tool_executor_prompt():
@@ -28,6 +30,10 @@ If you need to use a tool, please directly call the corresponding tool function.
     Prompt(tool_executor_prompt, "tool_executor_prompt")
 
 
+# 初始化提示词
+init_tool_executor_prompt()
+
+
 class ToolExecutor:
     """独立的工具执行器组件
 
@@ -51,9 +57,6 @@ class ToolExecutor:
             request_type="tool_executor",
         )
 
-        # 初始化工具实例
-        self.tool_instance = ToolUser()
-
         # 缓存配置
         self.enable_cache = enable_cache
         self.cache_ttl = cache_ttl
@@ -73,7 +76,7 @@ class ToolExecutor:
             return_details: 是否返回详细信息(使用的工具列表和提示词)
 
         Returns:
-            如果return_details为False: List[Dict] - 工具执行结果列表
+            如果return_details为False: Tuple[List[Dict], List[str], str] - (工具执行结果列表, 空, 空)
             如果return_details为True: Tuple[List[Dict], List[str], str] - (结果列表, 使用的工具, 提示词)
         """
 
@@ -82,15 +85,15 @@ class ToolExecutor:
         if cached_result := self._get_from_cache(cache_key):
             logger.info(f"{self.log_prefix}使用缓存结果，跳过工具执行")
             if not return_details:
-                return cached_result, [], "使用缓存结果"
+                return cached_result, [], ""
 
             # 从缓存结果中提取工具名称
             used_tools = [result.get("tool_name", "unknown") for result in cached_result]
-            return cached_result, used_tools, "使用缓存结果"
+            return cached_result, used_tools, ""
 
         # 缓存未命中，执行工具调用
         # 获取可用工具
-        tools = self.tool_instance._define_tools()
+        tools = self._get_tool_definitions()
 
         # 获取当前时间
         time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
@@ -112,6 +115,7 @@ class ToolExecutor:
         # 调用LLM进行工具决策
         response, other_info = await self.llm_model.generate_response_async(prompt=prompt, tools=tools)
 
+        # TODO: 在APIADA加入后完全修复这里！
         # 解析LLM响应
         if len(other_info) == 3:
             reasoning_content, model_name, tool_calls = other_info
@@ -133,6 +137,11 @@ class ToolExecutor:
             return tool_results, used_tools, prompt
         else:
             return tool_results, [], ""
+    
+    def _get_tool_definitions(self) -> List[Dict[str, Any]]:
+        all_tools = get_llm_available_tool_definitions()
+        user_disabled_tools = global_announcement_manager.get_disabled_chat_tools(self.chat_id)
+        return [parameters for name, parameters in all_tools if name not in user_disabled_tools]
 
     async def _execute_tool_calls(self, tool_calls) -> Tuple[List[Dict], List[str]]:
         """执行工具调用
@@ -172,7 +181,7 @@ class ToolExecutor:
                 logger.debug(f"{self.log_prefix}执行工具: {tool_name}")
 
                 # 执行工具
-                result = await self.tool_instance._execute_tool_call(tool_call)
+                result = await self._execute_tool_call(tool_call)
 
                 if result:
                     tool_info = {
@@ -205,6 +214,45 @@ class ToolExecutor:
 
         return tool_results, used_tools
 
+    async def _execute_tool_call(self, tool_call: Dict[str, Any]) -> Optional[Dict]:
+        # sourcery skip: use-assigned-variable
+        """执行单个工具调用
+
+        Args:
+            tool_call: 工具调用对象
+
+        Returns:
+            Optional[Dict]: 工具调用结果，如果失败则返回None
+        """
+        try:
+            function_name = tool_call["function"]["name"]
+            function_args = json.loads(tool_call["function"]["arguments"])
+            function_args["llm_called"] = True  # 标记为LLM调用
+
+            # 获取对应工具实例
+            tool_instance = get_tool_instance(function_name)
+            if not tool_instance:
+                logger.warning(f"未知工具名称: {function_name}")
+                return None
+
+            # 执行工具
+            result = await tool_instance.execute(function_args)
+            if result:
+                # 直接使用 function_name 作为 tool_type
+                tool_type = function_name
+
+                return {
+                    "tool_call_id": tool_call["id"],
+                    "role": "tool",
+                    "name": function_name,
+                    "type": tool_type,
+                    "content": result["content"],
+                }
+            return None
+        except Exception as e:
+            logger.error(f"执行工具调用时发生错误: {str(e)}")
+            return None
+
     def _generate_cache_key(self, target_message: str, chat_history: str, sender: str) -> str:
         """生成缓存键
 
@@ -272,15 +320,6 @@ class ToolExecutor:
         if expired_keys:
             logger.debug(f"{self.log_prefix}清理了{len(expired_keys)}个过期缓存")
 
-    def get_available_tools(self) -> List[str]:
-        """获取可用工具列表
-
-        Returns:
-            List[str]: 可用工具名称列表
-        """
-        tools = self.tool_instance._define_tools()
-        return [tool.get("function", {}).get("name", "unknown") for tool in tools]
-
     async def execute_specific_tool(
         self, tool_name: str, tool_args: Dict, validate_args: bool = True
     ) -> Optional[Dict]:
@@ -299,7 +338,7 @@ class ToolExecutor:
 
             logger.info(f"{self.log_prefix}直接执行工具: {tool_name}")
 
-            result = await self.tool_instance._execute_tool_call(tool_call)
+            result = await self._execute_tool_call(tool_call)
 
             if result:
                 tool_info = {
@@ -366,12 +405,8 @@ class ToolExecutor:
             logger.info(f"{self.log_prefix}缓存TTL修改为: {cache_ttl}")
 
 
-# 初始化提示词
-init_tool_executor_prompt()
-
-
 """
-使用示例：
+ToolExecutor使用示例：
 
 # 1. 基础使用 - 从聊天消息执行工具（启用缓存，默认TTL=3）
 executor = ToolExecutor(executor_id="my_executor")
@@ -400,7 +435,6 @@ result = await executor.execute_specific_tool(
 )
 
 # 6. 缓存管理
-available_tools = executor.get_available_tools()
 cache_status = executor.get_cache_status()  # 查看缓存状态
 executor.clear_cache()  # 清空缓存
 executor.set_cache_config(cache_ttl=5)  # 动态修改缓存配置

src/plugin_system/utils/manifest_utils.py

@@ -163,12 +163,11 @@ class VersionComparator:
                     version_normalized, max_normalized
                 )
 
-                if is_compatible:
-                    logger.info(f"版本兼容性检查：{compat_msg}")
-                    return True, compat_msg
-                else:
+                if not is_compatible:
                     return False, f"版本 {version_normalized} 高于最大支持版本 {max_normalized}，且无兼容性映射"
 
+                logger.info(f"版本兼容性检查：{compat_msg}")
+                return True, compat_msg
         return True, ""
 
     @staticmethod
@@ -358,14 +357,10 @@ class ManifestValidator:
 
         if self.validation_errors:
             report.append("❌ 验证错误:")
-            for error in self.validation_errors:
-                report.append(f"  - {error}")
-
+            report.extend(f"  - {error}" for error in self.validation_errors)
         if self.validation_warnings:
             report.append("⚠️ 验证警告:")
-            for warning in self.validation_warnings:
-                report.append(f"  - {warning}")
-
+            report.extend(f"  - {warning}" for warning in self.validation_warnings)
         if not self.validation_errors and not self.validation_warnings:
             report.append("✅ Manifest文件验证通过")
 

src/plugins/built_in/core_actions/_manifest.json

@@ -24,11 +24,6 @@
     "is_built_in": true,
     "plugin_type": "action_provider",
     "components": [
-      {
-        "type": "action",
-        "name": "reply",
-        "description": "参与聊天回复，发送文本进行表达"
-      },
       {
         "type": "action", 
         "name": "no_reply",

src/plugins/built_in/core_actions/emoji.py

@@ -9,7 +9,8 @@ from src.common.logger import get_logger
 
 # 导入API模块 - 标准Python包方式
 from src.plugin_system.apis import emoji_api, llm_api, message_api
-from src.plugins.built_in.core_actions.no_reply import NoReplyAction
+# 注释：不再需要导入NoReplyAction，因为计数器管理已移至heartFC_chat.py
+# from src.plugins.built_in.core_actions.no_reply import NoReplyAction
 from src.config.config import global_config
 
 
@@ -20,10 +21,14 @@ class EmojiAction(BaseAction):
     """表情动作 - 发送表情包"""
 
     # 激活设置
-    activation_type = ActionActivationType.RANDOM
+    if global_config.emoji.emoji_activate_type == "llm":
+        activation_type = ActionActivationType.LLM_JUDGE
+        random_activation_probability = 0
+    else:
+        activation_type = ActionActivationType.RANDOM
+        random_activation_probability = global_config.emoji.emoji_chance
     mode_enable = ChatMode.ALL
     parallel_action = True
-    random_activation_probability = 0.2  # 默认值，可通过配置覆盖
 
     # 动作基本信息
     action_name = "emoji"
@@ -115,7 +120,7 @@ class EmojiAction(BaseAction):
                     logger.error(f"{self.log_prefix} 未找到'utils_small'模型配置，无法调用LLM")
                     return False, "未找到'utils_small'模型配置"
 
-                success, chosen_emotion, _, _ = await llm_api.generate_with_model(
+                success, chosen_emotion = await llm_api.generate_with_model(
                     prompt, model_config=chat_model_config, request_type="emoji"
                 )
 
@@ -143,8 +148,8 @@ class EmojiAction(BaseAction):
                 logger.error(f"{self.log_prefix} 表情包发送失败")
                 return False, "表情包发送失败"
 
-            # 重置NoReplyAction的连续计数器
-            NoReplyAction.reset_consecutive_count()
+            # 注释：重置NoReplyAction的连续计数器现在由heartFC_chat.py统一管理
+            # NoReplyAction.reset_consecutive_count()
 
             return True, f"发送表情包: {emoji_description}"
 

src/plugins/built_in/core_actions/no_reply.py

@@ -1,6 +1,7 @@
 import random
 import time
-from typing import Tuple
+from typing import Tuple, List
+from collections import deque
 
 # 导入新插件系统
 from src.plugin_system import BaseAction, ActionActivationType, ChatMode
@@ -17,11 +18,15 @@ logger = get_logger("no_reply_action")
 
 
 class NoReplyAction(BaseAction):
-    """不回复动作，根据新消息的兴趣值或数量决定何时结束等待.
+    """不回复动作，支持waiting和breaking两种形式.
 
-    新的等待逻辑:
-    1. 新消息累计兴趣值超过阈值 (默认10) 则结束等待
-    2. 累计新消息数量达到随机阈值 (默认5-10条) 则结束等待
+    waiting形式:
+    - 只要有新消息就结束动作
+    - 记录新消息的兴趣度到列表（最多保留最近三项）
+    - 如果最近三次动作都是no_reply，且最近新消息列表兴趣度之和小于阈值，就进入breaking形式
+
+    breaking形式:
+    - 和原有逻辑一致，需要消息满足一定数量或累计一定兴趣值才结束动作
     """
 
     focus_activation_type = ActionActivationType.NEVER
@@ -35,112 +40,45 @@ class NoReplyAction(BaseAction):
 
     # 连续no_reply计数器
     _consecutive_count = 0
+    
+    # 最近三次no_reply的新消息兴趣度记录
+    _recent_interest_records: deque = deque(maxlen=3)
 
-    # 新增：兴趣值退出阈值
+    # 兴趣值退出阈值
     _interest_exit_threshold = 3.0
-    # 新增：消息数量退出阈值
-    _min_exit_message_count = 5
-    _max_exit_message_count = 10
+    # 消息数量退出阈值
+    _min_exit_message_count = 3
+    _max_exit_message_count = 6
 
     # 动作参数定义
     action_parameters = {}
 
     # 动作使用场景
-    action_require = ["你发送了消息，目前无人回复"]
+    action_require = [""]
 
     # 关联类型
     associated_types = []
 
     async def execute(self) -> Tuple[bool, str]:
         """执行不回复动作"""
-        import asyncio
 
         try:
-            # 增加连续计数
-            NoReplyAction._consecutive_count += 1
-            count = NoReplyAction._consecutive_count
-
             reason = self.action_data.get("reason", "")
             start_time = self.action_data.get("loop_start_time", time.time())
-            check_interval = 0.6  # 每秒检查一次
+            check_interval = 0.6
 
-            # 随机生成本次等待需要的新消息数量阈值
-            exit_message_count_threshold = random.randint(self._min_exit_message_count, self._max_exit_message_count)
-            logger.info(
-                f"{self.log_prefix} 本次no_reply需要 {exit_message_count_threshold} 条新消息或累计兴趣值超过 {self._interest_exit_threshold} 才能打断"
-            )
+            # 判断使用哪种形式
+            form_type = self._determine_form_type()
+            
+            logger.info(f"{self.log_prefix} 选择不回复(第{NoReplyAction._consecutive_count + 1}次)，使用{form_type}形式，原因: {reason}")
 
-            logger.info(f"{self.log_prefix} 选择不回复(第{count}次)，开始摸鱼，原因: {reason}")
+            # 增加连续计数（在确定要执行no_reply时才增加）
+            NoReplyAction._consecutive_count += 1
 
-            # 进入等待状态
-            while True:
-                current_time = time.time()
-                elapsed_time = current_time - start_time
-
-                # 1. 检查新消息
-                recent_messages_dict = message_api.get_messages_by_time_in_chat(
-                    chat_id=self.chat_id,
-                    start_time=start_time,
-                    end_time=current_time,
-                    filter_mai=True,
-                    filter_command=True,
-                )
-                new_message_count = len(recent_messages_dict)
-
-                # 2. 检查消息数量是否达到阈值
-                talk_frequency = global_config.chat.get_current_talk_frequency(self.chat_id)
-                if new_message_count >= exit_message_count_threshold / talk_frequency:
-                    logger.info(
-                        f"{self.log_prefix} 累计消息数量达到{new_message_count}条(>{exit_message_count_threshold / talk_frequency})，结束等待"
-                    )
-                    exit_reason = f"{global_config.bot.nickname}（你）看到了{new_message_count}条新消息，可以考虑一下是否要进行回复"
-                    await self.store_action_info(
-                        action_build_into_prompt=False,
-                        action_prompt_display=exit_reason,
-                        action_done=True,
-                    )
-                    return True, f"累计消息数量达到{new_message_count}条，结束等待 (等待时间: {elapsed_time:.1f}秒)"
-
-                # 3. 检查累计兴趣值
-                if new_message_count > 0:
-                    accumulated_interest = 0.0
-                    for msg_dict in recent_messages_dict:
-                        text = msg_dict.get("processed_plain_text", "")
-                        interest_value = msg_dict.get("interest_value", 0.0)
-                        if text:
-                            accumulated_interest += interest_value
-                    
-                    talk_frequency = global_config.chat.get_current_talk_frequency(self.chat_id)
-                    # 只在兴趣值变化时输出log
-                    if not hasattr(self, "_last_accumulated_interest") or accumulated_interest != self._last_accumulated_interest:
-                        logger.info(f"{self.log_prefix} 当前累计兴趣值: {accumulated_interest:.2f}, 当前聊天频率: {talk_frequency:.2f}")
-                        self._last_accumulated_interest = accumulated_interest
-                    
-                    if accumulated_interest >= self._interest_exit_threshold / talk_frequency:
-                        logger.info(
-                            f"{self.log_prefix} 累计兴趣值达到{accumulated_interest:.2f}(>{self._interest_exit_threshold / talk_frequency})，结束等待"
-                        )
-                        exit_reason = f"{global_config.bot.nickname}（你）感觉到了大家浓厚的兴趣（兴趣值{accumulated_interest:.1f}），决定重新加入讨论"
-                        await self.store_action_info(
-                            action_build_into_prompt=False,
-                            action_prompt_display=exit_reason,
-                            action_done=True,
-                        )
-                        return (
-                            True,
-                            f"累计兴趣值达到{accumulated_interest:.2f}，结束等待 (等待时间: {elapsed_time:.1f}秒)",
-                        )
-
-                # 每10秒输出一次等待状态
-                if int(elapsed_time) > 0 and int(elapsed_time) % 10 == 0:
-                    logger.debug(
-                        f"{self.log_prefix} 已等待{elapsed_time:.0f}秒，累计{new_message_count}条消息，继续等待..."
-                    )
-                    # 使用 asyncio.sleep(1) 来避免在同一秒内重复打印日志
-                    await asyncio.sleep(1)
-
-                # 短暂等待后继续检查
-                await asyncio.sleep(check_interval)
+            if form_type == "waiting":
+                return await self._execute_waiting_form(start_time, check_interval)
+            else:
+                return await self._execute_breaking_form(start_time, check_interval)
 
         except Exception as e:
             logger.error(f"{self.log_prefix} 不回复动作执行失败: {e}")
@@ -153,8 +91,191 @@ class NoReplyAction(BaseAction):
             )
             return False, f"不回复动作执行失败: {e}"
 
+    def _determine_form_type(self) -> str:
+        """判断使用哪种形式的no_reply"""
+        # 如果连续no_reply次数少于3次，使用waiting形式
+        if NoReplyAction._consecutive_count < 3:
+            return "waiting"
+        
+        # 如果最近三次记录不足，使用waiting形式
+        if len(NoReplyAction._recent_interest_records) < 3:
+            return "waiting"
+        
+        # 计算最近三次记录的兴趣度总和
+        total_recent_interest = sum(NoReplyAction._recent_interest_records)
+        
+        # 获取当前聊天频率和意愿系数
+        talk_frequency = global_config.chat.get_current_talk_frequency(self.chat_id)
+        willing_amplifier = global_config.chat.willing_amplifier
+        
+        # 计算调整后的阈值
+        adjusted_threshold = self._interest_exit_threshold / talk_frequency / willing_amplifier
+        
+        logger.info(f"{self.log_prefix} 最近三次兴趣度总和: {total_recent_interest:.2f}, 调整后阈值: {adjusted_threshold:.2f}")
+        
+        # 如果兴趣度总和小于阈值，进入breaking形式
+        if total_recent_interest < adjusted_threshold:
+            logger.info(f"{self.log_prefix} 兴趣度不足，进入breaking形式")
+            return "breaking"
+        else:
+            logger.info(f"{self.log_prefix} 兴趣度充足，继续使用waiting形式")
+            return "waiting"
+
+    async def _execute_waiting_form(self, start_time: float, check_interval: float) -> Tuple[bool, str]:
+        """执行waiting形式的no_reply"""
+        import asyncio
+        
+        logger.info(f"{self.log_prefix} 进入waiting形式，等待任何新消息")
+        
+        while True:
+            current_time = time.time()
+            elapsed_time = current_time - start_time
+
+            # 检查新消息
+            recent_messages_dict = message_api.get_messages_by_time_in_chat(
+                chat_id=self.chat_id,
+                start_time=start_time,
+                end_time=current_time,
+                filter_mai=True,
+                filter_command=True,
+            )
+            new_message_count = len(recent_messages_dict)
+
+            # waiting形式：只要有新消息就结束
+            if new_message_count > 0:
+                # 计算新消息的总兴趣度
+                total_interest = 0.0
+                for msg_dict in recent_messages_dict:
+                    interest_value = msg_dict.get("interest_value", 0.0)
+                    if msg_dict.get("processed_plain_text", ""):
+                        total_interest += interest_value * global_config.chat.willing_amplifier
+
+                # 记录到最近兴趣度列表
+                NoReplyAction._recent_interest_records.append(total_interest)
+                
+                logger.info(
+                    f"{self.log_prefix} waiting形式检测到{new_message_count}条新消息，总兴趣度: {total_interest:.2f}，结束等待"
+                )
+                
+                exit_reason = f"{global_config.bot.nickname}（你）看到了{new_message_count}条新消息，可以考虑一下是否要进行回复"
+                await self.store_action_info(
+                    action_build_into_prompt=False,
+                    action_prompt_display=exit_reason,
+                    action_done=True,
+                )
+                return True, f"waiting形式检测到{new_message_count}条新消息，结束等待 (等待时间: {elapsed_time:.1f}秒)"
+
+            # 每10秒输出一次等待状态
+            if int(elapsed_time) > 0 and int(elapsed_time) % 10 == 0:
+                logger.debug(f"{self.log_prefix} waiting形式已等待{elapsed_time:.0f}秒，继续等待新消息...")
+                await asyncio.sleep(1)
+
+            # 短暂等待后继续检查
+            await asyncio.sleep(check_interval)
+
+    async def _execute_breaking_form(self, start_time: float, check_interval: float) -> Tuple[bool, str]:
+        """执行breaking形式的no_reply（原有逻辑）"""
+        import asyncio
+        
+        # 随机生成本次等待需要的新消息数量阈值
+        exit_message_count_threshold = random.randint(self._min_exit_message_count, self._max_exit_message_count)
+        
+        logger.info(f"{self.log_prefix} 进入breaking形式，需要{exit_message_count_threshold}条消息或足够兴趣度")
+
+        while True:
+            current_time = time.time()
+            elapsed_time = current_time - start_time
+
+            # 检查新消息
+            recent_messages_dict = message_api.get_messages_by_time_in_chat(
+                chat_id=self.chat_id,
+                start_time=start_time,
+                end_time=current_time,
+                filter_mai=True,
+                filter_command=True,
+            )
+            new_message_count = len(recent_messages_dict)
+
+            # 检查消息数量是否达到阈值
+            talk_frequency = global_config.chat.get_current_talk_frequency(self.chat_id)
+            modified_exit_count_threshold = (exit_message_count_threshold / talk_frequency) / global_config.chat.willing_amplifier
+            
+            if new_message_count >= modified_exit_count_threshold:
+                # 记录兴趣度到列表
+                total_interest = 0.0
+                for msg_dict in recent_messages_dict:
+                    interest_value = msg_dict.get("interest_value", 0.0)
+                    if msg_dict.get("processed_plain_text", ""):
+                        total_interest += interest_value * global_config.chat.willing_amplifier
+                
+                NoReplyAction._recent_interest_records.append(total_interest)
+                
+                logger.info(
+                    f"{self.log_prefix} breaking形式累计消息数量达到{new_message_count}条(>{modified_exit_count_threshold})，结束等待"
+                )
+                exit_reason = f"{global_config.bot.nickname}（你）看到了{new_message_count}条新消息，可以考虑一下是否要进行回复"
+                await self.store_action_info(
+                    action_build_into_prompt=False,
+                    action_prompt_display=exit_reason,
+                    action_done=True,
+                )
+                return True, f"breaking形式累计消息数量达到{new_message_count}条，结束等待 (等待时间: {elapsed_time:.1f}秒)"
+
+            # 检查累计兴趣值
+            if new_message_count > 0:
+                accumulated_interest = 0.0
+                for msg_dict in recent_messages_dict:
+                    text = msg_dict.get("processed_plain_text", "")
+                    interest_value = msg_dict.get("interest_value", 0.0)
+                    if text:
+                        accumulated_interest += interest_value * global_config.chat.willing_amplifier
+                
+                # 只在兴趣值变化时输出log
+                if not hasattr(self, "_last_accumulated_interest") or accumulated_interest != self._last_accumulated_interest:
+                    logger.info(f"{self.log_prefix} breaking形式当前累计兴趣值: {accumulated_interest:.2f}, 当前聊天频率: {talk_frequency:.2f}")
+                    self._last_accumulated_interest = accumulated_interest
+                
+                if accumulated_interest >= self._interest_exit_threshold / talk_frequency:
+                    # 记录兴趣度到列表
+                    NoReplyAction._recent_interest_records.append(accumulated_interest)
+                    
+                    logger.info(
+                        f"{self.log_prefix} breaking形式累计兴趣值达到{accumulated_interest:.2f}(>{self._interest_exit_threshold / talk_frequency})，结束等待"
+                    )
+                    exit_reason = f"{global_config.bot.nickname}（你）感觉到了大家浓厚的兴趣（兴趣值{accumulated_interest:.1f}），决定重新加入讨论"
+                    await self.store_action_info(
+                        action_build_into_prompt=False,
+                        action_prompt_display=exit_reason,
+                        action_done=True,
+                    )
+                    return (
+                        True,
+                        f"breaking形式累计兴趣值达到{accumulated_interest:.2f}，结束等待 (等待时间: {elapsed_time:.1f}秒)",
+                    )
+
+            # 每10秒输出一次等待状态
+            if int(elapsed_time) > 0 and int(elapsed_time) % 10 == 0:
+                logger.debug(
+                    f"{self.log_prefix} breaking形式已等待{elapsed_time:.0f}秒，累计{new_message_count}条消息，继续等待..."
+                )
+                await asyncio.sleep(1)
+
+            # 短暂等待后继续检查
+            await asyncio.sleep(check_interval)
+
     @classmethod
     def reset_consecutive_count(cls):
-        """重置连续计数器"""
+        """重置连续计数器和兴趣度记录"""
         cls._consecutive_count = 0
-        logger.debug("NoReplyAction连续计数器已重置")
+        cls._recent_interest_records.clear()
+        logger.debug("NoReplyAction连续计数器和兴趣度记录已重置")
+
+    @classmethod
+    def get_recent_interest_records(cls) -> List[float]:
+        """获取最近的兴趣度记录"""
+        return list(cls._recent_interest_records)
+
+    @classmethod
+    def get_consecutive_count(cls) -> int:
+        """获取连续计数"""
+        return cls._consecutive_count

src/plugins/built_in/core_actions/plugin.py

@@ -8,9 +8,8 @@
 from typing import List, Tuple, Type
 
 # 导入新插件系统
-from src.plugin_system import BasePlugin, register_plugin, ComponentInfo, ActionActivationType
+from src.plugin_system import BasePlugin, register_plugin, ComponentInfo
 from src.plugin_system.base.config_types import ConfigField
-from src.config.config import global_config
 
 # 导入依赖的系统组件
 from src.common.logger import get_logger
@@ -18,7 +17,6 @@ from src.common.logger import get_logger
 # 导入API模块 - 标准Python包方式
 from src.plugins.built_in.core_actions.no_reply import NoReplyAction
 from src.plugins.built_in.core_actions.emoji import EmojiAction
-from src.plugins.built_in.core_actions.reply import ReplyAction
 
 logger = get_logger("core_actions")
 
@@ -52,10 +50,9 @@ class CoreActionsPlugin(BasePlugin):
     config_schema: dict = {
         "plugin": {
             "enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
-            "config_version": ConfigField(type=str, default="0.4.0", description="配置文件版本"),
+            "config_version": ConfigField(type=str, default="0.5.0", description="配置文件版本"),
         },
         "components": {
-            "enable_reply": ConfigField(type=bool, default=True, description="是否启用回复动作"),
             "enable_no_reply": ConfigField(type=bool, default=True, description="是否启用不回复动作"),
             "enable_emoji": ConfigField(type=bool, default=True, description="是否启用发送表情/图片动作"),
         },
@@ -64,24 +61,12 @@ class CoreActionsPlugin(BasePlugin):
     def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
         """返回插件包含的组件列表"""
 
-        if global_config.emoji.emoji_activate_type == "llm":
-            EmojiAction.random_activation_probability = 0.0
-            EmojiAction.focus_activation_type = ActionActivationType.LLM_JUDGE
-            EmojiAction.normal_activation_type = ActionActivationType.LLM_JUDGE
-
-        elif global_config.emoji.emoji_activate_type == "random":
-            EmojiAction.random_activation_probability = global_config.emoji.emoji_chance
-            EmojiAction.focus_activation_type = ActionActivationType.RANDOM
-            EmojiAction.normal_activation_type = ActionActivationType.RANDOM
         # --- 根据配置注册组件 ---
         components = []
-        if self.get_config("components.enable_reply", True):
-            components.append((ReplyAction.get_action_info(), ReplyAction))
         if self.get_config("components.enable_no_reply", True):
             components.append((NoReplyAction.get_action_info(), NoReplyAction))
         if self.get_config("components.enable_emoji", True):
             components.append((EmojiAction.get_action_info(), EmojiAction))
 
-        # components.append((DeepReplyAction.get_action_info(), DeepReplyAction))
 
         return components

src/plugins/built_in/core_actions/reply.py

@@ -1,149 +0,0 @@
-# 导入新插件系统
-from src.plugin_system import BaseAction, ActionActivationType, ChatMode
-from src.config.config import global_config
-import random
-import time
-from typing import Tuple
-import asyncio
-import re
-import traceback
-
-# 导入依赖的系统组件
-from src.common.logger import get_logger
-
-# 导入API模块 - 标准Python包方式
-from src.plugin_system.apis import generator_api, message_api
-from src.plugins.built_in.core_actions.no_reply import NoReplyAction
-from src.person_info.person_info import get_person_info_manager
-from src.mais4u.mai_think import mai_thinking_manager
-from src.mais4u.constant_s4u import ENABLE_S4U
-
-logger = get_logger("reply_action")
-
-
-class ReplyAction(BaseAction):
-    """回复动作 - 参与聊天回复"""
-
-    # 激活设置
-    focus_activation_type = ActionActivationType.NEVER
-    normal_activation_type = ActionActivationType.NEVER
-    mode_enable = ChatMode.FOCUS
-    parallel_action = False
-
-    # 动作基本信息
-    action_name = "reply"
-    action_description = ""
-
-    # 动作参数定义
-    action_parameters = {}
-
-    # 动作使用场景
-    action_require = [""]
-
-    # 关联类型
-    associated_types = ["text"]
-
-    def _parse_reply_target(self, target_message: str) -> tuple:
-        sender = ""
-        target = ""
-        # 添加None检查，防止NoneType错误
-        if target_message is None:
-            return sender, target
-        if ":" in target_message or "：" in target_message:
-            # 使用正则表达式匹配中文或英文冒号
-            parts = re.split(pattern=r"[:：]", string=target_message, maxsplit=1)
-            if len(parts) == 2:
-                sender = parts[0].strip()
-                target = parts[1].strip()
-        return sender, target
-
-    async def execute(self) -> Tuple[bool, str]:
-        """执行回复动作"""
-        logger.debug(f"{self.log_prefix} 决定进行回复")
-        start_time = self.action_data.get("loop_start_time", time.time())
-
-        user_id = self.user_id
-        platform = self.platform
-        # logger.info(f"{self.log_prefix} 用户ID: {user_id}, 平台: {platform}")
-        person_id = get_person_info_manager().get_person_id(platform, user_id)  # type: ignore
-        # logger.info(f"{self.log_prefix} 人物ID: {person_id}")
-        person_name = get_person_info_manager().get_value_sync(person_id, "person_name")
-        reply_to = f"{person_name}:{self.action_message.get('processed_plain_text', '')}"  # type: ignore
-        logger.info(f"{self.log_prefix} 决定进行回复,目标: {reply_to}")
-
-        try:
-            if prepared_reply := self.action_data.get("prepared_reply", ""):
-                reply_text = prepared_reply
-            else:
-                try:
-                    success, reply_set, _ = await asyncio.wait_for(
-                        generator_api.generate_reply(
-                            extra_info="",
-                            reply_to=reply_to,
-                            chat_id=self.chat_id,
-                            request_type="chat.replyer.focus",
-                            enable_tool=global_config.tool.enable_in_focus_chat,
-                        ),
-                        timeout=global_config.chat.thinking_timeout,
-                    )
-                except asyncio.TimeoutError:
-                    logger.warning(f"{self.log_prefix} 回复生成超时 ({global_config.chat.thinking_timeout}s)")
-                    return False, "timeout"
-
-                # 检查从start_time以来的新消息数量
-                # 获取动作触发时间或使用默认值
-                current_time = time.time()
-                new_message_count = message_api.count_new_messages(
-                    chat_id=self.chat_id, start_time=start_time, end_time=current_time
-                )
-
-                # 根据新消息数量决定是否使用reply_to
-                need_reply = new_message_count >= random.randint(2, 4)
-                if need_reply:
-                    logger.info(
-                        f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，使用引用回复"
-                    )
-                else:
-                    logger.debug(
-                        f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，不使用引用回复"
-                    )
-
-            # 构建回复文本
-            reply_text = ""
-            first_replied = False
-            reply_to_platform_id = f"{platform}:{user_id}"
-            for reply_seg in reply_set:
-                data = reply_seg[1]
-                if not first_replied:
-                    if need_reply:
-                        await self.send_text(
-                            content=data, reply_to=reply_to, reply_to_platform_id=reply_to_platform_id, typing=False
-                        )
-                    else:
-                        await self.send_text(content=data, reply_to_platform_id=reply_to_platform_id, typing=False)
-                    first_replied = True
-                else:
-                    await self.send_text(content=data, reply_to_platform_id=reply_to_platform_id, typing=True)
-                reply_text += data
-
-            # 存储动作记录
-            reply_text = f"你对{person_name}进行了回复：{reply_text}"
-
-            if ENABLE_S4U:
-                await mai_thinking_manager.get_mai_think(self.chat_id).do_think_after_response(reply_text)
-
-            await self.store_action_info(
-                action_build_into_prompt=False,
-                action_prompt_display=reply_text,
-                action_done=True,
-            )
-
-            # 重置NoReplyAction的连续计数器
-            NoReplyAction.reset_consecutive_count()
-
-            return success, reply_text
-
-        except Exception as e:
-            logger.error(f"{self.log_prefix} 回复动作执行失败: {e}")
-            traceback.print_exc()
-            return False, f"回复失败: {str(e)}"

src/plugins/built_in/knowledge/get_knowledge.py

@@ -1,4 +1,4 @@
-from src.tools.tool_can_use.base_tool import BaseTool
+from src.plugin_system.base.base_tool import BaseTool
 from src.chat.utils.utils import get_embedding
 from src.common.database.database_model import Knowledges  # Updated import
 from src.common.logger import get_logger
@@ -77,7 +77,7 @@ class SearchKnowledgeTool(BaseTool):
             Union[str, list]: 格式化的信息字符串或原始结果列表
         """
         if not query_embedding:
-            return "" if not return_raw else []
+            return [] if return_raw else ""
 
         similar_items = []
         try:
@@ -115,10 +115,10 @@ class SearchKnowledgeTool(BaseTool):
 
         except Exception as e:
             logger.error(f"从 Peewee 数据库获取知识信息失败: {str(e)}")
-            return "" if not return_raw else []
+            return [] if return_raw else ""
 
         if not results:
-            return "" if not return_raw else []
+            return [] if return_raw else ""
 
         if return_raw:
             # Peewee 模型实例不能直接序列化为 JSON，如果需要原始模型，调用者需要处理

src/plugins/built_in/knowledge/lpmm_get_knowledge.py

@@ -1,4 +1,4 @@
-from src.tools.tool_can_use.base_tool import BaseTool
+from src.plugin_system.base.base_tool import BaseTool
 
 # from src.common.database import db
 from src.common.logger import get_logger

src/plugins/built_in/plugin_management/_manifest.json

@@ -9,7 +9,7 @@
   },
   "license": "GPL-v3.0-or-later",
   "host_application": {
-    "min_version": "0.9.0"
+    "min_version": "0.9.1"
   },
   "homepage_url": "https://github.com/MaiM-with-u/maibot",
   "repository_url": "https://github.com/MaiM-with-u/maibot",

src/plugins/built_in/plugin_management/plugin.py

@@ -11,6 +11,7 @@ from src.plugin_system import (
     component_manage_api,
     ComponentInfo,
     ComponentType,
+    send_api,
 )
 
 
@@ -27,8 +28,15 @@ class ManagementCommand(BaseCommand):
             or not self.message.message_info.user_info
             or str(self.message.message_info.user_info.user_id) not in self.get_config("plugin.permission", [])  # type: ignore
         ):
-            await self.send_text("你没有权限使用插件管理命令")
+            await self._send_message("你没有权限使用插件管理命令")
             return False, "没有权限", True
+        if not self.message.chat_stream:
+            await self._send_message("无法获取聊天流信息")
+            return False, "无法获取聊天流信息", True
+        self.stream_id = self.message.chat_stream.stream_id
+        if not self.stream_id:
+            await self._send_message("无法获取聊天流信息")
+            return False, "无法获取聊天流信息", True
         command_list = self.matched_groups["manage_command"].strip().split(" ")
         if len(command_list) == 1:
             await self.show_help("all")
@@ -42,7 +50,7 @@ class ManagementCommand(BaseCommand):
                 case "help":
                     await self.show_help("all")
                 case _:
-                    await self.send_text("插件管理命令不合法")
+                    await self._send_message("插件管理命令不合法")
                     return False, "命令不合法", True
         if len(command_list) == 3:
             if command_list[1] == "plugin":
@@ -56,7 +64,7 @@ class ManagementCommand(BaseCommand):
                     case "rescan":
                         await self._rescan_plugin_dirs()
                     case _:
-                        await self.send_text("插件管理命令不合法")
+                        await self._send_message("插件管理命令不合法")
                         return False, "命令不合法", True
             elif command_list[1] == "component":
                 if command_list[2] == "list":
@@ -64,10 +72,10 @@ class ManagementCommand(BaseCommand):
                 elif command_list[2] == "help":
                     await self.show_help("component")
                 else:
-                    await self.send_text("插件管理命令不合法")
+                    await self._send_message("插件管理命令不合法")
                     return False, "命令不合法", True
             else:
-                await self.send_text("插件管理命令不合法")
+                await self._send_message("插件管理命令不合法")
                 return False, "命令不合法", True
         if len(command_list) == 4:
             if command_list[1] == "plugin":
@@ -81,28 +89,28 @@ class ManagementCommand(BaseCommand):
                     case "add_dir":
                         await self._add_dir(command_list[3])
                     case _:
-                        await self.send_text("插件管理命令不合法")
+                        await self._send_message("插件管理命令不合法")
                         return False, "命令不合法", True
             elif command_list[1] == "component":
                 if command_list[2] != "list":
-                    await self.send_text("插件管理命令不合法")
+                    await self._send_message("插件管理命令不合法")
                     return False, "命令不合法", True
                 if command_list[3] == "enabled":
                     await self._list_enabled_components()
                 elif command_list[3] == "disabled":
                     await self._list_disabled_components()
                 else:
-                    await self.send_text("插件管理命令不合法")
+                    await self._send_message("插件管理命令不合法")
                     return False, "命令不合法", True
             else:
-                await self.send_text("插件管理命令不合法")
+                await self._send_message("插件管理命令不合法")
                 return False, "命令不合法", True
         if len(command_list) == 5:
             if command_list[1] != "component":
-                await self.send_text("插件管理命令不合法")
+                await self._send_message("插件管理命令不合法")
                 return False, "命令不合法", True
             if command_list[2] != "list":
-                await self.send_text("插件管理命令不合法")
+                await self._send_message("插件管理命令不合法")
                 return False, "命令不合法", True
             if command_list[3] == "enabled":
                 await self._list_enabled_components(target_type=command_list[4])
@@ -111,11 +119,11 @@ class ManagementCommand(BaseCommand):
             elif command_list[3] == "type":
                 await self._list_registered_components_by_type(command_list[4])
             else:
-                await self.send_text("插件管理命令不合法")
+                await self._send_message("插件管理命令不合法")
                 return False, "命令不合法", True
         if len(command_list) == 6:
             if command_list[1] != "component":
-                await self.send_text("插件管理命令不合法")
+                await self._send_message("插件管理命令不合法")
                 return False, "命令不合法", True
             if command_list[2] == "enable":
                 if command_list[3] == "global":
@@ -123,7 +131,7 @@ class ManagementCommand(BaseCommand):
                 elif command_list[3] == "local":
                     await self._locally_enable_component(command_list[4], command_list[5])
                 else:
-                    await self.send_text("插件管理命令不合法")
+                    await self._send_message("插件管理命令不合法")
                     return False, "命令不合法", True
             elif command_list[2] == "disable":
                 if command_list[3] == "global":
@@ -131,10 +139,10 @@ class ManagementCommand(BaseCommand):
                 elif command_list[3] == "local":
                     await self._locally_disable_component(command_list[4], command_list[5])
                 else:
-                    await self.send_text("插件管理命令不合法")
+                    await self._send_message("插件管理命令不合法")
                     return False, "命令不合法", True
             else:
-                await self.send_text("插件管理命令不合法")
+                await self._send_message("插件管理命令不合法")
                 return False, "命令不合法", True
 
         return True, "命令执行完成", True
@@ -180,51 +188,51 @@ class ManagementCommand(BaseCommand):
                 )
             case _:
                 return
-        await self.send_text(help_msg)
+        await self._send_message(help_msg)
 
     async def _list_loaded_plugins(self):
         plugins = plugin_manage_api.list_loaded_plugins()
-        await self.send_text(f"已加载的插件: {', '.join(plugins)}")
+        await self._send_message(f"已加载的插件: {', '.join(plugins)}")
 
     async def _list_registered_plugins(self):
         plugins = plugin_manage_api.list_registered_plugins()
-        await self.send_text(f"已注册的插件: {', '.join(plugins)}")
+        await self._send_message(f"已注册的插件: {', '.join(plugins)}")
 
     async def _rescan_plugin_dirs(self):
         plugin_manage_api.rescan_plugin_directory()
-        await self.send_text("插件目录重新扫描执行中")
+        await self._send_message("插件目录重新扫描执行中")
 
     async def _load_plugin(self, plugin_name: str):
         success, count = plugin_manage_api.load_plugin(plugin_name)
         if success:
-            await self.send_text(f"插件加载成功: {plugin_name}")
+            await self._send_message(f"插件加载成功: {plugin_name}")
         else:
             if count == 0:
-                await self.send_text(f"插件{plugin_name}为禁用状态")
-            await self.send_text(f"插件加载失败: {plugin_name}")
+                await self._send_message(f"插件{plugin_name}为禁用状态")
+            await self._send_message(f"插件加载失败: {plugin_name}")
 
     async def _unload_plugin(self, plugin_name: str):
         success = await plugin_manage_api.remove_plugin(plugin_name)
         if success:
-            await self.send_text(f"插件卸载成功: {plugin_name}")
+            await self._send_message(f"插件卸载成功: {plugin_name}")
         else:
-            await self.send_text(f"插件卸载失败: {plugin_name}")
+            await self._send_message(f"插件卸载失败: {plugin_name}")
 
     async def _reload_plugin(self, plugin_name: str):
         success = await plugin_manage_api.reload_plugin(plugin_name)
         if success:
-            await self.send_text(f"插件重新加载成功: {plugin_name}")
+            await self._send_message(f"插件重新加载成功: {plugin_name}")
         else:
-            await self.send_text(f"插件重新加载失败: {plugin_name}")
+            await self._send_message(f"插件重新加载失败: {plugin_name}")
 
     async def _add_dir(self, dir_path: str):
-        await self.send_text(f"正在添加插件目录: {dir_path}")
+        await self._send_message(f"正在添加插件目录: {dir_path}")
         success = plugin_manage_api.add_plugin_directory(dir_path)
         await asyncio.sleep(0.5)  # 防止乱序发送
         if success:
-            await self.send_text(f"插件目录添加成功: {dir_path}")
+            await self._send_message(f"插件目录添加成功: {dir_path}")
         else:
-            await self.send_text(f"插件目录添加失败: {dir_path}")
+            await self._send_message(f"插件目录添加失败: {dir_path}")
 
     def _fetch_all_registered_components(self) -> List[ComponentInfo]:
         all_plugin_info = component_manage_api.get_all_plugin_info()
@@ -255,29 +263,29 @@ class ManagementCommand(BaseCommand):
     async def _list_all_registered_components(self):
         components_info = self._fetch_all_registered_components()
         if not components_info:
-            await self.send_text("没有注册的组件")
+            await self._send_message("没有注册的组件")
             return
 
         all_components_str = ", ".join(
             f"{component.name} ({component.component_type})" for component in components_info
         )
-        await self.send_text(f"已注册的组件: {all_components_str}")
+        await self._send_message(f"已注册的组件: {all_components_str}")
 
     async def _list_enabled_components(self, target_type: str = "global"):
         components_info = self._fetch_all_registered_components()
         if not components_info:
-            await self.send_text("没有注册的组件")
+            await self._send_message("没有注册的组件")
             return
 
         if target_type == "global":
             enabled_components = [component for component in components_info if component.enabled]
             if not enabled_components:
-                await self.send_text("没有满足条件的已启用全局组件")
+                await self._send_message("没有满足条件的已启用全局组件")
                 return
             enabled_components_str = ", ".join(
                 f"{component.name} ({component.component_type})" for component in enabled_components
             )
-            await self.send_text(f"满足条件的已启用全局组件: {enabled_components_str}")
+            await self._send_message(f"满足条件的已启用全局组件: {enabled_components_str}")
         elif target_type == "local":
             locally_disabled_components = self._fetch_locally_disabled_components()
             enabled_components = [
@@ -286,28 +294,28 @@ class ManagementCommand(BaseCommand):
                 if (component.name not in locally_disabled_components and component.enabled)
             ]
             if not enabled_components:
-                await self.send_text("本聊天没有满足条件的已启用组件")
+                await self._send_message("本聊天没有满足条件的已启用组件")
                 return
             enabled_components_str = ", ".join(
                 f"{component.name} ({component.component_type})" for component in enabled_components
             )
-            await self.send_text(f"本聊天满足条件的已启用组件: {enabled_components_str}")
+            await self._send_message(f"本聊天满足条件的已启用组件: {enabled_components_str}")
 
     async def _list_disabled_components(self, target_type: str = "global"):
         components_info = self._fetch_all_registered_components()
         if not components_info:
-            await self.send_text("没有注册的组件")
+            await self._send_message("没有注册的组件")
             return
 
         if target_type == "global":
             disabled_components = [component for component in components_info if not component.enabled]
             if not disabled_components:
-                await self.send_text("没有满足条件的已禁用全局组件")
+                await self._send_message("没有满足条件的已禁用全局组件")
                 return
             disabled_components_str = ", ".join(
                 f"{component.name} ({component.component_type})" for component in disabled_components
             )
-            await self.send_text(f"满足条件的已禁用全局组件: {disabled_components_str}")
+            await self._send_message(f"满足条件的已禁用全局组件: {disabled_components_str}")
         elif target_type == "local":
             locally_disabled_components = self._fetch_locally_disabled_components()
             disabled_components = [
@@ -316,12 +324,12 @@ class ManagementCommand(BaseCommand):
                 if (component.name in locally_disabled_components or not component.enabled)
             ]
             if not disabled_components:
-                await self.send_text("本聊天没有满足条件的已禁用组件")
+                await self._send_message("本聊天没有满足条件的已禁用组件")
                 return
             disabled_components_str = ", ".join(
                 f"{component.name} ({component.component_type})" for component in disabled_components
             )
-            await self.send_text(f"本聊天满足条件的已禁用组件: {disabled_components_str}")
+            await self._send_message(f"本聊天满足条件的已禁用组件: {disabled_components_str}")
 
     async def _list_registered_components_by_type(self, target_type: str):
         match target_type:
@@ -332,18 +340,18 @@ class ManagementCommand(BaseCommand):
             case "event_handler":
                 component_type = ComponentType.EVENT_HANDLER
             case _:
-                await self.send_text(f"未知组件类型: {target_type}")
+                await self._send_message(f"未知组件类型: {target_type}")
                 return
 
         components_info = component_manage_api.get_components_info_by_type(component_type)
         if not components_info:
-            await self.send_text(f"没有注册的 {target_type} 组件")
+            await self._send_message(f"没有注册的 {target_type} 组件")
             return
 
         components_str = ", ".join(
             f"{name} ({component.component_type})" for name, component in components_info.items()
         )
-        await self.send_text(f"注册的 {target_type} 组件: {components_str}")
+        await self._send_message(f"注册的 {target_type} 组件: {components_str}")
 
     async def _globally_enable_component(self, component_name: str, component_type: str):
         match component_type:
@@ -354,12 +362,12 @@ class ManagementCommand(BaseCommand):
             case "event_handler":
                 target_component_type = ComponentType.EVENT_HANDLER
             case _:
-                await self.send_text(f"未知组件类型: {component_type}")
+                await self._send_message(f"未知组件类型: {component_type}")
                 return
         if component_manage_api.globally_enable_component(component_name, target_component_type):
-            await self.send_text(f"全局启用组件成功: {component_name}")
+            await self._send_message(f"全局启用组件成功: {component_name}")
         else:
-            await self.send_text(f"全局启用组件失败: {component_name}")
+            await self._send_message(f"全局启用组件失败: {component_name}")
 
     async def _globally_disable_component(self, component_name: str, component_type: str):
         match component_type:
@@ -370,13 +378,13 @@ class ManagementCommand(BaseCommand):
             case "event_handler":
                 target_component_type = ComponentType.EVENT_HANDLER
             case _:
-                await self.send_text(f"未知组件类型: {component_type}")
+                await self._send_message(f"未知组件类型: {component_type}")
                 return
         success = await component_manage_api.globally_disable_component(component_name, target_component_type)
         if success:
-            await self.send_text(f"全局禁用组件成功: {component_name}")
+            await self._send_message(f"全局禁用组件成功: {component_name}")
         else:
-            await self.send_text(f"全局禁用组件失败: {component_name}")
+            await self._send_message(f"全局禁用组件失败: {component_name}")
 
     async def _locally_enable_component(self, component_name: str, component_type: str):
         match component_type:
@@ -387,16 +395,16 @@ class ManagementCommand(BaseCommand):
             case "event_handler":
                 target_component_type = ComponentType.EVENT_HANDLER
             case _:
-                await self.send_text(f"未知组件类型: {component_type}")
+                await self._send_message(f"未知组件类型: {component_type}")
                 return
         if component_manage_api.locally_enable_component(
             component_name,
             target_component_type,
             self.message.chat_stream.stream_id,
         ):
-            await self.send_text(f"本地启用组件成功: {component_name}")
+            await self._send_message(f"本地启用组件成功: {component_name}")
         else:
-            await self.send_text(f"本地启用组件失败: {component_name}")
+            await self._send_message(f"本地启用组件失败: {component_name}")
 
     async def _locally_disable_component(self, component_name: str, component_type: str):
         match component_type:
@@ -407,34 +415,40 @@ class ManagementCommand(BaseCommand):
             case "event_handler":
                 target_component_type = ComponentType.EVENT_HANDLER
             case _:
-                await self.send_text(f"未知组件类型: {component_type}")
+                await self._send_message(f"未知组件类型: {component_type}")
                 return
         if component_manage_api.locally_disable_component(
             component_name,
             target_component_type,
             self.message.chat_stream.stream_id,
         ):
-            await self.send_text(f"本地禁用组件成功: {component_name}")
+            await self._send_message(f"本地禁用组件成功: {component_name}")
         else:
-            await self.send_text(f"本地禁用组件失败: {component_name}")
+            await self._send_message(f"本地禁用组件失败: {component_name}")
+    
+    async def _send_message(self, message: str):
+        await send_api.text_to_stream(message, self.stream_id, typing=False, storage_message=False)
 
 
 @register_plugin
 class PluginManagementPlugin(BasePlugin):
     plugin_name: str = "plugin_management_plugin"
-    enable_plugin: bool = True
+    enable_plugin: bool = False
     dependencies: list[str] = []
     python_dependencies: list[str] = []
     config_file_name: str = "config.toml"
     config_schema: dict = {
         "plugin": {
-            "enable": ConfigField(bool, default=True, description="是否启用插件"),
-            "permission": ConfigField(list, default=[], description="有权限使用插件管理命令的用户列表"),
+            "enabled": ConfigField(bool, default=False, description="是否启用插件"),
+            "config_version": ConfigField(type=str, default="1.1.0", description="配置文件版本"),
+            "permission": ConfigField(
+                list, default=[], description="有权限使用插件管理命令的用户列表，请填写字符串形式的用户ID"
+            ),
         },
     }
 
     def get_plugin_components(self) -> List[Tuple[CommandInfo, Type[BaseCommand]]]:
         components = []
-        if self.get_config("plugin.enable", True):
+        if self.get_config("plugin.enabled", True):
             components.append((ManagementCommand.get_command_info(), ManagementCommand))
         return components

src/tools/tool_can_use/__init__.py

@@ -1,20 +0,0 @@
-from src.tools.tool_can_use.base_tool import (
-    BaseTool,
-    register_tool,
-    discover_tools,
-    get_all_tool_definitions,
-    get_tool_instance,
-    TOOL_REGISTRY,
-)
-
-__all__ = [
-    "BaseTool",
-    "register_tool",
-    "discover_tools",
-    "get_all_tool_definitions",
-    "get_tool_instance",
-    "TOOL_REGISTRY",
-]
-
-# 自动发现并注册工具
-discover_tools()

src/tools/tool_can_use/base_tool.py

@@ -1,115 +0,0 @@
-from typing import List, Any, Optional, Type
-import inspect
-import importlib
-import pkgutil
-import os
-from src.common.logger import get_logger
-from rich.traceback import install
-
-install(extra_lines=3)
-
-logger = get_logger("base_tool")
-
-# 工具注册表
-TOOL_REGISTRY = {}
-
-
-class BaseTool:
-    """所有工具的基类"""
-
-    # 工具名称，子类必须重写
-    name = None
-    # 工具描述，子类必须重写
-    description = None
-    # 工具参数定义，子类必须重写
-    parameters = None
-
-    @classmethod
-    def get_tool_definition(cls) -> dict[str, Any]:
-        """获取工具定义，用于LLM工具调用
-
-        Returns:
-            dict: 工具定义字典
-        """
-        if not cls.name or not cls.description or not cls.parameters:
-            raise NotImplementedError(f"工具类 {cls.__name__} 必须定义 name, description 和 parameters 属性")
-
-        return {
-            "type": "function",
-            "function": {"name": cls.name, "description": cls.description, "parameters": cls.parameters},
-        }
-
-    async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
-        """执行工具函数
-
-        Args:
-            function_args: 工具调用参数
-
-        Returns:
-            dict: 工具执行结果
-        """
-        raise NotImplementedError("子类必须实现execute方法")
-
-
-def register_tool(tool_class: Type[BaseTool]):
-    """注册工具到全局注册表
-
-    Args:
-        tool_class: 工具类
-    """
-    if not issubclass(tool_class, BaseTool):
-        raise TypeError(f"{tool_class.__name__} 不是 BaseTool 的子类")
-
-    tool_name = tool_class.name
-    if not tool_name:
-        raise ValueError(f"工具类 {tool_class.__name__} 没有定义 name 属性")
-
-    TOOL_REGISTRY[tool_name] = tool_class
-    logger.info(f"已注册: {tool_name}")
-
-
-def discover_tools():
-    """自动发现并注册tool_can_use目录下的所有工具"""
-    # 获取当前目录路径
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    package_name = os.path.basename(current_dir)
-
-    # 遍历包中的所有模块
-    for _, module_name, _ in pkgutil.iter_modules([current_dir]):
-        # 跳过当前模块和__pycache__
-        if module_name == "base_tool" or module_name.startswith("__"):
-            continue
-
-        # 导入模块
-        module = importlib.import_module(f"src.tools.{package_name}.{module_name}")
-
-        # 查找模块中的工具类
-        for _, obj in inspect.getmembers(module):
-            if inspect.isclass(obj) and issubclass(obj, BaseTool) and obj != BaseTool:
-                register_tool(obj)
-
-    logger.info(f"工具发现完成，共注册 {len(TOOL_REGISTRY)} 个工具")
-
-
-def get_all_tool_definitions() -> List[dict[str, Any]]:
-    """获取所有已注册工具的定义
-
-    Returns:
-        List[dict]: 工具定义列表
-    """
-    return [tool_class().get_tool_definition() for tool_class in TOOL_REGISTRY.values()]
-
-
-def get_tool_instance(tool_name: str) -> Optional[BaseTool]:
-    """获取指定名称的工具实例
-
-    Args:
-        tool_name: 工具名称
-
-    Returns:
-        Optional[BaseTool]: 工具实例，如果找不到则返回None
-    """
-    tool_class = TOOL_REGISTRY.get(tool_name)
-    if not tool_class:
-        return None
-    return tool_class()

src/tools/tool_can_use/compare_numbers_tool.py

@@ -1,49 +0,0 @@
-from src.tools.tool_can_use.base_tool import BaseTool
-from src.common.logger import get_logger
-from typing import Any
-
-logger = get_logger("compare_numbers_tool")
-
-
-class CompareNumbersTool(BaseTool):
-    """比较两个数大小的工具"""
-
-    name = "compare_numbers"
-    description = "使用工具 比较两个数的大小，返回较大的数"
-    parameters = {
-        "type": "object",
-        "properties": {
-            "num1": {"type": "number", "description": "第一个数字"},
-            "num2": {"type": "number", "description": "第二个数字"},
-        },
-        "required": ["num1", "num2"],
-    }
-
-    async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
-        """执行比较两个数的大小
-
-        Args:
-            function_args: 工具参数
-
-        Returns:
-            dict: 工具执行结果
-        """
-        num1: int | float = function_args.get("num1")  # type: ignore
-        num2: int | float = function_args.get("num2")  # type: ignore
-
-        try:
-            if num1 > num2:
-                result = f"{num1} 大于 {num2}"
-            elif num1 < num2:
-                result = f"{num1} 小于 {num2}"
-            else:
-                result = f"{num1} 等于 {num2}"
-
-            return {"type": "comparison_result", "id": f"{num1}_vs_{num2}", "content": result}
-        except Exception as e:
-            logger.error(f"比较数字失败: {str(e)}")
-            return {"type": "info", "id": f"{num1}_vs_{num2}", "content": f"比较数字失败，炸了: {str(e)}"}
-
-
-# 注册工具
-# register_tool(CompareNumbersTool)

src/tools/tool_can_use/rename_person_tool.py

@@ -1,104 +0,0 @@
-from src.tools.tool_can_use.base_tool import BaseTool
-from src.person_info.person_info import get_person_info_manager
-from src.common.logger import get_logger
-import time
-
-
-logger = get_logger("rename_person_tool")
-
-
-class RenamePersonTool(BaseTool):
-    name = "rename_person"
-    description = (
-        "这个工具可以改变用户的昵称。你可以选择改变对他人的称呼。你想给人改名，叫别人别的称呼，需要调用这个工具。"
-    )
-    parameters = {
-        "type": "object",
-        "properties": {
-            "person_name": {"type": "string", "description": "需要重新取名的用户的当前昵称"},
-            "message_content": {
-                "type": "string",
-                "description": "当前的聊天内容或特定要求，用于提供取名建议的上下文，尽可能详细。",
-            },
-        },
-        "required": ["person_name"],
-    }
-
-    async def execute(self, function_args: dict, message_txt=""):
-        """
-        执行取名工具逻辑
-
-        Args:
-            function_args (dict): 包含 'person_name' 和可选 'message_content' 的字典
-            message_txt (str): 原始消息文本 (这里未使用，因为 message_content 更明确)
-
-        Returns:
-            dict: 包含执行结果的字典
-        """
-        person_name_to_find = function_args.get("person_name")
-        request_context = function_args.get("message_content", "")  # 如果没有提供，则为空字符串
-
-        if not person_name_to_find:
-            return {"name": self.name, "content": "错误：必须提供需要重命名的用户昵称 (person_name)。"}
-        person_info_manager = get_person_info_manager()
-        try:
-            # 1. 根据昵称查找用户信息
-            logger.debug(f"尝试根据昵称 '{person_name_to_find}' 查找用户...")
-            person_info = await person_info_manager.get_person_info_by_name(person_name_to_find)
-
-            if not person_info:
-                logger.info(f"未找到昵称为 '{person_name_to_find}' 的用户。")
-                return {
-                    "name": self.name,
-                    "content": f"找不到昵称为 '{person_name_to_find}' 的用户。请确保输入的是我之前为该用户取的昵称。",
-                }
-
-            person_id = person_info.get("person_id")
-            user_nickname = person_info.get("nickname")  # 这是用户原始昵称
-            user_cardname = person_info.get("user_cardname")
-            user_avatar = person_info.get("user_avatar")
-
-            if not person_id:
-                logger.error(f"找到了用户 '{person_name_to_find}' 但无法获取 person_id")
-                return {"name": self.name, "content": f"找到了用户 '{person_name_to_find}' 但获取内部ID时出错。"}
-
-            # 2. 调用 qv_person_name 进行取名
-            logger.debug(
-                f"为用户 {person_id} (原昵称: {person_name_to_find}) 调用 qv_person_name，请求上下文: '{request_context}'"
-            )
-            result = await person_info_manager.qv_person_name(
-                person_id=person_id,
-                user_nickname=user_nickname,  # type: ignore
-                user_cardname=user_cardname,  # type: ignore
-                user_avatar=user_avatar,  # type: ignore
-                request=request_context,
-            )
-
-            # 3. 处理结果
-            if result and result.get("nickname"):
-                new_name = result["nickname"]
-                # reason = result.get("reason", "未提供理由")
-                logger.info(f"成功为用户 {person_id} 取了新昵称: {new_name}")
-
-                content = f"已成功将用户 {person_name_to_find} 的备注名更新为 {new_name}"
-                logger.info(content)
-                return {"type": "info", "id": f"rename_success_{time.time()}", "content": content}
-            else:
-                logger.warning(f"为用户 {person_id} 调用 qv_person_name 后未能成功获取新昵称。")
-                # 尝试从内存中获取可能已经更新的名字
-                current_name = await person_info_manager.get_value(person_id, "person_name")
-                if current_name and current_name != person_name_to_find:
-                    return {
-                        "name": self.name,
-                        "content": f"尝试取新昵称时遇到一点小问题，但我已经将 '{person_name_to_find}' 的昵称更新为 '{current_name}' 了。",
-                    }
-                else:
-                    return {
-                        "name": self.name,
-                        "content": f"尝试为 '{person_name_to_find}' 取新昵称时遇到了问题，未能成功生成。可能需要稍后再试。",
-                    }
-
-        except Exception as e:
-            error_msg = f"重命名失败: {str(e)}"
-            logger.error(error_msg, exc_info=True)
-            return {"type": "info_error", "id": f"rename_error_{time.time()}", "content": error_msg}

src/tools/tool_use.py

@@ -1,55 +0,0 @@
-import json
-from src.common.logger import get_logger
-from src.tools.tool_can_use import get_all_tool_definitions, get_tool_instance
-
-logger = get_logger("tool_use")
-
-
-class ToolUser:
-    @staticmethod
-    def _define_tools():
-        """获取所有已注册工具的定义
-
-        Returns:
-            list: 工具定义列表
-        """
-        return get_all_tool_definitions()
-
-    @staticmethod
-    async def _execute_tool_call(tool_call):
-        """执行特定的工具调用
-
-        Args:
-            tool_call: 工具调用对象
-            message_txt: 原始消息文本
-
-        Returns:
-            dict: 工具调用结果
-        """
-        try:
-            function_name = tool_call["function"]["name"]
-            function_args = json.loads(tool_call["function"]["arguments"])
-
-            # 获取对应工具实例
-            tool_instance = get_tool_instance(function_name)
-            if not tool_instance:
-                logger.warning(f"未知工具名称: {function_name}")
-                return None
-
-            # 执行工具
-            result = await tool_instance.execute(function_args)
-            if result:
-                # 直接使用 function_name 作为 tool_type
-                tool_type = function_name
-
-                return {
-                    "tool_call_id": tool_call["id"],
-                    "role": "tool",
-                    "name": function_name,
-                    "type": tool_type,
-                    "content": result["content"],
-                }
-            return None
-        except Exception as e:
-            logger.error(f"执行工具调用时发生错误: {str(e)}")
-            return None