添加主动思考的执行器喵~（你是一只猫娘喵）（）

src/plugins/built_in/proactive_thinker/proacive_thinker_event.py

@@ -13,6 +13,7 @@ from src.manager.async_task_manager import async_task_manager, AsyncTask
 from src.plugin_system import EventType, BaseEventHandler
 from src.plugin_system.apis import chat_api, person_api
 from src.plugin_system.base.base_event import HandlerResult
+from .proactive_thinker_executor import ProactiveThinkerExecutor
 
 logger = get_logger(__name__)
 
@@ -26,6 +27,7 @@ class ColdStartTask(AsyncTask):
     def __init__(self):
         super().__init__(task_name="ColdStartTask")
         self.chat_manager = get_chat_manager()
+        self.executor = ProactiveThinkerExecutor()
 
     async def run(self):
         """任务主循环，周期性地检查是否有需要“破冰”的新用户。"""
@@ -72,7 +74,7 @@ class ColdStartTask(AsyncTask):
                         # 创建后，该用户就进入了机器人的“好友列表”，后续将由 ProactiveThinkingTask 接管
                         await self.chat_manager.get_or_create_stream(platform, user_info)
 
-                        # TODO: 在这里调用LLM，生成一句自然的、符合人设的“破冰”问候语，并发送给用户。
+                        await self.executor.execute_cold_start(user_info)
                         logger.info(f"【冷启动】已为新用户 {chat_id} (昵称: {user_nickname}) 创建聊天流并发送问候。")
 
                     except ValueError:
@@ -100,6 +102,7 @@ class ProactiveThinkingTask(AsyncTask):
     def __init__(self):
         super().__init__(task_name="ProactiveThinkingTask")
         self.chat_manager = get_chat_manager()
+        self.executor = ProactiveThinkerExecutor()
 
     def _get_next_interval(self) -> float:
         """
@@ -174,7 +177,7 @@ class ProactiveThinkingTask(AsyncTask):
                     if time_since_last_active > next_interval:
                         logger.info(f"【日常唤醒】聊天流 {stream.stream_id} 已冷却 {time_since_last_active:.2f} 秒，触发主动对话。")
                         
-                        # TODO: 在这里调用LLM，生成一句自然的、符合上下文的问候语，并发送。
+                        await self.executor.execute_wakeup(stream.stream_id)
                         
                         # 【关键步骤】在触发后，立刻更新活跃时间并保存。
                         # 这可以防止在同一个检查周期内，对同一个目标因为意外的延迟而发送多条消息。

src/plugins/built_in/proactive_thinker/proactive_thinker_executor.py

@@ -0,0 +1,77 @@
+from typing import Optional
+
+from maim_message import UserInfo
+
+from src.chat.memory_system.memory_manager import MemoryManager
+from src.common.logger import get_logger
+from src.plugin_system.apis import chat_api, person_api, schedule_api, send_api, llm_api
+
+logger = get_logger(__name__)
+
+
+class ProactiveThinkerExecutor:
+    """
+    主动思考执行器，负责生成并发送主动消息。
+    """
+
+    def __init__(self):
+        self.memory_manager = MemoryManager()
+
+    async def _generate_prompt(self, stream_id: str) -> Optional[str]:
+        """
+        根据聊天流信息，生成包含记忆、日程和个人信息的提示词。
+        """
+        # 1. 获取用户信息
+        stream = chat_api.get_stream_by_stream_id(stream_id)
+        if not stream:
+            logger.warning(f"无法找到 stream_id 为 {stream_id} 的聊天流")
+            return None
+
+        user_info = stream.user_info
+        person_id = person_api.get_person_id(user_info.platform, int(user_info.user_id))
+
+        # 2. 获取记忆
+        memories = await self.memory_manager.get_memories(person_id)
+        memory_context = "\n".join([f"- {m.content}" for m in memories])
+
+        # 3. 获取日程
+        schedules = await schedule_api.get_today_schedule(person_id)
+        schedule_context = "\n".join([f"- {s.title} ({s.start_time}-{s.end_time})" for s in schedules])
+
+        # 4. 构建提示词
+        prompt = f"""
+        # Context
+        ## Memory
+        {memory_context}
+
+        ## Schedule
+        {schedule_context}
+
+        # Task
+        You are a proactive assistant. Based on the user's memory and schedule, initiate a conversation.
+        """
+        return prompt
+
+    async def execute_cold_start(self, user_info: UserInfo):
+        """
+        为新用户执行“破冰”操作。
+        """
+        logger.info(f"为新用户 {user_info.user_id} 执行“破冰”操作")
+        prompt = f"You are a proactive assistant. Initiate a conversation with a new friend named {user_info.user_nickname}."
+        
+        response = await llm_api.generate(prompt)
+        await send_api.send_message(user_info.platform, user_info.user_id, response)
+
+    async def execute_wakeup(self, stream_id: str):
+        """
+        为已冷却的聊天执行“唤醒”操作。
+        """
+        logger.info(f"为聊天流 {stream_id} 执行“唤醒”操作")
+        prompt = await self._generate_prompt(stream_id)
+        if not prompt:
+            return
+
+        response = await llm_api.generate(prompt)
+        
+        stream = chat_api.get_stream_by_stream_id(stream_id)
+        await send_api.send_message(stream.user_info.platform, stream.user_info.user_id, response)