Revert "feat(reminder): 增强定时提醒系统，实现智能用户识别与上下文感知回复"

This reverts commit e1ebf41f8d.

src/chat/chat_loop/proactive/proactive_thinker.py

@@ -139,6 +139,21 @@ class ProactiveThinker:
             logger.error(f"{self.context.log_prefix} 主动思考执行异常: {e}")
             logger.error(traceback.format_exc())
 
+    async def _get_reminder_context(self, message_id: str) -> str:
+        """获取提醒消息的上下文"""
+        try:
+            # 只获取那一条消息
+            message_record = await db_get(Messages, {"message_id": message_id}, single_result=True)
+            if message_record:
+                # 使用 build_readable_messages_with_id 来格式化单条消息
+                chat_context_block, _ = build_readable_messages_with_id(messages=[message_record])
+                return chat_context_block
+            return "无法加载相关的聊天记录。"
+        except Exception as e:
+            logger.error(f"{self.context.log_prefix} 获取提醒上下文失败: {e}")
+            return "无法加载相关的聊天记录。"
+    
+
     async def _generate_proactive_content_and_send(self, action_result: Dict[str, Any], trigger_event: ProactiveTriggerEvent):
         """
         获取实时信息，构建最终的生成提示词，并生成和发送主动回复。

src/plugins/built_in/at_user_plugin/plugin.py

@@ -69,49 +69,15 @@ class AtAction(BaseAction):
         if not member_list:
             return False, "群成员列表为空"
 
-        # 优化用户匹配逻辑
+        # 使用模糊匹配找到最接近的用户名
-        best_match = None
+        choices = {member["card"] or member["nickname"]: member["user_id"] for member in member_list}
-        user_id = None
+        best_match, score = process.extractOne(user_name, choices.keys())
         
-        # 1. 完全精确匹配
+        if score < 30: # 设置一个匹配度阈值
-        for member in member_list:
+            logger.info(f"找不到与 '{user_name}' 高度匹配的用户 (最佳匹配: {best_match}, 分数: {score})")
-            card = member.get("card", "")
-            nickname = member.get("nickname", "")
-            if user_name == card or user_name == nickname:
-                best_match = card if user_name == card else nickname
-                user_id = member["user_id"]
-                logger.info(f"找到完全精确匹配: '{user_name}' -> '{best_match}' (ID: {user_id})")
-                break
-        
-        # 2. 包含关系匹配
-        if not best_match:
-            containing_matches = []
-            for member in member_list:
-                card = member.get("card", "")
-                nickname = member.get("nickname", "")
-                if user_name in card:
-                    containing_matches.append((card, member["user_id"]))
-                elif user_name in nickname:
-                    containing_matches.append((nickname, member["user_id"]))
-            
-            if containing_matches:
-                # 选择最短的匹配项，因为通常更精确
-                best_match, user_id = min(containing_matches, key=lambda x: len(x[0]))
-                logger.info(f"找到包含关系匹配: '{user_name}' -> '{best_match}' (ID: {user_id})")
-
-        # 3. 模糊匹配作为兜底
-        if not best_match:
-            choices = {member["card"] or member["nickname"]: member["user_id"] for member in member_list}
-            fuzzy_match, score = process.extractOne(user_name, choices.keys())
-            if score >= 60: # 维持较高的阈值
-                best_match = fuzzy_match
-                user_id = choices[best_match]
-                logger.info(f"找到模糊匹配: '{user_name}' -> '{best_match}' (ID: {user_id}, Score: {score})")
-        
-        if not best_match:
-            logger.warning(f"所有匹配策略都未能找到用户: '{user_name}'")
             return False, "用户不存在"
             
+        user_id = choices[best_match]
         user_info = {"user_id": user_id, "user_nickname": best_match}
 
         try:
@@ -126,23 +92,13 @@ class AtAction(BaseAction):
                 return False, "聊天流不存在"
             
             replyer = DefaultReplyer(chat_stream)
-            # 优化提示词，消除记忆割裂感
+            extra_info = f"你需要艾特用户 {user_name} 并回复他们说: {at_message}"
-            reminder_task = at_message.replace("定时提醒：", "").strip()
-            extra_info = f"""你之前记下了一个提醒任务：'{reminder_task}'
-现在时间到了，你需要去提醒用户 '{user_name}'。
-
-**重要规则**：
-- 你的任务**只**是生成提醒的**内容**。
-- **绝对不要**在你的回复中包含任何`@`符号或者目标用户的名字。真正的@操作会由系统自动完成。
-- 像一个朋友一样，自然地完成这个提醒，而不是生硬地复述任务。
-
-请直接输出提醒的**内容**。"""
             
             success, llm_response, _ = await replyer.generate_reply_with_context(
-                reply_to=f"是时候提醒'{user_name}'了",  # 内部上下文，更符合执行任务的语境
+                reply_to=f"{user_name}:{at_message}",
                 extra_info=extra_info,
                 enable_tool=False,
-                from_plugin=True  # 标记为插件调用，以便LLM更好地理解上下文
+                from_plugin=False
             )
             
             if not success or not llm_response: