feat：处理器处理时间上限，处理并行模式，planner和思考prompt，循环观察器大优化

feat：增加了处理器处理时间上限，记忆处理并行模式，优化了planner和思考prompt，优化了循环观察器

src/chat/focus_chat/planners/actions/no_reply_action.py

@@ -26,8 +26,8 @@ class NoReplyAction(BaseAction):
     action_parameters = {}
     action_require = [
         "话题无关/无聊/不感兴趣/不懂",
-        "最后一条消息是你自己发的且无人回应你",
-        "你发送了太多消息，且无人回复",
+        "聊天记录中最新一条消息是你自己发的且无人回应你",
+        "你连续发送了太多消息，且无人回复",
     ]
     default = True
 

src/chat/focus_chat/planners/modify_actions.py

@@ -28,11 +28,8 @@ class ActionModifier:
     async def modify_actions(
         self,
         observations: Optional[List[Observation]] = None,
-        running_memorys: Optional[List[Dict]] = None,
         **kwargs: Any,
     ):
-        # print(f"observations: {observations}")
-        # processed_infos = []
 
         # 处理Observation对象
         if observations:

src/chat/focus_chat/planners/planner.py

@@ -26,9 +26,8 @@ def init_prompt():
         """
 你的自我认知是：
 {self_info_block}
-
 {extra_info_block}
-
+{memory_str}
 你需要基于以下信息决定如何参与对话
 这些信息可能会有冲突，请你整合这些信息，并选择一个最合适的action：
 {chat_content_block}
@@ -49,7 +48,7 @@ def init_prompt():
 请你以下面格式输出你选择的action：
 {{
     "action": "action_name",
-    "reasoning": "你的决策理由",
+    "reasoning": "说明你做出该action的原因",
     "参数1": "参数1的值",
     "参数2": "参数2的值",
     "参数3": "参数3的值",
@@ -84,13 +83,13 @@ class ActionPlanner:
 
         self.action_manager = action_manager
 
-    async def plan(self, all_plan_info: List[InfoBase], cycle_timers: dict) -> Dict[str, Any]:
+    async def plan(self, all_plan_info: List[InfoBase], running_memorys: List[Dict[str, Any]]) -> Dict[str, Any]:
         """
         规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
 
         参数:
             all_plan_info: 所有计划信息
-            cycle_timers: 计时器字典
+            running_memorys: 回忆信息
         """
 
         action = "no_reply"  # 默认动作
@@ -169,6 +168,7 @@ class ActionPlanner:
                 current_available_actions=current_available_actions,  # <-- Pass determined actions
                 cycle_info=cycle_info,  # <-- Pass cycle info
                 extra_info=extra_info,
+                running_memorys=running_memorys,
             )
 
             # --- 调用 LLM (普通文本生成) ---
@@ -259,10 +259,22 @@ class ActionPlanner:
         current_available_actions: Dict[str, ActionInfo],
         cycle_info: Optional[str],
         extra_info: list[str],
+        running_memorys: List[Dict[str, Any]],
     ) -> str:
         """构建 Planner LLM 的提示词 (获取模板并填充数据)"""
         try:
-            # --- Determine chat context ---
+            
+            memory_str = ""
+            if global_config.focus_chat.parallel_processing:
+                memory_str = ""
+                if running_memorys:
+                    memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
+                    for running_memory in running_memorys:
+                        memory_str += f"{running_memory['topic']}: {running_memory['content']}\n"
+            
+            
+            
+            
             chat_context_description = "你现在正在一个群聊中"
             chat_target_name = None  # Only relevant for private
             if not is_group_chat and chat_target_info:
@@ -324,6 +336,7 @@ class ActionPlanner:
             planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
             prompt = planner_prompt_template.format(
                 self_info_block=self_info_block,
+                memory_str=memory_str,
                 # bot_name=global_config.bot.nickname,
                 prompt_personality=personality_block,
                 chat_context_description=chat_context_description,