random qa

src/chat/focus_chat/expressors/default_expressor.py

@@ -181,11 +181,6 @@ class DefaultExpressor:
         (已整合原 HeartFCGenerator 的功能)
         """
         try:
-            # 1. 获取情绪影响因子并调整模型温度
-            # arousal_multiplier = mood_manager.get_arousal_multiplier()
-            # current_temp = float(global_config.model.normal["temp"]) * arousal_multiplier
-            # self.express_model.params["temperature"] = current_temp  # 动态调整温度
-
             # --- Determine sender_name for private chat ---
             sender_name_for_prompt = "某人"  # Default for group or if info unavailable
             if not self.is_group_chat and self.chat_target_info:

src/chat/focus_chat/info_processors/tool_processor.py

@@ -48,11 +48,13 @@ class ToolProcessor(BaseProcessor):
         self.structured_info = []
 
     async def process_info(
-        self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None, *infos
-    ) -> List[dict]:
+        self, observations: Optional[List[Observation]] = None, running_memories: Optional[List[Dict]] = None, *infos
+    ) -> List[StructuredInfo]:
         """处理信息对象
 
         Args:
+            observations: 可选的观察列表，包含ChattingObservation和StructureObservation类型
+            running_memories: 可选的运行时记忆列表，包含字典类型的记忆信息
             *infos: 可变数量的InfoBase类型的信息对象
 
         Returns:
@@ -60,15 +62,15 @@ class ToolProcessor(BaseProcessor):
         """
 
         working_infos = []
+        result = []
 
         if observations:
             for observation in observations:
                 if isinstance(observation, ChattingObservation):
-                    result, used_tools, prompt = await self.execute_tools(observation, running_memorys)
+                    result, used_tools, prompt = await self.execute_tools(observation, running_memories)
 
-            # 更新WorkingObservation中的结构化信息
             logger.debug(f"工具调用结果: {result}")
-
+            # 更新WorkingObservation中的结构化信息
             for observation in observations:
                 if isinstance(observation, StructureObservation):
                     for structured_info in result:
@@ -81,12 +83,7 @@ class ToolProcessor(BaseProcessor):
         structured_info = StructuredInfo()
         if working_infos:
             for working_info in working_infos:
-                # print(f"working_info: {working_info}")
-                # print(f"working_info.get('type'): {working_info.get('type')}")
-                # print(f"working_info.get('content'): {working_info.get('content')}")
                 structured_info.set_info(key=working_info.get("type"), value=working_info.get("content"))
-                # info = structured_info.get_processed_info()
-                # print(f"info: {info}")
 
         return [structured_info]
 

src/chat/focus_chat/planners/modify_actions.py

@@ -198,9 +198,7 @@ class ActionModifier:
 
         Args:
             actions_with_info: 带完整信息的动作字典
-            observed_messages_str: 观察到的聊天消息
-            chat_context: 聊天上下文信息
-            extra_context: 额外的上下文信息
+            chat_content: 聊天内容
 
         Returns:
             Dict[str, Any]: 过滤后激活的actions字典
@@ -320,9 +318,7 @@ class ActionModifier:
 
         Args:
             llm_judge_actions: 需要LLM判定的actions
-            observed_messages_str: 观察到的聊天消息
-            chat_context: 聊天上下文
-            extra_context: 额外上下文
+            chat_content: 聊天内容
 
         Returns:
             Dict[str, bool]: action名称到激活结果的映射

src/chat/focus_chat/planners/planner_simple.py

@@ -217,7 +217,6 @@ class ActionPlanner(BasePlanner):
 
                     # 提取决策，提供默认值
                     extracted_action = parsed_json.get("action", "no_reply")
-                    # extracted_reasoning = parsed_json.get("reasoning", "LLM未提供理由")
                     extracted_reasoning = ""
 
                     # 将所有其他属性添加到action_data