Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev

src/chat/focus_chat/heartFC_chat.py

@@ -215,8 +215,6 @@ class HeartFChatting:
         else:
             logger.warning(f"{self.log_prefix} 没有注册任何处理器。这可能是由于配置错误或所有处理器都被禁用了。")
 
-
-
     async def start(self):
         """检查是否需要启动主循环，如果未激活则启动。"""
         logger.debug(f"{self.log_prefix} 开始启动 HeartFChatting")
@@ -397,8 +395,6 @@ class HeartFChatting:
                         ("\n前处理器耗时: " + "; ".join(processor_time_strings)) if processor_time_strings else ""
                     )
 
-
-
                     logger.info(
                         f"{self.log_prefix} 第{self._current_cycle_detail.cycle_id}次思考,"
                         f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒, "
@@ -557,8 +553,6 @@ class HeartFChatting:
 
         return all_plan_info, processor_time_costs
 
-
-
     async def _observe_process_plan_action_loop(self, cycle_timers: dict, thinking_id: str) -> dict:
         try:
             loop_start_time = time.time()

src/chat/replyer/default_generator.py

@@ -429,10 +429,7 @@ class DefaultReplyer:
         try:
             # 使用工具执行器获取信息
             tool_results = await self.tool_executor.execute_from_chat_message(
-                sender = sender,
+                sender=sender, target_message=text, chat_history=chat_history, return_details=False
-                target_message=text,
-                chat_history=chat_history,
-                return_details=False
             )
 
             if tool_results:

src/tools/tool_executor.py

@@ -59,11 +59,7 @@ class ToolExecutor:
         logger.info(f"{self.log_prefix}工具执行器初始化完成，缓存{'启用' if enable_cache else '禁用'}，TTL={cache_ttl}")
 
     async def execute_from_chat_message(
-        self, 
+        self, target_message: str, chat_history: list[str], sender: str, return_details: bool = False
-        target_message: str,
-        chat_history: list[str],
-        sender: str,
-        return_details: bool = False
     ) -> List[Dict] | Tuple[List[Dict], List[str], str]:
         """从聊天消息执行工具
 
@@ -113,10 +109,7 @@ class ToolExecutor:
         logger.debug(f"{self.log_prefix}开始LLM工具调用分析")
 
         # 调用LLM进行工具决策
-        response, other_info = await self.llm_model.generate_response_async(
+        response, other_info = await self.llm_model.generate_response_async(prompt=prompt, tools=tools)
-            prompt=prompt, 
-            tools=tools
-        )
 
         # 解析LLM响应
         if len(other_info) == 3:
@@ -218,6 +211,7 @@ class ToolExecutor:
             str: 缓存键
         """
         import hashlib
+
         # 使用消息内容和群聊状态生成唯一缓存键
         content = f"{target_message}_{chat_history}_{sender}"
         return hashlib.md5(content.encode()).hexdigest()
@@ -256,11 +250,7 @@ class ToolExecutor:
         if not self.enable_cache:
             return
 
-        self.tool_cache[cache_key] = {
+        self.tool_cache[cache_key] = {"result": result, "ttl": self.cache_ttl, "timestamp": time.time()}
-            "result": result,
-            "ttl": self.cache_ttl,
-            "timestamp": time.time()
-        }
         logger.debug(f"{self.log_prefix}设置缓存，TTL: {self.cache_ttl}")
 
     def _cleanup_expired_cache(self):
@@ -289,10 +279,7 @@ class ToolExecutor:
         return [tool.get("function", {}).get("name", "unknown") for tool in tools]
 
     async def execute_specific_tool(
-        self, 
+        self, tool_name: str, tool_args: Dict, validate_args: bool = True
-        tool_name: str, 
-        tool_args: Dict,
-        validate_args: bool = True
     ) -> Optional[Dict]:
         """直接执行指定工具
 
@@ -305,10 +292,7 @@ class ToolExecutor:
             Optional[Dict]: 工具执行结果，失败时返回None
         """
         try:
-            tool_call = {
+            tool_call = {"name": tool_name, "arguments": tool_args}
-                "name": tool_name,
-                "arguments": tool_args
-            }
 
             logger.info(f"{self.log_prefix}直接执行工具: {tool_name}")
 
@@ -360,7 +344,7 @@ class ToolExecutor:
             "enabled": True,
             "cache_count": total_count,
             "cache_ttl": self.cache_ttl,
-            "ttl_distribution": ttl_distribution
+            "ttl_distribution": ttl_distribution,
         }
 
     def set_cache_config(self, enable_cache: bool = None, cache_ttl: int = None):