🤖 自动格式化代码 [skip ci]

src/chat/focus_chat/heartFC_chat.py

@@ -28,7 +28,6 @@ from src.chat.focus_chat.planners.action_manager import ActionManager
 from src.config.config import global_config
 from src.chat.focus_chat.hfc_performance_logger import HFCPerformanceLogger
 from src.chat.focus_chat.hfc_version_manager import get_hfc_version
-from src.chat.focus_chat.info.structured_info import StructuredInfo
 from src.person_info.relationship_builder_manager import relationship_builder_manager
 
 
@@ -218,8 +217,6 @@ 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")
@@ -400,8 +397,6 @@ 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, "
@@ -560,8 +555,6 @@ 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

@@ -163,14 +163,7 @@ class DefaultReplyer:
 
         self.heart_fc_sender = HeartFCSender()
         self.memory_activator = MemoryActivator()
-        self.tool_executor = ToolExecutor(
+        self.tool_executor = ToolExecutor(chat_id=self.chat_stream.stream_id, enable_cache=True, cache_ttl=3)
-            chat_id=self.chat_stream.stream_id,
-            enable_cache=True,
-            cache_ttl=3
-        )
-
-        
-        
 
     def _select_weighted_model_config(self) -> Dict[str, Any]:
         """使用加权随机选择来挑选一个模型配置"""
@@ -425,10 +418,7 @@ 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

@@ -2,7 +2,6 @@ from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config
 import time
 from src.common.logger import get_logger
-from src.individuality.individuality import get_individuality
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.tools.tool_use import ToolUser
 from src.chat.utils.json_utils import process_llm_tool_calls
@@ -60,11 +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
 
@@ -114,10 +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:
@@ -219,6 +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()
@@ -257,11 +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):
@@ -290,10 +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
 
@@ -306,10 +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}")
 
@@ -361,7 +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):