ruff

src/llm_models/model_client/base_client.py

@@ -189,11 +189,11 @@ class ClientRegistry:
             bool: 事件循环是否变化
         """
         current_loop_id = self._get_current_loop_id()
-        
+
         # 如果没有缓存的循环ID，说明是首次创建
         if provider_name not in self._event_loop_cache:
             return False
-        
+
         # 比较当前循环ID与缓存的循环ID
         cached_loop_id = self._event_loop_cache[provider_name]
         return current_loop_id != cached_loop_id
@@ -208,7 +208,7 @@ class ClientRegistry:
             BaseClient: 注册的API客户端实例
         """
         provider_name = api_provider.name
-        
+
         # 如果强制创建新实例，直接创建不使用缓存
         if force_new:
             if client_class := self.client_registry.get(api_provider.client_type):
@@ -224,7 +224,7 @@ class ClientRegistry:
             # 事件循环已变化，需要重新创建实例
             logger.debug(f"检测到事件循环变化，为 {provider_name} 重新创建客户端实例")
             self._loop_change_count += 1
-            
+
             # 移除旧实例
             if provider_name in self.client_instance_cache:
                 del self.client_instance_cache[provider_name]
@@ -237,7 +237,7 @@ class ClientRegistry:
                 self._event_loop_cache[provider_name] = self._get_current_loop_id()
             else:
                 raise KeyError(f"'{api_provider.client_type}' 类型的 Client 未注册")
-        
+
         return self.client_instance_cache[provider_name]
 
     def get_cache_stats(self) -> dict:

src/llm_models/model_client/mcp_sse_client.py

@@ -50,14 +50,16 @@ def _convert_messages_to_mcp(messages: list[Message]) -> list[dict[str, Any]]:
             for item in message.content:
                 if isinstance(item, tuple):
                     # 图片内容
-                    content_parts.append({
-                        "type": "image",
-                        "source": {
-                            "type": "base64",
-                            "media_type": f"image/{item[0].lower()}",
-                            "data": item[1],
-                        },
-                    })
+                    content_parts.append(
+                        {
+                            "type": "image",
+                            "source": {
+                                "type": "base64",
+                                "media_type": f"image/{item[0].lower()}",
+                                "data": item[1],
+                            },
+                        }
+                    )
                 elif isinstance(item, str):
                     # 文本内容
                     content_parts.append({"type": "text", "text": item})
@@ -138,9 +140,7 @@ async def _parse_sse_stream(
         async with session.post(url, json=payload, headers=headers) as response:
             if response.status != 200:
                 error_text = await response.text()
-                raise RespNotOkException(
-                    response.status, f"MCP SSE请求失败: {error_text}"
-                )
+                raise RespNotOkException(response.status, f"MCP SSE请求失败: {error_text}")
 
             # 解析SSE流
             async for line in response.content:
@@ -258,10 +258,7 @@ async def _parse_sse_stream(
         response.reasoning_content = reasoning_buffer.getvalue()
 
     if tool_calls_buffer:
-        response.tool_calls = [
-            ToolCall(call_id, func_name, args)
-            for call_id, func_name, args in tool_calls_buffer
-        ]
+        response.tool_calls = [ToolCall(call_id, func_name, args) for call_id, func_name, args in tool_calls_buffer]
 
     # 关闭缓冲区
     content_buffer.close()
@@ -351,9 +348,7 @@ class MCPSSEClient(BaseClient):
         url = f"{self.api_provider.base_url}/v1/messages"
 
         try:
-            response, usage_record = await _parse_sse_stream(
-                session, url, payload, headers, interrupt_flag
-            )
+            response, usage_record = await _parse_sse_stream(session, url, payload, headers, interrupt_flag)
         except Exception as e:
             logger.error(f"MCP SSE请求失败: {e}")
             raise

src/llm_models/model_client/openai_client.py

@@ -378,7 +378,7 @@ class OpenaiClient(BaseClient):
     # 类级别的全局缓存：所有 OpenaiClient 实例共享
     _global_client_cache: dict[int, AsyncOpenAI] = {}
     """全局 AsyncOpenAI 客户端缓存：config_hash -> AsyncOpenAI 实例"""
-    
+
     def __init__(self, api_provider: APIProvider):
         super().__init__(api_provider)
         self._config_hash = self._calculate_config_hash()
@@ -396,33 +396,31 @@ class OpenaiClient(BaseClient):
     def _create_client(self) -> AsyncOpenAI:
         """
         获取或创建 OpenAI 客户端实例（全局缓存）
-        
+
         多个 OpenaiClient 实例如果配置相同（base_url + api_key + timeout），
         将共享同一个 AsyncOpenAI 客户端实例，最大化连接池复用。
         """
         # 检查全局缓存
         if self._config_hash in self._global_client_cache:
             return self._global_client_cache[self._config_hash]
-        
+
         # 创建新的 AsyncOpenAI 实例
         logger.debug(
-            f"创建新的 AsyncOpenAI 客户端实例 "
-            f"(base_url={self.api_provider.base_url}, "
-            f"config_hash={self._config_hash})"
+            f"创建新的 AsyncOpenAI 客户端实例 (base_url={self.api_provider.base_url}, config_hash={self._config_hash})"
         )
-        
+
         client = AsyncOpenAI(
             base_url=self.api_provider.base_url,
             api_key=self.api_provider.get_api_key(),
             max_retries=0,
             timeout=self.api_provider.timeout,
         )
-        
+
         # 存入全局缓存
         self._global_client_cache[self._config_hash] = client
-        
+
         return client
-    
+
     @classmethod
     def get_cache_stats(cls) -> dict:
         """获取全局缓存统计信息"""

src/llm_models/utils_model.py

@@ -280,7 +280,9 @@ class _PromptProcessor:
 这有助于我判断你的输出是否被截断。请不要在 `{self.end_marker}` 前后添加任何其他文字或标点。
 """
 
-    async def prepare_prompt(self, prompt: str, model_info: ModelInfo, api_provider: APIProvider, task_name: str) -> str:
+    async def prepare_prompt(
+        self, prompt: str, model_info: ModelInfo, api_provider: APIProvider, task_name: str
+    ) -> str:
         """
         为请求准备最终的提示词。