fix typing of utils_model.py

src/llm_models/utils_model.py

@@ -4,7 +4,7 @@ import time
 
 from enum import Enum
 from rich.traceback import install
-from typing import Tuple, List, Dict, Optional, Callable, Any
+from typing import Tuple, List, Dict, Optional, Callable, Any, Set
 import traceback
 
 from src.common.logger import get_logger
@@ -82,9 +82,7 @@ class LLMRequest:
             message_builder = MessageBuilder()
             message_builder.add_text_content(prompt)
             message_builder.add_image_content(
-                image_base64=image_base64,
-                image_format=image_format,
-                support_formats=client.get_support_image_formats()
+                image_base64=image_base64, image_format=image_format, support_formats=client.get_support_image_formats()
             )
             return [message_builder.build()]
 
@@ -145,7 +143,7 @@ class LLMRequest:
             (Tuple[str, str, str, Optional[List[ToolCall]]]): 响应内容、推理内容、模型名称、工具调用列表
         """
         start_time = time.time()
-        
+
         def message_factory(client: BaseClient) -> List[Message]:
             message_builder = MessageBuilder()
             message_builder.add_text_content(prompt)
@@ -177,7 +175,7 @@ class LLMRequest:
                 endpoint="/chat/completions",
                 time_cost=time.time() - start_time,
             )
-        return content, (reasoning_content, model_info.name, tool_calls)
+        return content or "", (reasoning_content, model_info.name, tool_calls)
 
     async def get_embedding(self, embedding_input: str) -> Tuple[List[float], str]:
         """
@@ -206,7 +204,7 @@ class LLMRequest:
             raise RuntimeError("获取embedding失败")
         return embedding, model_info.name
 
-    def _select_model(self, exclude_models: set = None) -> Tuple[ModelInfo, APIProvider, BaseClient]:
+    def _select_model(self, exclude_models: Optional[Set[str]] = None) -> Tuple[ModelInfo, APIProvider, BaseClient]:
         """
         根据总tokens和惩罚值选择的模型
         """
@@ -224,7 +222,7 @@ class LLMRequest:
         )
         model_info = model_config.get_model_info(least_used_model_name)
         api_provider = model_config.get_provider(model_info.api_provider)
-        force_new_client = (self.request_type == "embedding")
+        force_new_client = self.request_type == "embedding"
         client = client_registry.get_client_class_instance(api_provider, force_new=force_new_client)
         logger.debug(f"选择请求模型: {model_info.name}")
         total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
@@ -246,13 +244,13 @@ class LLMRequest:
         max_tokens: Optional[int],
         embedding_input: str | None,
         audio_base64: str | None,
-        compressed_messages: Optional[List[Message]] = None,
     ) -> APIResponse:
         """
         在单个模型上执行请求，包含针对临时错误的重试逻辑。
         如果成功，返回APIResponse。如果失败（重试耗尽或硬错误），则抛出ModelAttemptFailed异常。
         """
         retry_remain = api_provider.max_retry
+        compressed_messages: Optional[List[Message]] = None
 
         while retry_remain > 0:
             try:
@@ -299,7 +297,9 @@ class LLMRequest:
                         logger.error(f"模型 '{model_info.name}' 在遇到 {e.status_code} 错误并用尽重试次数后仍然失败。")
                         raise ModelAttemptFailed(f"模型 '{model_info.name}' 重试耗尽", original_exception=e) from e
 
-                    logger.warning(f"模型 '{model_info.name}' 遇到可重试的HTTP错误: {str(e)}。剩余重试次数: {retry_remain}")
+                    logger.warning(
+                        f"模型 '{model_info.name}' 遇到可重试的HTTP错误: {str(e)}。剩余重试次数: {retry_remain}"
+                    )
                     await asyncio.sleep(api_provider.retry_interval)
                     continue
 
@@ -315,8 +315,8 @@ class LLMRequest:
                 raise ModelAttemptFailed(f"模型 '{model_info.name}' 遇到硬错误", original_exception=e) from e
 
             except Exception as e:
-                logger.error(traceback.format_exc()) 
-                
+                logger.error(traceback.format_exc())
+
                 logger.warning(f"模型 '{model_info.name}' 遇到未知的不可重试错误: {str(e)}")
                 raise ModelAttemptFailed(f"模型 '{model_info.name}' 遇到硬错误", original_exception=e) from e
 
@@ -338,12 +338,11 @@ class LLMRequest:
         """
         调度器函数，负责模型选择、故障切换。
         """
-        failed_models_this_request = set()
+        failed_models_this_request: Set[str] = set()
         max_attempts = len(self.model_for_task.model_list)
         last_exception: Optional[Exception] = None
-        compressed_messages: Optional[List[Message]] = None
 
-        for _attempt in range(max_attempts):
+        for _ in range(max_attempts):
             model_info, api_provider, client = self._select_model(exclude_models=failed_models_this_request)
 
             message_list = []
@@ -352,7 +351,10 @@ class LLMRequest:
 
             try:
                 response = await self._attempt_request_on_model(
-                    model_info, api_provider, client, request_type,
+                    model_info,
+                    api_provider,
+                    client,
+                    request_type,
                     message_list=message_list,
                     tool_options=tool_options,
                     response_format=response_format,
@@ -362,7 +364,6 @@ class LLMRequest:
                     max_tokens=max_tokens,
                     embedding_input=embedding_input,
                     audio_base64=audio_base64,
-                    compressed_messages=compressed_messages,
                 )
                 return response, model_info
 
@@ -430,4 +431,4 @@ class LLMRequest:
         match = re.search(r"(?:<think>)?(.*?)</think>", content, re.DOTALL)
         content = re.sub(r"(?:<think>)?.*?</think>", "", content, flags=re.DOTALL, count=1).strip()
         reasoning = match[1].strip() if match else ""
-        return content, reasoning
+        return content, reasoning