feat: 模型请求降级

src/llm_models/model_client/openai_client.py

@@ -530,6 +530,11 @@ class OpenaiClient(BaseClient):
         except APIStatusError as e:
             # 重封装APIError为RespNotOkException
             raise RespNotOkException(e.status_code) from e
+        except Exception as e:
+            # 添加通用异常处理和日志记录
+            logger.error(f"获取嵌入时发生未知错误: {str(e)}")
+            logger.error(f"错误类型: {type(e)}")
+            raise
 
         response = APIResponse()
 

src/llm_models/utils_model.py

@@ -518,7 +518,8 @@ class LLMRequest:
                 wait_interval, compressed_messages = self._default_exception_handler(
                     e,
                     self.task_name,
-                    model_name=model_info.name,
+                    model_info=model_info,
+                    api_provider=api_provider,
                     remain_try=retry_remain,
                     retry_interval=api_provider.retry_interval,
                     messages=(message_list, compressed_messages is not None) if message_list else None,
@@ -541,7 +542,8 @@ class LLMRequest:
         self,
         e: Exception,
         task_name: str,
-        model_name: str,
+        model_info: ModelInfo,
+        api_provider: APIProvider,
         remain_try: int,
         retry_interval: int = 10,
         messages: Tuple[List[Message], bool] | None = None,
@@ -551,13 +553,15 @@ class LLMRequest:
         Args:
             e (Exception): 异常对象
             task_name (str): 任务名称
-            model_name (str): 模型名称
+            model_info (ModelInfo): 模型信息
+            api_provider (APIProvider): API提供商
             remain_try (int): 剩余尝试次数
             retry_interval (int): 重试间隔
             messages (tuple[list[Message], bool] | None): (消息列表, 是否已压缩过)
         Returns:
             (等待间隔（如果为0则不等待，为-1则不再请求该模型）, 新的消息列表（适用于压缩消息）)
         """
+        model_name = model_info.name
 
         if isinstance(e, NetworkConnectionError):  # 网络连接错误
             return self._check_retry(
@@ -573,7 +577,8 @@ class LLMRequest:
             return self._handle_resp_not_ok(
                 e,
                 task_name,
-                model_name,
+                model_info,
+                api_provider,
                 remain_try,
                 retry_interval,
                 messages,
@@ -624,7 +629,8 @@ class LLMRequest:
         self,
         e: RespNotOkException,
         task_name: str,
-        model_name: str,
+        model_info: ModelInfo,
+        api_provider: APIProvider,
         remain_try: int,
         retry_interval: int = 10,
         messages: tuple[list[Message], bool] | None = None,
@@ -634,7 +640,8 @@ class LLMRequest:
         Args:
             e (RespNotOkException): 响应错误异常对象
             task_name (str): 任务名称
-            model_name (str): 模型名称
+            model_info (ModelInfo): 模型信息
+            api_provider (APIProvider): API提供商
             remain_try (int): 剩余尝试次数
             retry_interval (int): 重试间隔
             messages (tuple[list[Message], bool] | None): (消息列表, 是否已压缩过)
@@ -643,6 +650,23 @@ class LLMRequest:
         """
         # 响应错误
         if e.status_code in [400, 401, 402, 403, 404]:
+            model_name = model_info.name
+            if (
+                e.status_code == 403
+                and model_name.startswith("Pro/deepseek-ai")
+                and api_provider.base_url == "https://api.siliconflow.cn/v1/"
+            ):
+                old_model_name = model_name
+                new_model_name = model_name[4:]
+                model_info.name = new_model_name
+                logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {new_model_name}")
+                # 更新任务配置中的模型列表
+                for i, m_name in enumerate(self.model_for_task.model_list):
+                    if m_name == old_model_name:
+                        self.model_for_task.model_list[i] = new_model_name
+                        logger.warning(f"将任务 {self.task_name} 的模型列表中的 {old_model_name} 临时降级至 {new_model_name}")
+                        break
+                return 0, None # 立即重试
             # 客户端错误
             logger.warning(
                 f"任务-'{task_name}' 模型-'{model_name}': 请求失败，错误代码-{e.status_code}，错误信息-{e.message}"