修复硬编码错误

src/config/api_ada_configs.py

@@ -88,10 +88,10 @@ class ModelInfo(ValidatedConfigBase):
 class TaskConfig(ValidatedConfigBase):
     """任务配置类"""
 
-    model_list: List[str] = Field(default_factory=list, description="任务使用的模型列表")
-    max_tokens: int = Field(default=1024, ge=1, le=100000, description="任务最大输出token数")
-    temperature: float = Field(default=0.3, ge=0.0, le=2.0, description="模型温度")
-    concurrency_count: int = Field(default=1, ge=1, le=10, description="并发请求数量，默认为1（不并发）")
+    model_list: List[str] = Field(..., description="任务使用的模型列表")
+    max_tokens: int = Field(default=None, ge=1, le=100000, description="任务最大输出token数")
+    temperature: float = Field(default=None, ge=0.0, le=2.0, description="模型温度")
+    concurrency_count: int = Field(default=None, ge=1, le=10, description="并发请求数量")
 
     @field_validator('model_list')
     @classmethod
@@ -103,22 +103,30 @@ class TaskConfig(ValidatedConfigBase):
             raise ValueError("模型列表中不能有重复的模型")
         return v
 
-    @field_validator('max_tokens')
-    @classmethod
-    def validate_max_tokens(cls, v):
-        """验证最大token数"""
-        if v <= 0:
-            raise ValueError("最大token数必须大于0")
-        if v > 100000:
-            raise ValueError("最大token数不能超过100000")
-        return v
-
 
 class ModelTaskConfig(ValidatedConfigBase):
     """模型配置类"""
 
+    # 必需配置项
     utils: TaskConfig = Field(..., description="组件模型配置")
+    utils_small: TaskConfig = Field(..., description="组件小模型配置")
+    replyer_1: TaskConfig = Field(..., description="normal_chat首要回复模型模型配置")
+    replyer_2: TaskConfig = Field(..., description="normal_chat次要回复模型配置")
+    maizone: TaskConfig = Field(..., description="maizone专用模型")
+    emotion: TaskConfig = Field(..., description="情绪模型配置")
+    vlm: TaskConfig = Field(..., description="视觉语言模型配置")
+    voice: TaskConfig = Field(..., description="语音识别模型配置")
+    tool_use: TaskConfig = Field(..., description="专注工具使用模型配置")
+    planner: TaskConfig = Field(..., description="规划模型配置")
+    embedding: TaskConfig = Field(..., description="嵌入模型配置")
+    lpmm_entity_extract: TaskConfig = Field(..., description="LPMM实体提取模型配置")
+    lpmm_rdf_build: TaskConfig = Field(..., description="LPMM RDF构建模型配置")
+    lpmm_qa: TaskConfig = Field(..., description="LPMM问答模型配置")
+    schedule_generator: TaskConfig = Field(..., description="日程生成模型配置")
+    emoji_vlm: TaskConfig = Field(..., description="表情包识别模型配置")
+    anti_injection: TaskConfig = Field(..., description="反注入检测专用模型配置")
     
+<<<<<<< Updated upstream
     # 可选配置项（有默认值） 
     utils_small: TaskConfig = Field(
         default_factory=lambda: TaskConfig(
@@ -240,37 +248,87 @@ class ModelTaskConfig(ValidatedConfigBase):
         ),
         description="日程生成模型配置"
     )
+=======
+    # 处理配置文件中命名不一致的问题
+    utils_video: TaskConfig = Field(..., description="视频分析模型配置（兼容配置文件中的命名）")
+>>>>>>> Stashed changes
 
-    # 可选配置项（有默认值）
-    video_analysis: TaskConfig = Field(
-        default_factory=lambda: TaskConfig(
-            model_list=["qwen2.5-vl-72b"],
-            max_tokens=1500,
-            temperature=0.3
-        ),
-        description="视频分析模型配置"
-    )
-    emoji_vlm: TaskConfig = Field(
-        default_factory=lambda: TaskConfig(
-            model_list=["qwen2.5-vl-72b"],
-            max_tokens=800
-        ),
-        description="表情包识别模型配置"
-    )
-    anti_injection: TaskConfig = Field(
-        default_factory=lambda: TaskConfig(
-            model_list=["qwen2.5-vl-72b"],
-            max_tokens=200,
-            temperature=0.1
-        ),
-        description="反注入检测专用模型配置"
-    )
+    @property
+    def video_analysis(self) -> TaskConfig:
+        """视频分析模型配置（提供向后兼容的属性访问）"""
+        return self.utils_video
 
     def get_task(self, task_name: str) -> TaskConfig:
         """获取指定任务的配置"""
+        # 处理向后兼容性：如果请求video_analysis，返回utils_video
+        if task_name == "video_analysis":
+            task_name = "utils_video"
+        
         if hasattr(self, task_name):
             config = getattr(self, task_name)
             if config is None:
                 raise ValueError(f"任务 '{task_name}' 未配置")
             return config
         raise ValueError(f"任务 '{task_name}' 未找到对应的配置")
+
+
+class APIAdapterConfig(ValidatedConfigBase):
+    """API Adapter配置类"""
+
+    models: List[ModelInfo] = Field(..., min_items=1, description="模型列表")
+    model_task_config: ModelTaskConfig = Field(..., description="模型任务配置")
+    api_providers: List[APIProvider] = Field(..., min_items=1, description="API提供商列表")
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self.api_providers_dict = {provider.name: provider for provider in self.api_providers}
+        self.models_dict = {model.name: model for model in self.models}
+
+    @field_validator('models')
+    @classmethod
+    def validate_models_list(cls, v):
+        """验证模型列表"""
+        if not v:
+            raise ValueError("模型列表不能为空，请在配置中设置有效的模型列表。")
+        
+        # 检查模型名称是否重复
+        model_names = [model.name for model in v]
+        if len(model_names) != len(set(model_names)):
+            raise ValueError("模型名称存在重复，请检查配置文件。")
+        
+        # 检查模型标识符是否有效
+        for model in v:
+            if not model.model_identifier:
+                raise ValueError(f"模型 '{model.name}' 的 model_identifier 不能为空")
+        
+        return v
+
+    @field_validator('api_providers')
+    @classmethod
+    def validate_api_providers_list(cls, v):
+        """验证API提供商列表"""
+        if not v:
+            raise ValueError("API提供商列表不能为空，请在配置中设置有效的API提供商列表。")
+        
+        # 检查API提供商名称是否重复
+        provider_names = [provider.name for provider in v]
+        if len(provider_names) != len(set(provider_names)):
+            raise ValueError("API提供商名称存在重复，请检查配置文件。")
+        
+        return v
+
+    def get_model_info(self, model_name: str) -> ModelInfo:
+        """根据模型名称获取模型信息"""
+        if not model_name:
+            raise ValueError("模型名称不能为空")
+        if model_name not in self.models_dict:
+            raise KeyError(f"模型 '{model_name}' 不存在")
+        return self.models_dict[model_name]
+
+    def get_provider(self, provider_name: str) -> APIProvider:
+        """根据提供商名称获取API提供商信息"""
+        if not provider_name:
+            raise ValueError("API提供商名称不能为空")
+        if provider_name not in self.api_providers_dict:
+            raise KeyError(f"API提供商 '{provider_name}' 不存在")
+        return self.api_providers_dict[provider_name]

src/config/config.py

@@ -487,10 +487,11 @@ def api_ada_load_config(config_path: str) -> APIAdapterConfig:
     with open(config_path, "r", encoding="utf-8") as f:
         config_data = tomlkit.load(f)
 
-    # 创建APIAdapterConfig对象（各个配置类会自动进行 Pydantic 验证）
+    config_dict = dict(config_data)
+
     try:
         logger.info("正在解析和验证API适配器配置文件...")
-        config = APIAdapterConfig.from_dict(config_data)
+        config = APIAdapterConfig.from_dict(config_dict)
         logger.info("API适配器配置文件解析和验证完成")
         return config
     except Exception as e: