class EmbeddingService:
	def __init__(self):
		...
		self.mode = 'api' # 'api' or 'local'

	def _get_embedding_cache(self):
		if(self.model == 'api'):
			# api
		else:
			# local

from abc import ABC, abstractmethod

class BaseEmbeddingService(ABC):
	pass

class BaseEmbeddingService(ABC):
	def __init__(self):
        self.embedding_cache: Dict[str, Dict[str, np.ndarray]] = {}
        self._get_embedding_cache()

	@abstractmethod
    def _get_embedding_cache(self) -> None:
        """获取嵌入缓存,子类必须实现"""
        pass
	...

class LocalEmbeddingService(BaseEmbeddingService): # 继承自基类
	def __init__(self, model_name: Optional[str] = None):
        super().__init__()
        self.local_models = {}
        self.current_model = None
        self.selected_model = model_name or Config().models.default_model
        if self.is_model_downloaded(self.selected_model):
            try:
                self._load_model(self.selected_model)
            except Exception as e:
                print(f"模型加载失败: {str(e)}")

	def _get_embedding_cache(self) -> None: # 重写抽象方法
        """获取本地模型的嵌入缓存"""
        if not self.selected_model:
            return
        cache_file = Config().get_absolute_cache_file().replace('.pkl', f'_{self.selected_model}.pkl')
        verify_folder(cache_file)
        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as f:
                self.embedding_cache = pickle.load(f)
	...