Example:
        .. code-block:: python
            from langchain.embeddings import HuggingFaceBgeEmbeddings
            model_name = "BAAI/bge-large-en"
            model_kwargs = {'device': 'cpu'}
            encode_kwargs = {'normalize_embeddings': True}
            hf = HuggingFaceBgeEmbeddings(
                model_name=model_name,
                model_kwargs=model_kwargs,
                encode_kwargs=encode_kwargs
            )
    """
    client: Any  #: :meta private:
    model_name: str = DEFAULT_BGE_MODEL
    """Model name to use."""
    cache_folder: Optional[str] = None
    """Path to store models.
    Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Key word arguments to pass to the model."""
    encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Key word arguments to pass when calling the `encode` method of the model."""
    query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN
    """Instruction to use for embedding query."""
    def __init__(self, **kwargs: Any):
        """Initialize the sentence_transformer."""
        super().__init__(**kwargs)
        try:
            import sentence_transformers
        except ImportError as exc:
            raise ImportError(
                "Could not import sentence_transformers python package. "
                "Please install it with `pip install sentence_transformers`."
            ) from exc
        self.client = sentence_transformers.SentenceTransformer(
            self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
        )
        if "-zh" in self.model_name:
            self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
    class Config: