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."""
    multi_process: bool = False
    """Run encode() on multiple GPUs."""
    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
        )
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a HuggingFace transformer model.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        import sentence_transformers
        texts = list(map(lambda x: x.replace("\n", " "), texts))
        if self.multi_process:
            pool = self.client.start_multi_process_pool()
            embeddings = self.client.encode_multi_process(texts, pool)
            sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
        else:
            embeddings = self.client.encode(texts, **self.encode_kwargs)
        return embeddings.tolist()