texts = list(map(lambda x: x.replace("\n", " "), texts))
        _model_kwargs = self.model_kwargs or {}
        _endpoint_kwargs = self.endpoint_kwargs or {}
        body = self.content_handler.transform_input(texts, _model_kwargs)
        content_type = self.content_handler.content_type
        accepts = self.content_handler.accepts
        # send request
        try:
            response = self.client.invoke_endpoint(
                EndpointName=self.endpoint_name,
                Body=body,
                ContentType=content_type,
                Accept=accepts,
                **_endpoint_kwargs,
            )
        except Exception as e:
            raise ValueError(f"Error raised by inference endpoint: {e}")
        return self.content_handler.transform_output(response["Body"])
[docs]    def embed_documents(
        self, texts: List[str], chunk_size: int = 64
    ) -> List[List[float]]:
        """Compute doc embeddings using a SageMaker Inference Endpoint.
        Args:
            texts: The list of texts to embed.
            chunk_size: The chunk size defines how many input texts will
                be grouped together as request. If None, will use the
                chunk size specified by the class.
        Returns:
            List of embeddings, one for each text.
        """
        results = []
        _chunk_size = len(texts) if chunk_size > len(texts) else chunk_size
        for i in range(0, len(texts), _chunk_size):
            response = self._embedding_func(texts[i : i + _chunk_size])
            results.extend(response)
        return results
[docs]    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a SageMaker inference endpoint.
        Args:
            text: The text to embed.