"""Compute doc embeddings using a HuggingFace transformer model.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        texts = [t.replace("\n", " ") for t in texts]
        embeddings = self.client.encode(texts, **self.encode_kwargs)
        return embeddings.tolist()
[docs]    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace transformer model.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        text = text.replace("\n", " ")
        embedding = self.client.encode(
            self.query_instruction + text, **self.encode_kwargs
        )
        return embedding.tolist()