list.
        """
        response = self.client.infer_trained_model(
            model_id=self.model_id, docs=[{self.input_field: text} for text in texts]
        )
        embeddings = [doc["predicted_value"] for doc in response["inference_results"]]
        return embeddings
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """
        Generate embeddings for a list of documents.
        Args:
            texts (List[str]): A list of document text strings to generate embeddings
                for.
        Returns:
            List[List[float]]: A list of embeddings, one for each document in the input
                list.
        """
        return self._embedding_func(texts)
[docs]    def embed_query(self, text: str) -> List[float]:
        """
        Generate an embedding for a single query text.
        Args:
            text (str): The query text to generate an embedding for.
        Returns:
            List[float]: The embedding for the input query text.
        """
        return self._embedding_func([text])[0]