async def _aget_embedding(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.

        """
        try:
            import httpx
        except ImportError:
            raise ImportError(
                "The httpx library is required to use the async version of "
                "this function. Install it with `pip install httpx`."
            )

        try:
            async with httpx.AsyncClient() as client:
                response = await client.post(
                    "https://api.llmrails.com/v1/embeddings",
                    headers={"X-API-KEY": self.api_key},
                    json={"input": [text], "model": self.model_id},
                )

                response.raise_for_status()

            return response.json()["data"][0]["embedding"]

        except httpx._exceptions.HTTPError as e:
            logger.error(f"Error while embedding text {e}.")
            raise ValueError(f"Unable to embed given text {e}")

        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.

        
llama-index-integrations/embeddings/llama-index-embeddings-llm-rails/llama_index/embeddings/llm_rails/base.py