async def embeddings(
        self,
        input: Union[str, Sequence[AnyStr]] = "",
        payload: Optional[Dict[str, Any]] = None,
        headers: Optional[Dict[str, str]] = None,
    ) -> Union[Dict[str, Any], Iterator[Mapping[str, Any]]]:
        """
        Generate embeddings asynchronously by sending a request to the endpoint.

        Args:
            input (Union[str, Sequence[AnyStr]], optional): The input text or sequence of texts for which to generate embeddings.
                Defaults to "".
            payload (Optional[Dict[str, Any]], optional): Additional parameters to include in the request payload.
                Defaults to None.
            headers (Optional[Dict[str, str]], optional): HTTP headers to include in the request.
                Defaults to None.

        Returns:
            Union[Dict[str, Any], Iterator[Mapping[str, Any]]]: The server's response, typically including the generated embeddings.

        """
        logger.debug(f"Generating embeddings with input: {input}, payload: {payload}")
        payload = {**(payload or {}), "input": input}
        return await self._request(payload=payload, headers=headers)

        Generate embeddings asynchronously by sending a request to the endpoint.

        Args:
            input (Union[str, Sequence[AnyStr]], optional): The input text or sequence of texts for which to generate embeddings.
                Defaults to "".
            payload (Optional[Dict[str, Any]], optional): Additional parameters to include in the request payload.
                Defaults to None.
            headers (Optional[Dict[str, str]], optional): HTTP headers to include in the request.
                Defaults to None.

        Returns:
            Union[Dict[str, Any], Iterator[Mapping[str, Any]]]: The server's response, typically including the generated embeddings.

        
llama-index-integrations/embeddings/llama-index-embeddings-oci-data-science/llama_index/embeddings/oci_data_science/client.py