def __init__(
        self,
        model_name: str,
        base_url: str = DEFAULT_URL,
        embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
        timeout: float = 60.0,
        callback_manager: Optional[CallbackManager] = None,
        auth_token: Optional[Union[str, Callable[[str], str]]] = None,
    ):
        """
        Initializes the TextEmbedEmbedding object with specified parameters.

        Args:
            model_name (str): The name of the model to be used for embeddings.
            base_url (str): The base URL of the embedding service.
            embed_batch_size (int): The batch size for embedding requests.
            timeout (float): Timeout for requests.
            callback_manager (Optional[CallbackManager]): Manager for handling callbacks.
            auth_token (Optional[Union[str, Callable[[str], str]]]): Authentication token or function for generating it.

        """
        super().__init__(
            base_url=base_url,
            model_name=model_name,
            embed_batch_size=embed_batch_size,
            timeout=timeout,
            callback_manager=callback_manager,
            auth_token=auth_token,
        )

        Initializes the TextEmbedEmbedding object with specified parameters.

        Args:
            model_name (str): The name of the model to be used for embeddings.
            base_url (str): The base URL of the embedding service.
            embed_batch_size (int): The batch size for embedding requests.
            timeout (float): Timeout for requests.
            callback_manager (Optional[CallbackManager]): Manager for handling callbacks.
            auth_token (Optional[Union[str, Callable[[str], str]]]): Authentication token or function for generating it.

        
llama-index-integrations/embeddings/llama-index-embeddings-textembed/llama_index/embeddings/textembed/base.py