def get_embeddings(
    api_key: str,
    api_base: str,
    model_name: str,
    input: List[str],
    timeout: int = 60,
    **kwargs: Any,
) -> List[List[float]]:
    """
    Retrieve embeddings for a given list of input strings using the specified model.

    Args:
        api_key (str): The API key for authentication.
        api_base (str): The base URL of the LiteLLM proxy server.
        model_name (str): The name of the model to use for generating embeddings.
        input (List[str]): A list of input strings for which embeddings are to be generated.
        timeout (float): The timeout value for the API call, default 60 secs.
        **kwargs (Any): Additional keyword arguments to be passed to the embedding function.

    Returns:
        List[List[float]]: A list of embeddings, where each embedding corresponds to an input string.

    """
    response = embedding(
        api_key=api_key,
        api_base=api_base,
        model=model_name,
        input=input,
        timeout=timeout,
        **kwargs,
    )
    return [result["embedding"] for result in response.data]

    Retrieve embeddings for a given list of input strings using the specified model.

    Args:
        api_key (str): The API key for authentication.
        api_base (str): The base URL of the LiteLLM proxy server.
        model_name (str): The name of the model to use for generating embeddings.
        input (List[str]): A list of input strings for which embeddings are to be generated.
        timeout (float): The timeout value for the API call, default 60 secs.
        **kwargs (Any): Additional keyword arguments to be passed to the embedding function.

    Returns:
        List[List[float]]: A list of embeddings, where each embedding corresponds to an input string.

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