LLM backend signature not unified

[allenanie]

Currently LLM backend __init__ signature is not unified (this is a legacy backward compatibility issue mostly with AutoGen).

For three backends, the signatures are:

class AutoGenLLM(AbstractModel):
    def __init__(self, config_list: List = None, filter_dict: Dict = None, reset_freq: Union[int, None] = None) -> None

class LiteLLM(AbstractModel):
    def __init__(self, model: Union[str, None] = None, reset_freq: Union[int, None] = None,
                 cache=True) -> None:

class CustomLLM(AbstractModel):
    def __init__(self, model: Union[str, None] = None, reset_freq: Union[int, None] = None,
                 cache=True) -> None:

Essentially two of them allow user to set model, but one can't. It is true that with AutoGen, the model setup/configuration is from the config_list side -- but we could consider unifying the AutoGen interface by dynamically creating a config_list by grabbing the API key from environment variables, see this convenience function:

def auto_construct_oai_config_list_from_env() -> List:
    """
    Collect various API keys saved in the environment and return a format like:
    [{"model": "gpt-4", "api_key": xxx}, {"model": "claude-3.5-sonnet", "api_key": xxx}]

    Note this is a lazy function that defaults to gpt-40 and claude-3.5-sonnet.
    If you want to specify your own model, please provide an OAI_CONFIG_LIST in the environment or as a file
    """
    config_list = []
    if os.environ.get("OPENAI_API_KEY") is not None:
        config_list.append(
            {"model": "gpt-4o", "api_key": os.environ.get("OPENAI_API_KEY")}
        )
    if os.environ.get("ANTHROPIC_API_KEY") is not None:
        config_list.append(
            {
                "model": "claude-3-5-sonnet-latest",
                "api_key": os.environ.get("ANTHROPIC_API_KEY"),
            }
        )
    return config_list

If you approve this solution, I can implement this! Otherwise, we can stay with the current design.

[allenanie]

Ohhh I believe the current AutoGen model specification is through:

AutoGenLLM(filter_dict={"model": [model]})

Then if this is the case, a simple modification would work -- just change rewrite the logic for filter_dict to allow it to accept model instead.

[chinganc]

Sounds good. Let's make this change to make the API consistent.

[chinganc]

@allenanie Let's resolve this in 0.1.3.7 update. Can you push a fix there?

[allenanie]

Unified