backend/config.py

import logging
import os


def process_config(config):

    if "CLIENT_TOKEN" not in os.environ:
        raise ValueError("Please set the CLIENT_TOKEN environment variable.")
    if "GUIDE_TOKEN" not in os.environ:
        raise ValueError("Please set the GUIDE_TOKEN environment variable.")
    if "CLASSIFIER_TOKEN" not in os.environ:
        raise ValueError("Please set the CLASSIFIER_TOKEN environment variable.")
    
    client_kwargs = {}
    if "client_llm" in config:
        if "model_id" in config["client_llm"]:
            client_kwargs["model_id"] = config["client_llm"]["model_id"]
        else:
            raise ValueError("config.yaml is missing client model_id.")
        if "url" in config["client_llm"]:
            client_kwargs["inference_server_url"] = config["client_llm"]["url"]
        else:
            raise ValueError("config.yaml is missing client url.")
        if "backend" in config["client_llm"]:
            client_kwargs["llm_backend"] = config["client_llm"]["backend"]
        else:
            raise ValueError("config.yaml is missing client backend.")
        client_kwargs["api_key"] = os.getenv("CLIENT_TOKEN")
        client_kwargs["temperature"] = config["client_llm"].get("temperature",.6)
        client_kwargs["max_tokens"] = config["client_llm"].get("max_tokens",800)
    else:
        raise ValueError("config.yaml is missing client_llm settings.")
    
    guide_kwargs = {"classifier_kwargs": {}}
    if "expert_llm" in config:
        if "model_id" in config["expert_llm"]:
            guide_kwargs["expert_model"] = config["expert_llm"]["model_id"]
        else:
            raise ValueError("config.yaml is missing expert model_id.")
        if "url" in config["expert_llm"]:
            guide_kwargs["inference_server_url"] = config["expert_llm"]["url"]
        else:
            raise ValueError("config.yaml is missing expert url.")
        if "backend" in config["expert_llm"]:
            guide_kwargs["llm_backend"] = config["expert_llm"]["backend"]
        else:
            raise ValueError("config.yaml is missing expert backend.")
        guide_kwargs["api_key"] = os.getenv("GUIDE_TOKEN")
    else:
        raise ValueError("config.yaml is missing expert_llm settings.")

    if "classifier_llm" in config:
        if "model_id" in config["classifier_llm"]:
            guide_kwargs["classifier_kwargs"]["model_id"] = config["classifier_llm"]["model_id"]
        else:
            raise ValueError("config.yaml is missing classifier model_id.")
        if "url" in config["classifier_llm"]:
            guide_kwargs["classifier_kwargs"]["inference_server_url"] = config["classifier_llm"]["url"]
        else:
            raise ValueError("config.yaml is missing classifier url.")
        if "batch_size" in config["classifier_llm"]:
            guide_kwargs["classifier_kwargs"]["batch_size"] = int(config["classifier_llm"]["batch_size"])
        else:
            raise ValueError("config.yaml is missing classifier batch_size.")
        guide_kwargs["classifier_kwargs"]["api_key"] = os.getenv("CLASSIFIER_TOKEN")  # classifier api key
    else:
        raise ValueError("config.yaml is missing classifier_llm settings.")

    return client_kwargs, guide_kwargs