AutoGPT/autogpt/llm/utils/__init__.py

from __future__ import annotations

import functools
import time
from typing import List, Literal, Optional
from unittest.mock import patch

import openai
import openai.api_resources.abstract.engine_api_resource as engine_api_resource
import openai.util
from colorama import Fore, Style
from openai.error import APIError, RateLimitError
from openai.openai_object import OpenAIObject

from autogpt.config import Config
from autogpt.logs import logger

from ..api_manager import ApiManager
from ..base import ChatSequence, Message
from .token_counter import *


def metered(func):
    """Adds ApiManager metering to functions which make OpenAI API calls"""
    api_manager = ApiManager()

    openai_obj_processor = openai.util.convert_to_openai_object

    def update_usage_with_response(response: OpenAIObject):
        try:
            usage = response.usage
            logger.debug(f"Reported usage from call to model {response.model}: {usage}")
            api_manager.update_cost(
                response.usage.prompt_tokens,
                response.usage.completion_tokens if "completion_tokens" in usage else 0,
                response.model,
            )
        except Exception as err:
            logger.warn(f"Failed to update API costs: {err.__class__.__name__}: {err}")

    def metering_wrapper(*args, **kwargs):
        openai_obj = openai_obj_processor(*args, **kwargs)
        if isinstance(openai_obj, OpenAIObject) and "usage" in openai_obj:
            update_usage_with_response(openai_obj)
        return openai_obj

    def metered_func(*args, **kwargs):
        with patch.object(
            engine_api_resource.util,
            "convert_to_openai_object",
            side_effect=metering_wrapper,
        ):
            return func(*args, **kwargs)

    return metered_func


def retry_openai_api(
    num_retries: int = 10,
    backoff_base: float = 2.0,
    warn_user: bool = True,
):
    """Retry an OpenAI API call.

    Args:
        num_retries int: Number of retries. Defaults to 10.
        backoff_base float: Base for exponential backoff. Defaults to 2.
        warn_user bool: Whether to warn the user. Defaults to True.
    """
    retry_limit_msg = f"{Fore.RED}Error: " f"Reached rate limit, passing...{Fore.RESET}"
    api_key_error_msg = (
        f"Please double check that you have setup a "
        f"{Fore.CYAN + Style.BRIGHT}PAID{Style.RESET_ALL} OpenAI API Account. You can "
        f"read more here: {Fore.CYAN}https://docs.agpt.co/setup/#getting-an-api-key{Fore.RESET}"
    )
    backoff_msg = (
        f"{Fore.RED}Error: API Bad gateway. Waiting {{backoff}} seconds...{Fore.RESET}"
    )

    def _wrapper(func):
        @functools.wraps(func)
        def _wrapped(*args, **kwargs):
            user_warned = not warn_user
            num_attempts = num_retries + 1  # +1 for the first attempt
            for attempt in range(1, num_attempts + 1):
                try:
                    return func(*args, **kwargs)

                except RateLimitError:
                    if attempt == num_attempts:
                        raise

                    logger.debug(retry_limit_msg)
                    if not user_warned:
                        logger.double_check(api_key_error_msg)
                        user_warned = True

                except APIError as e:
                    if (e.http_status != 502) or (attempt == num_attempts):
                        raise

                backoff = backoff_base ** (attempt + 2)
                logger.debug(backoff_msg.format(backoff=backoff))
                time.sleep(backoff)

        return _wrapped

    return _wrapper


def call_ai_function(
    function: str, args: list, description: str, model: str | None = None
) -> str:
    """Call an AI function

    This is a magic function that can do anything with no-code. See
    https://github.com/Torantulino/AI-Functions for more info.

    Args:
        function (str): The function to call
        args (list): The arguments to pass to the function
        description (str): The description of the function
        model (str, optional): The model to use. Defaults to None.

    Returns:
        str: The response from the function
    """
    cfg = Config()
    if model is None:
        model = cfg.smart_llm_model
    # For each arg, if any are None, convert to "None":
    args = [str(arg) if arg is not None else "None" for arg in args]
    # parse args to comma separated string
    arg_str: str = ", ".join(args)

    prompt = ChatSequence.for_model(
        model,
        [
            Message(
                "system",
                f"You are now the following python function: ```# {description}"
                f"\n{function}```\n\nOnly respond with your `return` value.",
            ),
            Message("user", arg_str),
        ],
    )
    return create_chat_completion(prompt=prompt, temperature=0)


@metered
@retry_openai_api()
def create_text_completion(
    prompt: str,
    model: Optional[str],
    temperature: Optional[float],
    max_output_tokens: Optional[int],
) -> str:
    cfg = Config()
    if model is None:
        model = cfg.fast_llm_model
    if temperature is None:
        temperature = cfg.temperature

    if cfg.use_azure:
        kwargs = {"deployment_id": cfg.get_azure_deployment_id_for_model(model)}
    else:
        kwargs = {"model": model}

    response = openai.Completion.create(
        **kwargs,
        prompt=prompt,
        temperature=temperature,
        max_tokens=max_output_tokens,
        api_key=cfg.openai_api_key,
    )
    return response.choices[0].text


# Overly simple abstraction until we create something better
# simple retry mechanism when getting a rate error or a bad gateway
@metered
@retry_openai_api()
def create_chat_completion(
    prompt: ChatSequence,
    model: Optional[str] = None,
    temperature: float = None,
    max_tokens: Optional[int] = None,
) -> str:
    """Create a chat completion using the OpenAI API

    Args:
        messages (List[Message]): The messages to send to the chat completion
        model (str, optional): The model to use. Defaults to None.
        temperature (float, optional): The temperature to use. Defaults to 0.9.
        max_tokens (int, optional): The max tokens to use. Defaults to None.

    Returns:
        str: The response from the chat completion
    """
    cfg = Config()
    if model is None:
        model = prompt.model.name
    if temperature is None:
        temperature = cfg.temperature

    logger.debug(
        f"{Fore.GREEN}Creating chat completion with model {model}, temperature {temperature}, max_tokens {max_tokens}{Fore.RESET}"
    )
    for plugin in cfg.plugins:
        if plugin.can_handle_chat_completion(
            messages=prompt.raw(),
            model=model,
            temperature=temperature,
            max_tokens=max_tokens,
        ):
            message = plugin.handle_chat_completion(
                messages=prompt.raw(),
                model=model,
                temperature=temperature,
                max_tokens=max_tokens,
            )
            if message is not None:
                return message
    api_manager = ApiManager()
    response = None

    if cfg.use_azure:
        kwargs = {"deployment_id": cfg.get_azure_deployment_id_for_model(model)}
    else:
        kwargs = {"model": model}

    response = api_manager.create_chat_completion(
        **kwargs,
        messages=prompt.raw(),
        temperature=temperature,
        max_tokens=max_tokens,
    )

    resp = response.choices[0].message["content"]
    for plugin in cfg.plugins:
        if not plugin.can_handle_on_response():
            continue
        resp = plugin.on_response(resp)
    return resp


def check_model(
    model_name: str, model_type: Literal["smart_llm_model", "fast_llm_model"]
) -> str:
    """Check if model is available for use. If not, return gpt-3.5-turbo."""
    api_manager = ApiManager()
    models = api_manager.get_models()

    if any(model_name in m["id"] for m in models):
        return model_name

    logger.typewriter_log(
        "WARNING: ",
        Fore.YELLOW,
        f"You do not have access to {model_name}. Setting {model_type} to "
        f"gpt-3.5-turbo.",
    )
    return "gpt-3.5-turbo"