matrix-reportbot/classes/openai.py

import openai
import requests

import json

from .logging import Logger

from typing import Dict, List, Tuple, Generator, Optional

class OpenAI:
    api_key: str
    chat_model: str = "gpt-3.5-turbo"
    logger: Logger

    api_code: str = "openai"

    @property
    def chat_api(self) -> str:
        return self.chat_model
    
    classification_api = chat_api
    image_api: str = "dalle"

    operator: str = "OpenAI ([https://openai.com](https://openai.com))"

    def __init__(self, api_key, chat_model=None, logger=None):
        self.api_key = api_key
        self.chat_model = chat_model or self.chat_model
        self.logger = logger or Logger()

    def generate_chat_response(self, messages: List[Dict[str, str]], user: Optional[str] = None) -> Tuple[str, int]:
        """Generate a response to a chat message.

        Args:
            messages (List[Dict[str, str]]): A list of messages to use as context.

        Returns:
            Tuple[str, int]: The response text and the number of tokens used.
        """

        self.logger.log(f"Generating response to {len(messages)} messages using {self.chat_model}...")

        response = openai.ChatCompletion.create(
            model=self.chat_model,
            messages=messages,
            api_key=self.api_key,
            user = user
        )

        result_text = response.choices[0].message['content']
        tokens_used = response.usage["total_tokens"]
        self.logger.log(f"Generated response with {tokens_used} tokens.")
        return result_text, tokens_used

    def classify_message(self, query: str, user: Optional[str] = None) -> Tuple[Dict[str, str], int]:
        system_message = """You are a classifier for different types of messages. You decide whether an incoming message is meant to be a prompt for an AI chat model, an image generation AI, or a calculation for WolframAlpha. You respond with a JSON object like this:

{ "type": event_type, "prompt": prompt }

- If the message you received is meant for the AI chat model, the event_type is "chat", and the prompt is the literal content of the message you received. This is also the default if none of the other options apply.
- If it is a prompt for a calculation that can be answered better by WolframAlpha than an AI chat bot, the event_type is "calculate". Optimize the message you received for input to WolframAlpha, and return it as the prompt attribute.
- If it is a prompt for an AI image generation, the event_type is "imagine". Optimize the message you received for use with DALL-E, and return it as the prompt attribute.
- If for any reason you are unable to classify the message (for example, if it infringes on your terms of service), the event_type is "error", and the prompt is a message explaining why you are unable to process the message.

Only the event_types mentioned above are allowed, you must not respond in any other way."""

        messages = [
            {
                "role": "system", 
                "content": system_message
            },
            {
                "role": "user",
                "content": query
            }
        ]

        self.logger.log(f"Classifying message '{query}'...")

        response = openai.ChatCompletion.create(
            model=self.chat_model,
            messages=messages,
            api_key=self.api_key,
            user = user
        )

        try:
            result = json.loads(response.choices[0].message['content'])
        except:
            result = {"type": "chat", "prompt": query}

        tokens_used = response.usage["total_tokens"]

        self.logger.log(f"Classified message as {result['type']} with {tokens_used} tokens.")

        return result, tokens_used

    def generate_image(self, prompt: str, user: Optional[str] = None) -> Generator[bytes, None, None]:
        """Generate an image from a prompt.

        Args:
            prompt (str): The prompt to use.

        Yields:
            bytes: The image data.
        """

        self.logger.log(f"Generating image from prompt '{prompt}'...")

        response = openai.Image.create(
            prompt=prompt,
            n=1,
            api_key=self.api_key,
            size="1024x1024",
            user = user
        )

        images = []

        for image in response.data:
            image = requests.get(image.url).content
            images.append(image)

        return images, len(images)