gists/openai_agent_example.py

"""
Written in 2024 by retoor@molodetz.nl.

MIT license. Enjoy!

Purpose of this file is to be a native part of your application 
instead of the nth-library. It's just not worth a library.
Especially not another one. Just modify and use it! 

The docstrings of all methods contain tips and important facts.
This document contains all URL's for all services that you need.

You'll need:
 - OpenAI account.
 - Named a project in OpenAI dashboard.
 - Requested an api key and created an assistant.

URL's to all these services are described in the class for convenience.
It can be hard to find initially.

The API keys described in this document are fake but are in the correct format for educational purposes.

How to start:
 - sudo apt install python3.12-venv python3-pip -y
 - python3 -m venv .venv
 - . .venv/bin/activate
 - pip install openapi

"""

# AGAIN. NOT REAL DATA, ONLY LOOKS THAT WAY FOR EDUCATIVE PURPOSES. 
# Not required to use the Agent class. Agent class accepts api_key as parameter.
API_KEY = "sk-proj-V1Jc3my22xSvtfZ3dxXNHgLWZIhEopmJVIMlcNrft_q-7p8dDT_-AQCE8wo9cKpO3v05egDm7CT3BlbkFjN21maiSZqS4oz8FSGiblOeKMH2i6BzIGdQWMcVbKHnRqWy0KiSwKQywJ7XEf792UgGFtwLtxkA"
# Not required to use the Agent class. Agent class accepts assistant_id as parameter.
ASSISTANT_ID = "asst_NgncvKEN8CTf642RE8a4PgAp"


import asyncio
import functools
from collections.abc import Generator
from typing import Optional

from openai import OpenAI


class Agent:
    """
    This class translates into an instance a single user session with its own memory.

    The messages property of this class is a list containing the full chat history about
    what the user said and what the assistant (agent) said. This can be used in future to continue
    where you left off. Format is described in the docs of __init__ function below.

    Introduction API usage for if you want to extend this class:
    https://platform.openai.com/docs/api-reference/introduction
    """

    def __init__(
        self, api_key: str, assistant_id: int, messages: Optional[list] = None
    ):
        """
        You can find and create API keys here:
        https://platform.openai.com/api-keys

        You can find assistant_id (agent_id) here. It is the id that starts with 'asst_', not your custom name:
        https://platform.openai.com/assistants/

        Messages are optional in this format, this is to keep a message history that you can later use again:
        [
            {"role": "user", "message": "What is choking the chicken?"},
            {"role": "assistant", "message": "Lucky for the cock."}
        ]
        """

        self.assistant_id = assistant_id
        self.api_key = api_key
        self.client = OpenAI(api_key=self.api_key)
        self.messages = messages or []
        self.thread = self.client.beta.threads.create(messages=self.messages)

    async def dalle2(
        self, prompt: str, width: Optional[int] = 512, height: Optional[int] = 512
    ) -> dict:
        """
        In my opinion dall-e-2 produces unusual results.
        Sizes: 256x256, 512x512 or 1024x1024.
        """
        result = self.client.images.generate(
            model="dall-e-2", prompt=prompt, n=1, size=f"{width}x{height}"
        )
        return result

    @property
    async def models(self):
        """
        List models in dict format. That's more convenient than the original
        list method because this can be directly converted to json to be used
        in your front end or api. That's not the original result which is a
        custom list with unserializable models.
        """
        return [
            {
                "id": model.id,
                "owned_by": model.owned_by,
                "object": model.object,
                "created": model.created,
            }
            for model in self.client.models.list()
        ]

    async def dalle3(
        self, prompt: str, height: Optional[int] = 1024, width: Optional[int] = 1024
    ) -> dict:
        """
        Sadly only big sizes allowed. Is more pricy.
        Sizes: 1024x1024, 1792x1024, or 1024x1792.
        """
        result = self.client.images.generate(
            model="dall-e-3", prompt=prompt, n=1, size=f"{width}x{height}"
        )
        print(result)
        return result

    async def chat(
        self, message: str, interval: Optional[float] = 0.2
    ) -> Generator[None, None, str]:
        """
        Chat with the agent. It yields on given interval to inform the caller it' still busy so you can
        update the user with live status. It doesn't hang. You can use this fully async with other
        instances of this class.

        This function also updates the self.messages list with chat history for later use.
        """
        message_object = {"role": "user", "content": message}
        self.messages.append(message_object)
        self.client.beta.threads.messages.create(
            self.thread.id,
            role=message_object["role"],
            content=message_object["content"],
        )
        run = self.client.beta.threads.runs.create(
            thread_id=self.thread.id, assistant_id=self.assistant_id
        )

        while run.status != "completed":
            run = self.client.beta.threads.runs.retrieve(
                thread_id=self.thread.id, run_id=run.id
            )
            yield None
            await asyncio.sleep(interval)

        response_messages = self.client.beta.threads.messages.list(
            thread_id=self.thread.id
        ).data
        last_message = response_messages[0]
        self.messages.append({"role": "assistant", "content": last_message})
        yield last_message

    async def chatp(self, message: str) -> str:
        """
        Just like regular chat function but with progress indication and returns string directly.
        This is handy for interactive usage or for a process log.
        """
        asyncio.get_event_loop()
        print("Processing", end="")
        async for message in self.chat(message):
            if not message:
                print(".", end="", flush=True)
                continue
            print("")
            break
        return message

    async def read_line(self, ps: Optional[str] = "> "):
        """
        Non blocking read_line.
        Blocking read line can break web socket connections.
        That's why.
        """
        loop = asyncio.get_event_loop()
        patched_input = functools.partial(input, ps)
        return await loop.run_in_executor(None, patched_input)

    async def cli(self):
        """
        Interactive client. Can be used on terminal by user or a different process.
        The bottom new line is so that a process can check for \n\n to check if it's end response
        and there's nothing left to wait for and thus can send next prompt if the '>' shows.
        """
        while True:
            try:
                message = await self.read_line("> ")
                if not message.strip():
                    continue
                response = await self.chatp(message)
                print(response.content[0].text.value)
                print("")
            except KeyboardInterrupt:
                print("Exiting..")
                break

async def main():
    """
    Example main function. The keys here are not real but look exactly like 
    the real ones for example purposes and that you're sure your key is in the 
    right format.
    """
    agent = Agent(api_key=API_KEY, assistant_id=ASSISTANT_ID)
    
    # Generate an image. Use dalle3, dalle2 is almost unusable. For image sizes look at the class method docstring.
    list_containing_dicts_with_url_to_images = await agent.dalle3("Make photo realistic image of a rust dev")
    
    # Run interactive chat
    await agent.cli()


if __name__ == "__main__":
    # Only gets executed by direct execution of script. Not when imported.
    asyncio.run(main())