large_response_demo.py

import logging
import os

import chainlit as cl
from dotenv import load_dotenv
from openai import AzureOpenAI

load_dotenv()

SYSTEM_MESSAGE = """
You are a skilled Python engineer who excels in writing code.
Your job is to generate application code based on a user input. 
Before you generate code, make sure the user is providing you with the right set of requirements for the use-case they have requested. 
"""

chat_history = []


def get_chat_completion(messages, stream=False):
    try:
        client = AzureOpenAI(
            api_key=os.environ["AZURE_OPENAI_API_KEY"],
            api_version=os.environ["AZURE_OPENAI_API_VERSION"],
            azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
        )
        messages = (
            [{"role": "system", "content": SYSTEM_MESSAGE}]
            + chat_history
            + [{"role": "user", "content": messages}]
        )

        completion = client.chat.completions.create(
            model=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
            messages=messages,
            stream=stream,
        )
        return completion.choices[0].message.content
    except Exception as e:
        logging.error(e)


@cl.on_chat_end
def on_chat_end():
    chat_history.clear()


@cl.on_message
async def main(message: cl.Message):

    chat_history.append({"role": "user", "content": message.content})
    logging.info(f"Incoming message:\n {message.content}")

    response = get_chat_completion(message.content)
    chat_history.append({"role": "assistant", "content": str(response)})
    logging.info(f"Response:\n {response}")

    await cl.Message(content=response).send()