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# You can find this code for Chainlit python streaming here (https://docs.chainlit.io/concepts/streaming/python)
# OpenAI Chat completion
import os
from openai import AsyncOpenAI # importing openai for API usage
import chainlit as cl # importing chainlit for our app
from chainlit.prompt import Prompt, PromptMessage # importing prompt tools
from chainlit.playground.providers import ChatOpenAI # importing ChatOpenAI tools
from dotenv import load_dotenv
load_dotenv()
# ChatOpenAI Templates
system_template = """You are a helpful assistant who:
1. Provides concise, clear responses
2. Uses formatting for better readability
3. Adapts your style to match the question type
4. Shows your work clearly for mathematical problems
5. Balances creativity with clarity
For any response:
- Use appropriate formatting with line breaks
- Be precise and avoid unnecessary words
- Include step-by-step reasoning when needed
- Stay focused on the core question"""
user_template = """{input}
Think through your response step by step.
"""
@cl.on_chat_start # marks a function that will be executed at the start of a user session
async def start_chat():
settings = {
"model": "gpt-3.5-turbo",
"temperature": 0,
"max_tokens": 500,
"top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
}
cl.user_session.set("settings", settings)
@cl.on_message # marks a function that should be run each time the chatbot receives a message from a user
async def main(message: cl.Message):
settings = cl.user_session.get("settings")
client = AsyncOpenAI()
print(message.content)
prompt = Prompt(
provider=ChatOpenAI.id,
messages=[
PromptMessage(
role="system",
template=system_template,
formatted=system_template,
),
PromptMessage(
role="user",
template=user_template,
formatted=user_template.format(input=message.content),
),
],
inputs={"input": message.content},
settings=settings,
)
print([m.to_openai() for m in prompt.messages])
msg = cl.Message(content="")
# Call OpenAI
async for stream_resp in await client.chat.completions.create(
messages=[m.to_openai() for m in prompt.messages], stream=True, **settings
):
token = stream_resp.choices[0].delta.content
if not token:
token = ""
await msg.stream_token(token)
# Update the prompt object with the completion
prompt.completion = msg.content
msg.prompt = prompt
# Send and close the message stream
await msg.send()
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