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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
import sys
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()
from rag import retrieval_augmented_qa_pipeline
# Add path to the root of the repo to the system path
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__))))
# ChatOpenAI Templates
system_template = """You are a helpful assistant who always speaks in a pleasant tone!
"""
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()
client = ChatOpenAI()
print(message.content)
response = retrieval_augmented_qa_pipeline(client).run_pipeline(message.content)
msg = cl.Message(content="")
msg.stream_token(response)
# Update the prompt object with the completion
msg.prompt = response
# Send and close the message stream
await msg.send()
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