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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
from src.retrieval_lib import initialize_index, load_pdf_to_text, split_text, load_text_to_index, query_index, create_answer_prompt, generate_answer
load_dotenv()
retriever = initialize_index()
@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)
#print([m.to_openai() for m in prompt.messages])
query = message.content
# query = "what is the reason for the lawsuit"
retrieved_docs = query_index(retriever, query)
print("retrieved_docs: \n", len(retrieved_docs))
answer_prompt = create_answer_prompt()
print("answer_prompt: \n", answer_prompt)
result = generate_answer(retriever, answer_prompt, query)
print("result: \n", result["response"].content)
msg = cl.Message(content="")
msg.content = result["response"].content
# Send and close the message stream
await msg.send()
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