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# You can find this code for Chainlit python streaming here (https://docs.chainlit.io/concepts/streaming/python)
import sys
import os
sys.path.append('../../lutil')
import openai # 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 aimakerspace.text_utils import TextFileLoader, CharacterTextSplitter
from aimakerspace.vectordatabase import VectorDatabase
import asyncio
from raq_qa_reterieval_wandb import RetrievalAugmentedQAPipeline,raqa_prompt,user_prompt
from aimakerspace.openai_utils.chatmodel import ChatOpenAI
import wandb
load_dotenv()
openai.api_key = os.environ["OPENAI_API_KEY"]
os.environ["WANDB_API_KEY"] = os.environ["WANDB_API_KEY"]
@cl.on_chat_start # marks a function that will be executed at the start of a user session
async def start_chat():
msg = cl.Message(
content=f"Loading Dataset ...", disable_human_feedback=True
)
await msg.send()
text_loader = TextFileLoader("../../data/KingLear.txt")
documents = text_loader.load_documents()
text_splitter = CharacterTextSplitter()
split_documents = text_splitter.split_texts(documents)
vector_db = VectorDatabase()
vector_db = asyncio.run(vector_db.abuild_from_list(split_documents))
chat_openai = ChatOpenAI()
retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
vector_db_retriever=vector_db,
llm=chat_openai,
wandb_project="RAQ in pure python HF")
msg.content = f"Dataset loading is done. You can now ask questions!"
await msg.update()
cl.user_session.set("chain", retrieval_augmented_qa_pipeline)
@cl.on_message # marks a function that should be run each time the chatbot receives a message from a user
async def main(message: str):
# settings = cl.user_session.get("settings")
chain = cl.user_session.get("chain")
output = chain.run_pipeline(message)
print(output)
msg = cl.Message(content=f"{output}")
# msg.prompt = output
await msg.send()
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