app.py

import os
import chainlit as cl
from dotenv import load_dotenv
from operator import itemgetter

from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_core.prompts import PromptTemplate
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.runnable.config import RunnableConfig
from utilities.utilities import process_documents

# GLOBAL SCOPE - ENTIRE APPLICATION HAS ACCESS TO VALUES SET IN THIS SCOPE #
# ---- ENV VARIABLES ---- # 
"""
This function will load our environment file (.env) if it is present.

NOTE: Make sure that .env is in your .gitignore file - it is by default, but please ensure it remains there.
"""

load_dotenv()

use_document = True
use_qdrant = True

lcel_rag_chain= None

if use_document:
    lcel_rag_chain = process_documents(use_qdrant)


@cl.author_rename
def rename(original_author: str):
    rename_dict = {
        "Assistant" : "Paul Graham Essay Bot"
    }
    return rename_dict.get(original_author, original_author)

@cl.on_chat_start
async def start_chat():
    cl.user_session.set("lcel_rag_chain", lcel_rag_chain)

@cl.on_message  
async def main(message: cl.Message):
    lcel_rag_chain = cl.user_session.get("lcel_rag_chain")
    msg = cl.Message(content="")

    if lcel_rag_chain:

        async for chunk in lcel_rag_chain.astream(
            {"query": message.content},
            config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
        ):
            await msg.stream_token(chunk)

        await msg.send()
    else:

        await cl.Message(content=f"You entered: {message}").send()