app.py

import os
import logging

#pip install pypdf
#export HNSWLIB_NO_NATIVE = 1

from langchain.document_loaders import PyPDFDirectoryLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

from langchain.vectorstores import Chroma
from langchain.memory import ChatMessageHistory, ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
import chainlit as cl

from src.utils import get_docsearch, get_source

# text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
# embeddings = OpenAIEmbeddings()

welcome_message = """Welcome"""


@cl.on_chat_start
async def start():
    await cl.Message("test").send()
    files = None
    files = await cl.AskFileMessage(
        content=welcome_message,
        accept=["text/plain", "application/pdf"],
    ).send()

    logging.info("file uploaded")

    file = files[0]

    msg = cl.Message(content=f"Processing {file.name}")
    await msg.send()

    logging.info("file processing")

    
    docsearch = await cl.make_async(get_docsearch)(file)

    message_history = ChatMessageHistory()

    memory = ConversationBufferMemory(
        memory_key="chat_history",
        output_key="answer",
        chat_memory=message_history,
        return_messages=True
    )

    ## create chain that uses chroma vector store
    chain = ConversationalRetrievalChain.from_llm(
        ChatOpenAI(model_name="gpt-3.5-turbo",temperature=0, streaming=True),
        chain_type="stuff",
        retriever=docsearch.as_retriever(),
        memory=memory,
        return_source_documents=True,
    )


    msg.content = f"Processing {file.name} completed. Start asking questions!"
    await msg.update()

    logging.info("file processed success")

    cl.user_session.set("chain",chain)

    logging.info("saved chain in currrent session")


@cl.on_message
async def main(message: cl.Message):

    ## get chain
    chain = cl.user_session.get("chain")

    logging.info("loaded chain")

    cb = cl.AsyncLangchainCallbackHandler()

    logging.info("loaded callbacks")

    res = await chain.acall(message.content, callbacks=[cb])

    answer = res["answer"]
    source_documents = res["source_documents"]

    
    
    text_elements = get_source(answer, source_documents)
    await cl.Message(content=answer, elements=text_elements).send()