app.py

import os
from typing import List

from langchain_chroma import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_groq import ChatGroq

from langchain_community.document_loaders import PyPDFLoader
from langchain.memory import ChatMessageHistory, ConversationBufferMemory
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.prompts import PromptTemplate
from langchain import hub

import chainlit as cl
from io import BytesIO

##################################### Load the embeddings and model #####################################

groq_api_key = os.getenv("GROQ_API_KEY")
embeddings_api_key = os.getenv('GOOGLE_API_KEY')

embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
llm = ChatGroq(model="mixtral-8x7b-32768", temperature=0)




##################################### on_chat_start event handler #######################################

@cl.on_chat_start
async def on_chat_start():
    files = None

    while files is None:
        files = await cl.AskFileMessage(
            content="Please upload a text file to begin",
            accept=["application/pdf"],
            max_size_mb=20,
            timeout=300
        ).send()

    file = files[0]
    msg = cl.Message(content=f"Processing `{file.name}` ...")
    await msg.send()

    ##################################### Load the text from the file ####################################

    pdf_loader = PyPDFLoader(file.path).load()

    ##################################### Split the text into chunks #####################################

    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    chunks = text_splitter.split_documents(pdf_loader)

    ##################################### Chroma DB setup ################################################

    docsearch = await cl.make_async(Chroma.from_documents)(
        chunks, embedding_model
    )

    message_history = ChatMessageHistory()

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

    ##################################### Chain setup ###################################################

    # Define your custom prompt template
    custom_prompt_template = """
    Based on the provided context please answer . if you don't know the answer. just say i don't know.
    {context}

    Question: {question}
    """
    custom_prompt = PromptTemplate(
    template=custom_prompt_template,
    input_variables=["context", "question"],)

    chain = ConversationalRetrievalChain.from_llm(
        llm,
        chain_type="stuff",
        retriever=docsearch.as_retriever(),
        memory=memory,
        return_source_documents=True,
        combine_docs_chain_kwargs={"prompt": custom_prompt}
        
    )

    msg.content = f"Processing `{file.name}` ... Done!✅ You can ask questions now!"
    await msg.update()

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


##################################### On message event handler ###########################################

@cl.on_message
async def main(message: cl.Message):
    chain = cl.user_session.get("chain")
    cb = cl.AsyncLangchainCallbackHandler()

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

    source_documents = res["source_documents"]  # type: List[Document]

    text_elements = []  # type: List[cl.Text]

    if source_documents:
        for source_idx, source_doc in enumerate(source_documents):
            source_name = f"source_{source_idx}"
            # Create the text element referenced in the message
            text_elements.append(
                cl.Text(content=source_doc.page_content, name=source_name, display="side")
            )
        source_names = [text_el.name for text_el in text_elements]

        if source_names:
            answer += f"\nSources: {', '.join(source_names)}"
        else:
            answer += "\nNo sources found"

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