app.py

"""
Question Answering with Retrieval QA and LangChain Language Models featuring Chroma Vector Stores
This script uses the LangChain Language Model API to answer questions using Retrieval QA and Chroma Vector Stores.
"""

import os
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub


def get_pdf_text(pdf_docs):
    """
    Extract text from a list of PDF documents.

    Parameters
    ----------
    pdf_docs : list
        List of PDF documents to extract text from.

    Returns
    -------
    str
        Extracted text from all the PDF documents.

    """
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text


def get_text_chunks(text):
    """
    Split the input text into chunks.

    Parameters
    ----------
    text : str
        The input text to be split.

    Returns
    -------
    list
        List of text chunks.

    """
    text_splitter = CharacterTextSplitter(
        separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks


def get_vectorstore(text_chunks):
    """
    Generate a vector store from a list of text chunks using HuggingFace BgeEmbeddings.

    Parameters
    ----------
    text_chunks : list
        List of text chunks to be embedded.

    Returns
    -------
    FAISS
        A FAISS vector store containing the embeddings of the text chunks.

    """
    # embeddings = OpenAIEmbeddings()
    model = "BAAI/bge-base-en-v1.5"
    encode_kwargs = {
        "normalize_embeddings": True
    }  # set True to compute cosine similarity
    embeddings = HuggingFaceBgeEmbeddings(
        model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
    )
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore


def get_conversation_chain(vectorstore):
    """
    Create a conversational retrieval chain using a vector store and a language model.

    Parameters
    ----------
    vectorstore : FAISS
        A FAISS vector store containing the embeddings of the text chunks.

    Returns
    -------
    ConversationalRetrievalChain
        A conversational retrieval chain for generating responses.

    """
    llm = HuggingFaceHub(
        repo_id="mistralai/Mistral-7B-Instruct-v0.1",
        model_kwargs={"temperature": 0.5, "max_length": 512},
    )
    # llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")

    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=llm, retriever=vectorstore.as_retriever(), memory=memory
    )
    return conversation_chain


def handle_userinput(user_question):
    """
    Handle user input and generate a response using the conversational retrieval chain.

    Parameters
    ----------
    user_question : str
        The user's question.

    """
    response = st.session_state.conversation({"question": user_question})
    st.session_state.chat_history = response["chat_history"]

    for i, message in enumerate(st.session_state.chat_history):
        if i % 2 == 0:
            st.write(
                user_template.replace("{{MSG}}", message.content),
                unsafe_allow_html=True,
            )
        else:
            st.write(
                bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True
            )


def main():
    
    st.set_page_config(
        page_title="Chat with a Bot that tries to answer questions about multiple PDFs",
        page_icon=":books:",
    )

    st.markdown("# Chat with a Bot")
    st.markdown("This bot tries to answer questions about multiple PDFs.")

    st.write(css, unsafe_allow_html=True)

    # set huggingface hub token in st.text_input widget
    # then hide the input
    huggingface_token = st.text_input("Enter your HuggingFace Hub token", type="password")
    #openai_api_key = st.text_input("Enter your OpenAI API key", type="password")

    # set this key as an environment variable
    os.environ["HUGGINGFACEHUB_API_TOKEN"] = huggingface_token
    #os.environ["OPENAI_API_KEY"] = openai_api_key

    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None

    st.header("Chat with a Bot 🤖🦾 that tries to answer questions about multiple PDFs :books:")
    user_question = st.text_input("Ask a question about your documents:")
    if user_question:
        handle_userinput(user_question)

    with st.sidebar:
        st.subheader("Your documents")
        pdf_docs = st.file_uploader(
            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True
        )
        if st.button("Process"):
            with st.spinner("Processing"):
                # get pdf text
                raw_text = get_pdf_text(pdf_docs)

                # get the text chunks
                text_chunks = get_text_chunks(raw_text)

                # create vector store
                vectorstore = get_vectorstore(text_chunks)

                # create conversation chain
                st.session_state.conversation = get_conversation_chain(vectorstore)


if __name__ == "__main__":
    main()