finalapp.py

import streamlit as st
import os, time
from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA
from langchain_community.document_loaders import PyPDFDirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain.chains import create_retrieval_chain
from langchain_community.vectorstores import FAISS

from dotenv import load_dotenv
load_dotenv()

os.environ['NVIDIA_API_KEY'] = os.getenv('NVIDIA_API_KEY')

llm = ChatNVIDIA(model = "nvidia/llama-3.1-nemotron-70b-instruct")

def vector_embedding():
    if 'vectors' not in st.session_state:
        st.session_state.embeddings = NVIDIAEmbeddings()
        st.session_state.loader = PyPDFDirectoryLoader("./books")
        st.session_state.docs = st.session_state.loader.load()
        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size = 700, chunk_overlap = 50)
        st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs[:30])
        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)

st.title("NVIDIA-NIM Chabot")
prompt=ChatPromptTemplate.from_template(
    """
    Answer the questions based on the provided context only.
    Please provide the most accurate response based on the question
    <context>
    {context}
    <context>
    Question : {input}
    """
)

prompt1 = st.text_input("Enter your Question from the documents")
if st.button("document Embedding"):
    vector_embedding()
    st.write("FAISS-Vector Store DB is ready using Nvidia embeddings")


if prompt1:
    document_chain = create_stuff_documents_chain(llm,prompt)
    retriever = st.session_state.vectors.as_retriever()
    retrieval_chain = create_retrieval_chain(retriever,document_chain)
    start = time.process_time()
    response = retrieval_chain.invoke({'input':prompt1})
    print("response time :", time.process_time()-start)
    st.write(response['answer'])

    with st.expander('Document Similarity Search'):
        for i, doc in enumerate(response["context"]):
            st.write(doc.page_content)
            st.write("--------------------------------------")