app.py

import streamlit as st
import os
from langchain_openai import ChatOpenAI
from langchain_openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain_objectbox.vectorstores import ObjectBox
from langchain_community.document_loaders import PyPDFDirectoryLoader

from dotenv import load_dotenv
load_dotenv()
## load the Groq And OpenAI Api Key
os.environ['OPEN_API_KEY']=os.getenv("OPENAI_API_KEY")
groq_api_key=os.getenv('GROQ_API_KEY')

st.title("Objectbox VectorstoreDB With Llama3 Demo")
llm = ChatOpenAI(model="gpt-4o") ## Calling Gpt-4o
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>
    Questions:{input}
    """
)

## Vector Enbedding and Objectbox Vectorstore db
def vector_embedding():
    if "vectors" not in st.session_state:
        st.session_state.embeddings=OpenAIEmbeddings()
        st.session_state.loader=PyPDFDirectoryLoader("./us_census") ## Data Ingestion
        st.session_state.docs=st.session_state.loader.load() ## Documents Loading
        st.session_state.text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200)
        st.session_state.final_documents=st.session_state.text_splitter.split_documents(st.session_state.docs[:20])
        st.session_state.vectors=ObjectBox.from_documents(st.session_state.final_documents,st.session_state.embeddings,embedding_dimensions=768)


input_prompt=st.text_input("Enter Your Question From Documents")

if st.button("Documents Embedding"):
    vector_embedding()
    st.write("ObjectBox Database is ready")

import time
if input_prompt:
    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':input_prompt})

    print("Response time :",time.process_time()-start)
    st.write(response['answer'])

    # With a streamlit expander
    with st.expander("Document Similarity Search"):
        # Find the relevant chunks
        for i, doc in enumerate(response["context"]):
            st.write(doc.page_content)
            st.write("--------------------------------")