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import streamlit as st
import os
from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA
from langchain_community.document_loaders import PyPDFLoader
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_community.vectorstores import FAISS
from dotenv import load_dotenv
import tempfile
import time
load_dotenv()
# load the Nvidia API key
os.environ['NVIDIA_API_KEY'] = os.getenv('NVIDIA_API_KEY')
llm = ChatNVIDIA(model="meta/llama3-70b-instruct")
def vector_embedding(pdf_file):
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
tmp_file.write(pdf_file.getvalue())
tmp_file_path = tmp_file.name
st.session_state.embeddings = NVIDIAEmbeddings()
st.session_state.loader = PyPDFLoader(tmp_file_path)
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)
st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
os.unlink(tmp_file_path)
st.title("Chat with PDF")
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}
"""
)
uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
if uploaded_file is not None:
if st.button("Process PDF"):
with st.spinner("Processing PDF..."):
vector_embedding(uploaded_file)
st.success("FAISS Vector Store DB is ready using NvidiaEmbedding")
prompt1 = st.text_input("Enter your question about the uploaded document")
if prompt1 and 'vectors' in st.session_state:
document_chain = create_stuff_documents_chain(llm, prompt)
retriever = st.session_state.vectors.as_retriever()
retrieval_chain = create_retrieval_chain(retriever, document_chain)
with st.spinner("Generating answer..."):
start = time.process_time()
response = retrieval_chain.invoke({'input': prompt1})
end = time.process_time()
st.write("Answer:", response['answer'])
st.write(f"Response time: {end - start:.2f} seconds")
with st.expander("Document Similarity Search"):
for i, doc in enumerate(response["context"]):
st.write(f"Chunk {i + 1}:")
st.write(doc.page_content)
st.write("------------------------------------------")
else:
if prompt1:
st.warning("Please upload and process a PDF document first.") |