main app file and requirements file

.env

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+NVIDIA_API_KEY = "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"

app.py

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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("NVIDIA NIM Demo")
+
+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.")

requirements.txt

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+openai
+python-dotenv
+langchain_nvidia_ai_endpoints
+langchain_community
+faiss-cpu
+streamlit
+pypdf
+langchain