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app.py

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+import os
+import streamlit as st
+from langchain_groq import ChatGroq
+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 langchain_community.document_loaders import PyPDFLoader
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import tempfile
+from dotenv import load_dotenv
+
+load_dotenv()
+
+## Load the GROQ and Google API key
+
+groq_api_key = os.getenv('GROQ_API_KEY')
+os.environ["GOOGLE_API_KEY"] = os.getenv('GOOGLE_API_KEY')
+
+st.title("Gemma Model Document Q&A")
+
+llm = ChatGroq(groq_api_key=groq_api_key, model_name="gemma2-9b-it")
+
+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}
+    """
+)
+
+def vector_embedding(pdf_files):
+    st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    docs = []
+    for pdf_file in pdf_files:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
+            temp_file.write(pdf_file.read())
+            temp_file_path = temp_file.name
+        loader = PyPDFLoader(temp_file_path)
+        docs.extend(loader.load())
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    final_documents = text_splitter.split_documents(docs[:20])
+    st.session_state.vectors = FAISS.from_documents(final_documents, st.session_state.embeddings)
+
+# File uploader
+uploaded_files = st.file_uploader("Upload PDF files", accept_multiple_files=True, type=["pdf"])
+
+if uploaded_files and st.button("Process Uploaded Files"):
+    vector_embedding(uploaded_files)
+    st.write("Vector Store DB is Ready")
+
+prompt1 = st.text_input("What do you want to ask from the documents?")
+
+import time
+
+if prompt1:
+    if "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)
+        
+        start = time.process_time()
+        response = retrieval_chain.invoke({'input': prompt1})
+        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("--------------------------------")
+    else:
+        st.write("Please upload and process PDF files first.")

requirements.txt

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+faiss-cpu
+groq
+langchain-groq
+PyPDF2
+langchain_google_genai
+langchain
+streamlit
+langchain_community
+python-dotenv
+pypdf