Create app.py

app.py

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+import streamlit as st
+import os
+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 PyPDFDirectoryLoader
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from dotenv import load_dotenv
+import os
+load_dotenv()
+
+## load the GROQ And OpenAI 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="Llama3-8b-8192")
+
+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():
+
+    if "vectors" not in st.session_state:
+
+        st.session_state.embeddings=GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+        st.session_state.loader=PyPDFDirectoryLoader("./us_census") ## Data Ingestion
+        st.session_state.docs=st.session_state.loader.load() ## Document Loading
+        st.session_state.text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200) ## Chunk Creation
+        st.session_state.final_documents=st.session_state.text_splitter.split_documents(st.session_state.docs[:20]) #splitting
+        st.session_state.vectors=FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings) #vector OpenAI embeddings
+
+
+
+
+
+prompt1=st.text_input("Enter Your Question From Doduments")
+
+
+if st.button("Documents Embedding"):
+    vector_embedding()
+    st.write("Vector Store DB Is Ready")
+
+import time
+
+
+
+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 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("--------------------------------")