import streamlit as st import utils # https://github.com/serkanyasr/RAG-with-LangChain-URL-PDF/blob/main/utils.py st.set_page_config(layout="centered") st.markdown("

RAG with LangChain & GenAI: Any url

", unsafe_allow_html=True) # st.title("RAG with LangChain & GenAI: Any url") # URL text box for user input url_input = st.text_input("Enter a URL to be queried:", "") # Input text box for user input user_input = st.text_input("Enter your Question below:", "") # Display the user input # st.write("You entered:", user_input) # st.write("URL entered:", url_input) sumbit_btn = st.button(label="Submit",key="url_btn") if sumbit_btn: with st.spinner("Processing..."): st.success("Response: Answering with RAG...") response = utils.rag_with_url(url_input,user_input) st.markdown(response) # st.title("Retrieval-Augmented Generation (RAG) with LangChain : PDF ") # st.divider() # col_input , col_rag , col_normal = st.columns([3,5,5]) # with col_input: # selected_file = st.file_uploader("PDF File", type=["pdf"]) # st.divider() # prompt = st.text_input("Prompt",key="pdf_prompt") # st.divider() # sumbit_btn = st.button(label="Submit",key="pdf_btn") # if sumbit_btn: # with col_rag: # with st.spinner("Processing..."): # st.success("Response: Answering with RAG...") # response,relevant_documents = utils.rag_with_pdf(file_path=f"./data/{selected_file.name}", # prompt=prompt) # st.markdown(response) # st.divider() # st.info("Documents") # for doc in relevant_documents: # st.caption(doc.page_content) # st.markdown(f"Source: {doc.metadata}") # st.divider() # with col_normal: # with st.spinner("Processing..."): # st.info("Response: Answering without RAG...") # response = utils.ask_gemini(prompt) # st.markdown(response) # st.divider()