rag_chatbot_app.py

import streamlit as st  # all streamlit commands will be available through the "st" alias
import rag_chatbot_lib as glib  # reference to local lib script


st.set_page_config(page_title="RAG Chatbot")  # HTML title
st.title("RAG Chatbot")  # page title


if "memory" not in st.session_state:  # see if the memory hasn't been created yet
    st.session_state.memory = glib.get_memory()  # initialize the memory


if (
    "chat_history" not in st.session_state
):  # see if the chat history hasn't been created yet
    st.session_state.chat_history = []  # initialize the chat history

if (
    "vector_index" not in st.session_state
):  # see if the vector index hasn't been created yet
    with st.spinner(
        "Indexing document..."
    ):  # show a spinner while the code in this with block runs
        st.session_state.vector_index = glib.get_index()  # retrieve the index through the supporting library and store in the app's session cache


# Re-render the chat history (Streamlit re-runs this script, so need this to preserve previous chat messages)
for message in st.session_state.chat_history:  # loop through the chat history
    with st.chat_message(
        message["role"]
    ):  # renders a chat line for the given role, containing everything in the with block
        st.markdown(message["text"])  # display the chat content


input_text = st.chat_input("Chat with your bot here")  # display a chat input box

if input_text:  # run the code in this if block after the user submits a chat message
    with st.chat_message("user"):  # display a user chat message
        st.markdown(input_text)  # renders the user's latest message

    st.session_state.chat_history.append(
        {"role": "user", "text": input_text}
    )  # append the user's latest message to the chat history

    chat_response = glib.get_rag_chat_response(
        input_text=input_text,
        memory=st.session_state.memory,
        index=st.session_state.vector_index,
    )  # call the model through the supporting library

    with st.chat_message("assistant"):  # display a bot chat message
        st.markdown(chat_response)  # display bot's latest response

    st.session_state.chat_history.append(
        {"role": "assistant", "text": chat_response}
    )  # append the bot's latest message to the chat history