app_3.py

import streamlit as st

# Start with empty messages, stored in session state
if 'messages' not in st.session_state:
    st.session_state.messages = []

# Draw a title and some markdown
st.markdown("""# Your Enterprise Co-Pilot 🚀
Generative AI is considered to bring the next Industrial Revolution.  
Why? Studies show a **37% efficiency boost** in day to day work activities!

### Security and safety
This Chatbot is safe to work with sensitive data. Why?
- First of all it makes use of [Ollama, a local inference engine](https://ollama.com);
- On top of the inference engine, we're running [Mistral, a local and open Large Language Model (LLM)](https://mistral.ai/);
- Also the LLM does not contain any sensitive or enterprise data, as there is no way to secure it in a LLM;
- Instead, your sensitive data is stored securely within the firewall inside [DataStax Enterprise v7 Vector Database](https://www.datastax.com/blog/get-started-with-the-datastax-enterprise-7-0-developer-vector-search-preview);
- And lastly, the chains are built on [RAGStack](https://www.datastax.com/products/ragstack), an enterprise version of Langchain and LLamaIndex, supported by [DataStax](https://www.datastax.com/).""")
st.divider()

# Draw all messages, both user and bot so far (every time the app reruns)
for message in st.session_state.messages:
    st.chat_message(message['role']).markdown(message['content'])

# Draw the chat input box
if question := st.chat_input("What's up?"):
    
    # Store the user's question in a session object for redrawing next time
    st.session_state.messages.append({"role": "human", "content": question})

    # Draw the user's question
    with st.chat_message('human'):
        st.markdown(question)

    # Generate the answer
    answer = f"""You asked: {question}"""

    # Store the bot's answer in a session object for redrawing next time
    st.session_state.messages.append({"role": "ai", "content": answer})

    # Draw the bot's answer
    with st.chat_message('assistant'):
        st.markdown(answer)