app_2.py

import streamlit as st

# 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 the chat input box
if question := st.chat_input("What's up?"):
    
    # Draw the user's question
    with st.chat_message('human'):
        st.markdown(question)

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

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