RAG with Python Cookbook

Chapter 11. RAG Web Apps

Most generative AI projects start as experiments, and most never reach production. Fast prototyping matters, especially when your team lacks full-stack engineering support. You need a setup that lets data scientists build RAG-powered apps that real users can test before committing to production infrastructure.

Streamlit is a Python framework for rapid web app development. It lets you build functional interfaces with a single script that covers the frontend and backend. The framework provides prebuilt components you can assemble without low-level web development, though you’re limited to Streamlit’s current offerings.

Streamlit reruns your entire script on every user interaction. This simplifies development but creates overhead that doesn’t scale beyond a few dozen concurrent users. For validating whether a GenAI app provides value, this trade-off works in your favor.

For production applications with thousands or millions of users, alternative frameworks provide better scalability and performance. Django offers full-featured capabilities with authentication and complex workflows. Flask provides a lightweight and flexible foundation. FastAPI is optimized for API endpoints.

The recipes in this chapter build progressively. You’ll start by creating a basic Streamlit app to understand the framework’s core mechanics, then build a working RAG chatbot, and finally extend it to handle document uploads and queries.

Tip

In the early days of RAG, many applications ...