Lab 4: Launching Your Research Assistant

In this lab, you'll transform your terminal-based research assistant into a professional web application!

Application Overview

The web application uses:

1. Backend: FastAPI Python server with WebSocket support
2. Frontend: HTML/CSS/JavaScript with Tailwind CSS for styling

Your launched application should look like this when running your research:

Running the Application

To launch the web application:

1. Start the FastAPI server:

   uvicorn app.main:app --reload
   

2. Open your browser and navigate to:

   http://localhost:8000
   

Using the Web Interface

The web application features a clean, intuitive interface:

1. Research Input: Enter a research topic in the input field and click "Research"
2. Progress Tracking: Watch as the system progresses through each research step
3. Live Updates: See real-time updates as the research is conducted
4. Thinking Process: Click the thought bubble icon to view the AI's reasoning process
5. Final Report: View the comprehensive research report with citations

Optional Step: Deploy Your App To Azure

You can deploy the full application to the cloud and generate a shareable link by following the instructions in the official Deep Research Azure sample repostory:

• Deploy the app to Azure

Congratulations!

You've successfully built a Deep Researcher using DeepSeek R1, LangChain, and FastAPI!

Checkout the Summary to see an overview of what you learnt and navigate to the Resources page for links to access this lab and more at home!