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    RAG Application in AI Workbench

    30 MIN

    Install and use AI Workbench to clone and run a reproducible RAG application

    • DGX
    • Spark
    View on GitHub
    OverviewOverviewInstructionsInstructionsTroubleshootingTroubleshooting

    Basic idea

    This walkthrough demonstrates how to set up and run an agentic retrieval-augmented generation (RAG) project using NVIDIA AI Workbench. You'll use AI Workbench to clone and run a pre-built agentic RAG application that intelligently routes queries, evaluates responses for relevancy and hallucination, and iterates through evaluation and generation cycles. The project uses a Gradio web interface and can work with both NVIDIA-hosted API endpoints or self-hosted models.

    What you'll accomplish

    You'll have a fully functional agentic RAG application running in NVIDIA AI Workbench with a web interface where you can submit queries and receive intelligent responses. The system will demonstrate advanced RAG capabilities including query routing, response evaluation, and iterative refinement, giving you hands-on experience with both AI Workbench's development environment and sophisticated RAG architectures.

    What to know before starting

    • Basic familiarity with retrieval-augmented generation (RAG) concepts
    • Understanding of API keys and how to generate them
    • Comfort working with web applications and browser interfaces
    • Basic understanding of containerized development environments

    Prerequisites

    Hardware Requirements:

    • NVIDIA Grace Blackwell GB10 Superchip System

    Software Requirements:

    • NVIDIA AI Workbench installed or ready to install
    • Free NVIDIA API key: Generate at NGC API Keys
    • Free Tavily API key: Generate at Tavily
    • Internet connection for cloning repositories and accessing APIs
    • Web browser for accessing the Gradio interface

    Verification commands

    • Verify the NVIDIA AI Workbench application exists on your DGX Spark system
    • Verify your API keys are valid and up-to-date

    Time & risk

    • Estimated time: 30-45 minutes (including AI Workbench installation if needed)
    • Risk level: Low - Uses pre-built containers and established APIs
    • Rollback: Simply delete the cloned project from AI Workbench to remove all components. No system changes are made outside the AI Workbench environment.
    • Last Updated: 10/28/2025
      • Minor copyedits

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