dgx-spark-playbooks/nvidia/rag-ai-workbench

RAG Application in AI Workbench

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

Table of Contents

• Overview
• Instructions
• Troubleshooting

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Overview

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

Instructions

Step 1. Install NVIDIA AI Workbench

Install AI Workbench on your DGX Spark system and complete the initial setup wizard.

On your DGX Spark, open the NVIDIA AI Workbench application and click "Begin Installation".

1. The installation wizard will prompt for authentication
2. Wait for the automated install to complete (several minutes)
3. Click "Let's Get Started" when installation finishes

Note

If you encounter the following error message, reboot your DGX Spark and then reopen NVIDIA AI Workbench: "An error occurred ... container tool failed to reach ready state. try again: docker is not running"

Step 2. Verify API key requirements

Next, you should ensure you have both required API keys before proceeding with the project setup. Keep these keys safe!

• Tavily API Key: https://tavily.com/
• NVIDIA API Key: https://org.ngc.nvidia.com/setup/api-keys
• Ensure this key has Public API Endpoints permissions

Keep both keys available for the next step.

Step 3. Clone the agentic RAG project

You'll then clone the pre-built agentic RAG project from GitHub into your AI Workbench environment.

From the AI Workbench landing page, select the Local location, if not done so already, then click "Clone Project" from the top right corner.

Paste this Git repository URL in the clone dialog: https://github.com/NVIDIA/workbench-example-agentic-rag

Click "Clone" to begin the clone and build process.

Step 4. Configure project secrets

You can then configure the API keys required for the agentic RAG application to function properly.

While the project builds, configure the API keys using the yellow warning banner that appears:

1. Click "Configure" in the yellow banner
2. Enter your NVIDIA_API_KEY
3. Enter your TAVILY_API_KEY
4. Save the configuration

Wait for the project build to complete before proceeding.

Step 5. Launch the chat application

You can now start the web-based chat interface where you can interact with the agentic RAG system.

Navigate to Environment > Project Container > Apps > Chat and start the web application.

A browser window will open automatically and load with the Gradio chat interface.

Step 6. Test the basic functionality

Verify the agentic RAG system is working by submitting a sample query.

In the chat application, click on or type a sample query such as: How do I add an integration in the CLI?

Wait for the agentic system to process and respond. The response, while general, should demonstrate intelligent routing and evaluation.

Step 7. Validate project

Confirm your setup is working correctly by testing the core features.

Verify the following components are functioning:

• Web application loads without errors
• Sample queries return responses
• No API authentication errors appear
• The agentic reasoning process is visible in the interface under "Monitor"

Step 8. Complete optional quickstart

You can evaluate advanced features by uploading data, retrieving context, and testing custom queries.

Substep A: Upload sample dataset Complete the in-app quickstart instructions to upload the sample dataset and test improved RAG-based responses.

Substep B: Test custom dataset (optional) Upload a custom dataset, adjust the Router prompt, and submit custom queries to test customization.

Step 10. Cleanup and rollback

You can remove the project if needed.

Warning

This will permanently delete the project and all associated data.

To remove the project completely:

1. In AI Workbench, click on the three dots next to a project
2. Select "Delete Project"
3. Confirm deletion when prompted

Note

All changes are contained within AI Workbench. No system-level modifications were made outside the AI Workbench environment.

Step 11. Next steps

You can also explore further advanced features and development options with the agentic RAG system:

• Modify component prompts in the project code
• Upload different documents to test routing and customization
• Experiment with different query types and complexity levels
• Review the agentic reasoning logs in the "Monitor" tab to understand decision-making

Consider customizing the Gradio UI or integrating the agentic RAG components into your own projects.

Troubleshooting

Symptom	Cause	Fix
Tavily API Error	Internet connection or DNS issues	Wait and retry query
401 Unauthorized	Wrong or malformed API key	Replace key in Project Secrets and restart
403 Unauthorized	API key lacks permissions	Generate new key with proper access
Agentic loop timeout	Complex query exceeding time limit	Try simpler query or retry

For latest known issues, please review the DGX Spark User Guide.