RAG Application in AI Workbench

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

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.

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.

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.

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.

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.

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"

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.

Cleanup and rollback

You can remove the project if needed.

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

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.