This guide provides step-by-step instructions for installing the Red Hat Golden Template path using the RHEcosystemAppEng/RHDH-templates repository.

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✅ Prerequisites

Before getting started, ensure you have the following:

• OpenShift CLI (oc): Download and install Openshift command-line interface
• Platform Access: Access to either TAP or a running RHDH instance. Helm Chart installation available here
• Hugging Face API Token: A valid authentication token from Hugging Face

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🚀 Step-by-Step Instructions

1. Create a Kubernetes secret for HF token

🔓 Without Vault

Set up your Hugging Face authentication:

1. Configure your token as an environment variable:

   export HF_TOKEN=<your huggingface token>

   Replace with your actual Hugging Face API token.

2. Create the secret in your OpenShift namespace:

   oc create secret generic huggingface-secret \
     -n <your-namespace> \
     --from-literal=HF_TOKEN=$HF_TOKEN

   Replace with the namespace where your RAG application is deployed.

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🔐 With Vault + External Secrets Operator

Note: Use this approach if you have Vault and External Secrets Operator configured in your cluster for centralized secret management.

1. Access Vault UI:

   # Get the Vault route
   oc get route -n vault
   
   # Get the Vault token
   oc get secret -n vault vault-token -o jsonpath="{.data.token}" | base64 --decode

   Open the Vault route in your browser and log in using the token method with the retrieved token.

   

2. Create the secret in Vault:

   • Select the KV secret engine

   • Navigate to: secret/

   • Set path as: secrets/ai-kickstart

   • Click Create secret (Shown on image 1)

   • Add secret data:

     • Key: hf_token
     • Value: <your-huggingface-token>

   • Click Save (Shown on image 2)

     (1)

     

     (2)

     

Note: The ExternalSecret Operator will map hf_token → Kubernetes key HF_TOKEN

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2. Login to Developer Hub

• Sign in to Developer Hub via GitLab using your GitLab credentials

  

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3. Register AI Templates

1. Navigate to Create:

   • From the Developer Hub sidebar, click "Create"

     

2. Register templates:

   • Click "Register Existing Component"

3. Import the template repository:

   • Paste this URL into the input field:

     https://github.com/RHEcosystemAppEng/RHDH-templates/blob/main/showcase-templates.yaml
     

   • Click "Analyze"

     

   • Click "Import" to complete registration

     

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4. Available Templates

Once registered, you'll see these AI-powered templates in the Catalog->Template page:

• 🤖 RAG Chatbot Kickstart (chatbot-rag-kickstart-template)
  Deploy a complete RAG (Retrieval Augmented Generation) architecture using LLaMA Stack, OpenShift AI, and PGVector. Includes document ingestion pipeline and vector database for intelligent question-answering.

• 🎯 AI Virtual Agent (ai-virtual-agent-kickstart-template)
  Create an intelligent virtual assistant powered by OpenShift AI and PGVector. Perfect for building conversational AI applications with advanced reasoning capabilities.

• 📊 AI Metrics Summarizer (ai-metric-summarizer-kickstart-template)
  Build a specialized chatbot that analyzes AI model performance metrics from Prometheus and generates human-readable summaries using LLaMA models. Ideal for AI observability and monitoring.

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5. Launch a Template

Once you've registered the templates, follow these steps to deploy an AI application:

Navigate to Self-Service Catalog

• From the Developer Hub sidebar, click "Create"
• You'll see the available AI templates listed

Choose Your Template

Select one of the registered templates:

• Chatbot-Rag Kickstart - for RAG document-based Q&A systems
• AI Virtual Agent - for conversational AI assistants
• AI Metrics Summarizer - for AI observability and monitoring

Configure Template Parameters

Fill in the guided form with your specifications:

Application Information:

• Name: Unique identifier for your component (e.g., my-ai-chatbot)
• Description: Brief description of your application

Repository Details:

• Host Type: Choose GitHub or GitLab
• Repository Owner: Your organization name
• Repository Name: Name for the source repository
• Namespace: Kubernetes namespace for deployment

AI Model Configuration:

• Language Model: Select from available LLaMA variants
• Safety Model: Optional LLaMA Guard for content filtering
• GPU Tolerance: Configure hardware requirements

Review and Create

• Review all configured parameters
• Click "Review" to validate your inputs
• Click "Create" to initiate the template deployment

Automatic Deployment Process

The template will automatically:

1. Build the software component with your specifications
2. Publish source and GitOps repositories to your chosen platform
3. Register the component in the Developer Hub catalog
4. Deploy via ArgoCD using GitOps workflows

Access Your Application

Once complete, use the provided links to:

• View source repository
• Monitor GitOps deployment
• Access the component in the catalog
• Review ArgoCD applications