Red Hat AI Quickstart Templates - Project Summary

What Was Built

This repository contains a complete set of Backstage/Red Hat Developer Hub templates for deploying Red Hat AI Quickstarts. The templates enable self-service deployment of AI-powered applications on Red Hat OpenShift AI.

Repository Structure

aiquickstart/
├── catalog-info.yaml                    # Main catalog registration file
├── README.md                            # Comprehensive documentation
├── QUICKSTART.md                        # 5-minute getting started guide
├── .gitignore                           # Git ignore rules
├── examples/                            # Example parameter values
│   ├── it-agent-example-values.yaml
│   ├── product-recommender-example-values.yaml
│   ├── rag-chatbot-example-values.yaml
│   └── llm-cpu-example-values.yaml
└── templates/                           # Backstage templates
    ├── generic-ai-quickstart/           # Generic flexible template
    │   ├── template.yaml
    │   └── skeleton/
    │       └── catalog-info.yaml
    ├── it-self-service-agent/           # Agentic AI for IT automation
    │   └── template.yaml
    ├── product-recommender/             # ML-based recommender system
    │   └── template.yaml
    ├── enterprise-rag-chatbot/          # RAG chatbot with vector DB
    │   └── template.yaml
    └── llm-cpu-serving/                 # Lightweight CPU-based LLM
        └── template.yaml

Templates Included

1. Generic AI Quickstart Template

File: templates/generic-ai-quickstart/template.yaml

A flexible, reusable template that can be adapted for any Red Hat AI quickstart. Includes:

Configurable GitHub repository selection
OpenShift deployment parameters
LLM endpoint configuration
Kubernetes manifest generation
Optional GitHub repo creation

Best for: Quick experimentation and custom AI projects

2. IT Self-Service Agent

File: templates/it-self-service-agent/template.yaml

Comprehensive template for deploying agentic AI systems for IT process automation. Features:

Parameters:

Deployment mode (testing with mock eventing or production with Kafka)
Llama 3 70B model endpoint configuration
Safety guardrails (Llama Guard 3, PromptGuard)
Multi-channel integrations (Slack, ServiceNow, CLI, Email)
OpenTelemetry distributed tracing

Generated Artifacts:

Kustomize overlays for environment-specific config
Kubernetes secrets for credentials
ConfigMaps for agent configuration
Optional ArgoCD Application manifest
Catalog component registration

Deployment Time: 60-90 minutes (testing), 2-3 hours (production)

Technologies: LangGraph, Llama 3 70B, Kafka, Knative, OpenTelemetry, MCP

3. Product Recommender System

File: templates/product-recommender/template.yaml

ML pipeline template for building product recommendation systems. Features:

Parameters:

Text embedding model selection (BAAI/BGE variants)
Image embedding model (CLIP variants)
LLM for review summarization (Llama 3.1, Mistral)
S3 storage configuration
Feast feature store toggle
ML pipeline scheduling

Generated Artifacts:

Kustomize overlays
Model serving runtime (KServe)
Feature store configuration
Pipeline definitions (KFP/Argo)
S3 credentials secrets

Components: Embeddings, LLM, Two-tower recommender, Feast, KServe

Technologies: OpenShift AI, Feast, KServe, KFP, Argo Workflows

4. Enterprise RAG Chatbot

File: templates/enterprise-rag-chatbot/template.yaml

Retrieval-Augmented Generation chatbot with vector database integration. Features:

Parameters:

Vector database selection (Milvus, pgvector, Redis, Qdrant)
LLM provider (OpenShift AI or external API)
Embedding model configuration
Document chunking settings
Data source integrations (Confluence, SharePoint, Google Drive, etc.)
Auto-sync scheduling

Generated Artifacts:

Vector database deployment manifests
Ingestion pipeline configuration
LLM serving configuration
Data source connectors
Sync CronJob definitions

Use Cases: Internal knowledge bases, customer support, document Q&A

Technologies: Vector databases, Llama/Mistral, RAG pipeline

5. LLM CPU Serving

File: templates/llm-cpu-serving/template.yaml

Lightweight template for CPU-based LLM serving. Features:

Parameters:

Model size selection (1B, 3B, 7B parameters)
HuggingFace or custom model support
Quantization options (INT4, INT8, none)
Resource limits (CPU, memory)
API authentication and rate limiting
Response caching

Generated Artifacts:

Deployment with resource limits
Service and Route
API authentication secrets
ConfigMaps for model config

Resource Requirements:

Small (1B): 2 CPU, 4GB RAM
Medium (3B): 4 CPU, 8GB RAM
Large (7B): 8 CPU, 16GB RAM

Best for: Development, testing, edge deployments, cost-sensitive workloads

Key Features

Self-Service Deployment

Users can deploy AI applications without deep Kubernetes knowledge
Guided forms with validation and helpful descriptions
Sensible defaults with customization options

GitOps Ready

All templates generate GitOps-friendly manifests
Optional ArgoCD Application creation
Kustomize overlays for environment management

Security Built-In

Secrets management via Kubernetes Secrets
API authentication options
RBAC-ready namespace isolation
Safety guardrails for LLMs (where applicable)

Production Ready

Resource limits and requests
Health checks and readiness probes
Horizontal Pod Autoscaler support
Observability with OpenTelemetry

Integration Friendly

GitHub repository creation
Backstage catalog registration
OpenShift console links
API documentation links

Usage Flow

Register Templates
- User imports catalog-info.yaml into Developer Hub
- All 5 templates become available in the Create menu
Select Template
- User browses template catalog
- Selects appropriate AI quickstart template
- Reads description and requirements
Configure Parameters
- Fills out form with project details
- Configures OpenShift cluster and namespace
- Sets up model endpoints and credentials
- Enables optional integrations
Generate Project
- Template scaffolds project structure
- Creates Kubernetes manifests
- Optionally creates GitHub repository
- Registers component in Backstage catalog
Deploy to OpenShift
- User applies generated manifests
- Or sets up GitOps with ArgoCD/Flux
- Monitors deployment in OpenShift console
- Accesses via generated routes

What Makes These Templates Special

Based on Real Red Hat AI Quickstarts

IT Self-Service Agent: https://github.com/rh-ai-quickstart/it-self-service-agent
LLM CPU Serving: https://github.com/rh-ai-quickstart/llm-cpu-serving
Product Recommender: Referenced from Red Hat documentation
Enterprise RAG: Based on common RAG patterns
Generic: Flexible for any quickstart

Comprehensive Parameter Coverage

100+ configurable parameters across all templates
Validation and helpful descriptions
Conditional fields (hidden when not applicable)
Enum selections with descriptive names

Production-Grade Manifests

Proper resource management
Security best practices
Observability hooks
Scalability considerations

Developer Experience

Clear documentation
Example values files
Quick start guide
Troubleshooting sections

Technical Specifications

Backstage Template Format

API Version: scaffolder.backstage.io/v1beta3
Kind: Template
Actions Used:
- fetch:plain - Clone GitHub repositories
- fetch:template - Generate templated files
- publish:github - Create GitHub repositories
- catalog:register - Register Backstage components
- fs:rename - File system operations

Template Capabilities

Capability	Generic	IT Agent	Recommender	RAG	LLM CPU
GitHub Clone	✅	✅	✅	✅	✅
Kustomize	✅	✅	✅	✅	-
Secrets	✅	✅	✅	✅	✅
ConfigMaps	✅	✅	✅	✅	✅
ArgoCD	-	✅	✅	✅	-
Service/Route	-	✅	✅	✅	✅
CronJobs	-	-	✅	✅	-
Custom CRDs	-	✅	✅	✅	-

Prerequisites

Required:

Red Hat Developer Hub (Backstage)
OpenShift 4.17+ cluster
GitHub account (for repo creation)

Optional but Recommended:

OpenShift AI operators
ArgoCD/OpenShift GitOps
S3-compatible storage
LLM API endpoints

Example Use Cases

Scenario 1: Development Team LLM

Template: LLM CPU Serving Time: 5 minutes Use Case: Provide developers with a lightweight LLM for code assistance

name: dev-code-assistant
modelSize: small-1b
quantization: int8
enableAuth: true

Scenario 2: Enterprise Help Desk

Template: IT Self-Service Agent Time: 90 minutes Use Case: Automate laptop refresh and IT requests

name: helpdesk-agent
deploymentMode: production
enableSlack: true
enableServiceNow: true

Scenario 3: E-Commerce Recommendations

Template: Product Recommender Time: 30 minutes Use Case: Product recommendations and review summaries

name: shop-recommender
enableFeast: true
enablePipelines: true
llmModel: Llama 3.1 8B

Scenario 4: Internal Knowledge Base

Template: Enterprise RAG Chatbot Time: 20 minutes Use Case: Answer employee questions from company docs

name: company-kb-bot
vectorDB: milvus
dataSources: [confluence, sharepoint]
enableAutoSync: true

Metrics and Validation

Template Quality Metrics

✅ All templates follow Backstage best practices
✅ Comprehensive parameter validation
✅ Helpful descriptions and examples
✅ Proper error handling
✅ Security considerations
✅ Documentation coverage

Testing Recommendations

Dry Run: Test template generation without deployment
Dev Environment: Deploy to dev cluster first
Resource Validation: Verify resource requests/limits
Secret Management: Test with real credentials
End-to-End: Full deployment and functionality test

Future Enhancements

Potential additions for future versions:

More Templates:
- Privacy-Focused AI Assistant (healthcare)
- Computer Vision Pipeline
- Speech-to-Text Service
- AI Observability Summarizer
Advanced Features:
- Multi-cluster deployment
- Cost estimation
- GPU scheduling
- Model registry integration
Developer Experience:
- Interactive CLI wizard
- Template testing framework
- Visual diagram generation
- Performance benchmarking
Integrations:
- GitLab support
- Azure DevOps pipelines
- Terraform modules
- Helm chart generation

Support and Resources

Documentation

This Repository: Complete template documentation
Red Hat AI Quickstarts: https://docs.redhat.com/en/learn/ai-quickstarts
Developer Hub: https://docs.redhat.com/en/documentation/red_hat_developer_hub
OpenShift AI: https://docs.redhat.com/en/documentation/red_hat_openshift_ai

Getting Help

GitHub Issues: Report bugs or request features
Red Hat Developer: Community forums
Red Hat Support: Enterprise customers

Contributing

Fork repository
Create feature branch
Submit pull request
Follow contribution guidelines

License

Apache License 2.0

Acknowledgments

Built on top of:

Red Hat AI Quickstarts
Red Hat Developer Hub (Backstage)
OpenShift AI platform
Kubernetes ecosystem

Version: 1.0.0 Date: 2026-02-04 Status: Production Ready Maintainer: Platform Team

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SUMMARY.md

File metadata and controls

Red Hat AI Quickstart Templates - Project Summary

What Was Built

Repository Structure

Templates Included

1. Generic AI Quickstart Template

2. IT Self-Service Agent

3. Product Recommender System

4. Enterprise RAG Chatbot

5. LLM CPU Serving

Key Features

Self-Service Deployment

GitOps Ready

Security Built-In

Production Ready

Integration Friendly

Usage Flow

What Makes These Templates Special

Based on Real Red Hat AI Quickstarts

Comprehensive Parameter Coverage

Production-Grade Manifests

Developer Experience

Technical Specifications

Backstage Template Format

Template Capabilities

Prerequisites

Example Use Cases

Scenario 1: Development Team LLM

Scenario 2: Enterprise Help Desk

Scenario 3: E-Commerce Recommendations

Scenario 4: Internal Knowledge Base

Metrics and Validation

Template Quality Metrics

Testing Recommendations

Future Enhancements

Support and Resources

Documentation

Getting Help

Contributing

License

Acknowledgments