https://github.com/redhatquickcourses/lean-rag
Repository created by RCB
https://github.com/redhatquickcourses/lean-rag
Last synced: 12 days ago
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Repository created by RCB
- Host: GitHub
- URL: https://github.com/redhatquickcourses/lean-rag
- Owner: RedHatQuickCourses
- Created: 2025-12-09T21:50:02.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-12-12T16:39:04.000Z (6 months ago)
- Last Synced: 2025-12-14T07:39:12.203Z (6 months ago)
- Language: CSS
- Homepage: https://redhatquickcourses.github.io/lean-rag/
- Size: 1.72 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Lean RAG Accelerator - Training & QuickStart
This repository contains both **training content** and **deployable quickstart code** for the Lean RAG Accelerator.
## Repository Structure
```
lean-rag/
├── README.md # This file
├── lean-rag-accelerator/ # Deployable QuickStart code
│ ├── README.md # QuickStart main documentation
│ ├── bootstrap.sh # Deployment script
│ ├── Makefile # Make targets for deployment
│ ├── examples/ # Deployment YAML files
│ ├── docs/ # QuickStart documentation
│ ├── scripts/ # Utility scripts
│ └── charts/ # Helm charts
├── modules/ # Training modules (Antora)
│ ├── module-1-*/ # Module 1: Inference Economics
│ ├── module-2-*/ # Module 2: Model Optimization
│ ├── module-3-*/ # Module 3: High-Performance Serving
│ ├── module-4-*/ # Module 4: Standardized RAG
│ └── module-5-*/ # Module 5: Validation & Benchmarking
└── antora.yml # Antora configuration
```
## Two Ways to Use This Repository
### 1. Training Content (Antora Documentation)
The training modules provide conceptual learning and best practices:
- **View Online**: The rendered training content is available via GitHub Pages (when configured)
- **Build Locally**: Use Antora to build and view the documentation locally
- **Modules**: Cover inference economics, model optimization, serving, RAG implementation, and benchmarking
**To view training content:**
- Navigate to the rendered documentation (GitHub Pages)
- Or build locally: `antora antora-playbook.yml`
### 2. Deployable QuickStart Code
The `lean-rag-accelerator/` directory contains ready-to-deploy code:
- **README.md**: Complete quickstart guide with deployment instructions
- **bootstrap.sh**: Interactive deployment script
- **Makefile**: Automated deployment targets
- **examples/**: Kubernetes YAML files for model optimization, inference serving, and RAG application
- **docs/**: Business value, architecture, and benchmarking documentation
**To deploy the quickstart:**
```bash
cd lean-rag-accelerator
./bootstrap.sh
```
Or use the Makefile:
```bash
cd lean-rag-accelerator
make help
make deploy-all NAMESPACE=lean-rag-accelerator
```
## Quick Links
### Training Content
- [Course Home](modules/ROOT/pages/index.adoc) - Start here for training modules
- [Module 1: Inference Economics](modules/module-1-inference-economics-rhoai-30-architecture/pages/module-1-inference-economics-rhoai-30-architecture.adoc)
- [Module 2: Model Optimization](modules/module-2-model-optimization-with-llm-compressor/pages/module-2-model-optimization-with-llm-compressor.adoc)
- [Module 3: High-Performance Serving](modules/module-3-high-performance-serving-with-vllm-llm-d/pages/module-3-high-performance-serving-with-vllm-llm-d.adoc)
- [Module 4: Standardized RAG](modules/module-4-standardized-rag-implementation/pages/module-4-standardized-rag-implementation.adoc)
- [Module 5: Validation & Benchmarking](modules/module-5-validation-benchmarking/pages/module-5-validation-benchmarking.adoc)
### QuickStart Code
- [QuickStart README](lean-rag-accelerator/README.md) - Main quickstart documentation
- [Business Value](lean-rag-accelerator/docs/business-value-driver.md) - ROI and problem statement
- [Architecture](lean-rag-accelerator/docs/architecture.md) - Technical architecture
- [Deployment Guide](lean-rag-accelerator/docs/deployment-guide.md) - Step-by-step deployment
- [Benchmarking](lean-rag-accelerator/docs/benchmarks.md) - Performance validation
## Getting Started
### For Training (Learning)
1. Review the training modules in order (Module 1 → Module 5)
2. Follow along with the concepts and best practices
3. Reference the quickstart code for hands-on practice
### For Deployment (QuickStart)
1. Navigate to `lean-rag-accelerator/` directory
2. Read the [QuickStart README](lean-rag-accelerator/README.md)
3. Follow the deployment instructions
4. Use `bootstrap.sh` or `Makefile` for automated deployment
## Prerequisites
- **For Training**: No prerequisites - the modules explain concepts
- **For QuickStart**:
- Red Hat OpenShift AI 3.0
- Kubernetes cluster with GPU nodes
- kubectl/oc CLI configured
- See [QuickStart Requirements](lean-rag-accelerator/README.md#requirements) for details
## Support
- **Training Questions**: Review the training modules
- **QuickStart Issues**: See [Troubleshooting Guide](lean-rag-accelerator/docs/troubleshooting.md)
- **General**: Refer to the main repository or contact Red Hat support
## Related Resources
- [Red Hat OpenShift AI 3.0 Documentation](https://docs.redhat.com/)
- [Reference RAG QuickStart](https://github.com/rh-ai-quickstart/RAG)
- [KServe Documentation](https://kserve.github.io/website/)
- [vLLM Documentation](https://docs.vllm.ai/)