🚀 RAGForge – Kubernetes-Native Hybrid RAG Lab

RAGForge is a Kubernetes-native Hybrid Retrieval-Augmented Generation (RAG) system built using official Kubernetes documentation as a real-world technical corpus.

This project is designed with AI Infrastructure principles from day one:

• Container-first architecture
• Kubernetes deployment (Minikube for lab)
• Modular RAG design
• Production-ready mindset
• Observability-ready structure

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🎯 Project Objective

Build a fully containerized Hybrid RAG system running on Kubernetes (Minikube) using:

📄 Official Kubernetes Basics Documentation
Source: https://kubernetes.io/docs/tutorials/kubernetes-basics/_print/

Initial Dataset: Printable version (~24 pages)

Why start with this dataset?

• Controlled size
• Structured technical content
• DevOps-aligned domain
• Ideal for parameter tuning

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🧠 Architecture Overview (Kubernetes-Native)

Flow:

User
↓
Ingress (Minikube)
↓
Service (ClusterIP / LoadBalancer)
↓
RAGForge API Pod (FastAPI)
├── Retriever
├── RAG Logic
└── LLM Client
↓
Vector DB Pod (Chroma / FAISS)
↓
External LLM API

Observability (Optional – Stage 4+):

• Prometheus
• Grafana
• Structured Logging

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☸️ Why Kubernetes From Day One?

• Clean isolation from local OS
• Portability across environments
• Easy scaling (HPA later)
• Future-ready production design
• Strong alignment with AI Infra / MLOps roles

Minikube provides:

• Local cluster
• Safe experimentation
• No pollution of host system
• Real Kubernetes workflow

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📂 Project Structure

ragforge/ │ ├── README.md ├── ROADMAP.md ├── requirements.txt ├── .env.example ├── .gitignore │ ├── data/ │ ├── raw/ │ │ └── kubernetes_basics.pdf │ └── processed/ │ ├── src/ │ ├── ingestion/ │ ├── embeddings/ │ ├── retriever/ │ ├── generation/ │ ├── evaluation/ │ └── config/ │ ├── k8s/ │ ├── namespace.yaml │ ├── ragforge-deployment.yaml │ ├── ragforge-service.yaml │ ├── vector-db-deployment.yaml │ ├── vector-db-service.yaml │ └── ingress.yaml │ └── tests/

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🧩 Core Modules

1️⃣ Ingestion

• Extract text from PDF
• Clean formatting artifacts
• Normalize text
• Remove duplicated headers
• Section-aware chunking

Key Parameters:

• chunk_size
• chunk_overlap
• section tagging
• page metadata

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2️⃣ Embeddings

• Generate vector representations
• Batch processing
• Cache embeddings
• Normalize vectors

Key Concepts:

• Cosine similarity
• Embedding dimensionality
• Semantic proximity

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3️⃣ Retriever

• Top-K similarity search
• Similarity threshold filtering
• Metadata filtering
• Section-aware retrieval

Why: Retrieval precision > model size.

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4️⃣ Generation

• Grounded prompt template
• Context injection
• Citation enforcement
• Anti-hallucination rules

Security:

• Prompt injection mitigation
• Input validation
• Context isolation

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5️⃣ Evaluation

Metrics:

• Context relevance
• Faithfulness
• Latency
• Token usage

MLOps mindset from start.

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🔐 Security Best Practices

• No secrets in Git
• .env excluded
• Kubernetes secrets for API keys
• Input length control
• Prompt injection mitigation
• Mandatory citation policy

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⚙️ Configurable Parameters

Parameter	Impact
chunk_size	Retrieval granularity
chunk_overlap	Context continuity
top_k	Retrieval depth
similarity_threshold	Noise filtering
temperature	Determinism
max_tokens	Cost control
embedding_model	Vector quality

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🚀 Running on Minikube

1. Start Minikube
2. Build Docker image
3. Apply namespace
4. Deploy vector DB
5. Deploy RAGForge API
6. Expose via Ingress
7. Query via browser / curl

This simulates a real production deployment workflow.

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🔮 Future Expansion

• Hybrid Search (BM25 + Vector)
• Re-ranking layer
• HPA autoscaling
• Prometheus metrics export
• Grafana dashboards
• CI/CD with GitHub Actions
• Helm chart
• Multi-environment setup (dev/stage/prod)

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🎯 Engineering Outcome

After completion, this project demonstrates:

• Kubernetes-native AI system design
• Hybrid RAG architecture
• AI Infrastructure thinking
• Observability awareness
• Production-oriented DevOps skills

Target Roles: AI Infrastructure Engineer
MLOps Engineer
LLMOps Engineer
Platform Engineer (AI Focus)