🚀 Free RAG Learning Path: From Basic to Multi-Agent Systems (143 Files, 70+ Technologies)

Are you a CS student or aspiring AI engineer? I just released a completely free GitHub repository that takes you from RAG basics to production-grade multi-agent systems.

🎯 What You Get (100% Free)

Repository: https://github.com/KlementMultiverse/rag-mastery-hub

This isn't another tutorial collection. This is 8,263 lines of production-ready code covering:

✅ Level 1: Basic RAG (Start Here)

• 
  Simple keyword-based RAG - TF-IDF + Grok API


• 
  Vector database RAG - ChromaDB & Pinecone integration


• 
  Production RAG - Circuit breakers, Redis caching, Prometheus metrics

✅ Level 2: Advanced RAG Techniques

• 
  Query Rewriting - HyDE, multi-query expansion


• 
  Reranking - Cross-encoders, Cohere Rerank, RRF fusion


• 
  Chunking Strategies - Semantic, recursive, sliding window


• 
  Knowledge Graphs - Neo4j integration, entity extraction


• 
  Hybrid Search - BM25 + semantic search fusion


• 
  Multimodal RAG - Text + images with CLIP & GPT-4 Vision

✅ Level 3: Multi-Agent Systems (ALL Frameworks)

• 
  LangChain - ReAct agents, research workflows


• 
  AutoGen - Microsoft's conversational agents & group chat


• 
  CrewAI - Role-based agent crews


• 
  LangGraph - Graph-based workflows with state management


• 
  Amazon Bedrock - AWS-native orchestration

✅ Level 4: Production Pipelines

• 
  Ingestion - Batch & streaming with Kafka, Celery


• 
  Evaluation - RAGAS metrics, A/B testing


• 
  Monitoring - Prometheus, Grafana, OpenTelemetry

✅ Level 5: Cloud Deployments

• 
  AWS - Lambda, SageMaker, Bedrock, CloudFormation


• 
  GCP - Vertex AI, Cloud Run, Terraform


• 
  Azure - OpenAI Service, Container Apps, Bicep

✅ Level 6: Real Use Cases

• 
  Customer Support Bot - Sentiment analysis, ticket routing


• 
  Research Assistant - arXiv API, citation extraction


• 
  Code Assistant - AST parsing, GitHub integration


• 
  Legal Document Analyzer - Contract analysis, entity extraction

🔥 Why This Repository Is Perfect for Students

1. Learn By Doing, Not Just Reading

Every module has working code you can run immediately. No half-baked tutorials.

2. Cover Every Framework (Stand Out in Interviews)

• 
  LangChain ✅


• 
  AutoGen ✅


• 
  CrewAI ✅


• 
  LangGraph ✅


• 
  Amazon Bedrock ✅

Most students know one framework. You'll know ALL five.

3. Production-Grade Code (Not Toy Examples)

• ✅ 100% type hints (Python best practices)


• ✅ SOLID principles throughout


• ✅ Comprehensive error handling
  


• ✅ Structured logging (production-ready)


• ✅ Environment-based config (no hardcoded secrets)


• ✅ Docker & CI/CD (DevOps skills)

4. Cloud Skills (AWS, GCP, Azure)

Most bootcamps teach you theory. This repo gives you deployment code for all three major clouds.

5. 70+ Technologies in One Place

Vector Databases: Pinecone, ChromaDB, Weaviate, Qdrant, OpenSearch

LLMs: Grok, OpenAI, Claude, PaLM, Azure OpenAI

Frameworks: LangChain, AutoGen, CrewAI, LangGraph, Bedrock

Graph DBs: Neo4j, NetworkX

NLP: SpaCy, NLTK, Unstructured.io

Cloud: AWS (Lambda, SageMaker), GCP (Vertex AI), Azure (Functions)

Monitoring: Prometheus, Grafana, OpenTelemetry, LangSmith

DevOps: Docker, GitHub Actions, Terraform, CloudFormation

📊 What Makes This Different?

🎓 Perfect For

CS Students

• Senior project material


• Portfolio piece for internships


• Interview preparation


• Learn industry best practices

Bootcamp Graduates

• Fill knowledge gaps


• Stand out from other candidates


• Demonstrate production skills


• Build portfolio depth

Self-Taught Developers

• Structured learning path


• Industry-standard patterns


• Real-world use cases


• Complete reference implementation

Job Seekers

This single repository proves proficiency in:

• ✅ LLM application development


• ✅ Vector database management


• ✅ Multi-agent orchestration


• ✅ Cloud architecture (3 platforms)


• ✅ Production system design


• ✅ DevOps & CI/CD


• ✅ Code quality & best practices

🚀 Quick Start (5 Minutes)

# Clone the repo

git clone https://github.com/KlementMultiverse/rag-mastery-hub.git

cd rag-mastery-hub



# Install dependencies

pip install -r requirements.txt



# Set up environment

cp .env.example .env

# Edit .env with your API keys (Grok API is free tier)



# Run your first RAG system

python 01_basic_rag/level_1_simple/simple_rag.py

That's it. You just ran a production RAG system.

💡 Learning Path Recommendation

Week 1: Basic RAG (Level 1)

Week 2: Advanced RAG Techniques (Level 2)

Week 3: Multi-Agent Systems (Level 3)

Week 4: Production Pipelines (Level 4)

Week 5: Cloud Deployments (Level 5)

Week 6: Build Your Own Use Case (Level 6)

In 6 weeks, you'll go from RAG beginner to production engineer.

🎁 What's Included

Documentation

• ✅ Comprehensive README with badges


• ✅ Architecture documentation


• ✅ Setup instructions per module


• ✅ API reference


• ✅ Technology breakdown by module

Code Quality

• ✅ 100% type hint coverage


• ✅ Docstrings everywhere


• ✅ SOLID principles


• ✅ Error handling patterns


• ✅ Production logging

DevOps

• ✅ Dockerfile & Docker Compose


• ✅ GitHub Actions CI/CD


• ✅ Makefile (install, test, lint, format, docker)


• ✅ Testing setup (unit + integration)

💰 Cost: $0.00

Everything uses free/open-source tools:

• ✅ Grok API (free tier available)


• ✅ Pinecone (free tier: 1 pod)


• ✅ ChromaDB (free & open-source)


• ✅ All Python libraries (free)


• ✅ Cloud examples use free tiers

Zero cost to learn production AI engineering.

📈 Repository Stats

• 
  Total Files: 143


• 
  Total Lines: 8,263


• 
  Core Implementation: 16 files, 6,900+ lines


• 
  Frameworks: 5 (LangChain, AutoGen, CrewAI, LangGraph, Bedrock)


• 
  Cloud Platforms: 3 (AWS, GCP, Azure)


• 
  Use Cases: 4 (Support, Research, Code, Legal)


• 
  Technologies: 70+

🌟 Why I Built This

I'm a developer building AI systems in production. I kept seeing students struggle because:

1. 
   Tutorials use toy examples - Not production code


2. 
   Single framework focus - Can't compare/choose


3. 
   No cloud deployment - Theory only


4. 
   No error handling - Breaks in real life


5. 
   Scattered resources - No learning path

This repository solves all five problems.

🎯 Use This Repository To

Build Your Portfolio

Fork it. Extend it. Add your own use cases. Show employers you understand:

• RAG systems (basic → advanced)


• Multi-agent orchestration


• Cloud deployments


• Production engineering

Ace Technical Interviews

Common interview questions this repo prepares you for:

• "How would you build a RAG system?"


• "What's the difference between LangChain and AutoGen?"


• "How do you handle errors in production LLM systems?"


• "Explain semantic chunking vs. fixed-size chunking"


• "How would you deploy this to AWS/GCP/Azure?"

Start Freelancing

Use these implementations as templates for client projects:

• Customer support bots → $2-5K per project


• Research assistants → $3-7K per project


• Code assistants → $5-10K per project


• Legal document analysis → $5-15K per project

Land Your First AI Job

This repository demonstrates skills that most senior engineers don't have:

• ✅ Multi-framework proficiency


• ✅ Production patterns


• ✅ Cloud deployments


• ✅ Real use cases


• ✅ Code quality

🔗 Links

Repository: https://github.com/KlementMultiverse/rag-mastery-hub

⭐ Star the repo if this helps you!

🍴 Fork it and build your own use cases

💬 Questions? Open an issue or discussion

📢 Share This

Know a CS student or bootcamp grad looking to break into AI? Share this repository.

It could be the difference between:

• ❌ "I don't have experience"


• ✅ "Here's my production RAG implementation with 5 frameworks"

Built with ❤️ for the AI learning community

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