klement gunndu

Posted on Oct 6

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

#rag #ai #machinelearning #python

🚀 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?

Most Tutorials	This Repository
Single framework	5 frameworks
Basic examples	Production code
No cloud deployment	AWS + GCP + Azure
Toy projects	Real use cases
No error handling	Enterprise patterns
100-200 lines	8,263 lines

🎓 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:

Tutorials use toy examples - Not production code
Single framework focus - Can't compare/choose
No cloud deployment - Theory only
No error handling - Breaks in real life
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

RAG #AI #MachineLearning #LLM #MultiAgent #LangChain #AutoGen #CrewAI #Python #AWS #GCP #Azure #StudentResources #LearnAI #ProductionAI #VectorDatabase #OpenSource

DEV Community

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

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

🎯 What You Get (100% Free)

✅ Level 1: Basic RAG (Start Here)

✅ Level 2: Advanced RAG Techniques

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

✅ Level 4: Production Pipelines

✅ Level 5: Cloud Deployments

✅ Level 6: Real Use Cases

🔥 Why This Repository Is Perfect for Students

1. Learn By Doing, Not Just Reading

2. Cover Every Framework (Stand Out in Interviews)

3. Production-Grade Code (Not Toy Examples)

4. Cloud Skills (AWS, GCP, Azure)

5. 70+ Technologies in One Place

📊 What Makes This Different?

🎓 Perfect For

CS Students

Bootcamp Graduates

Self-Taught Developers

Job Seekers

🚀 Quick Start (5 Minutes)

💡 Learning Path Recommendation

🎁 What's Included

Documentation

Code Quality

DevOps

💰 Cost: $0.00

📈 Repository Stats

🌟 Why I Built This

🎯 Use This Repository To

Build Your Portfolio

Ace Technical Interviews

Start Freelancing

Land Your First AI Job

🔗 Links

📢 Share This

RAG #AI #MachineLearning #LLM #MultiAgent #LangChain #AutoGen #CrewAI #Python #AWS #GCP #Azure #StudentResources #LearnAI #ProductionAI #VectorDatabase #OpenSource

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