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

πŸš€ 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:

βœ… 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

βœ… 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

βœ… 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

βœ… Level 4: Production Pipelines

  • Ingestion - Batch & streaming with Kafka, Celery
  • Evaluation - RAGAS metrics, A/B testing
  • Monitoring - Prometheus, Grafana, OpenTelemetry

βœ… Level 5: Cloud Deployments

  • AWS - Lambda, SageMaker, Bedrock, CloudFormation
  • GCP - Vertex AI, Cloud Run, Terraform
  • Azure - OpenAI Service, Container Apps, Bicep

βœ… 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

πŸ”₯ 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 βœ…
  • AutoGen βœ…
  • CrewAI βœ…
  • LangGraph βœ…
  • Amazon Bedrock βœ…

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

3. Production-Grade Code (Not Toy Examples)

  • βœ… 100% type hints (Python best practices)
  • βœ… SOLID principles throughout
  • βœ… Comprehensive error handling
  • βœ… Structured logging (production-ready)
  • βœ… Environment-based config (no hardcoded secrets)
  • βœ… 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


πŸ“Š 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

πŸŽ“ 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:

  • βœ… LLM application development
  • βœ… Vector database management
  • βœ… Multi-agent orchestration
  • βœ… Cloud architecture (3 platforms)
  • βœ… Production system design
  • βœ… DevOps & CI/CD
  • βœ… Code quality & best practices

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


πŸ’‘ 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

  • βœ… Comprehensive README with badges
  • βœ… Architecture documentation
  • βœ… Setup instructions per module
  • βœ… API reference
  • βœ… Technology breakdown by module

Code Quality

  • βœ… 100% type hint coverage
  • βœ… Docstrings everywhere
  • βœ… SOLID principles
  • βœ… Error handling patterns
  • βœ… Production logging

DevOps

  • βœ… Dockerfile & Docker Compose
  • βœ… GitHub Actions CI/CD
  • βœ… Makefile (install, test, lint, format, docker)
  • βœ… Testing setup (unit + integration)

πŸ’° Cost: $0.00

Everything uses free/open-source tools:

  • βœ… Grok API (free tier available)
  • βœ… Pinecone (free tier: 1 pod)
  • βœ… ChromaDB (free & open-source)
  • βœ… All Python libraries (free)
  • βœ… Cloud examples use free tiers

Zero cost to learn production AI engineering.


πŸ“ˆ 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 β†’ 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 β†’ $2-5K per project
  • Research assistants β†’ $3-7K per project
  • Code assistants β†’ $5-10K per project
  • Legal document analysis β†’ $5-15K per project

Land Your First AI Job

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

  • βœ… Multi-framework proficiency
  • βœ… Production patterns
  • βœ… Cloud deployments
  • βœ… Real use cases
  • βœ… Code quality

πŸ”— Links

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

⭐ Star the repo if this helps you!

🍴 Fork it and build your own use cases

πŸ’¬ Questions? Open an issue or discussion


πŸ“’ 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"
  • βœ… "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

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