π 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.
π― 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:
β 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
β 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
β 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
β Level 4: Production Pipelines
- Ingestion - Batch & streaming with Kafka, Celery
- Evaluation - RAGAS metrics, A/B testing
- Monitoring - Prometheus, Grafana, OpenTelemetry
β Level 5: Cloud Deployments
- AWS - Lambda, SageMaker, Bedrock, CloudFormation
- GCP - Vertex AI, Cloud Run, Terraform
- Azure - OpenAI Service, Container Apps, Bicep
β 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
π₯ 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 β
- AutoGen β
- CrewAI β
- LangGraph β
- Amazon Bedrock β
Most students know one framework. You'll know ALL five.
3. Production-Grade Code (Not Toy Examples)
- β 100% type hints (Python best practices)
- β SOLID principles throughout
- β Comprehensive error handling
- β Structured logging (production-ready)
- β Environment-based config (no hardcoded secrets)
- β 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
π 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 |
π 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:
- β LLM application development
- β Vector database management
- β Multi-agent orchestration
- β Cloud architecture (3 platforms)
- β Production system design
- β DevOps & CI/CD
- β Code quality & best practices
π 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.
π‘ 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.
π What's Included
Documentation
- β Comprehensive README with badges
- β Architecture documentation
- β Setup instructions per module
- β API reference
- β Technology breakdown by module
Code Quality
- β 100% type hint coverage
- β Docstrings everywhere
- β SOLID principles
- β Error handling patterns
- β Production logging
DevOps
- β Dockerfile & Docker Compose
- β GitHub Actions CI/CD
- β Makefile (install, test, lint, format, docker)
- β Testing setup (unit + integration)
π° Cost: $0.00
Everything uses free/open-source tools:
- β Grok API (free tier available)
- β Pinecone (free tier: 1 pod)
- β ChromaDB (free & open-source)
- β All Python libraries (free)
- β Cloud examples use free tiers
Zero cost to learn production AI engineering.
π 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+
π 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.
π― 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:
- β Multi-framework proficiency
- β Production patterns
- β Cloud deployments
- β Real use cases
- β Code quality
π Links
Repository: https://github.com/KlementMultiverse/rag-mastery-hub
β Star the repo if this helps you!
π΄ Fork it and build your own use cases
π¬ Questions? Open an issue or discussion
π’ Share This
Know a CS student or bootcamp grad looking to break into AI? Share this repository.
It could be the difference between:
- β "I don't have experience"
- β "Here's my production RAG implementation with 5 frameworks"
Built with β€οΈ for the AI learning community
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