🧠 RAG-Mastery

A complete end-to-end learning repository covering Retrieval-Augmented Generation (RAG) from fundamentals to advanced concepts.

This repository is built by following and extending the "LangChain & RAG Mastery" Udemy course by Krish Naik.

It includes all the concepts taught in the course along with my personal notes, improvements, and additional experiments.

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✨ What You'll Learn

• Core RAG architecture and components
• Data ingestion, parsing, and advanced chunking strategies
• Vector embeddings, hybrid search, and query enhancement
• Multimodal RAG
• Building AI Agents and Agentic RAG
• LangGraph for complex workflows
• Multi-agent systems, Corrective RAG, Adaptive RAG
• Persistent memory, caching, and RAG evaluation
• Graph Databases with Cypher queries using LangChain

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📁 Project Structure

RAG-Mastery/
├── 01. Introduction to RAG/
├── 02. Core Components in RAG/
├── 03. Data Ingestion and Data Parsing Techniques/
├── 04. Vector Embeddings and Vector Databases/
├── 05. Vector Stores and Vector Databases/
├── 06. Advance Chunking and Preprocessing Techniques/
├── 07. Hybrid Search Strategies/
├── 08. Query Enhancement/
├── 09. MultiModal Introduction and Multi-Model RAG/
├── 10. Introduction to AI Agents and Agentic AI/
├── 11. Updated Langchain Hands On with Version V1/
├── 12. LangGraph Basics/
├── 13. Agents Architecture/
├── 14. Agentic RAG/
├── 15. Autonomous RAG/
├── 16. Multi Agents RAGS/
├── 17. Corrective RAG/
├── 18. Adaptive RAG/
├── 19. RAG With Persistant Memory/
├── 20. Cache RAG With LangGraph/
├── 21. ChatBot and RAG Evaluation/
├── 22. Introduction to Graph Databases and Cypher Query Language with Langchain/
├── 23. Practical Implementation with GraphDB with Langchain/
├── requirements.txt
├── .gitignore
└── README.md

Each folder contains well-commented Jupyter notebooks (.ipynb) for hands-on learning.

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🚀 Setup Instructions

1. Clone the Repository

git clone https://github.com/mani24singh/RAG-Mastery.git
cd RAG-Mastery

2. Create Virtual Environment (Recommended with uv)

# Install uv
pip install uv

# Create virtual environment
uv venv --python 3.11.13

# Activate the environment
# Windows:
.venv\Scripts\activate
# macOS / Linux:
source .venv/bin/activate

3. Install Dependencies

uv pip install -r requirements.txt

4. Register Jupyter Kernel

uv pip install ipykernel

uv run python -m ipykernel install --user --name "RAG-Mastery" --display-name "RAG-Mastery (.venv)"

Tip: Close and reopen VS Code after registering the kernel, then select "RAG-Mastery (.venv)" as the notebook kernel.

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🔑 Environment Variables

Create a .env file in the root directory:

# LLMs
OPENAI_API_KEY="your-openai-key"
GROQ_API_KEY="your-groq-key"
GOOGLE_API_KEY="your-google-key"

# Frameworks
LANGSMITH_API_KEY="your-langsmith-key"

# Vector Databases
PINECONE_API_KEY="your-pinecone-key"
ASTRA_DB_API_ENDPOINT="your-astra-endpoint"
ASTRA_DB_APPLICATION_TOKEN="your-astra-token"

# AI Agents
TAVILY_API_KEY="your-tavily-key"

.env is already added to .gitignore.

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🙏 Special Thanks

This repository is created while learning from the "LangChain & RAG Mastery" Udemy course by Krish Naik.

Huge thanks to Krish Naik for creating such a comprehensive and practical course on RAG and LangChain.

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🤝 Contributing

Contributions, improvements, and suggestions are welcome!
Feel free to fork the repo and submit a pull request.

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📜 License

This project is licensed under the MIT License.

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⭐ Show Your Support

If this repository helped you in mastering RAG concepts, please give it a ⭐ on GitHub!

Your support encourages me to keep improving and adding more content.

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Happy Learning & Building Powerful RAG Systems! 🚀

Made with ❤️ by Mani