Advanced RAG : Vector to Graph RAG LangChain Neo4j AutoGen

In this course, you will learn how to master Retrieval-Augmented Generation (RAG), a cutting-edge AI technique that combines retrieval-based methods with generative models. This course is designed for developers, data scientists, and AI enthusiasts, quality engineers, Students who want to build practical applications using RAG, ranging from simple vector RAG chatbot to advanced chatbot with Graph RAG and Self Reflective RAG. You’ll explore the theoretical foundations, practical implementations, and real-world use cases of RAG. By the end of this course, you will have the skills to create  RAG-based AI applications.

After completing the course, you will be able to create chatbot with multiple RAG techniques using Streamlit, LangChain, LangGraph, Groq API and many more. Along with that you will also learn fundamentals and concepts.

Course Objectives

• Understand the fundamental concepts of RAG and NLP.
• Understand concepts of NLP with examples like tokenization, chunking, TF-IDF, embedding.
• Understand evaluation of NLP models from rule based to transformer model.
• Understand transformer model and components with examples.
• Environment setup for hands on implementation.
• Build first chatbot with Streamlit and Langchain.
• Build a vector RAG with Streamlit chatbot with Groq API.
• Understand Graph RAG and implement Graph RAG with Neo4j.
• Understand Self Reflective or Adaptive RAG and implement with LangGraph.
• Real world use cases of RAG.
• Re-ranking RAG technique
• Agentic RAG or Agent based RAG. AutoGen RAG.
• Check your understanding with Quizzes.

Lets deep dive into world of RAG to understand it.