Name: Advance RAG : Vector to Graph RAG Neo4j Adaptive AutoGen AWS
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Created bySoumen Kumar Mondal

Last updated 4/2025

English

What you'll learn

Fundamentals of RAG (Retrieval-Augmented Generation) and NLP: Understand core concepts to build strong foundation of NLP and RAG.
Understand process of NLP like tokenization, embedding, POS, TF-IDF, chunking etc.
Understand evaluation of NLP models from Rule based to Transformer model.
Understand transformer model with simple RAG example.
Environment setup for hands on implementation of RAG application using Python and VS Code
Learn to build vector based RAG application with Streamlit chatbot, langchain and vectordb.
Learn advance RAG technique with Graph RAG , LLM and Streamlit chatbot. Learn how to setup Neo4j, create Graph RAG , show graph in your chatbot.
Learn advance RAG with hybrid search technique using Graph RAG. Learn self reflective RAG with Langgraph. Practical use cases with python code of RAG.
Re ranking RAG with cohere API to improve retrieval process of RAG.
Practical use cases on RAG.
Quizzes to check learning.
Build Agent based RAG application with Autogen. Agentic RAG.

Course content

11 sections • 27 lectures • 3h 26m total length

Introduction to course4:47
In this course you'll master RAG a cutting edge AI technique. This course is tailored to help you to build practical RAG application from simple to advance chatbot. Along with that you will understand fundamental of NLP and RAG.

What is NLP?1:43
This is a short lecture on definition and applications of NLP.
POS , NER , Chunking, BoW, TF-IDF and Embedding7:12
Lets deep dive into NLP processes like POS (Parts of Speech), NER, Chunking, BoW, TF-IDF and Embedding with examples.
Tokenization, Stemming and Lemmatization3:31
Tokenization is important process to understand for RAG. In this lecture, we will understand tokenization with example.
Evaluation of NLP5:47
Natural Language Processing, or NLP, has come a long way in helping machines understand and generate human language. But how do we evaluate the effectiveness of different NLP approaches? Let’s take a journey through the evolution of NLP models, from simple rule-based systems to advanced Transformer models
Transformer Model8:10
We have seen Rule based and RNN model. In this module we will cover Transformer model.

What is Graph RAG3:47
Now that we’ve explored the basics of Vector RAG, let’s take it a step further by diving into Graph RAG, an advanced approach that leverages the power of graph databases and graph-based data structures to enhance the retrieval-augmented generation process.
Implement Graph RAG chatbot to build and show graph with Neo4j13:47
Hands-on implementation of RAG chatbot with Neo4j. Create graph and display graph in chatbot.
Implement hybrid search with Graph RAG and Neo4j18:45
Lets implement hybrid search technique of Graph RAG.
Vector RAG vs Graph RAG4:09
Compare vector RAG and graph RAG to see how embeddings and semantic similarity drive retrieval in vector databases, versus knowledge graphs and graph traversal in graph databases.

Understand adaptive or self-reflective flow1:56
Self-reflective RAG or Adaptive RAG is advance RAG technique to improve response of user query. With multiple level of checking into every layer will improve RAG response. Lets understand the flow of Adaptive RAG.
Implement Self-reflective RAG chatbot with Langgraph20:36
Hands on of self reflective RAG. Will build a chatbot with the flow of adaptive RAG.

Use case with Bedrock - Knowledge Base6:40
Use case architecture1:49
Describe the use case architecture for a stream-based question answering pipeline on AWS, routing user queries through Lambda to Bedrock's retrieve and generate API, with knowledge base access.
Knowledge Base Setup4:57
Set up a knowledge base by uploading a document to an S3 bucket, linking the data source, and configuring a vector store with OpenSearch serverless and cost considerations.
How to sync Knowledge Base7:51
Environment setup - Boto34:59
RAG - Streamlit chatbot, knowledge base, s317:25

Requirements

No prior RAG experience required.
Very basic python knowledge will help.
Don't worry without python knowledge also you will learn how to implement RAG chatbot.

Description

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.
Create RAG application with AWS Bedrock Knowledge Base , BOTO3, Streamlit, Lamda, S3
Check your understanding with Quizzes.

Lets deep dive into world of RAG to understand it.

Who this course is for:

Data Scientists
Machine Learning Engineers
AI and NLP Enthusiasts
Developers and Software Engineers
Researchers and Academics
Product Managers and Technical Leads
Students and Learners
AI Practitioners and Consultants
Quality Engineers

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 5min

Fundamentals of RAG2 lectures • 7min

Fundamental of NLP5 lectures • 26min

Environment setup2 lectures • 21min

Implement chatbot with Vector RAG2 lectures • 14min

Implement RAG chatbot with Graph RAG4 lectures • 40min

Implement Self-Reflective RAG or Adaptive RAG2 lectures • 23min

Cohere ranking RAG(newly added)1 lecture • 14min

Autogen RAG: Agentic RAG (newly added)1 lecture • 8min

RAG with AWS Bedrock (Newly added)6 lectures • 44min

Requirements

Description

Who this course is for: