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Udemy – Advance RAG : Vector to Graph RAG Neo4j Adaptive AutoGen RAG 2024-10

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Lecture 7/21

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Lecture 7 of 21

POS , NER , Chunking, BoW, TF-IDF and Embedding

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Course Content

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Section 1: Fundamentals of RAG2 videos

What is RAG RAG Process

12m

Generative AI without RAG. Why RAG

11m

Section 2: Introduction1 videos

Introduction to course

33m

Section 3: Fundamental of NLP5 videos

Tokenization, Stemming and Lemmatization

10m

What is NLP

Evaluation of NLP

14m

POS , NER , Chunking, BoW, TF-IDF and Embedding

30mNow Playing

Transformer Model

38m

Section 4: Environment setup2 videos

Create simple streamlit chatbot

20m

Setup VS code , Python, Neo4j, Streamlit, PIP packages

1h 8m

Section 5: Implement chatbot with Vector RAG2 videos

What is vector RAG

11m

Develop vector RAG with Groq API and Langchain

53m

Section 6: Implement RAG chatbot with Graph RAG4 videos

Vector RAG vs Graph RAG

10m

What is Graph RAG

17m

Implement Graph RAG chatbot to build and show graph with Neo4j

1h 14m

Implement hybrid search with Graph RAG and Neo4j

1h 30m

Section 7: Implement Self-Reflective RAG or Adaptive RAG2 videos

Understand adaptive or self-reflective flow

Implement Self-reflective RAG chatbot with Langgraph

1h 30m

Section 8: Autogen RAG Agentic RAG (newly added)1 videos

Autogen RAG Agentic RAG

39m

Section 9: Cohere ranking RAG(newly added)1 videos

Flow of Ranking RAG and LangChain Python coding

1h 8m

Section 10: Source Code , Advance Use Cases and Conclusion1 videos

Use Cases and Conclusion

21m

About this Lecture

Section: Fundamental of NLP • Size: 65.17 MB • Duration: 30m

Course Summary

Advance RAG course: Vector to Graph RAG Neo4j Adaptive AutoGen RAG. In this course, you will learn how to master Retrieval Augmented Generation (RAG), an advanced artificial intelligence technique that combines retrieval-based methods with generative models. This course is designed for developers, data scientists, AI enthusiasts, quality engineers, students who want to build practical applications using RAG, from a simple chatbot based on vector RAG to advanced chatbot with Graph RAG and self-reflective RAG. You will explore the theoretical principles, practical implementation and real-world use cases of RAG. At the end of this course, you will have the skills to create RAG-based AI applications.

Course Content

0 / 21 completed

Section 1: Fundamentals of RAG2 videos

What is RAG RAG Process

12m

Generative AI without RAG. Why RAG

11m

Section 2: Introduction1 videos

Introduction to course

33m

Section 3: Fundamental of NLP5 videos

Tokenization, Stemming and Lemmatization

10m

What is NLP

Evaluation of NLP

14m

POS , NER , Chunking, BoW, TF-IDF and Embedding

30mNow Playing

Transformer Model

38m

Section 4: Environment setup2 videos

Create simple streamlit chatbot

20m

Setup VS code , Python, Neo4j, Streamlit, PIP packages

1h 8m

Section 5: Implement chatbot with Vector RAG2 videos

What is vector RAG

11m

Develop vector RAG with Groq API and Langchain

53m

Section 6: Implement RAG chatbot with Graph RAG4 videos

Vector RAG vs Graph RAG

10m

What is Graph RAG

17m

Implement Graph RAG chatbot to build and show graph with Neo4j

1h 14m

Implement hybrid search with Graph RAG and Neo4j

1h 30m

Section 7: Implement Self-Reflective RAG or Adaptive RAG2 videos

Understand adaptive or self-reflective flow

Implement Self-reflective RAG chatbot with Langgraph

1h 30m

Section 8: Autogen RAG Agentic RAG (newly added)1 videos

Autogen RAG Agentic RAG

39m

Section 9: Cohere ranking RAG(newly added)1 videos

Flow of Ranking RAG and LangChain Python coding

1h 8m

Section 10: Source Code , Advance Use Cases and Conclusion1 videos

Use Cases and Conclusion

21m