RAG (Retrieval Augmented Generation) with Vector Similarity and Knowledge Graph using Spring AI, Neo4J, and Temporal

What you'll learn

Understand Retrieval Augmented Generation (RAG) for Generative AI

Understand Knowledge Graph and How It Enhances RAG to Form GraphRAG

Implements Retrieval Augmented Generation (RAG) Using OpenAI, Spring Boot 3 and Spring AI

Implements Knowledge Graph RAG Using Neo4j

Requirements

Basic Java Programming

Basic Spring Boot Programming

Basic Understanding of Using Large Language Models like OpenAI

Description

Enhance Your Generative AI Expertise with Retrieval Augmented Generation (RAG) and Knowledge GraphRetrieval-augmented generation (RAG) is a powerful approach for utilizing generative AI to access information beyond the pre-trained data of Large Language Models (LLMs) while avoiding over-reliance on these models for factual content. The effectiveness of RAG hinges on the ability to quickly identify and provide the most relevant context to the LLM. Knowledge Graphs transforms RAG systems with improved performance, accuracy, traceability, and completeness.The RAG with Knowledge Graph, also known as GraphRAG, is an effective way to improve the capability of Generative AI. Take your AI skills to the next level with this ultimate course, designed to help you unlock the potential of LLMs by leveraging Knowledge Graphs and RAG systems.In this course, you will learn:Introduction to RAG Systems: Discover why Retrieval Augmented Generation is a groundbreaking tool for enhancing AI.Foundations of Knowledge Graphs: Grasp the basics of knowledge graphs, including their structure and data relationships. Understand how these graphs enhance data modeling for RAG.Implementing GraphRAG from Scratch: Build a fully operational RAG system with knowledge graphs. Use LLMs to extract and organize information.Building Knowledge From Multiple Data Sources: Learn to integrate knowledge graphs with unstructured and structured data sources.Querying Knowledge Graphs: Gain practical experience with leading tools and techniques.Technology Highlights:Spring AI: A new technology from famous Java Spring to help engineers work easily with various Generative AI and Large Language ModelsOpen AI: The innovative Generative AI that everyone loves. A groundbreaking tool for Large Language Models and AI.Neo4J: Graph database and Vector store that integrates easily with Spring AI to form RAG and Knowledge GraphTemporal: A workflow orchestrator platform to help engineers build a reliable GrahRAG pipeline.Mastering advanced AI techniques offers a significant edge in today's fast-paced, data-driven world. This course provides actionable insights to enhance your career or innovate in your field.

Overview

Section 1: Introduction

Lecture 1 Welcome To This Course

Lecture 2 Course Structure & Coverage

Lecture 3 Technology In This Course

Lecture 4 Download Source Code & Scripts

Lecture 5 Tips : How To Get Maximum Value From This Course

Section 2: Using Artificial Intelligence (AI) Assistant

Lecture 6 AI Assistant In This Course

Lecture 7 About This Section

Lecture 8 Important Points on Course With AI Assistant

Lecture 9 Download AI Prompts

Section 3: How AI Assistant Change The Way We Work

Lecture 10 AI Assistant in Software Engineering

Lecture 11 AI Assistant in Software Testing

Lecture 12 ChatGPT & Github Copilot Introduction

Lecture 13 ChatGPT & Github Copilot Installation

Section 4: Tools Installation

Lecture 14 What & Why Docker

Lecture 15 Install Visual Studio Code

Lecture 16 Visual Studio Code With GitHub Copilot

Section 5: Generative AI & Large Language Models

Lecture 17 Generative AI (Gen AI)

Lecture 18 Large Language Models (LLM)

Lecture 19 Prompt Engineering

Lecture 20 LLM Limitations

Section 6: Retrieval Augmented Generation (RAG)

Lecture 21 Retrieval Augmented Generation (RAG) Introduction

Lecture 22 RAG System Design

Section 7: Spring AI First Steps

Lecture 23 Start With Spring AI

Lecture 24 Hello OpenAI

Section 8: Basic RAG

Lecture 25 Basic Indexing Pipeline - Theory

Lecture 26 Basic Indexing Pipeline - Hands On

Lecture 27 Basic Indexing Pipeline - Test The Application

Lecture 28 Basic RAG Processor - Theory

Lecture 29 Basic RAG Processor - Hands On

Lecture 30 Basic RAG Processor - Test The Application

Section 9: Vector RAG

Lecture 31 Vector Indexing Pipeline - Theory

Lecture 32 Neo4j Installation

Lecture 33 Vector Indexing Pipeline - Hands On

Lecture 34 Vector Indexing Pipeline - Test The Application

Lecture 35 Vector RAG Processor - Theory

Lecture 36 Vector RAG Processor - Hands On

Lecture 37 Vector RAG Processor - Test The Application

Lecture 38 It Works, But .

Lecture 39 Avoid Duplicates

Lecture 40 Test The Enhanced Pipeline

Lecture 41 Tips: Vector Store

Section 10: Reliable Pipeline App

Lecture 42 Dedicated Pipeline App - Hands On

Lecture 43 Dedicated Pipeline App - Test The Pipeline

Section 11: Knowledge Graph (KG)

Lecture 44 Knowledge Graph

Lecture 45 Building Knowledge Graph - Theory

Lecture 46 Neo4J Introduction

Lecture 47 Building Static Knowledge Graph - Hands On

Lecture 48 Building Static Knowledge Graph - Test The Application

Lecture 49 Building Knowledge Graph From Structured Data - Hands On

Lecture 50 Building Knowledge Graph From Structured Data - Test The Application

Lecture 51 Knowledge Graph RAG Processor - Theory

Lecture 52 Knowledge Graph RAG Processor - Hands On

Lecture 53 Knowledge Graph RAG Processor - Test The Application

Lecture 54 Building Knowledge Graph From Unstructured Data - Theory

Lecture 55 Building Knowledge Graph From Unstructured Data - Before Hands On

Lecture 56 Preparing Neo4J Knowledge Graph Builder

Section 12: Resources & Reference

Lecture 57 Download Source Code & Scripts

Lecture 58 Bonus Lectures

Software Developers / Engineers (particularly on Java Spring),AI Enthusiasts,Technical Lead / Managers