Java Spring Ai, Neo4J, And Openai For Knowledge Graph Rag
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.16 GB | Duration: 6h 16m
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 скачать 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 скачать 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 скачать Source Code & Scripts
Lecture 58 Bonus Lectures
Software Developers / Engineers (particularly on Java Spring),AI Enthusiasts,Technical Lead / Managers
RapidGator
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