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Java Spring AI, Neo4J, and OpenAI for Knowledge Graph RAG - OneDDL - 11-24-2024
![[Image: 75230a1b42a484f7f3362efd13cff092.jpeg]](https://i124.fastpic.org/big/2024/1124/92/75230a1b42a484f7f3362efd13cff092.jpeg)
Free Download Java Spring AI, Neo4J, and OpenAI for Knowledge Graph RAG
Published 11/2024
Created by Timotius Pamungkas
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 58 Lectures ( 6h 15m ) | Size: 2.9 GB
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.
Who this course is for
Software Developers / Engineers (particularly on Java Spring)
AI Enthusiasts
Technical Lead / Managers
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