AI Enhancement with Knowledge Graphs - Mastering RAG Systems

Overview

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Unleash the potential of AI systems by mastering Retrieval-Augmented Generation (RAG) techniques with Knowledge Graphs in this comprehensive course. You'll learn how to design, build, and query advanced Knowledge Graphs while integrating them with AI systems to boost contextual understanding and improve retrieval efficiency. The course begins with a solid introduction to Knowledge Graphs, including their structure, construction, and applications. You'll set up your development environment, dive into practical Neo4j implementations, and programmatically generate Knowledge Graphs. Through guided exercises, you'll extract real-world data, transform it into graph structures, and visually explore their interconnections. Moving further, you'll explore the synergy between Knowledge Graphs and RAG systems, creating vector indexes, embeddings, and integrating them into databases. Learn advanced querying methods, visualizations, and workflows for AI-powered use cases. By the end, you'll build a RAG-powered Knowledge Graph project, combining Neo4j and LangChain, to showcase the full flow of data transformation, retrieval, and application. This course is perfect for AI enthusiasts, data engineers, and developers eager to enhance their AI models with Knowledge Graphs. Prior experience with Python and basic AI concepts is recommended. Whether you’re at an intermediate or advanced level, you'll gain valuable, industry-relevant skills.

Syllabus

Introduction

In this module, we will set the stage for the course by reviewing the essential prerequisites and introducing the core concepts of Knowledge Graphs and RAG systems. You'll gain a clear roadmap of the course's objectives and structure, ensuring you're fully prepared to embark on this learning journey.

Development Environment Setup

In this module, we will guide you through the setup of a robust development environment, including the creation and configuration of your OpenAI account. You’ll learn how to acquire and use your API key effectively, ensuring you have the technical foundation to build and experiment with RAG systems.

Knowledge Graph Deep Dive

In this module, we will delve deeply into the world of Knowledge Graphs, exploring their definition, core principles, and key components. You will gain insights into their structure, learn how they are constructed, and uncover their applications in real-world AI scenarios. This foundational knowledge is essential for mastering RAG systems.

Hands-On: Knowledge Graph Deep Dive - Neo4j Introduction and Overview

In this module, we will provide a hands-on experience with Neo4j, a leading graph database platform. You'll start with the fundamentals and progressively learn how to set up a Neo4j environment, programmatically build Knowledge Graphs, and execute queries to explore entities and relationships. By the end, you'll have practical skills in creating and querying Knowledge Graphs using Neo4j.

Knowledge Graphs & RAG Systems

In this module, we will bridge the gap between Knowledge Graphs and RAG systems, providing a comprehensive overview of their synergy. You’ll engage in hands-on tasks, including extracting data from CSV files to build Knowledge Graphs, visualizing them using Neo4j Browser, and leveraging LangChain wrappers for advanced querying. This module equips you with the skills to create and query Knowledge Graphs in the context of AI systems.

Knowledge Graph & RAG - Index Creation and Vector Store - Embeddings

In this module, we will focus on the integration of vector embeddings with Knowledge Graphs, a critical component of RAG systems. You’ll learn how to create vector indexes, populate them with embeddings, and query these alongside your Knowledge Graph. This combination enhances the retrieval capabilities and functionality of RAG systems for advanced AI applications.

Hands-on: User Cases - Graph Retrieval and RAG Systems - The Whole Flow

In this module, we will walk you through the process of building a complete RAG system using a Knowledge Graph, with a hands-on project centered around the Roman Empire. You’ll set up the project, extract and visualize graph data, create indexes and retrievers, and ultimately define a full GraphRAG workflow. By the end of this module, you will have a comprehensive understanding of how to create an end-to-end RAG system powered by Knowledge Graphs.

Wrap up

In this module, we will conclude the course by revisiting the core topics and achievements, ensuring you have a clear understanding of your progress. You'll also receive guidance on next steps to deepen your expertise and explore advanced applications of Knowledge Graphs and RAG systems.