Author Description

LLM Graph RAG: A Hands-On Guide to Building Advanced, Graph-Based Retrieval-Augmented Generation with LLMs Unlock the power of Graph-Based Retrieval-Augmented Generation (RAG) to build intelligent AI systems that retrieve, reason, and generate knowledge like never before! In the era of Large Language Models (LLMs), retrieval-augmented generation (RAG) has emerged as a game-changing technique to enhance accuracy, reduce hallucinations, and provide reliable responses. But what if we could go beyond traditional retrieval techniques and integrate the power of knowledge graphs and Graph Neural Networks (GNNs) for even deeper reasoning and richer knowledge representation? This comprehensive, hands-on guide takes you through the entire journey of Graph-Based RAG, from foundations to real-world applications. Whether you're an AI developer, machine learning researcher, data scientist, or knowledge engineer, this book equips you with the skills and tools to leverage knowledge graphs, advanced retrieval techniques, and multimodal AI architectures to build next-generation AI systems. What You'll Learn Inside This Book: Part I: Foundations of Graph-Based RAG The evolution of Retrieval-Augmented Generation (RAG) and why traditional approaches fall short. Introduction to graph theory, knowledge graphs, and their role in AI retrieval. How to build, query, and optimize graph databases (Neo4j, SPARQL, and Cypher). Part II: Building Graph-Based RAG Systems Understanding Graph Neural Networks (GNNs) and their application in retrieval. Implementing knowledge graph embeddings (Node2Vec, GraphSAGE, and GATs) for efficient search. Integrating GNNs with LLMs to enhance response accuracy and reasoning. Part III: Hands-On Implementation Setting up FAISS, PyTorch Geometric, and Neo4j to power Graph-Based RAG. End-to-end implementation of a knowledge-driven RAG pi