LLM Graph RAG: A Hands-on Guide to Building Advanced, Graph-Based Retrieval-Augmented Generation with LLMs (Paperback) (ISBN-13: 9798309538270)

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 pipeline.
✔ Deploying scalable Graph-Based RAG systems in cloud environments.

Part IV: Advanced Topics & Future Directions

✔ Optimizing retrieval using hybrid methods (dense + sparse search).
✔ Exploring multimodal RAG with text, images, and video.
✔ Addressing bias, fairness, explainability, and ethical concerns in Graph-Based RAG.
✔ The future of LLMs, knowledge graphs, and AI-driven reasoning.

Why This Book?

✅ Comprehensive & Up-to-Date - Covers the latest techniques in AI retrieval, knowledge graphs, and multimodal RAG.
✅ Hands-On & Practical - Includes fully explained code examples, real-world projects, and step-by-step tutorials.
✅ Real-World Applications - Explore use cases in healthcare, finance, research, and enterprise AI.
✅ Scalable & Production-Ready - Learn how to optimize, deploy, and scale Graph-Based RAG systems.

Who Is This Book For?

✔ AI Developers & Engineers - Build advanced AI retrieval systems with knowledge graphs and LLMs.
✔ Machine Learning Practitioners - Improve retrieval quality using GNNs and vector search.
✔ Data Scientists & Researchers - Leverage Graph-Based RAG for data-intensive AI applications.
✔ NLP Enthusiasts - Enhance text retrieval and question-answering systems with graph-based reasoning.

If you're looking to push the boundaries of Retrieval-Augmented Generation (RAG) and integrate the power of graphs and neural networks into AI-driven retrieval systems, this is the book you've been waiting for.

Author: Maxime Lane
ISBN-13: 9798309538270
Publisher: Independently Published
Language: English
Published: 02/05/2025
Pages: 128
Format: Paperback
Weight: 0.52lbs
Size: 10.00h x 7.00w x 0.27d