In this comprehensive guide, discover how to seamlessly integrate Knowledge Graphs with Large Language Models (LLMs) to build smarter, context-aware AI systems.

This book takes you on a transformative journey, covering everything from the foundations of LLMs and knowledge graphs to advanced topics like multi-hop reasoning, graph neural networks, and real-world applications in healthcare, e-commerce, and beyond.

What You'll Learn:

The principles behind Graph RAG and why it's the future of AI workflows.
How to design and build effective Knowledge Graphs using tools like Neo4j, SPARQL, and RDFLib.
Best practices for integrating retrieved graph data into LLMs to enhance contextual reasoning and output accuracy.
Advanced graph-based reasoning techniques, including temporal knowledge graphs and dynamic updates.
Practical applications across industries, from personalized recommendations to scientific discovery.

Key Features:

Hands-On Projects: Build real-world Graph RAG systems with step-by-step tutorials.
Code Examples: Clear, well-documented Python code for graph creation, querying, and integration with LLMs.
Visual Aids: Diagrams, flowcharts, and case studies to simplify complex concepts.
Practice Problems: Reinforce your learning with challenges and solutions designed for practitioners.

Who This Book Is For:

AI Developers and Researchers: Build smarter and more context-aware LLM applications.
Data Scientists: Leverage knowledge graphs for better insights and data-driven reasoning.
Tech Enthusiasts and Students: Gain a deep understanding of cutting-edge AI technologies.

As AI systems grow more complex, the ability to integrate structured knowledge into LLMs is critical. This book equips you with the knowledge and tools to master Graph RAG, empowering you to innovate and lead in the evolving AI landscape.

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