Overview

Unlock the full power of Retrieval-Augmented Generation (RAG) with knowledge graphs, vector search, and large language models (LLMs) in this definitive guide for AI engineers, developers, and data scientists. Mastering Graph-RAG Foundations takes you from conceptual understanding to practical mastery, offering a structured, hands-on approach to designing AI systems that can intelligently retrieve, reason, and generate knowledge. Whether you're building advanced chatbots, knowledge-intensive agents, or production-grade AI workflows, this book equips you with the tools and frameworks you need to succeed. Inside, you'll discover: How knowledge graphs enhance RAG workflows for accurate and context-aware AI outputs. Step-by-step guidance on vector search, embeddings, and LLM integration. Hands-on Python and LangGraph examples to implement real-world RAG systems. Practical insights into designing scalable, maintainable AI architectures. Expert commentary, best practices, and caveats from a senior AI engineer's perspective. Designed for advanced learners and technical professionals, this book bridges the gap between theory and practice. Start your journey to mastering Graph-RAG today and unlock new levels of AI system intelligence and reliability.

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