SKU/Artículo: AMZ-B0FXXZG28C
KNOWLEDGE GRAPH INTELLIGENCE: BUILDING THE REASONING LAYER FOR AI WITH NEO4J, RAG, AND LARGE LANGUAGE MODELS (Agentic AI & Retrieval Systems Series Book 2)
Format:
Kindle
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Fuera de stock
Fuera de stock
Peso con empaque:
0.15 kg
0.15 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- What if your AI could not only recall data — but truly reason with it? Welcome to the era of Knowledge Graph Intelligence, where context, relationships, and meaning combine to form the reasoning layer of artificial intelligence.In this groundbreaking guide, Robertto Tech unveils how to merge Neo4j, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) to build AI systems that don’t just retrieve — they understand. This book redefines what it means to make AI intelligent, showing you how to design architectures that connect facts, relationships, and logic into a unified cognitive framework. 💡 Inside, You’ll Learn How To:Grasp the foundations of graph-based reasoning and how it powers contextual AI.Model entities, relationships, and ontologies using Neo4j for real-world intelligence.Combine semantic embeddings and vector search with graph databases for hybrid reasoning.Build Graph-Powered RAG Systems that deliver grounded, explainable AI responses.Integrate LLMs with Cypher and graph queries, allowing natural language to drive retrieval.Architect scalable, transparent cognitive systems that think in relationships — not keywords.Explore the future of AI reasoning, from graph memory to autonomous agent intelligence. 🧩 Who This Book Is ForWhether you’re an AI engineer, data scientist, Neo4j developer, or technical founder, this book gives you the tools to move beyond surface-level machine learning into the realm of graph reasoning. You’ll gain the insight to design systems that can explain, connect, and evolve — the true hallmarks of next-generation AI. ⚙️ What You’ll BuildThrough detailed examples, Python + Cypher demonstrations, and hands-on projects, you’ll create:A semantic search engine using embeddings and Neo4jA RAG-powered assistant that uses graphs for contextual accuracyA knowledge reasoning framework capable of powering enterprise-grade AI 🚀 Why This Book WinsWhile other AI books stop at models and prompts, Knowledge Graph Intelligence goes deeper — teaching you how to give AI a memory, a context, and a mind. This is the blueprint for the next wave of intelligent systems — ones that reason like humans, not just compute like machines.If you’re ready to build the reasoning layer of AI, this is your guide. Turn data into knowledge. Turn knowledge into intelligence. Welcome to Graph Intelligence.
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