Knowledge Graph Architectures: Designing Real-World AI Systems with Neo4j, Llms, and Rag for Semantic Search and Recommendation

From Concept to Deployment - Build AI Systems That Think in Context.

Artificial Intelligence is no longer just about models and data - it's about reasoning.
In a world dominated by large language models, the missing link is structure: the ability for AI to understand, connect, and recall knowledge intelligently.

Knowledge Graph Architectures is your complete guide to designing and deploying real-world AI systems powered by Neo4j, Graph Data Science, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs).
Written by Robertto Tech, a seasoned AI systems engineer, this hands-on manual transforms abstract theory into concrete engineering blueprints for developers, data scientists, and architects.

✅ Graph Thinking for AI Systems - Understand how to model relationships, context, and semantics as the backbone of reasoning.

✅ Data Modeling for Semantic Search - Design flexible, schema-light structures that capture real-world intelligence.

✅ Cypher + Python Integration - Learn how to query, analyze, and automate your graphs with production-ready code.

✅ Embeddings and Hybrid Retrieval - Combine the power of vectors, graphs, and LLMs for state-of-the-art search and recommendation.

✅ Graph-Powered RAG Systems - Build retrieval-augmented generation pipelines that understand both structure and meaning.

✅ Graph Algorithms for Insight - Apply Neo4j Graph Data Science for fraud detection, recommendation, and community discovery.

✅ Deploying Graph-Based AI Apps - Architect scalable APIs and microservices that bring semantic reasoning to production.

✅ Future of AI Reasoning - Explore agentic AI, graph memory, and explainable intelligence for next-generation systems.

• AI Developers who want to integrate Neo4j and LLMs in real projects.

• Solution Architects building scalable, reasoning-aware applications.

• Data Engineers exploring hybrid retrieval and semantic graph pipelines.

• Technical Leaders defining the architecture of cognitive AI systems.

Whether you're building an enterprise search engine, a personalized recommender, or an intelligent assistant, this book shows you how to architect the reasoning layer that turns AI into understanding.

• Hands-on projects with Neo4j, LangChain, and Python.

• Instructor-style explanations that teach by doing.

• Real-world diagrams, schema patterns, and best practices.

• Designed for AI professionals who think in systems, not silos.

Knowledge Graph Architectures is more than a technical manual - it's a roadmap for engineers shaping the next wave of intelligent systems.