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Paperback. Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: new. Paperback. In today's data-driven world, relationships matter more than ever. From social networks to fraud detection, recommendation engines to knowledge graphs, traditional databases fall short when it comes to highly connected data. That's where graph databases excel and Neo4j, the world's leading native graph database, stands at the forefront. With its powerful property graph model and expressive Cypher query language, Neo4j enables you to explore and extract insights from your data like never before.Written by a seasoned practitioner with hands-on experience deploying real-world graph systems, this guide goes beyond theory. Every chapter reflects best practices rooted in real projects-from startups to enterprise systems-and is designed for professionals who want more than just academic examples. Whether you're migrating from relational databases, integrating graphs into full-stack apps, or building intelligent systems, this book provides a grounded, practical approach that gets results.Hands-On Graph Databases with Neo4j is your complete guide to understanding, building, and leveraging graph databases for real-world applications. You'll start with the fundamentals-nodes, relationships, and properties-and quickly advance to querying with Cypher, importing data from CSV, JSON, APIs, and modeling complex use cases like social networks, knowledge graphs, and fraud detection. From data enrichment with APOC procedures to scalable analysis with GDS, and from Neo4j Desktop to Aura and Docker deployments, this book equips you with the full toolkit.What's Inside: Mastering Cypher: Writing efficient and expressive graph queriesData Import: Handling CSVs, JSON, REST APIs, and bulk loadersGraph Algorithms: Centrality, community detection, similarity, and traversalsReal-World Projects: Build knowledge graphs, social networks, and full-stack appsNeo4j Integrations: REST APIs, GraphQL, Python, Node.js, and visualization with D3.js & NeovisProduction Guidance: Indexing, scaling, security, backups, and monitoringAdvanced Use Cases: LLM integration, Bloom visualization, and GDS pipelinesThis book is for developers, data engineers, architects, and analysts who want to build real-world graph applications with Neo4j. No prior graph experience is required, but basic familiarity with Python or JavaScript and general database concepts will be helpful. Whether you're coming from SQL, NoSQL, or a completely different background, this book bridges the gap and accelerates your journey into graph data modeling.In just a few focused sessions, you'll go from graph novice to confidently building production-grade Neo4j systems. You'll learn not only how to write Cypher queries but also how to think graph-first-a paradigm shift that transforms how you solve problems and uncover connections. From first node to deployed app, this book respects your time and delivers immediate, actionable knowledge.Ready to master Neo4j and unlock the full power of connected data? Whether you're building a recommendation engine, visualizing a knowledge graph, or deploying a graph-powered backend, this book gives you everything you need. Grab your copy of Hands-On Graph Databases with Neo4j today and start turning complex data into meaningful insights one relationship at a time. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condizione: New.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. Retrieval-Augmented Generation (RAG) 2.0 is the cornerstone of modern AI agent architecture-enabling language models to access external knowledge, maintain long-term context, and perform real-world tasks with accuracy and trust. As LLMs evolve, the fusion of retrieval systems, memory, tool use, and agent orchestration defines the next generation of intelligent applications-across research, diagnostics, enterprise automation, and beyond.This guide is written by a leading expert in AI systems and data engineering, drawing from production-grade implementations, academic research, and enterprise deployment experience. It reflects 2025's most current technologies covering architectures adopted by OpenAI, Meta, Anthropic, Google DeepMind, and leading open-source innovators.Architecting RAG 2.0 for AI Agent is your complete, professional guide to designing scalable, modular, and intelligent RAG-based pipelines. You'll learn how to combine vector databases, hybrid retrievers, and LLMs into robust systems with memory, reasoning, and planning. Whether you're developing a chatbot, knowledge assistant, or autonomous agent, this book teaches you how to bridge language understanding with real-time knowledge and tool use.Modular RAG 2.0 system architecture (Retriever Augmenter LLM)Long-context handling with vector stores + context-aware modelsMetadata and provenance tracking for reliability and auditabilityRetriever tuning, embeddings, and hybrid indexing strategiesLLM integration: streaming, token management, and API orchestrationAdvanced agent workflows, tool use, and planning techniquesMultimodal RAG systems (text, image, audio, video)Deployment strategies, containerization, and performance monitoringA/B testing, evaluation frameworks, and error debuggingCase studies in legal, medical, research, and enterprise AI agentsThis book is ideal for AI engineers, data scientists, ML architects, and advanced developers who want to build retrieval-augmented AI agents. It's also suited for technical product managers, researchers, and enterprise teams deploying LLM-powered systems with RAG capabilities in production.Don't waste months experimenting through trial and error. This guide distills the most effective strategies, architectures, and patterns into actionable insights-saving you countless hours and giving you a competitive edge in deploying AI agent systems that work at scale. Whether you're augmenting GPT, Claude, or open-source LLMs with your own data, this book gives you the blueprint to build robust, high-performance RAG 2.0 systems today.Buy Architecting RAG 2.0 for AI Agent now and lead the future of intelligent automation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condizione: new. Paperback. In today's data-driven world, relationships matter more than ever. From social networks to fraud detection, recommendation engines to knowledge graphs, traditional databases fall short when it comes to highly connected data. That's where graph databases excel and Neo4j, the world's leading native graph database, stands at the forefront. With its powerful property graph model and expressive Cypher query language, Neo4j enables you to explore and extract insights from your data like never before.Written by a seasoned practitioner with hands-on experience deploying real-world graph systems, this guide goes beyond theory. Every chapter reflects best practices rooted in real projects-from startups to enterprise systems-and is designed for professionals who want more than just academic examples. Whether you're migrating from relational databases, integrating graphs into full-stack apps, or building intelligent systems, this book provides a grounded, practical approach that gets results.Hands-On Graph Databases with Neo4j is your complete guide to understanding, building, and leveraging graph databases for real-world applications. You'll start with the fundamentals-nodes, relationships, and properties-and quickly advance to querying with Cypher, importing data from CSV, JSON, APIs, and modeling complex use cases like social networks, knowledge graphs, and fraud detection. From data enrichment with APOC procedures to scalable analysis with GDS, and from Neo4j Desktop to Aura and Docker deployments, this book equips you with the full toolkit.What's Inside: Mastering Cypher: Writing efficient and expressive graph queriesData Import: Handling CSVs, JSON, REST APIs, and bulk loadersGraph Algorithms: Centrality, community detection, similarity, and traversalsReal-World Projects: Build knowledge graphs, social networks, and full-stack appsNeo4j Integrations: REST APIs, GraphQL, Python, Node.js, and visualization with D3.js & NeovisProduction Guidance: Indexing, scaling, security, backups, and monitoringAdvanced Use Cases: LLM integration, Bloom visualization, and GDS pipelinesThis book is for developers, data engineers, architects, and analysts who want to build real-world graph applications with Neo4j. No prior graph experience is required, but basic familiarity with Python or JavaScript and general database concepts will be helpful. Whether you're coming from SQL, NoSQL, or a completely different background, this book bridges the gap and accelerates your journey into graph data modeling.In just a few focused sessions, you'll go from graph novice to confidently building production-grade Neo4j systems. You'll learn not only how to write Cypher queries but also how to think graph-first-a paradigm shift that transforms how you solve problems and uncover connections. From first node to deployed app, this book respects your time and delivers immediate, actionable knowledge.Ready to master Neo4j and unlock the full power of connected data? Whether you're building a recommendation engine, visualizing a knowledge graph, or deploying a graph-powered backend, this book gives you everything you need. Grab your copy of Hands-On Graph Databases with Neo4j today and start turning complex data into meaningful insights one relationship at a time. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.