9798278139621 - graph rag projects engineering advanced retrieval systems with vector databases and llms: build semantic search tools, structured reasoning engines, and enterprise ai solutions von colton, zhao (4 Ergebnisse)
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Graph RAG Projects Engineering Advanced Retrieval Systems with Vector Databases and LLMs (Paperback)
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Paperback. Zustand: new. Paperback. Graph RAG Projects: Engineering Advanced Retrieval Systems with Vector Databases and LLMs is a comprehensive, hands-on guide for developers, AI engineers, data scientists, and enterprise teams building next-generation retrieval systems powered by knowledge graphs, vector databases, and large l…anguage models (LLMs).In this deep, practical resource, author Zhao Colton introduces a complete blueprint for designing, implementing, and deploying Graph RAG (Graph Retrieval-Augmented Generation) systems capable of semantic understanding, knowledge reasoning, and enterprise-grade retrieval performance. Unlike traditional vector-only RAG setups, Graph RAG brings together graph structures, entity relationships, context linking, semantic indexing, and structured reasoning, creating far more accurate and explainable AI tools.This book is built around real-world projects, code workflows, and production patterns to help you master advanced retrieval architectures, graph construction techniques, graph embeddings, multi-hop reasoning, knowledge extraction, and hybrid search pipelines. Whether you're building semantic search engines, structured reasoning agents, knowledge-aware chatbots, research assistants, or enterprise AI solutions, this guide gives you the tools to engineer sophisticated retrieval workflows at scale.What You Will LearnHow to build Graph RAG pipelines that combine graph databases, embeddings, and language modelsTechniques for knowledge graph modeling, entity extraction, relationship mapping, and ontology designMethods for integrating vector search, graph traversal, topology-based ranking, and hybrid retrievalHow to implement semantic search tools, reasoning engines, and context-aware AI assistantsPractical applications using Neo4j, ArangoDB, NetworkX, and modern vector storesApproaches to structured retrieval, context routing, and LLM reasoning over graph dataWorkflows for building scalable enterprise solutions with graph reasoning, semantic indexing, and multi-step retrieval logicPatterns for real-world deployment, optimization, and evaluation of Graph RAG systemsEvery chapter combines conceptual clarity with implementation depth, ensuring you understand not just what to build, but how to build it effectivelyWho This Book Is ForAI/ML EngineersData ScientistsKnowledge EngineersEnterprise Software TeamsNLP ResearchersDevelopers building retrieval-based AI systemsAnyone interested in knowledge graphs, semantic search, or advanced RAG architecturesWhether you're upgrading an existing RAG pipeline or designing a new retrieval system from scratch, this book will help you create high-performance, knowledge-aware solutions ready for production. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Graph RAG Projects Engineering Advanced Retrieval Systems with Vector Databases and LLMs (Paperback)
- Softcover
- Print-on-Demand
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Paperback. Zustand: new. Paperback. Graph RAG Projects: Engineering Advanced Retrieval Systems with Vector Databases and LLMs is a comprehensive, hands-on guide for developers, AI engineers, data scientists, and enterprise teams building next-generation retrieval systems powered by knowledge graphs, vector databases, and large l…anguage models (LLMs).In this deep, practical resource, author Zhao Colton introduces a complete blueprint for designing, implementing, and deploying Graph RAG (Graph Retrieval-Augmented Generation) systems capable of semantic understanding, knowledge reasoning, and enterprise-grade retrieval performance. Unlike traditional vector-only RAG setups, Graph RAG brings together graph structures, entity relationships, context linking, semantic indexing, and structured reasoning, creating far more accurate and explainable AI tools.This book is built around real-world projects, code workflows, and production patterns to help you master advanced retrieval architectures, graph construction techniques, graph embeddings, multi-hop reasoning, knowledge extraction, and hybrid search pipelines. Whether you're building semantic search engines, structured reasoning agents, knowledge-aware chatbots, research assistants, or enterprise AI solutions, this guide gives you the tools to engineer sophisticated retrieval workflows at scale.What You Will LearnHow to build Graph RAG pipelines that combine graph databases, embeddings, and language modelsTechniques for knowledge graph modeling, entity extraction, relationship mapping, and ontology designMethods for integrating vector search, graph traversal, topology-based ranking, and hybrid retrievalHow to implement semantic search tools, reasoning engines, and context-aware AI assistantsPractical applications using Neo4j, ArangoDB, NetworkX, and modern vector storesApproaches to structured retrieval, context routing, and LLM reasoning over graph dataWorkflows for building scalable enterprise solutions with graph reasoning, semantic indexing, and multi-step retrieval logicPatterns for real-world deployment, optimization, and evaluation of Graph RAG systemsEvery chapter combines conceptual clarity with implementation depth, ensuring you understand not just what to build, but how to build it effectivelyWho This Book Is ForAI/ML EngineersData ScientistsKnowledge EngineersEnterprise Software TeamsNLP ResearchersDevelopers building retrieval-based AI systemsAnyone interested in knowledge graphs, semantic search, or advanced RAG architecturesWhether you're upgrading an existing RAG pipeline or designing a new retrieval system from scratch, this book will help you create high-performance, knowledge-aware solutions ready for production. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
