SKU/Artículo: AMZ-B0G5QRQ5TR

Graph-RAG Unleashed: Designing Knowledge Graphs, Smart Retrieval Systems, and High-Accuracy AI Workflows

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Kindle

Paperback

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En stock

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0.15 kg

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Sí

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Producto de:
Amazon

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USA

Sobre este producto

Master the next frontier of Retrieval-Augmented Generation with the definitive guide to Graph-RAG systems. In an era where large language models frequently hallucinate and vector-only RAG hits its accuracy ceiling, Graph-RAG has emerged as the most powerful paradigm for achieving factual consistency, explainable reasoning, and enterprise-grade precision. This book delivers a comprehensive, implementation-ready blueprint for building production Graph-RAG pipelines that dramatically outperform traditional RAG, Hybrid Search, and Agentic workflows. From ontology engineering and automated knowledge graph population to hypergraph embeddings, GNN-based node representations, real-time traversal-augmented retrieval, and LLM fine-tuning with graph-injected context, every layer of the modern Graph-RAG stack is dissected with mathematical rigor, production code patterns (PyTorch, LangChain, LlamaIndex, Neo4j, cuGraph), and battle-tested architectural decisions. Key topics include: Graph Neural Networks · Knowledge Graph Construction · Entity Resolution · Relation Extraction · Temporal & Dynamic Graphs · Graph Embeddings (TransE, ComplEx, GraphSAGE, HyTE) · Distributed Graph Databases (JanusGraph, TigerGraph, NebulaGraph) · Path Ranking Algorithms · Graph + Vector Hybrid Retrieval · Subgraph Retrieval · Fuzzy & Probabilistic Retrieval · Prompt Chaining with Graph Context · Hallucination Detection via Graph Consistency Checks · Evaluation Frameworks (RAGAS, GraphRAG-Eval, DeepEval) · GPU-Accelerated Graph Computing · Federated Graph-RAG · Multimodal Knowledge Graphs · Bias Auditing in Graph Structures · Quantum Graph Algorithms outlook Whether you are a machine learning engineer, data scientist, AI architect, or researcher pushing the boundaries of trustworthy generative AI, Graph-RAG Unleashed equips you with the advanced techniques required to design, scale, and deploy knowledge-graph-powered retrieval systems that deliver verifiable, high-accuracy AI workflows in production. Foreword by a leading Graph ML researcher (pending) Includes 150+ diagrams, 80+ complete code repositories, and 12 end-to-end case studies.

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Graph-RAG Unleashed: Designing Knowledge Graphs, Smart Retrieval Systems, and High-Accuracy AI Workflows

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GRAPH-RAG Foundations: From Beginners to Expert: Master Knowledge Graphs, Retrieval-Augmented Generation, Vector Search, and Next-Gen AI Reasoning Systems for Real-World Applications

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