SKU/Artículo: AMZ-B0GD7DRNFC
Graph RAG Foundations: The Practical Handbook for Graph Retrieval-Augmented Generation with Knowledge Graphs
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Paperback
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En stock
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0.26 kg
0.26 kg
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Amazon
Amazon
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USA
USA
Sobre este producto
- Unlock the next level of intelligent AI systems — where relationships matter as much as raw text.In an era where large language models (LLMs) power everything from enterprise search to scientific discovery, standard Retrieval-Augmented Generation (RAG) frequently falls short on complex, interconnected questions. Naive vector search delivers fragments; it struggles with multi-hop reasoning, entity disambiguation, global summarization, and traceable provenance. Graph RAG changes that by explicitly modeling entities, relationships, and hierarchies — turning scattered documents into a connected knowledge fabric that enables dramatically more accurate, reliable, and explainable LLM responses.Graph RAG Foundations is the comprehensive, production-focused guide that bridges theory and real-world engineering. Written for ML engineers, data scientists, AI architects, and technical leaders who already understand LLMs and basic RAG, this book delivers the depth and recipes needed to build next-generation, knowledge-aware systems.You will learn how to:Construct robust knowledge graphs from unstructured text using LLM-powered entity & relation extraction, schema-guided prompting, and hybrid deterministic approachesImplement the major Graph RAG families — from index-time hierarchical summarization (Microsoft GraphRAG style) to query-time lazy construction and hybrid vector+graph retrievalDesign advanced patterns: multi-hop path traversal, community-aware global search, agentic workflows with graphs as memory/tools, and multi-modal extensions (text + tables + images)Build and evaluate end-to-end systems with tools such as Neo4j, Memgraph, LlamaIndex PropertyGraphIndex, LangChain/LangGraph, and the official Microsoft GraphRAG libraryOptimize for production: cost/latency/accuracy trade-offs, incremental updates, caching, pruning, security, privacy, governance, and distributed scalingApply Graph RAG to high-impact domains — biomedical literature & PubMed-scale KGs, legal clause dependencies & case law networks, financial ownership/transaction graphs, enterprise knowledge bases, and customer support/documentationPacked with Python code examples, architectural diagrams, prompt templates, evaluation frameworks (including graph-aware RAGAS extensions), and domain-adapted case studies, this handbook equips you to move beyond "it works on my toy dataset" to scalable, trustworthy Graph RAG deployments that deliver measurable business value.Whether you're upgrading legacy RAG pipelines, reducing hallucination risk in regulated industries, or researching the frontier of knowledge-aware AI, Graph RAG Foundations provides the conceptual clarity, practical blueprints, and forward-looking insights to build systems that are not just smarter — but truly relational.Ideal for intermediate-to-advanced practitioners with Python proficiency and familiarity with LLMs, embeddings, vector databases, or graph concepts.Keywords (for Amazon backend & discoverability): GraphRAG, Graph Retrieval-Augmented Generation, knowledge graph RAG, Microsoft GraphRAG, agentic RAG, hybrid vector graph retrieval, Neo4j RAG, LlamaIndex PropertyGraph, LangChain GraphRAG, multi-hop reasoning, hallucination reduction, production RAG, enterprise knowledge graphs, continual learning graphs, multi-modal RAG
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AR$69.900
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AR$27.960
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