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GraphRAG in Practice: Harness Knowledge Graphs to Build Explainable, Scalable, and Enterprise-Ready Retrieval-Augmented Generation Systems
Paperback
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ISBN13: 9798292092117
Publisher: Independently Published
Published: Jul 11 2025
Pages: 356
Weight: 1.82
Height: 0.74 Width: 8.50 Depth: 11.00
Language: English
Publisher: Independently Published
Published: Jul 11 2025
Pages: 356
Weight: 1.82
Height: 0.74 Width: 8.50 Depth: 11.00
Language: English
GraphRAG in Practice: Build Explainable, Scalable, and Enterprise-Ready Retrieval-Augmented Generation with Knowledge Graphs
Unlock the next frontier of Retrieval-Augmented Generation (RAG) with GraphRAG-the most advanced, explainable, and regulation-ready approach to GenAI.
This definitive guide explores how knowledge graphs, ontologies, and hybrid retrieval strategies are reshaping the future of large language models (LLMs). Unlike traditional vector-based RAG systems that struggle with explainability, traceability, and regulatory alignment, GraphRAG enables structured reasoning, transparent outputs, and semantic control-making it the gold standard for enterprise, healthcare, finance, legal, and government-grade AI applications.
Whether you're a machine learning engineer, AI architect, NLP researcher, or enterprise technology leader, this book gives you the end-to-end blueprint to design, deploy, and govern GraphRAG systems that are powerful, safe, and future-proof.
What You'll Learn:
Scroll up and get your copy now - and future-proof your AI systems with GraphRAG.
What You'll Learn:
- Strategic Advantages of GraphRAG: Understand the limitations of chunk-based RAG and why structured knowledge graphs outperform vectors in explainability and compliance.
- End-to-End Pipeline Design: Master ingestion, ontology modeling, triplet extraction, indexing, and graph-augmented prompting.
- Query Languages for Retrieval: Use Cypher, Gremlin, and GraphQL to drive dynamic, fine-grained subgraph retrieval with secure access controls.
- Hybrid Fusion Architectures: Learn when and how to combine vectors with graph paths using rerankers, retrieval routers, and memory-enhanced generation.
- Hallucination Control & Grounding: Engineer prompts with citations, fact-linking, and feedback-aware generation to ensure trustworthiness.
- Monitoring, CI/CD & AutoEval: Implement evaluation loops, cost tracking, drift detection, and version-controlled deployment workflows.
- Security & Compliance Ready: Align your GraphRAG pipelines with ISO 42001, EU AI Act, GDPR, and CCPA-with role-based access, redaction, and audit trails built-in.
- Enterprise Scaling Models: Deploy across teams, clouds, and regions with modular patterns, edge optimizations, and CoE frameworks.
Scroll up and get your copy now - and future-proof your AI systems with GraphRAG.
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