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Mastering Retrieval-Augmented Generation Workflows with GraphRAG: A Practical Guide to Knowledge-Graph-Powered Retrieval, Context Modeling, and High-A
Paperback
Publisher: Independently Published
Published: Dec 4 2025
Pages: 148
Weight: 0.59
Height: 0.32 Width: 7.00 Depth: 10.00
Language: English
Written in a practical, hands-on style, GraphRAG Essentials delivers the tools, patterns, and architectures you need to design, optimize, and deploy knowledge-graph-augmented AI systems at scale.
Inside this book, you will learn:
- Core GraphRAG principles
How knowledge graphs enhance retrieval, improve grounding, and deliver richer context to LLMs.
- Practical workflows and architectures
Step-by-step pipelines for entity extraction, graph building, retrieval integration, and generation refinement.
- Key algorithms and techniques
Graph traversal, semantic similarity search, embeddings, scoring methods, and hybrid retrieval models.
- Knowledge graph engineering
Schema design, ontology modeling, graph storage, indexing, and integration with LLM-based systems.
- Building GraphRAG applications
Real-world examples in search, analytics, chat systems, enterprise AI, and domain-specific intelligence.
- Performance optimization
How to improve accuracy, reduce hallucinations, boost retrieval quality, and scale GraphRAG pipelines.
- Tooling and frameworks
Practical guidance on Neo4j, NetworkX, LangChain, LlamaIndex, and modern graph infrastructure.
Who this book is for
- AI engineers and ML practitioners
- NLP and knowledge-graph researchers
- Developers building advanced RAG-based applications
- Architects designing scalable contextual AI systems
- Anyone exploring the frontier of AI retrieval and structured reasoning
Whether you're enhancing enterprise search, building domain-expert chatbots, or developing custom generative AI applications, this book will help you unlock the full power of GraphRAG.
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