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GraphRAG Mastery: Building Production-Ready Knowledge Graph-Enhanced Retrieval Augmented Generation Systems
Format:
Kindle
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.97 kg
0.97 kg
Devolución:
No
No
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Unlock the full potential of Retrieval Augmented Generation (RAG) by combining the structural power of knowledge graphs with cutting-edge large language models. In an era where basic vector RAG often falls short on complex queries, hallucination-free answers, and relationship-aware reasoning, GraphRAG has emerged as the gold standard for enterprise-grade AI applications. Written under the pen name Andrew Ming, this comprehensive, hands-on guide takes you from beginner concepts to production-ready GraphRAG systems that consistently outperform traditional RAG pipelines. Whether you're battling poor recall on multi-hop questions, struggling with context traceability, or looking to implement Microsoft’s celebrated GraphRAG pattern at scale, this book delivers everything you need. What you’ll learn and build:Design and implement end-to-end GraphRAG architectures from scratchAutomatically extract and construct high-quality knowledge graphs using LLMsMaster hybrid retrieval: combining vector similarity, graph traversal, and BM25Generate accurate Cypher queries from natural language with LLMsBuild Agentic RAG systems that reason over structured and unstructured dataImplement and customize Microsoft’s GraphRAG pipelineEvaluate and benchmark RAG performance with rigorous metrics (RAGAS, correctness, faithfulness, answer relevancy)Deploy traceable, auditable, and explainable GraphRAG applications in productionPacked with complete Python code examples, Neo4j best practices, real-world datasets, and step-by-step projects you can run today, GraphRAG Mastery bridges the gap between research papers and battle-tested implementations. Perfect for data scientists, ML engineers, prompt engineers, and AI developers with intermediate Python skills and basic familiarity with graph databases (Neo4j experience helpful but not required). Stop settling for fragile RAG. Start building intelligent, relationship-aware AI systems that understand your data the way humans do—with GraphRAG Mastery.
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