Graph-RAG Engineering shows how to combine structured knowledge from Knowledge Graphs with Large Language Models to build context-aware, explainable, and high-precision AI applications. The book covers graph modeling (RDF, property graphs), building and maintaining knowledge graphs with Neo4j/RDFLib, querying with SPARQL and Cypher, and creating Graph-RAG pipelines that fuse graph retrieval with dense vector search. Learn multi-hop reasoning, graph neural networks (GNN) for link prediction and entity disambiguation, ...
Read More
Graph-RAG Engineering shows how to combine structured knowledge from Knowledge Graphs with Large Language Models to build context-aware, explainable, and high-precision AI applications. The book covers graph modeling (RDF, property graphs), building and maintaining knowledge graphs with Neo4j/RDFLib, querying with SPARQL and Cypher, and creating Graph-RAG pipelines that fuse graph retrieval with dense vector search. Learn multi-hop reasoning, graph neural networks (GNN) for link prediction and entity disambiguation, temporal and streaming graph updates, and strategies for keeping graphs consistent and fresh. Practical projects include personalized recommendation systems, scientific discovery assistants, legal & regulatory search, and enterprise knowledge hubs. The book also addresses schema design, entity linking, provenance, versioning, and production considerations (ETL, connectors, monitoring). Key topics: knowledge graph design, Neo4j/Cypher, RDF/SPARQL, entity linking & canonicalization, Graph-RAG fusion, vector + graph hybrid retrieval, GNNs, temporal graphs, production ETL & governance.
Read Less
Add this copy of Graph-RAG Engineering: Integrating Knowledge Graphs to cart. $18.92, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
New. Print on demand Trade paperback (US). Glued binding. 428 p. Agentic AI and Graph-Powered Workflows Series: Practical Guides to Multi-Agent Systems, Langflow, R, 4.
