Graph RAG from Scratch

Traditional Retrieval-Augmented Generation (RAG) systems excel at semantic search, but they often struggle with complex relationships, multi-hop reasoning, and maintaining contextual consistency. Graph RAG addresses these limitations by combining knowledge graphs with hybrid retrieval to create AI systems that are more accurate, explainable, and intelligent.

Graph RAG from Scratch is a hands-on guide designed to help developers build Graph RAG systems from the ground up. Beginning with the fundamentals of knowledge graphs and graph databases, you'll learn how to transform unstructured data into connected knowledge, engineer hybrid retrieval pipelines, and integrate graph-powered context into Large Language Model (LLM) applications.

Throughout the book, you'll explore graph modeling, entity and relationship extraction, vector embeddings, graph traversal, ontology design, and practical implementation techniques using modern development tools. By the end, you'll have the knowledge and confidence to design and build your own production-ready Graph RAG applications.

Inside you'll learn how to:

• Understand the principles behind Graph RAG and modern retrieval systems.
• Build knowledge graphs from unstructured documents.
• Design ontologies and graph schemas for AI applications.
• Combine vector search, keyword search, and graph traversal.
• Implement Graph RAG pipelines for LLMs.
• Optimize retrieval quality and contextual relevance.
• Deploy scalable Graph RAG applications.

Whether you're an AI engineer, software developer, data engineer, or machine learning practitioner, this book provides the practical foundation needed to build the next generation of intelligent retrieval systems.