preface

This book came about because we (Oskar and Tomaž) had been working together for a few years at Neo4j and kept arriving at the same thought: someone should write a book about combining knowledge graphs with retrieval-augmented generation (RAG). We figured it might as well be us. The idea wasn’t born from some grand epiphany—it was just a practical realization. We’d both spent enough time with graphs, machine learning, and generative AI to see that large language models (LLMs) had real limitations, like outdated info or missing domain-specific details. Knowledge graphs seemed like an obvious way to fix that, and it wasn’t that hard to put the two together.

Our backgrounds made it a natural fit. Oskar, with over 20 years as a software engineer and a decade at Neo4j, leads the generative AI engineering team, focused on helping developers build GenAI apps with graphs. Tomaž has deep experience in graph algorithms, machine learning, and LLMs, contributing to frameworks like LangChain and LlamaIndex while writing about practical LLM applications. Together, we’d already been tinkering with these ideas—extracting structured data from text, plugging it into graphs, and using it to boost RAG. It worked well enough in our day-to-day that we thought others could use it too.