This paper presents a knowledge graph and retrieval-augmented generation (KG-RAG) system for question-answering (QA) in crime movies. Our implementation is demonstrated through the application of KG-RAG on the script of crime films, enabling an in-depth understanding of crime cinema. Unlike traditional analysis methods, our system supports nuanced, open-ended, and non-factual questioning that goes beyond simple fact retrieval. It can delve into subtle thematic elements, uncover plot twists, and even analyze emotional undertones within crime cinema. By integrating structured knowledge graphs with advanced generative retrieval techniques, we have created a framework that allows rich, context-driven film analysis. KG-RAG is ideal for this application, as it allows flexible, context-driven exploration of film content and enhances interactive storytelling research and film analysis. This approach can be a valuable tool for film enthusiasts, researchers, and content creators in the domain of crime films.