GraphRAG is a game-changing retrieval technology that builds upon vector search by incorporating reasoning and relationships. It starts with vector search to narrow down relevant documents or nodes, but then uses large language models (LLMs) to generate Cypher queries based on the ontology of a knowledge graph. This enables GraphRAG to traverse multi-hop relationships, enrich responses with structured context, execute precise queries over a graph, and generate answers that are grounded, explainable, and connected. The author has reworked a movie chatbot using Neo4j and Google Vertex AI to incorporate GraphRAG, resulting in a system that understands the plot of movies, pulls in vector-similar movies, identifies directors, genres, and timeframes, and responds with contextual, multi-hop, personalized suggestions. Deploying the chatbot to Google Cloud Run provides serverless and scalable deployment capabilities.