Graph data models such as parent-child, question-based, and topic-summary can be used to enhance retrieval augmented generation (RAG) applications powered by knowledge graphs. These models provide unique benefits, including vector search capabilities with Neo4j's graph database, which allows for the performance of vector search against the embedding properties of Document nodes to find the most relevant text to return as context to a large language model (LLM). The Parent-Child model further breaks down text chunks into smaller lengths, providing more exact vector search matches and context-rich results. The Questions model links Question nodes to Documents containing useful context for an answer, allowing for two retrieval options: matching LLM-generated questions or real user questions with relationships to highly rated LLM Response nodes. The Topics & Summaries model uses Neo4j's Graph Data Science library to find communities of Document nodes and generate summaries for each community, providing a keyword search in addition to vector search. These models can perform individually or in combination to enhance RAG application capabilities.