The text explores the role of knowledge graphs within the framework of retrieval-augmented generation (RAG) systems in AI, emphasizing the importance of contextual data in enhancing large language models (LLMs). It contrasts traditional full-text search (FTS) with vector search, which uses neural networks to translate text into vectors for similarity measurement, and discusses the benefits and complexities of using data structures like graphs to extract and organize information. The text describes two main types of graphs: lexical graphs, which capture the structure of text documents, and domain graphs, which focus on entities and their relationships. It highlights the advantages of using graphs for precise data retrieval, despite the challenges of building and maintaining them, and introduces hybrid graphs that combine lexical and domain elements for more comprehensive data traversal. Additionally, the text outlines advanced strategies like graph queries, vector search followed by graph algorithms, and the derivation of subgraphs to improve RAG outputs, concluding with a nod to future discussions on practical applications and query writing.