The Graph RAG Project and its GraphRetriever make it easy to connect documents and knowledge in an intuitive and lightweight way, expanding the capabilities of RAG systems without creating much additional complexity. They combine RAG techniques with graph-structured knowledge to help LLMs retrieve connected, meaningful information — not just isolated text chunks. This is achieved by traversing relationships between topics, entities, events, or ideas using metadata alone, without requiring a pre-existing knowledge graph or separate graph database. The GraphRetriever builds an in-memory, relevant subgraph at query time based on simple rules that define how documents are related through their metadata fields, keeping the architecture lightweight and flexible. This enables developers to start adding graph-style retrieval to their RAG systems immediately, without having to pre-build a heavy, manually curated knowledge graph. The GraphRetriever allows for flexible, domain-specific ways of connecting documents by defining multiple types of edges based on metadata fields, such as author or topic links. By blending semantic similarity and graph-connected reasoning, the GraphRetriever helps AI systems provide stronger grounding of responses, better reasoning over complex information, and more trustworthy linking and citations.