Graph retrieval-augmented generation (RAG) is a powerful tool that integrates multiple data sources to produce more accurate results by leveraging a knowledge graph to reveal hidden relationships and structures in documents. This new perspective aids in document visualization and enhances RAG performance for large language model queries, particularly in capturing hierarchical or relational connections within documents. Unstructured excels at parsing and interpreting different types of documents, extracting both content and metadata, which lays the groundwork for building a graph that captures document hierarchies and connections often missed by purely semantic embeddings. Astra DB complements this by providing a scalable, hybrid graph and vector store, ensuring scalability as data grows. By combining these tools, users can create a rich, interconnected perspective of their documents, enabling smarter, more context-aware results for LLMs. Graph RAG provides a new way to visualize and organize documents, uncovering hidden relationships and structures, which enables more effective understanding of the data while enhancing traditional RAG performance.