But why would you want to extract structured information from text and represent it as a graph? One key reason is to power retrieval-augmented generation (RAG) applications. While using text embedding models over unstructured text is a useful approach, it can fall short when it comes to answering complex, multi-hop questions that require understanding connections across multiple entities or question where structured operations like filtering, sorting, and aggregation is required. By extracting structured information from text and constructing knowledge graphs, you not only organise data more effectively but also create a powerful framework for understanding complex relationships between entities. This structured approach makes it much easier to retrieve and leverage specific information, expanding the types of questions you can answer while providing greater accuracy.