A graph agent rendered with ChatGPT Retrieval-augmented generation (RAG) is here to stay — and for good reason. It’s a powerful framework that blends advanced language models with targeted information retrieval techniques, enabling quicker access to relevant data and producing more accurate, context-aware responses. While RAG applications often focus on unstructured data, I’m a big fan of integrating structured data into the mix, a vital yet frequently overlooked approach. One of my favorite ...
Read it: Building Knowledge Graph Agents With LlamaIndex Workflows