The blog post by Matea Pesic explains how to build a single-agent retrieval-augmented generation (RAG) system by integrating Memgraph, a graph database, with LlamaIndex, a framework designed for optimizing information retrieval for large language models (LLMs). The tutorial outlines the process of setting up Memgraph as a graph store for structured knowledge retrieval and using LlamaIndex to create a Property Graph Index, enabling vector searches on embedded data. It includes a step-by-step guide on implementing an agent capable of arithmetic operations and semantic retrieval, using tools like Docker, OpenAI's GPT-4, and function tools for calculations. The post further demonstrates creating a RAG pipeline for efficient data retrieval and contextual responses and concludes by highlighting the potential of integrating LlamaIndex with Memgraph for building advanced AI applications.