How to build Agentic RAG with Pagerank using LlamaIndex?

The blog post provides a tutorial on creating an agentic Retrieval Augmented Generation (RAG) system utilizing graph algorithms, specifically Memgraph's PageRank, within a multi-agent workflow framework that leverages LlamaIndex. It builds upon previous setups by first incorporating Memgraph as a graph store and creating a sample dataset, then using LlamaIndex to define function agents for tasks such as data retrieval and arithmetic operations. A retriever agent is tasked with executing the PageRank algorithm to extract and rank nodes, while a calculator agent processes numerical data from these nodes. The tutorial includes setting up the environment with necessary dependencies and establishing connections to Memgraph using Python, along with detailed implementation of the agents and workflow needed for automated query execution and data processing. The post emphasizes the potential of integrating graph intelligence into agent systems and encourages further experimentation to enhance the capabilities of GenAI pipelines.

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