Introducing Agent-based RAG

An implementation with LangGraph, Azure AI Search and Azure OpenAI GPT-4o

Among the various architectural patterns in the field of Generative AI, Retrieval Augmented Generation (RAG) was the first and probably still most polular to be around.

RAG is a technique that allows the generative models to access external knowledge sources, such as documents, databases, or web pages, and use them as additional inputs for generating responses. By doing so, RAG can improve the quality, diversity, and reliability of the generated content, as well as provide transparency and verifiability for the users.

Over the last months, many variations of RAG have been developed (GraphRAG, Adaptive RAG, Corrective RAG…), with the goal of improving some weaknesses of the “traditional” RAG pipeline.

In this article, we are going to see one of these variations: Agentic RAG. Before diving into the topic, let’s refresh how the two main ingredients of this solution — RAG and Agents — are defined.

What is RAG?

Retrieval Augmented Generation (RAG) is a powerful technique in LLM-powered applications scenarios that addresses the following problem: “what if I want to ask my LLM something that is not part of the training set where the LLM was…