Agentic RAG/

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Basic RAG vs. Agentic RAG Implementation with LlamaIndex

Basic RAG vs. Agentic RAG Implementation with LlamaIndex

Implement and directly compare a standard static RAG pipeline against a basic, single-tool agentic RAG system using LlamaIndex.

We'll cover the following...

In our previous lessons, we built a solid theoretical foundation. We deconstructed the agent into its core components and explored the sophisticated ReAct reasoning strategy that brings it to life.

Now, it’s time to bridge the gap between theory and practice. In this lesson, we will get our hands on the keyboard and write the code for our very first agentic RAG system. By implementing a standard RAG pipeline and an agentic system side by side, we will focus on the crucial differences in their architecture and code, understanding how the agentic approach provides a more flexible and scalable foundation.

An overview of agentic frameworks

Before we write our first line of code, it’s helpful to understand the landscape. Building an agent from scratch would require us to manually handle complex prompt templating, LLM response parsing, and the logic for the reasoning loop. Fortunately, several powerful open-source frameworks do this heavy lifting for us.

The two most prominent frameworks in the Python ecosystem are:

Agent implementation frameworks
Agent implementation frameworks

  • LangChain: One of the earliest and most popular frameworks for building LLM applications. For creating sophisticated agents, its companion library, LangGraph, is typically used. LangGraph allows developers to define agentic workflows as stateful graphs, enabling complex cycles, branching, and human-in-the-loop interactions.

  • LlamaIndex: While it also has broad agentic capabilities, LlamaIndex has its roots specifically in optimizing RAG pipelines. It provides highly advanced and easy-to-use components for data ingestion, indexing, and retrieval. Its agentic layer is designed to integrate seamlessly with these powerful RAG components.

If you’re interested in exploring these frameworks, you might find dedicated courses on LlamaIndex and LangChain with LangGraph helpful.

For this course, we will be using LlamaIndex. Its data-centric focus makes it the perfect choice for mastering agentic RAG. It allows us to build state-of-the-art retrieval pipelines and then elegantly layer agentic reasoning on top of them.

Essential LlamaIndex syntax

To get started, let’s familiarize ourselves with two core LlamaIndex concepts that we will be using.

  • The query engine: This is the object that represents our complete, runnable RAG pipeline. We will use a VectorStoreIndex to create a query_engine, and we can interact with it directly using query_engine.query("Your question"). A query engine encapsulates the entire RAG process, handling both the retrieval of information from the index and the final generation of the answer.

  • The tool: ...