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Mar 25, 2025
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🤖 Agentic RAG with LlamaIndex

A hands-on tutorial series on building advanced Retrieval-Augmented Generation (RAG) systems using LlamaIndex, with a focus on agentic capabilities and multi-document reasoning.

📚 Description

This repository contains a progressive series of Jupyter notebooks that demonstrate how to build increasingly sophisticated RAG systems using LlamaIndex. The course starts with basic router query engines and progresses to advanced multi-document agents with dynamic tool selection.

Each lesson builds upon the previous one, introducing new concepts and techniques for working with document-based knowledge and LLM agents.

🔍 Features

  • Router Query Engines: Learn how to route queries to the most appropriate query engine based on the query content
  • Tool Calling: Implement function-calling capabilities to enable LLMs to use tools for retrieving information
  • Agent Reasoning Loop: Build an agent that can reason over documents and maintain a conversation
  • Multi-Document Agent: Create a sophisticated agent that can work with multiple documents and dynamically select the most relevant tools

🛠️ Prerequisites

  • Python 3.9+
  • OpenAI API key (stored in a .env file)
  • Required packages (see requirements.txt)

🚀 Setup Guide

  1. Clone this repository
  2. Install the required packages: 
    pip install -r requirements.txt
  3. Create a .env file in the root directory with your OpenAI API key: 
    OPENAI_API_KEY=your_api_key_here
  4. Run the Jupyter notebooks in order:
    • L1_Router_Engine.ipynb
    • L2_Tool_Calling.ipynb
    • L3_Building_an_Agent_Reasoning_Loop.ipynb
    • L4_Building_a_Multi-Document_Agent.ipynb

📖 Lesson Overview

Lesson 1: Router Engine

Learn how to create a router query engine that can direct queries to either a summary index or a vector index based on the query content.

Lesson 2: Tool Calling

Explore how to define and use tools with LLMs, including simple function tools and auto-retrieval tools for querying documents.

Lesson 3: Building an Agent Reasoning Loop

Build an agent that can reason over documents, maintain a conversation, and provide detailed responses with source attribution.

Lesson 4: Building a Multi-Document Agent

Create a sophisticated agent that can work with multiple documents, dynamically select the most relevant tools, and compare information across different sources.

🔗 Resources

📝 License

This project is open source and available for educational purposes.