New course: Building Agentic RAG with LlamaIndex
Enroll today: https://bit.ly/3JN6gaN
Introducing Building Agentic RAG with LlamaIndex, made in collaboration with LlamaIndex and taught by its co-founder and CEO, Jerry Liu.
Unlike the standard retrieval augmented generation (RAG) pipeline—suitable for simple queries across a few documents—agentic RAG adapts based on initial findings to enhance further data retrieval. You'll use this framework to build research agents skilled in tool use, reasoning, and decision-making with your data.
Explore how to:
Build the simplest form of agentic RAG: a router. Given a query, the router will pick one of two query engines, Q&A or summarization, to execute it over a single document.
Add tool calling to your router agent, where you will use an LLM to pick a function to execute and infer an argument to pass to the function.
Build a research assistant agent. Instead of tool calling in a single-shot setting, an agent is able to reason over tools in multiple steps.
Build a multi-document agent where you will learn how to extend the research agent to handle multiple documents.
You'll build agents capable of intelligently navigating, summarizing, and comparing information across multiple research papers from arXiv, and learn how to debug these agents, ensuring you can guide their actions effectively.
Learn more: https://bit.ly/3JN6gaN
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