Build a router agent that can help you with Q&A and summarization tasks, and extend it to handle passing arguments to this agent. Join our new sh

Building Agentic RAG with LlamaIndex

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2024-05-09 00:00:19

Build a router agent that can help you with Q&A and summarization tasks, and extend it to handle passing arguments to this agent.

Join our new short course and learn from Jerry Liu, co-founder and CEO at LlamaIndex to start using agentic RAG, a framework designed to build research agents skilled in tool use, reasoning, and decision-making with your data.

Unlike the standard RAG pipeline—suitable for simple queries across a few documents—this intelligent approach adapts based on initial findings to enhance further data retrieval. You’ll learn to develop an autonomous research agent, enhancing your ability to engage with and analyze your data comprehensively.

You’ll practice building agents capable of intelligently navigating, summarizing, and comparing information across multiple research papers from arXiv. Additionally, you’ll learn how to debug these agents, ensuring you can guide their actions effectively.

Anyone who has basic Python knowledge and wants to learn how to quickly build agents that can reason over their own documents.