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Create an AI Agent with RAG in Two Simple Steps
Using the framework Agno, it is simple to augment the agent’s capabilities.
I remember the first time I heard about the word RAG, it sounded like something from another world.
If you also feel that way, you have come to the right place. Stay with me in this tutorial, so we can demystify this concept together.
In this article, we will build an agent that can answer questions about a PDF file by retrieving information from it. Let’s dive in.
What is RAG?
Retrieval-Augmented Generation, or just RAG, is nothing more than adding some more information in a box that the Large Language Model (LLM) can access and get information from.
RAG is a store where the LLM can shop for information before returning an answer
Machines can store a lot more information than we do. But they (still) can’t store all the information available. Besides being impractical and probably very costly, there is also the problem of information security and new information being generated every single day.
For example, if we ask anything about our daily jobs to an LLM, it will probably say it does not have that information, or it will hallucinate and invent…
