Agentic RAG: Enhancing Retrieval Augmented Generation with AI Agents
In the rapidly evolving field of artificial intelligence, new techniques are constantly emerging to improve the capabilities of language models. One such innovation is Agentic RAG, a powerful enhancement to the traditional Retrieval Augmented Generation (RAG) approach.
What is Agentic RAG?
Agentic RAG is an advanced version of the Retrieval Augmented Generation technique. To understand Agentic RAG, let's first recap what RAG is:
RAG is a method that enhances the accuracy and reliability of Large Language Models (LLMs) by connecting them to external data sources. When a user asks a question, RAG retrieves relevant information from these sources and uses it to generate a more informed response.
Agentic RAG takes this concept a step further by incorporating AI agents into the process. An AI agent in this context is a system with an LLM as its "brain," access to memory, and a collection of tools it can use to make decisions and perform tasks autonomously.
Components of an Agentic RAG System
An Agentic RAG system typically consists of three main components:
LLM (Large Language Model): This serves as the "brain" of the system, capable of understanding queries and generating responses.
Memory: This can include both internal memory (like chat history) and external memory that can be updated and accessed as needed.
Tools: These are specific functions that the agent can use to perform tasks, such as online search, calculation, or text summarization.
Curious to learn more?
Join Professor Mehdi and myself for a discussion about this topic below:
What you’ll learn🤓:
🔎 System architecture of Agentic RAG
🚀 Components of an Agentic RAG system: LLM, memory, tools
🛠 Implementing Agentic RAG from scratch vs using frameworks like LangChain
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🛠️✨ Happy practicing and happy building! 🚀🌟
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