Agentic RAG

Large language models (LLMs) like ChatGPT retrieve answers from vast datasets they’re trained on, but their responses can sometimes be vague or even confidently incorrect—an issue known as AI hallucination. To ensure users get accurate, specific answers, a method called Retrieval Augmented Generation (RAG) allows LLMs to pull information directly from provided knowledge bases, like documents or databases. Agentic RAG extends this approach by incorporating AI agents to handle queries that require multiple sources or more complex workflows.

Suppose you want your customers to get only specific, non-hallucinated, and accurate information about your organization, business, documentation, enterprise data, etc. In that case, you can provide a knowledge base for these language models. Providing an LLM as a source of knowledge, like a document (PDF or Excel), to answer customers' queries is known as Retrieval Augmented Generation (RAG). Giving a single document/source to an LLM to provide accurate answers can make users happy.

However, if a user's query is multi-part or requires further information from several sources, you will be in trouble because RAG supports only a single document information retrieval. You can build upon your existing RAG system by extending it with AI agents to overcome this issue. A RAG system consisting of an AI agent's orchestration and workflow is called an agentic RAG. Now that you have RAG combined with agents, you can provide as many documents/databases as you want to these LLMs for more precise and satisfactory responses to complex user queries.

What is an Agent?

An agent is an AI assistant powered by an LLM that uses specific tools and functions to perform certain tasks. An agentic workflow can consist of one or more agents collaborating to execute and orchestrate tasks. For example, a multi-agent system for language translation may consist of an agent coordinator responsible for transferring instructions to other AI agents equipped with tools to help them answer user queries in specific languages.

What is RAG?

RAG is an information retrieval approach aiming to enhance AI applications' abilities using external knowledge sources. It bridges the gap between information retrieval and generation by large and small language models. The main purpose of RAG is to give AI-based applications broader knowledge and information for improved accuracy of results.

The RAG technique instructs LLMs to answer users' prompts by retrieving information from specified knowledge bases. In RAG, the knowledge base can be a relational database (SQL or Postgres) or vector database (LanceDB, Pinecone) with a document like PDF for retrieval.

What Is Agentic RAG, and Why Is It Useful? 

Agentic RAG is a standard RAG implementation that uses AI agents to solve complex problems by extending the capabilities of LLMs. A classical RAG approach relies solely on LLMs. The agentic RAG method converts LLMs into AI agents and empowers them to use tools, functions, and external knowledge sources. Imagine having a system of tax AI agents at your disposal. This system can be designed to consist of different agents specialized in solving tax problems. There can be an agent who is an expert in making tax cards, an agent who handles tax collections and returns, and another who solves tax problems in a specified language like Swedish or Spanish.

An agentic RAG system aims to address the problems of a conventional RAG by utilizing more intelligent agents to tackle complex queries from multiple data sources and using various tools.

Agentic RAG Components 

An agentic RAG has four main components. These components work together as a single unit for information retrieval, augmentation, and generation.

• Prompt: What a user specifies as a query for an LLM.
• LLM: A large or small language model responsible for generating responses from the user's query. 
• Agent: AI chatbots that use tools and functions to retrieve information to solve tasks.
• Knowledge base: Source of information for unstructured, semi-structured, and structured data.

Why Do We Need RAG Agents, and What Do They Solve? 

A traditional RAG has knowledge gaps because it cannot access multiple knowledge bases. Implementing agents helps leverage external tools to fix these gaps in traditional RAG systems. If a user's prompt cannot be answered, these external tools can be called immediately to respond to the query. External vector databases and standard data sources can also be called, and information can be retrieved to help resolve the problem.

• The existence of multi-knowledge bases and multi-agent approaches helps to minimize hallucination. 
• It makes it easy to retrieve, compare, and contrast information.
• Context comprehension: Using a diverse knowledge base for AI agents to retrieve information helps to provide more accurate responses to multi-part queries.
• Easily scalable: The system's ability to handle multiple agents, document sources, and databases makes building AI applications for millions of users possible.

Key Features of an Agentic RAG System 

• Ability to plan: Agents in this approach can plan and reason for tasks requiring multi-steps and reasoning. 
• Orchestration: A multi-agent RAG approach uses an agent coordinator or orchestrator to assign tasks to appropriate member agents. The coordinator can handle task planning and flow across the system.
• Expert agents: Agents get the required tools to pursue specific goals instead of acting as generalists. Their ability to solve particular tasks helps produce satisfied responses to user prompts. 
• Perform multiple tasks: Multiple RAG agents can perform calculations, find weather information, recommend stock and market trends, analyze data, and more.
• Multi-knowledge base support: The system can connect to several vector and standard databases, such as SQL, Superbase Vector, Pinecone, and LanceDB.

RAG Agents Common Use Cases

Agent-based RAG applications can be built to automate specific tasks within the enterprise ecosystem. Deploying these systems helps organizations minimize manual workloads and improve team efficiency and productivity. The following highlights a few areas where RAG agent technologies can be used across enterprises.

• Generate FAQs: Automatically use RAG agents to generate frequently asked questions from customer tickets and feedback. 
• Meeting notes and summary generator: Build an agent-based RAG application that extracts information from video conferencing and audio room platforms.
• Questioning and answering system: In an agentic RAG system, agents can often be provided with structured, semi-structured, or unstructured data, such as PDF or HTML pages, to answer user queries. LLMs and AI agents can also provide answers to internal private company information.  
• Appointment booking and scheduling platform: Build a retrieval service for a telemedicine platform that helps doctors schedule their availability and enables patients to book appointments. 
• AI chatbot: Build an agent-powered RAG system for to-and-fro questioning, answering, and follow-up questions. Chatbots equipped with RAG agents help to produce more accurate answers.  
• A RAG agent customer support system can help users find specific information from unstructured, semi-unstructured, and structured data formats.
• Agent-powered RAG systems are excellent at looking at and retrieving information from heterogeneous enterprise data. 
• Collaboration: Intelligent agents in a RAG application can seamlessly collaborate to fix issues in enterprise systems. 
• Enterprise retrieval-based chatbot: Empowering these intelligent agents with memory, reasoning, and planning capabilities makes them excellent for 24/7 enterprise questioning and answering systems and for ensuring coherent conversations. 
• Data analysis: It is excellent for analyzing complex unstructured data in videos, images, and text. An agentic RAG system can analyze participants' sentiments in a video conferencing app like Zoom.

Traditional RAG vs RAG Agents

​​Agentic retrieval works much better than traditional RAG workflows because it can connect with several databases, external tools, and functions. If your industry has an existing vanilla RAG implementation, it is time to overhaul it for an agent-powered RAG.

The table below distinguishes a classic RAG application from an agentic RAG system.

| Classic RAG System | Agentic RAG System | |:---------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------:| | Excellent at retrieving and comparing information in a single document. | It can seamlessly handle information retrieval from several data sources. It can also compare information across multiple documents. | | Only one retrieval step is possible at a time, and having a single retrieval step can affect the quality of the response. | It has multiple retrieval steps. If a response is bad, it can re-retrieve information to improve the response. | | There is no way to validate information retrieval. | It can validate the information it retrieves, ensuring users get correct responses to their queries. | | An LLM in a standard RAG system can still hallucinate. | It minimizes hallucination. | | It relies heavily on users’ queries. Prompt engineering may be needed for high-quality results. | It is less dependent on prompts because it can retrieve relevant information from different knowledge sources. |

How To Implement Agentic RAG

Generally, you can build a RAG agent using Python frameworks instead of creating one from scratch. Agentic frameworks like Phidata, OpenAI Swarm, Autogen, CrewAI, LangGraph, and others support multi-agent RAG workflows. Building agents within a RAG system from scratch can be complex and time-consuming because there are many aspects to consider. A fully featured agentic system for RAG must implement reasoning, memory management, LLMs' performance tracking dashboard, multi-deployment options, etc. Luckily, most of these Python-based frameworks have built-in support for these functionalities or integrate with other external tools to cover the support.

Frequently Asked Questions