Next-Generation AI Technology: The Future of Information Retrieval and Response Systems Transformed by Agentic RAG

Introduction

Current AI technology has evolved, providing new solutions across many industries. In particular, information retrieval and question-answering systems require the ability to extract appropriate information from vast amounts of data quickly and accurately. Among these, Retrieval-Augmented Generation (RAG) is gaining attention. RAG is a method that combines the generative capabilities of Large Language Models (LLMs) with information retrieval technology, offering flexibility and accuracy that exceed traditional search systems.

In this article, we will cover a new approach called Agentic RAG. This approach overcomes the challenges of traditional Naive RAG and improves the ability to handle complex queries by utilizing multi-agent systems. The purpose of this article is to detail the basic concepts, technical implementation, practical application examples, and the future of Agentic RAG.

Basic Concepts of Agentic RAG

Agentic RAG was developed to overcome the limitations of traditional Naive RAG. The main differences are listed below.

1. Understanding complex queries: Naive RAG processes complex queries in a single step, making granular analysis difficult. In contrast, Agentic RAG adopts an approach that breaks down queries into multiple sub-steps to reach a final conclusion.

2. Use of dynamic tools: While Naive RAG has limited integration with external APIs and databases, Agentic RAG can dynamically utilize these according to the conditions of the workflow.

3. Error correction and reflection: Agentic RAG incorporates mechanisms to verify the accuracy of generated responses and correct errors or hallucinations.

Due to these features, Agentic RAG is attracting attention as a highly reliable solution for enterprise use.

Project Overview

In this project, we implemented Agentic RAG using a framework called LangGraph. LangGraph is a tool for efficiently handling state management and data flow between nodes.

1. Query analysis and routing: User queries are classified and routed to the appropriate nodes.

2. Generation of research plans: For complex requests, a step-by-step research plan is generated.

3. Execution of subgraphs: Necessary documents are retrieved for each step, and highly relevant information is extracted.

4. Response generation and verification: A response is generated based on the retrieved information, and its accuracy is verified.

One of the key features of LangGraph is maintaining consistency between steps by sharing state. This enables efficient and consistent processing even when complex query processing or multiple sub-processes are required.

Details of Technical Implementation

In this chapter, we will explain in detail the technical implementation required to build Agentic RAG.

1. Document analysis and construction of a vector store

First, we used an open-source library called Docling for document analysis. This library provides the functionality to convert documents in various formats such as PDF, DOCX, and XLSX into Markdown or JSON. This conversion process is performed in the following steps.

1. Document Import: Accepts documents in various formats as input and converts them to Markdown format using Docling.

2. Paragraph-level Segmentation: Splits the converted Markdown text into paragraphs, treating each paragraph as an individual data chunk.

3. Saving to Vector Store: Uses the LangChain framework to encode the segmented data chunks as vectors and saves them to ChromaDB. This vector store enables efficient information retrieval in subsequent search processes.

2. Query Routing and Response Generation

Next, we will explain the routing and response generation processes for appropriately handling user queries.

1. Query Classification: User queries are classified into three categories: "General Information," "Environment-Related," and "Request for Additional Information." This classification is performed by a router defined within LangGraph.

2. Research Plan Generation: Based on the classified query, necessary research steps are generated. This includes searching documents and comparing data.

3. Response Generation and Verification: The final response is generated by an LLM based on the retrieved data. The generated response undergoes a verification process for hallucinations to ensure accuracy.

Through these processes, Agentic RAG functions as an information retrieval system that combines reliability and efficiency.

Case Study

This chapter details actual operational examples of Agentic RAG.

1. Actual Operational Example and Results

Using Google's annual environmental sustainability report, we tested the following query.

In response to this query, Agentic RAG generated the following steps.

1. Step 1: Search for the PUE values of Singapore Facility 2 in 2019 and 2022.

2. Step 2: Search for the 2023 CFE average for the Asia-Pacific region.

As a result, the retrieved data is as follows.

• PUE Value: The PUE for Singapore Facility 2 in 2022 was 1.21.

• CFE Average Value: The CFE for the Asia-Pacific region in 2023 was 12%.

2. Comparison with ChatGPT

When the same query was submitted to ChatGPT, the response contained several hallucinations. For example, there were cases where the provided numerical values were incorrect. Agentic RAG is superior in that it can prevent such errors through a hallucination-checking step.

This demonstrated the reliability and accuracy of Agentic RAG.

Challenges and Future Outlook

While Agentic RAG holds much potential, there are also several challenges.

1. Technical Challenges

1. Latency: Due to the complexity of multi-agent systems, processing time can become long. Especially for enterprise use, where response speed is critical, balancing speed and accuracy is a challenge.

2. Scalability: When processing large volumes of queries or data, system scalability is required. In particular, efficient management and scaling of vector stores are key.

3. Improving Hallucination Detection Accuracy: While the current hallucination detection process is effective, the development of new algorithms is necessary to further increase accuracy.

2. Future Possibilities

1. Expansion into New Application Fields: Agentic RAG is expected to see further applications in fields such as healthcare, education, and finance. It is particularly promising for scenarios requiring complex question-answering.

2. Real-time Processing: Moving beyond current batch processing to enable real-time response generation is expected to further improve usability.

3. Collaboration with Humans: Further strengthening the collaboration between humans and agents will enable the handling of more advanced tasks. In particular, improvements in user interaction will enhance the system's reliability and ease of use.

As the next frontier of AI technology, Agentic RAG holds great potential. Its evolution will significantly change the future of AI applications.