Summary of A Study on the Implementation Method Of An Agent-based Advanced Rag System Using Graph, by Cheonsu Jeong
A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph
by Cheonsu Jeong
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study aims to enhance knowledge-based question-answering (QA) systems by overcoming limitations of existing Retrieval-Augmented Generation (RAG) models. The proposed approach utilizes Graph technology to develop high-quality generative AI services, addressing issues with accuracy degradation and real-time data incorporation. The enhanced RAG system employs LangGraph to evaluate retrieved information reliability and synthesizes diverse data to generate more accurate responses. This study provides a detailed explanation of the system’s operation, implementation steps, and examples through code and validation results. The approach offers practical guidelines for implementing advanced RAG systems in corporate services. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research wants to make better question-answering systems that can find answers using information from the internet. Right now, these systems are good at providing accurate and natural-sounding responses, but they have some problems. They may not always provide the best answer because they’re limited by what they know beforehand, and they can’t incorporate new information after they’ve been set up. To fix this, the researchers created a new system that uses graph technology to find and use relevant information more effectively. This new system is designed to generate better responses by considering multiple sources of information. The study explains how this system works and provides examples to help people understand how it can be applied in real-world situations. |
Keywords
» Artificial intelligence » Question answering » Rag » Retrieval augmented generation
