A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph

• URL: http://arxiv.org/abs/2407.19994v3
• Date: Fri, 13 Sep 2024 12:19:26 GMT
• Title: A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph
• Authors: Cheonsu Jeong,
• Abstract summary: This study implements an advanced RAG system based on Graph technology to develop high-quality generative AI services. It employs LangGraph to evaluate the reliability of retrieved information and synthesizes diverse data to generate more accurate and enhanced responses.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study aims to improve knowledge-based question-answering (QA) systems by overcoming the limitations of existing Retrieval-Augmented Generation (RAG) models and implementing an advanced RAG system based on Graph technology to develop high-quality generative AI services. While existing RAG models demonstrate high accuracy and fluency by utilizing retrieved information, they may suffer from accuracy degradation as they generate responses using pre-loaded knowledge without reprocessing. Additionally, they cannot incorporate real-time data after the RAG configuration stage, leading to issues with contextual understanding and biased information. To address these limitations, this study implemented an enhanced RAG system utilizing Graph technology. This system is designed to efficiently search and utilize information. Specifically, it employs LangGraph to evaluate the reliability of retrieved information and synthesizes diverse data to generate more accurate and enhanced responses. Furthermore, the study provides a detailed explanation of the system's operation, key implementation steps, and examples through implementation code and validation results, thereby enhancing the understanding of advanced RAG technology. This approach offers practical guidelines for implementing advanced RAG systems in corporate services, making it a valuable resource for practical application.

Related papers

• Leveraging Retrieval-Augmented Generation for University Knowledge Retrieval [2.749898166276854]
  This paper introduces an innovative approach using Retrieval-Augmented Generation (RAG) pipelines with Large Language Models (LLMs) By systematically extracting data from the university official webpage, we generate accurate, contextually relevant responses to user queries. Our experimental results demonstrate significant improvements in the precision and relevance of generated responses.
  arXiv  Detail & Related papers  (2024-11-09T17:38:01Z)
• Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation [72.70046559930555]
  We propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks. Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes. In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration.
  arXiv  Detail & Related papers  (2024-10-11T14:03:29Z)
• A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation [6.544757635738911]
  Retrieval-augmented generation (RAG) is an umbrella of different components, design decisions, and domain-specific adaptations. There is currently no generally accepted methodology for RAG evaluation despite a growing interest in this technology. We propose a first blueprint of a methodology for a sound and reliable evaluation of RAG systems.
  arXiv  Detail & Related papers  (2024-10-11T13:36:13Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [69.4501863547618]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios. With a focus on factual accuracy, we propose three novel metrics Completeness, Hallucination, and Irrelevance. Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• Retrieval-Augmented Generation for AI-Generated Content: A Survey [38.50754568320154]
  Retrieval-Augmented Generation (RAG) has emerged as a paradigm to address such challenges. RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores. In this paper, we comprehensively review existing efforts that integrate RAG technique into AIGC scenarios.
  arXiv  Detail & Related papers  (2024-02-29T18:59:01Z)
• EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems [82.76483989905961]
  Current Sequential Recommender Systems (SRSs) suffer from computational and resource inefficiencies. We develop the Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems (EASRec) EASRec introduces data-aware gates that leverage historical information from input data batch to improve the performance of the recommendation network.
  arXiv  Detail & Related papers  (2024-02-01T07:22:52Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)
• Retrieval-Augmented Generation for Large Language Models: A Survey [17.82361213043507]
  Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases.
  arXiv  Detail & Related papers  (2023-12-18T07:47:33Z)
• A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture [0.0]
  This study presents a method for implementing generative AI services by utilizing the Large Language Models (LLM) application architecture. The research delves into strategies for mitigating the issue of inadequate data, offering tailored solutions. A significant contribution of this work is the development of a Retrieval-Augmented Generation (RAG) model.
  arXiv  Detail & Related papers  (2023-09-03T07:03:17Z)
• Retrieval-Enhanced Machine Learning [110.5237983180089]
  We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2022-05-02T21:42:45Z)
• Graph Learning based Recommender Systems: A Review [111.43249652335555]
  Graph Learning based Recommender Systems (GLRS) employ advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics for recommendations. We provide a systematic review of GLRS, by discussing how they extract important knowledge from graph-based representations to improve the accuracy, reliability and explainability of the recommendations.
  arXiv  Detail & Related papers  (2021-05-13T14:50:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.