Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks

• URL: http://arxiv.org/abs/2407.21059v1
• Date: Fri, 26 Jul 2024 03:45:30 GMT
• Title: Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks
• Authors: Yunfan Gao, Yun Xiong, Meng Wang, Haofen Wang,
• Abstract summary: Retrieval-augmented Generation (RAG) has markedly enhanced the capabilities of Large Language Models (LLMs) This paper examines the limitations of the existing RAG paradigm and introduces the modular RAG framework.
• Score: 15.241520961365051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented Generation (RAG) has markedly enhanced the capabilities of Large Language Models (LLMs) in tackling knowledge-intensive tasks. The increasing demands of application scenarios have driven the evolution of RAG, leading to the integration of advanced retrievers, LLMs and other complementary technologies, which in turn has amplified the intricacy of RAG systems. However, the rapid advancements are outpacing the foundational RAG paradigm, with many methods struggling to be unified under the process of "retrieve-then-generate". In this context, this paper examines the limitations of the existing RAG paradigm and introduces the modular RAG framework. By decomposing complex RAG systems into independent modules and specialized operators, it facilitates a highly reconfigurable framework. Modular RAG transcends the traditional linear architecture, embracing a more advanced design that integrates routing, scheduling, and fusion mechanisms. Drawing on extensive research, this paper further identifies prevalent RAG patterns-linear, conditional, branching, and looping-and offers a comprehensive analysis of their respective implementation nuances. Modular RAG presents innovative opportunities for the conceptualization and deployment of RAG systems. Finally, the paper explores the potential emergence of new operators and paradigms, establishing a solid theoretical foundation and a practical roadmap for the continued evolution and practical deployment of RAG technologies.

Related papers

• Synergizing RAG and Reasoning: A Systematic Review [8.842022673771147]
  Recent breakthroughs in large language models (LLMs) have propelled Retrieval-Augmented Generation (RAG) to unprecedented levels. This paper presents a systematic review of the collaborative interplay between RAG and reasoning.
  arXiv  Detail & Related papers  (2025-04-22T13:55:13Z)
• UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation [64.79921229760332]
  Retrieval-Augmented Generation (RAG) significantly enhances the performance of large language models (LLMs) in downstream tasks. Existing RAG toolkits lack support for knowledge adaptation tailored to specific application scenarios. We propose UltraRAG, a RAG toolkit that automates knowledge adaptation throughout the entire workflow.
  arXiv  Detail & Related papers  (2025-03-31T03:49:49Z)
• RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration [4.604003661048267]
  RAG-KG-IL is a novel multi-agent hybrid framework designed to enhance the reasoning capabilities of Large Language Models. It integrates Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs) with an Incremental Learning (IL) approach. We evaluate the framework using real-world case studies involving health-related queries.
  arXiv  Detail & Related papers  (2025-03-14T11:50:16Z)
• Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning [51.54046200512198]
  Retrieval-augmented generation (RAG) is extensively utilized to incorporate external, current knowledge into large language models. A standard RAG pipeline may comprise several components, such as query rewriting, document retrieval, document filtering, and answer generation. To overcome these challenges, we propose treating the RAG pipeline as a multi-agent cooperative task, with each component regarded as an RL agent.
  arXiv  Detail & Related papers  (2025-01-25T14:24:50Z)
• Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG [0.8463972278020965]
  Large Language Models (LLMs) have revolutionized artificial intelligence (AI) by enabling human like text generation and natural language understanding. Retrieval Augmented Generation (RAG) has emerged as a solution, enhancing LLMs by integrating real time data retrieval to provide contextually relevant responses. Agentic Retrieval-Augmented Generation (RAG) transcends these limitations by embedding autonomous AI agents into the RAG pipeline.
  arXiv  Detail & Related papers  (2025-01-15T20:40:25Z)
• An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms [62.878616839799776]
  We propose SynthRAG, an innovative framework designed to enhance Question Answering (QA) performance. SynthRAG improves on conventional models by employing adaptive outlines for dynamic content structuring. An online deployment on the Zhihu platform revealed that SynthRAG's answers achieved notable user engagement.
  arXiv  Detail & Related papers  (2024-10-23T09:14:57Z)
• StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [94.31508613367296]
  Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) We propose StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure. Experiments show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios.
  arXiv  Detail & Related papers  (2024-10-11T13:52:44Z)
• A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions [0.0]
  RAG combines retrieval mechanisms with generative language models to enhance the accuracy of outputs. Recent research breakthroughs are discussed, highlighting novel methods for improving retrieval efficiency. Future research directions are proposed, focusing on improving the robustness of RAG models.
  arXiv  Detail & Related papers  (2024-10-03T22:29:47Z)
• A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph [0.0]
  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.
  arXiv  Detail & Related papers  (2024-07-29T13:26:43Z)
• LLM-enhanced Reranking in Recommender Systems [49.969932092129305]
  Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms. We introduce a comprehensive reranking framework, designed to seamlessly integrate various reranking criteria. A customizable input mechanism is also integrated, enabling the tuning of the language model's focus to meet specific reranking needs.
  arXiv  Detail & Related papers  (2024-06-18T09:29:18Z)
• Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning [49.3242278912771]
  We introduce a novel multimodal RAG framework named RMR (Retrieval Meets Reasoning) The RMR framework employs a bi-modal retrieval module to identify the most relevant question-answer pairs. It significantly boosts the performance of various vision-language models across a spectrum of benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-31T14:23:49Z)
• FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research [70.6584488911715]
  retrieval-augmented generation (RAG) has attracted considerable research attention. Existing RAG toolkits are often heavy and inflexibly, failing to meet the customization needs of researchers. Our toolkit has implemented 16 advanced RAG methods and gathered and organized 38 benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-22T12:12:40Z)
• A Survey on Retrieval-Augmented Text Generation for Large Language Models [1.4579344926652844]
  Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements. This paper organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval, and generation. It outlines RAG's evolution and discusses the field's progression through the analysis of significant studies.
  arXiv  Detail & Related papers  (2024-04-17T01:27:42Z)
• 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)
• 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)
• REX: Rapid Exploration and eXploitation for AI Agents [103.68453326880456]
  We propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance.
  arXiv  Detail & Related papers  (2023-07-18T04:26:33Z)
• RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection [66.08293086254851]
  We propose a reciprocal learning framework to reinforce the learning of a RefSR network. The newly proposed module aligns reference-input images at multi-scale feature spaces and performs reference-aware feature selection. We empirically show that multiple recent state-of-the-art RefSR models can be consistently improved with our reciprocal learning paradigm.
  arXiv  Detail & Related papers  (2022-11-08T12:39:35Z)

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.