Seventy3

Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。

今天的主题是：

Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

Summary

The provided text is a survey of Agentic Retrieval-Augmented Generation (RAG), a paradigm that enhances large language models by integrating autonomous AI agents into the RAG pipeline. This allows for dynamic retrieval strategies, contextual understanding, and iterative refinement, addressing the limitations of traditional RAG systems. The survey covers the evolution of RAG paradigms, detailed Agentic RAG architectures, and applications across industries like healthcare, finance, and education. It also explores implementation strategies, challenges in scaling, ethical considerations, performance optimization, and relevant frameworks and tools. Finally, the survey provides an overview of benchmarks and datasets used to evaluate RAG systems.

这篇文章是关于代理化检索增强生成（Agentic RAG）的综述，介绍了一种通过将自主AI代理集成到RAG流程中来增强大型语言模型的范式。通过这种方式，RAG能够实现动态的检索策略、上下文理解和迭代优化，克服了传统RAG系统的局限性。综述涵盖了RAG范式的演变、详细的代理化RAG架构以及在医疗、金融和教育等行业中的应用。文章还探讨了实现策略、扩展中的挑战、伦理考量、性能优化，以及相关的框架和工具。最后，文章提供了评估RAG系统所使用的基准和数据集的概述。

原文链接：https://arxiv.org/abs/2501.09136