代理RAG值得吗？RAG方法的实验比较
Is Agentic RAG worth it? An experimental comparison of RAG approaches
AI总结：
本文通过多场景多维度实验比较了增强型和代理型RAG方法，揭示了两者在性能与成本上的权衡，为实际应用提供选择指导。
摘要：
检索增强生成（RAG）系统通常由生成器和检索组件组成，后者从知识库中提取文本上下文以回答用户查询。然而，基本实现存在噪声或次优检索、检索用于超出范围查询、弱查询-文档匹配以及生成器的可变性或成本等局限。这些不足促使了
英文摘要：
Retrieval-Augmented Generation (RAG) systems are usually defined by the combination of a generator and a retrieval component that extracts textual context from a knowledge base to answer user queries. However, such basic implementations exhibit several limitations, including noisy or suboptimal retrieval, misuse of retrieval for out-of-scope queries, weak query-document matching, and variability or cost associated with the generator. These shortcomings have motivated the development of "Enhanced" RAG, where dedicated modules are introduced to address specific weaknesses in the workflow. More recently, the growing self-reflective capabilities of Large Language Models (LLMs) have enabled a new paradigm, often referred to as "Agentic" RAG. In this approach, an LLM orchestrates the entire process, deciding which actions to perform, when to perform them, and whether to iterate. Despite the rapid adoption of both paradigms, it remains unclear which approach is preferable under which conditions. In this work, we conduct an empirically driven evaluation of "Enhanced" and "Agentic" RAG across multiple scenarios and dimensions. Our results provide practical insights into the trade-offs between the two paradigms, offering guidance on selecting the most effective RAG design for real-world applications, considering both performance and costs.
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