用关键词搜索替代向量库，也能达到90%的RAG效果
Keyword search is all you need: Achieving RAG-Level Performance without vector databases using agentic tool use
- 用智能体+关键词搜索替代向量数据库进行检索
- 性能达到传统RAG系统的90%以上
- 适合需要频繁更新知识库的场景
尽管检索增强生成（RAG）在基于现有知识库生成准确、上下文相关的回答方面表现出色，但仍存在对检索质量的依赖、集成复杂性和成本高等问题。近期基于智能体的RAG和工具增强型大模型架构提出了信息检索与处理的新方法。本文探讨了在问答任务中，向量数据库和语义搜索相较于简单、智能体驱动的关键词搜索，在RAG中究竟带来了多大额外价值。我们系统比较了基于RAG的系统与工具增强型大模型智能体，特别评估了仅具备基础关键词搜索工具的智能体在检索机制和响应质量上的表现。实证分析表明，在智能体框架内使用工具化关键词搜索，无需持久化向量数据库，即可达到传统RAG系统超过90%的性能指标。该方法实现简单、成本低，尤其适用于需频繁更新知识库的场景。
原文摘要 · Abstract (English)
While Retrieval-Augmented Generation (RAG) has proven effective for generating accurate, context-based responses based on existing knowledge bases, it presents several challenges including retrieval quality dependencies, integration complexity and cost. Recent advances in agentic-RAG and tool-augmented LLM architectures have introduced alternative approaches to information retrieval and processing. We question how much additional value vector databases and semantic search bring to RAG over simple, agentic keyword search in documents for question-answering. In this study, we conducted a systematic comparison between RAG-based systems and tool-augmented LLM agents, specifically evaluating their retrieval mechanisms and response quality when the agent only has access to basic keyword search tools. Our empirical analysis demonstrates that tool-based keyword search implementations within an agentic framework can attain over $90\%$ of the performance metrics compared to traditional RAG systems without using a standing vector database. Our approach is simple to implement, cost effective, and is particularly useful in scenarios requiring frequent updates to knowledge bases.
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