让大模型自主检索企业知识库，提升问答准确率与效率。
AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
- 用轻量级代理工具层替代固定检索，支持模型迭代搜索与文档分析。
- 在BRIGHT上召回率提升至49.6%（+21.8个百分点），金融问答正确率达92%。
- 适合需要高可靠、可落地的企业知识系统部署场景。
我们提出AgenticRAG，一种面向企业知识库的实用型智能检索与分析框架。传统RAG将检索依赖过度集中于搜索模块，使语言模型仅能基于早期检索结果进行生成。本方法在现有企业搜索架构上叠加轻量级代理工具层，赋予推理大模型搜索、查找、打开和摘要等能力，实现信息的迭代获取、文档内导航与证据分析的自主执行。在三个公开基准上均取得显著提升：BRIGHT上召回率@1达49.6%（较最佳嵌入基线提升21.8个百分点），WixQA事实性得分0.96（相对提升13%），FinanceBench答案正确率达92%，仅比使用真实证据的最优情况低2个百分点。消融实验表明，从单次检索转向代理式工具调用是主要提升因素（性能提升5.9倍），多查询检索与文档内导航则同时改善质量与效率。设计选择基于实际生产环境的部署经验，证明其适用于真实企业生产场景。
原文摘要 · Abstract (English)
We present AgenticRAG, a practical agentic harness for retrieval and analysis over enterprise knowledge bases. Standard RAG pipelines place significant burden of grounding on the search stack, constraining the language model to a fixed candidate set chosen deep in the retrieval process. Our approach reduces this overdependence by layering a lightweight harness on top of existing enterprise search infrastructure, equipping a reasoning LLM with search, find, open, and summarize tools enabling the model to iteratively retrieve information, navigate within documents, and analyze evidence autonomously. On three open benchmarks we observe substantial gains: $49.6\%$ recall@1 on BRIGHT (+21.8 pp over the best embedding baseline), 0.96 factuality on WixQA ($+13\%$ relative improvement), and $92\%$ answer correctness on FinanceBench--within 2 pp of oracle access to true evidence. Ablation studies show that the most significant factor is the shift from single-shot retrieval to agentic tool use ($5.9\times$ improvement), while multi-query search and in-document navigation contribute to both quality and efficiency. We present various design choices in our agentic harness that were informed by pre-production deployments. Our results demonstrate its suitability for real-world enterprise production environments.
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