AgenticRAG：企业知识库中的代理检索
AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
AI总结：
AgenticRAG通过在现有企业检索基础设施上叠加轻量级代理框架，使语言模型能够自主迭代检索信息、导航文档并分析证据，提升了检索准确率和事实性。
摘要：
我们提出了AgenticRAG，一种实用的代理框架，用于在企业知识库上进行检索和分析。传统RAG流程将大量基础工作置于搜索栈上，限制了语言模型只能在检索过程深处的固定候选集上进行操作。我们的方法通过在现有企业搜索基础设施上叠加一个轻量级框架，使推理LLM具备搜索、查找、打开和总结工具，使其能够自主迭代检索信息、在文档中导航并分析证据。在三个公开基准测试中，我们观察到显著提升：在BRIGHT上召回率@1达到49.6%（比最佳嵌入基线高21.8个百分点），在WixQA上的事实性达到0.96（相对改进13%），在FinanceBench上的回答正确率达到92%（距离真实证据的oracle访问仅差2个百分点）。消融研究显示，从单次检索转向代理工具使用是最重要的因素（改进5.9倍），而多查询搜索和文档内导航对质量和效率都有贡献。我们展示了代理框架中的各种设计选择，这些选择受到预生产部署的启发。我们的结果证明了其在真实企业生产环境中的适用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
