用轻量路径连接碎片化文档，提升知识检索与生成效果。
Orion-RAG: Path-Aligned Hybrid Retrieval for Graphless Data
- 通过提取轻量级路径关联分散文档中的概念
- 在FinanceBench上相比强基线提升25.2%精度
- 支持实时更新与人工介入，成本低效率高
检索增强生成（RAG）在知识融合中表现优异，但在数据离散且碎片化的实际场景中面临挑战。多数信息分布于独立文件（如报告、日志），缺乏显式关联。传统搜索引擎独立处理文件，忽略跨文件联系；而手动构建知识图谱对海量数据不现实。为此，我们提出Orion-RAG。核心思想是：无需复杂算法组织数据，仅通过低复杂度策略提取轻量路径，自然连接相关概念。实验证明，该方法足以将碎片化文档转化为半结构化数据，有效实现跨文件信息关联。在多个领域测试中，Orion-RAG持续优于主流框架，支持实时更新与人工验证，具备高成本效益。在FinanceBench上的实验显示，其精度相较强基线有25.2%的相对提升。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has proven effective for knowledge synthesis, yet it encounters significant challenges in practical scenarios where data is inherently discrete and fragmented. In most environments, information is distributed across isolated files like reports and logs that lack explicit links. Standard search engines process files independently, ignoring the connections between them. Furthermore, manually building Knowledge Graphs is impractical for such vast data. To bridge this gap, we present Orion-RAG. Our core insight is simple yet effective: we do not need heavy algorithms to organize this data. Instead, we use a low-complexity strategy to extract lightweight paths that naturally link related concepts. We demonstrate that this streamlined approach suffices to transform fragmented documents into semi-structured data, enabling the system to link information across different files effectively. Extensive experiments demonstrate that Orion-RAG consistently outperforms mainstream frameworks across diverse domains, supporting real-time updates and explicit Human-in-the-Loop verification with high cost-efficiency. Experiments on FinanceBench demonstrate superior precision with a 25.2% relative improvement over strong baselines.
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