Orion-RAG: Path-Aligned Hybrid Retrieval for Graphless Data

TL;DR

Orion-RAG introduces a simple, low-complexity method to connect fragmented, discrete data across files, enhancing retrieval-augmented generation with improved accuracy and efficiency in real-world scenarios.

Contribution

It proposes a lightweight path extraction strategy that transforms unstructured documents into semi-structured data, enabling effective cross-file information linking without complex algorithms.

Findings

Outperforms mainstream frameworks across multiple domains.

Achieves 25.2% relative improvement on FinanceBench.

Supports real-time updates and human-in-the-loop verification.

Abstract

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.