PathRAG: A Graph-Based Reasoning Approach for Enhancing Retrieval in Software User Manuals

Abstract

In modern software systems, large-scale technical manuals and complex user workflows impose a significant cognitive barrier for end-users, particularly children and the elderly. While integrated automated assistants have emerged as a promising service paradigm to abstract these system complexities, traditional Retrieval-Augmented Generation (RAG) and existing GraphRAG architectures heavily focus on relational knowledge, failing to capture the strict conditional execution paths and procedural control-flows necessary for accurate system orchestration. To bridge this gap, we propose PathRAG, a lightweight, service-oriented graph reasoning framework designed to enable adaptive, condition-aware guide services over structured software procedures. Rather than processing manuals as flat textual chunks, PathRAG models software tasks as an explicit Conditional Directed Graph, enabling a single-pass graph traversal service that efficiently navigates non-linear software states. Experiments show that PathRAG achieves high accuracy in graph construction (F1 up to 0.956 for nodes and 0.936 for edges) and strong performance in path reasoning (Average Path F1 of 0.82 on simple and 0.63 on complex cases). Notably, the low average path distance (1.53) suggests that most errors correspond to minor deviations from the ground-truth sequence. Despite promising results, limitations include a small dataset, domain specificity, and reliance on rule-based parsing, which may affect generalization to noisy or multi-document scenarios.