LabSage: Structural-Semantic Decoupling for Enhanced Retrieval-Augmented Generation in Clinical Laboratories

Modern clinical laboratory operates within a stringent regulatory ecosystem requiring precise adherence to Standard Operating Procedures (SOPs). However, standard Retrieval-Augmented Generation (RAG) approaches frequently fail in this domain due to context fragmentation, where fixed-size segmentation arbitrarily severs critical procedural dependencies. To address this, we introduce LabSage, a domain-adapted framework implementing structural-semantic decoupling. This hierarchical architecture indexes compact search units for optimal retrieval precision while dynamically retrieving expanded context units to preserve procedural completeness during inference. Evaluated on authentic laboratory queries using Qwen-2.5-7B as backbone, LabSage achieved an Answer Accuracy of 0.780 and Context Recall of 0.909, outperforming standard RAG by 8.3% and 5.7%, respectively. These findings demonstrate that decoupling vector search from reasoning context is a critical architectural advancement in regulated medical domains to mitigate safety-critical omissions and ensure compliance.