This paper presents Oph-Guid-RAG, a multimodal visual retrieval-augmented generation system designed for ophthalmology clinical decision support. The system treats guideline pages as independent evidence units and retrieves page images while preserving visual elements like tables and flowcharts. It implements a controllable retrieval framework with routing and filtering mechanisms to reduce noise while selectively introducing external evidence. The system combines query decomposition, rewriting, retrieval, reranking, and multimodal reasoning to provide traceable outputs with guideline references. Evaluated on HealthBench with doctor-based scoring, the approach significantly outperforms GPT-5.2 and GPT-5.4 on hard subsets, achieving +30.0% improvement in overall score and +10.4% to +24.4% gains in accuracy.