The precision of contextual information is crucial for the results of Large Language Models (LLMs). However, to achieve more precise results in the field of medication instructions, which are characterized by their specificity in covering multiple drug characteristics, it is necessary to refine these instructions. Additionally, most current methods for constructing indexes do not consider sentence-level semantic information, which can easily ignore important details. Therefore, we propose a retrieval enhancement method based on ontology subdivision for LLM(OAGLLM). In the domain of Medication Instructions, we propose an ontology-based subdivision approach for constructing a specialized ontology and systematically storing expert knowledge, accurately match various aspects of Medication Instructions. To capture sentence-level semantic information and improve retrieval accuracy, we have introduced a hierarchical construction indexing method, which is designed to enhance text relevance and coherence. Lastly, we develop a retrieval augmentation system that integrates the Medication Instructions ontology with the hierarchical database. To validate the effectiveness of OAGLLM, we constructed a dataset on medication instructions. Our experiments demonstrate that our method outperforms other models in overall performance and excels across various types of data.