This paper propose using promptable image embeddings guided by questions generated by an LLM, which help Multimodal models focus on specific visual attributes. They also implement a linear approximation strategy to reduce the high computational costs associated with using multimodal large language models (MLLMs) for large-scale searches. Experimental results demonstrate that these techniques significantly improve retrieval precision on complex queries compared to traditional baseline methods. Ultimately, this research aims to bridge the gap between global semantic understanding and the recognition of non-dominant visual details in digital images.