How to Build a Semantic Search Engine for Emojis

The article explores the development of a semantic search engine specifically for emojis, addressing the limitations of existing emoji search tools that rely solely on exact text matches. The author outlines the challenges faced in creating an engine that integrates both textual and visual data, highlighting the unique dual nature of emojis as both text and images. By utilizing models like CLIP and generating high-resolution images, the search engine attempts to bridge the gap between these modalities for more effective retrieval. The process involves generating candidate emojis based on image similarity, reordering them through text-based similarity evaluations, and using a cross-encoder for refined rankings. The final result is an open-source emoji search engine available in UI and CLI versions, which, despite its imperfections, represents a step forward in multimodal search technology. The article emphasizes the broader implications of understanding the intersection of text and images, with emojis serving as a unique test case for multimodal models.

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