Multimodal Embeddings — Embed Text and Images in a Shared Space

I will implement multimodal embeddings for your application — using CLIP or a similar vision-language model to embed both text and images in a shared vector space, enabling text-to-image search, image-to-image similarity, and cross-modal retrieval from a single unified index. Multimodal embedding systems unlock retrieval capabilities that separate text and image indexes cannot provide — a user describing a product in natural language finding visually matching products, an image query finding conceptually similar content regardless of whether it's text or image, and a catalogue where images and descriptions are interchangeable query modalities.

The implementation covers CLIP or vision-language model selection, text and image encoding pipelines, shared vector space verification, cross-modal retrieval API, a search interface supporting both modalities, and retrieval quality evaluation on text-to-image and image-to-image queries.