Multimodal Semantic Search — Search Images & Text

I will build a multimodal semantic search system — enabling search across mixed text and image content using CLIP embeddings, supporting text-to-image, image-to-image, and image-to-text queries from a single unified index. Multimodal search is the right architecture for any content catalogue where text and images coexist and users might reasonably use either modality to find relevant content — product catalogues, media libraries, social content platforms, and design asset systems all benefit from search that treats text descriptions and images as equivalent search modalities rather than maintaining separate keyword and image search systems.

The system covers CLIP embedding generation for all content types, unified multimodal vector index, query interface supporting text and image inputs, cross-modal result presentation, and retrieval quality evaluation across all query type combinations.