Multimodal Semantic Search: Combining MetaCLIP & BLIP with Vector Databases

## Bridging Text and Vision in Modern Search Users do not think purely in text or purely in images. When looking for fashion items, they want to provide a reference image and say "find something like this in emerald green". ### Unified Latent Space with MetaCLIP By projecting both image features and text descriptions into a shared embedding space, we can compute cosine distance between arbitrary modalities without intermediate translations. ### Automated Captioning via BLIP For catalogs with sparse metadata, leveraging BLIP generates synthetic detailed descriptions that enrich both vector embeddings and keyword search indexes. ### Scalable Vector Search Indexing vectors into ChromaDB allows sub-50ms retrieval latencies across tens of thousands of items with filtered metadata scopes.