How does image similarity search work in production?

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Answer

$(1) Extract embeddings: CLIP / DINOv2 / SigLIP \to 512-1024 dim vectors.$
$(2) L2-normalize + index in FAISS / Qdrant / pgvector (HNSW / IVF-PQ).$
$(3) Query: embed uploaded image, ANN search, rerank with cross-encoder if precision needed.$
$(4) Filter by metadata (category, brand).$
$Production examples: Pinterest visual search, Google Images similar, Bing 'search by image'.$

Check yourself — multiple choice

Same as text search
Extract CLIP/DINOv2 embeddings → L2 norm + FAISS/Qdrant/pgvector (HNSW/IVF-PQ) → query embed + ANN + optional cross-encoder rerank
Random
Not possible

Image similarity: DINOv2/CLIP embed + ANN + rerank; Pinterest / Google standard.

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