Image embeddings for visual search: how 'find similar' actually works

The mechanics behind a 'find visually similar items' feature: embedding images into a shared vector space and searching it the same way text RAG does.

Written by

Pranjul Rathour

GenAI Engineer at SCULT INDIA, based in Kanpur, India. Builds production RAG systems, fine-tunes open LLMs and ships agentic AI products; has mentored 200+ college developers. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks.

pranjulrathour41@gmail.com LinkedIn GitHub X Instagram Bluesky Dev.to

Pranjul Rathour — GenAI engineer, Kanpur

'Find items that look like this one' is one of the most requested features in student e-commerce and catalog projects, and it works on the same principle as text retrieval: embed, store, search by similarity.

The pipeline

A vision embedding model turns each image into a fixed-length vector such that visually similar images land close together in that vector space. Store these vectors the same way you'd store text embeddings, and search with cosine similarity.

CLIP-family models embed both images and text into the same space, which also enables text-to-image search ('red running shoes').
Pure vision embedding models (not CLIP) can capture finer visual detail if your search is purely image-to-image.
Pre-process images consistently — same crop, same resolution — before embedding, or similarity scores get noisy.

See choosing between CLIP, ViT and multimodal LLMs for picking the right embedding model for this.

— Pranjul Rathour, GenAI Engineer from Kanpur, India. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at any campus: pranjulrathour41@gmail.com.