'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.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
