From Image to Vector: Building Image Similarity Search with Python and MySQL

Modern applications increasingly rely on vector embeddings to search and compare data such as text, images, and audio. For example: finding visually similar images semantic document search recommendation systems In this article we will: Convert an image to a vector embedding Store the vector in MySQL Compare images using vector similarity Stacks used: Python sentence-transformers PyTorch MySQL vector support Final Result After storing the vectors in MySQL we can run a similarity query. Example output: +------------+-------------+ | image_name | similarity | +------------+-------------+ | img/t3.jpg | 1.00 | | img/t2.jpg | 0.96 | | img/t1.jpg | 0.96 | | img/t4.jpg | 0.92 | System Architecture The overall pipeline is simple: An image is processed by a multimodal embedding model The model converts the image into a numerical vector The vector is stored in MySQL SQL queries compute similarity between vectors AI → ML → DL → Neural Networks → LLM Let’s briefly explain each level. Artificial I