This project implements a visual search system using deep learning feature extraction. A Faster-RCNN model with VGG-16 backbone extracts feature vectors from images, which are then stored in Elasticsearch with a custom distance-computing plugin. The entire stack is dockerized, with the Python-based feature extraction service communicating with an Elasticsearch container over a Docker network to enable approximate nearest-neighbor image search.