How to Build a Semantic Image Search Engine with Supabase and OpenAI CLIP

Building a semantic image search engine has become more accessible with advancements such as OpenAI's Contrastive Language-Image Pre-Training (CLIP) model, which can identify semantically related images to a given user query. By utilizing embeddings—numeric representations of data like text and images—created via the CLIP model, developers can effectively search images based on textual queries. This guide explains how to set up a semantic search engine using the Roboflow-hosted API for embedding generation and Supabase for storing and retrieving these embeddings. The process involves calculating embeddings for images in a dataset, comparing them to text embeddings from user queries, and using Supabase's pg_vector extension to efficiently search and match images with high semantic similarity. By implementing these steps, users can build a scalable and efficient semantic image search system, leveraging tools available for free like the CLIP API and Supabase's extensions.