How to Build a Semantic Image Search Engine with Roboflow and CLIP

Building a robust search engine for images has evolved significantly with the advent of neural networks, such as OpenAI's CLIP model, which can identify semantically related images to a user's query. CLIP, trained on millions of text-image pairs, encodes semantics from both text and images, enabling the creation of a semantic search engine with minimal code. This guide demonstrates how to construct a semantic search engine using CLIP by calculating embeddings for images and text queries, storing them in a vector store, and retrieving the most relevant images based on similarity. The process is explained using two methods: querying a CLIP embedding search API from Roboflow and manually implementing the search engine with CLIP. The guide also highlights considerations such as storage requirements and the need for an API or web interface when running a self-hosted search engine, while emphasizing the scalability of Roboflow's API as an out-of-the-box solution.

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