Build an Image Search Engine with CLIP using Intel Gaudi2 HPUs

Modern computer vision models, such as Contrastive Language Image Pre-training (CLIP) developed by OpenAI, enable sophisticated image search capabilities by using semantic details encoded in image embeddings. This guide demonstrates how to build an image search engine using CLIP and Intel's Gaudi2 system, a hardware optimized for large-scale image processing. By calculating and storing image and text embeddings in a vector database like Faiss, users can efficiently search for images related to specific text prompts or other images. The Gaudi2 system's architecture, featuring 24 Tensor processor cores and substantial memory, significantly speeds up these computations, making it ideal for enterprise applications handling millions of images. The process involves setting up CLIP, computing embeddings, storing them in a vector database, and implementing logic to conduct searches. Such capabilities can enhance media search engines and Retrieval Augmented Generation (RAG) systems, providing rich, semantic search experiences in various applications, from travel exploration to media archive management.