A multimodal search solution using NLP, BigQuery and embeddings

The blog discusses the advancement of search technologies beyond text, incorporating images and videos into search capabilities through multimodal embeddings. Traditional enterprise search engines were designed for text-based queries, limiting their ability to handle visual content. By integrating natural language processing (NLP) and multimodal embeddings, it is now possible to perform cross-modal semantic searches, allowing users to search for images and videos as they would with text. The blog demonstrates a system that can perform text-to-image, text-to-video, and combined searches by using Google Cloud Storage for media storage and BigQuery for indexing. A multimodal embedding model is employed to generate embeddings for media files, enabling efficient similarity searches. The architecture supports seamless cross-modal search experiences, making content discovery more intuitive. The user’s text input is converted into an embedding, and a vector search is performed to match the query with the stored media data. Finally, results are presented to the user with the most relevant image or video URIs and their similarity scores. This approach enhances the search experience, unlocking new possibilities for searching visual content.