We will explore the use of embeddings generated from multiple modalities such as text, images, and audio for efficient and accurate search. The session will cover how to create and process multimodal embeddings, index them in a vector database, and perform similarity search operations across diverse data types. We will also discuss various use cases and advantages of using multimodal embeddings in vector search, including improved accuracy in information retrieval and cross-modal connections.