Yes, vector search can effectively handle multimodal data - information that exists in different forms or modalities. The core principle is that any type of data, regardless of its original format, can be converted into a common vector space representation. This allows for unified search and comparison across different modalities. The system can process combinations of text, images, audio, and other data types simultaneously as long as they can be embedded into the same vector space with comparable dimensionality.
Vector search moves beyond traditional keyword matching to understand semantic relationships and context across different types of data. This is particularly powerful for applications like recommendation systems that need to consider multiple types of user interaction data, or content retrieval systems that match queries across different media formats.
The key is that the embedding models used must be able to capture the relevant semantic features of each modality in a way that makes them comparable in the vector space. While the source readings focus primarily on single-modality examples like word embeddings or image vectors, the principles extend naturally to multiple modalities through appropriate embedding techniques and distance metrics.