Vector search can handle virtually any type of unstructured data that can be converted into vector embeddings. This includes text (documents, emails, social media posts), images (photos, diagrams, medical scans), audio (voice recordings, music, sound effects), video content, sensor data from IoT devices, and even protein structures or DNA sequences. The key requirement is that the data can be transformed into a numerical vector representation through machine learning models or other embedding techniques.
For example, images can be converted into vectors using models like ResNet-50, while text can be embedded using models like Word2Vec or BERT. Machine-generated data like sensor readings, log files, and application metrics can also be vectorized and searched. For example. a single photo of an Eastern Towhee bird can be represented as a vector of 2048 numbers using ResNet-50, allowing for similarity-based retrieval. The flexibility to handle diverse data types makes vector search particularly powerful for modern applications that need to process and analyze many different kinds of information.
Vector search can understand semantic relationships in the data, like finding similar actors to Marlon Brando or understanding that "apple" can refer to both a fruit and a technology company.