In context of vector search, what is the primary function of an embedding model?

The primary function of an embedding model is to transform text or data into numerical vector representations. This transformation is essential because vector search relies on representing various forms of data—such as words, sentences, images, or other entities—as high-dimensional vectors. These vectors capture the semantic meaning and relationships of the data in a way that allows similar items to be found efficiently through mathematical operations.

By converting data into embeddings, the model creates a form that can be effectively processed by algorithms designed for similarity search. The resultant numerical vectors enable comparisons based on distance metrics, allowing for the identification of items that are close in the semantic space defined by the model. This capability is foundational for tasks such as semantic search, recommendation systems, and various AI applications where understanding relationships among data points is crucial.