What kind of data do vector embeddings represent?

Vector embeddings primarily represent unstructured data. This data type lacks a predefined model or organization, making it complex and diverse, including text, images, and audio.

When data is transformed into vector embeddings, it is typically done to capture the semantic meaning or characteristics of the unstructured data. For example, the textual content of documents or sentences can be transformed into high-dimensional vectors that encode the relationships and context within the text. These vectors can then be used for various tasks such as similarity search, clustering, or machine learning applications, allowing algorithms to process and analyze the data effectively.

The choice of unstructured data is significant because traditional data types, such as structured data, follow a rigid schema or organization (like tables in a relational database), which does not apply to the nature of unstructured data. The essence of vector embeddings lies in their ability to serve as representations of these complex, unstructured inputs, providing a way for machines to understand and work with data that does not fit neatly into traditional formats.