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The primary output of an embedding model in vector search is numerical vector representations of text or data. This output is crucial because embedding models transform various forms of data—such as text, images, or other types—into dense, fixed-length vectors in a high-dimensional space. These vector representations preserve the semantic and contextual relationships between the data points, enabling the models to capture the meaning and similarities between them effectively.

This vectorization process allows for efficient similarity searches, where the results can be derived based on the proximity of these vectors in the vector space. Therefore, the ability to convert input data into numerical vectors is foundational for vector search technology, as it sets the stage for subsequent operations like similarity searches, clustering, and other machine learning tasks.

The alternatives presented, such as schemas for managing data, results of similarity search operations, or logs of executed queries, do not directly represent the output of the embedding model itself but rather reflect different aspects of data management and operational processes within a system utilizing vector search technology.