Using Embedding Models

Embedding models convert input data into high-dimensional vector representations. They are useful for a variety of tasks, such as clustering, semantic search, and more.

We support several embedding models, usable in the same way as the other models with the only difference being that the output is a tensor, not a string. These will be returned as a list of floats with a length equal to the embedding dimension.

See our pricing page for a list of available embedding models.