embed_images(images: url, {
    "param": value,
    ...
}) -> (embedding: list[number])

An embedding vector is a numerical representation of an image (or text etc.), such that different numerical components of the vector capture different dimensions of the image’s content. Embeddings can be used, for example, to calculate the semantic similarity between pairs of images (see link_embeddings, for example, to create a network of images connected by similarity).

In its current form the step calculates image embeddings using Clip, which has been trained on 400M image/text pairs to pick out an image’s correct caption from a list of candidates.

embed_images(images: url, {
    "param": value,
    ...
}) -> (embedding: list[number])
​
normalize
boolean
default:"true"

Whether to normalize embedding vectors (to length/norm of 1.0).

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