Once your model is trained, you must deploy the model on Abacus.AI platform to generate predictions. You can use the prediction dashboard to generate the predictions from the trained model. In this section the underlying prediction API and all other additional prediction API methods are discussed for the use case in consideration:
Returns the k nearest neighbors for the provided embedding vector.
REQUIRED | KEY | TYPE | DESCRIPTION |
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Yes | deploymentToken | str | The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website. |
Yes | deploymentId | str | The unique identifier to a deployment created under the project. |
Yes | vector | List[float] | Input vector to perform the k nearest neighbors with. |
No | k | int | Overrideable number of items to return. |
No | distance | str | Specify the distance function to use. Options include “dot“, “cosine“, “euclidean“, and “manhattan“. Default = “dot“ |
No | includeScore | bool | If True, will return the score alongside the resulting embedding value. |
No | catalogId | str | An optional parameter honored only for embeddings that provide a catalog id |
KEY | TYPE | DESCRIPTION |
---|---|---|
success | Boolean | true if the call succeeded, false if there was an error |
List[dict] |
TYPE | WHEN |
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DataNotFoundError |
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DataNotFoundError |
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