Classification use case

The classification use case lets you use embedding models to compute similarity scores between the incoming text and the labels. It returns the labels ranked in order of most similar to least similar.

The classification use case is compatible with all Lucidworks hosted pre-trained and custom embedding models. The default behavior of embedding models is to always have a score returned.

The topK and similarityCutoff parameters can be used to achieve behaviors where only the following are returned:

• The single most applicable label

• Labels with similarities that exceed a threshold

• A set number of items

• A set number if it exceeds the threshold

POST /ai/prediction/classification/{MODEL_ID}

Classification use case

Python

import requests

url = "https://application_id.applications.lucidworks.com/ai/prediction/classification/{MODEL_ID}"

payload = {}
headers = {
    "Authorization": "<authorization>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

200

{
  "predictions": [
    {
      "tokensUsed": {
        "inputTokens": 10,
        "labelsTokens": 26
      },
      "labels": {
        "Lord of the Rings": 0.40380859375,
        "Harry Potter": 0.3681640625
      },
      "response": "Lord of the Rings: 0.40, Harry Potter: 0.37"
    }
  ]
}

Headers

Authorization · string · required

Bearer token used for authentication. Format: Authorization: Bearer ACCESS_TOKEN.

Content-Type · string

application/json

Example:

"application/json"

Path Parameters

MODEL_ID · string · required

Unique identifier for the model.

Body

application/json

batch · object[]

The batch of key:value pairs used as inputs in the prediction. Up to 32 inputs per request are allowed.

Maximum array length: 32

Show child attributes

useCaseConfig · UseCaseConfigClassification · object

Show child attributes

Response

200 - application/json

OK

The response is of type object.