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In the Summarization use case of the Lucidworks AI Async Prediction API, the LLM ingests text and returns a summary of the text as a response. The context length is 2048 tokens. No options can be configured. This use case can be used to condense the text of a large document into a summarized response. The Summarization use case contains two requests:
  • POST request - submits a prediction task for a specific useCase and modelId. The API responds with the following information:
    • predictionId. A unique UUID for the submitted prediction task that can be used later to retrieve the results.
    • status. The current state of the prediction task.
  • GET request - uses the predictionId you submit from a previously-submitted POST request and returns the results associated with that previous request.
For detailed API specifications in Swagger/OpenAPI format, see Platform APIs.

Prerequisites

To use this API, you need:
  • The unique APPLICATION_ID for your Lucidworks AI application, which is provided by Lucidworks.
  • A bearer token generated with a scope value of machinelearning.predict. For more information, see Authentication API.
  • The USE_CASE and MODEL_ID fields in the /async-prediction for the POST request. The path is /ai/async-prediction/USE_CASE/MODEL_ID. A list of supported modes is returned in the Lucidworks AI Use Case API. For more information about supported models, see Generative AI models.

Common POST request parameters and fields

Some parameters in the /ai/async-prediction/USE_CASE/MODEL_ID POST request are common to all of the generative AI (Gen-AI) use cases, such as the modelConfig parameter. Also referred to as hyperparameters, these fields set certain controls on the response. Refer to the API spec for more information.

Unique values for the summarization use case

Some parameter values available in the summarization use case are unique to this use case, including values for the useCaseConfig parameter. Refer to the API spec for more information.

Example POST request

This example does not include modelConfig parameters, but you can submit requests that include parameters described in Common POST request parameters and fields.

Example GET request