com.pulumi.gcp.diagflow.kotlin.outputs.CxFlowNluSettings.kt Maven / Gradle / Ivy

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Build cloud applications and infrastructure by combining the safety and reliability of infrastructure as code with the power of the Kotlin programming language.

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@file:Suppress("NAME_SHADOWING", "DEPRECATION")

package com.pulumi.gcp.diagflow.kotlin.outputs

import kotlin.Double
import kotlin.String
import kotlin.Suppress

/**
 *
 * @property classificationThreshold To filter out false positive results and still get variety in matched natural language inputs for your agent, you can tune the machine learning classification threshold.
 * If the returned score value is less than the threshold value, then a no-match event will be triggered. The score values range from 0.0 (completely uncertain) to 1.0 (completely certain). If set to 0.0, the default of 0.3 is used.
 * @property modelTrainingMode Indicates NLU model training mode.
 * * MODEL_TRAINING_MODE_AUTOMATIC: NLU model training is automatically triggered when a flow gets modified. User can also manually trigger model training in this mode.
 * * MODEL_TRAINING_MODE_MANUAL: User needs to manually trigger NLU model training. Best for large flows whose models take long time to train.
 * Possible values are: `MODEL_TRAINING_MODE_AUTOMATIC`, `MODEL_TRAINING_MODE_MANUAL`.
 * @property modelType Indicates the type of NLU model.
 * * MODEL_TYPE_STANDARD: Use standard NLU model.
 * * MODEL_TYPE_ADVANCED: Use advanced NLU model.
 * Possible values are: `MODEL_TYPE_STANDARD`, `MODEL_TYPE_ADVANCED`.
 */
public data class CxFlowNluSettings(
    public val classificationThreshold: Double? = null,
    public val modelTrainingMode: String? = null,
    public val modelType: String? = null,
) {
    public companion object {
        public fun toKotlin(javaType: com.pulumi.gcp.diagflow.outputs.CxFlowNluSettings): CxFlowNluSettings = CxFlowNluSettings(
            classificationThreshold = javaType.classificationThreshold().map({ args0 -> args0 }).orElse(null),
            modelTrainingMode = javaType.modelTrainingMode().map({ args0 -> args0 }).orElse(null),
            modelType = javaType.modelType().map({ args0 -> args0 }).orElse(null),
        )
    }
}