com.pulumi.gcp.diagflow.kotlin.inputs.CxFlowNluSettingsArgs.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.inputs

import com.pulumi.core.Output
import com.pulumi.core.Output.of
import com.pulumi.gcp.diagflow.inputs.CxFlowNluSettingsArgs.builder
import com.pulumi.kotlin.ConvertibleToJava
import com.pulumi.kotlin.PulumiTagMarker
import kotlin.Double
import kotlin.String
import kotlin.Suppress
import kotlin.jvm.JvmName

/**
 *
 * @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 CxFlowNluSettingsArgs(
    public val classificationThreshold: Output? = null,
    public val modelTrainingMode: Output? = null,
    public val modelType: Output? = null,
) : ConvertibleToJava {
    override fun toJava(): com.pulumi.gcp.diagflow.inputs.CxFlowNluSettingsArgs =
        com.pulumi.gcp.diagflow.inputs.CxFlowNluSettingsArgs.builder()
            .classificationThreshold(classificationThreshold?.applyValue({ args0 -> args0 }))
            .modelTrainingMode(modelTrainingMode?.applyValue({ args0 -> args0 }))
            .modelType(modelType?.applyValue({ args0 -> args0 })).build()
}

/**
 * Builder for [CxFlowNluSettingsArgs].
 */
@PulumiTagMarker
public class CxFlowNluSettingsArgsBuilder internal constructor() {
    private var classificationThreshold: Output? = null

    private var modelTrainingMode: Output? = null

    private var modelType: Output? = null

    /**
     * @param value 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.
     */
    @JvmName("ijqeelfgkckphfuj")
    public suspend fun classificationThreshold(`value`: Output) {
        this.classificationThreshold = value
    }

    /**
     * @param value 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`.
     */
    @JvmName("fhhthggxrlcekkcf")
    public suspend fun modelTrainingMode(`value`: Output) {
        this.modelTrainingMode = value
    }

    /**
     * @param value 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`.
     */
    @JvmName("luwmyoitbhvcixqm")
    public suspend fun modelType(`value`: Output) {
        this.modelType = value
    }

    /**
     * @param value 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.
     */
    @JvmName("igbktwstrcofijpi")
    public suspend fun classificationThreshold(`value`: Double?) {
        val toBeMapped = value
        val mapped = toBeMapped?.let({ args0 -> of(args0) })
        this.classificationThreshold = mapped
    }

    /**
     * @param value 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`.
     */
    @JvmName("fadtsvkkenrsholx")
    public suspend fun modelTrainingMode(`value`: String?) {
        val toBeMapped = value
        val mapped = toBeMapped?.let({ args0 -> of(args0) })
        this.modelTrainingMode = mapped
    }

    /**
     * @param value 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`.
     */
    @JvmName("igfuhqxgqcndromj")
    public suspend fun modelType(`value`: String?) {
        val toBeMapped = value
        val mapped = toBeMapped?.let({ args0 -> of(args0) })
        this.modelType = mapped
    }

    internal fun build(): CxFlowNluSettingsArgs = CxFlowNluSettingsArgs(
        classificationThreshold = classificationThreshold,
        modelTrainingMode = modelTrainingMode,
        modelType = modelType,
    )
}