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com.pulumi.awsnative.bedrock.kotlin.inputs.KnowledgeBaseOpenSearchServerlessFieldMappingArgs.kt Maven / Gradle / Ivy
@file:Suppress("NAME_SHADOWING", "DEPRECATION")
package com.pulumi.awsnative.bedrock.kotlin.inputs
import com.pulumi.awsnative.bedrock.inputs.KnowledgeBaseOpenSearchServerlessFieldMappingArgs.builder
import com.pulumi.core.Output
import com.pulumi.core.Output.of
import com.pulumi.kotlin.ConvertibleToJava
import com.pulumi.kotlin.PulumiNullFieldException
import com.pulumi.kotlin.PulumiTagMarker
import kotlin.String
import kotlin.Suppress
import kotlin.jvm.JvmName
/**
* A mapping of Bedrock Knowledge Base fields to OpenSearch Serverless field names
* @property metadataField The name of the field in which Amazon Bedrock stores metadata about the vector store.
* @property textField The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.
* @property vectorField The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.
*/
public data class KnowledgeBaseOpenSearchServerlessFieldMappingArgs(
public val metadataField: Output,
public val textField: Output,
public val vectorField: Output,
) :
ConvertibleToJava {
override fun toJava(): com.pulumi.awsnative.bedrock.inputs.KnowledgeBaseOpenSearchServerlessFieldMappingArgs =
com.pulumi.awsnative.bedrock.inputs.KnowledgeBaseOpenSearchServerlessFieldMappingArgs.builder()
.metadataField(metadataField.applyValue({ args0 -> args0 }))
.textField(textField.applyValue({ args0 -> args0 }))
.vectorField(vectorField.applyValue({ args0 -> args0 })).build()
}
/**
* Builder for [KnowledgeBaseOpenSearchServerlessFieldMappingArgs].
*/
@PulumiTagMarker
public class KnowledgeBaseOpenSearchServerlessFieldMappingArgsBuilder internal constructor() {
private var metadataField: Output? = null
private var textField: Output? = null
private var vectorField: Output? = null
/**
* @param value The name of the field in which Amazon Bedrock stores metadata about the vector store.
*/
@JvmName("sncnsuoyjyckcsip")
public suspend fun metadataField(`value`: Output) {
this.metadataField = value
}
/**
* @param value The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.
*/
@JvmName("qldputxfceknjyur")
public suspend fun textField(`value`: Output) {
this.textField = value
}
/**
* @param value The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.
*/
@JvmName("gvevfprgssqokvjt")
public suspend fun vectorField(`value`: Output) {
this.vectorField = value
}
/**
* @param value The name of the field in which Amazon Bedrock stores metadata about the vector store.
*/
@JvmName("rwlrbwnlyxrmwqbt")
public suspend fun metadataField(`value`: String) {
val toBeMapped = value
val mapped = toBeMapped.let({ args0 -> of(args0) })
this.metadataField = mapped
}
/**
* @param value The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.
*/
@JvmName("lkcwmmgffsweumly")
public suspend fun textField(`value`: String) {
val toBeMapped = value
val mapped = toBeMapped.let({ args0 -> of(args0) })
this.textField = mapped
}
/**
* @param value The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.
*/
@JvmName("cvngunjdrfnvvxnn")
public suspend fun vectorField(`value`: String) {
val toBeMapped = value
val mapped = toBeMapped.let({ args0 -> of(args0) })
this.vectorField = mapped
}
internal fun build(): KnowledgeBaseOpenSearchServerlessFieldMappingArgs =
KnowledgeBaseOpenSearchServerlessFieldMappingArgs(
metadataField = metadataField ?: throw PulumiNullFieldException("metadataField"),
textField = textField ?: throw PulumiNullFieldException("textField"),
vectorField = vectorField ?: throw PulumiNullFieldException("vectorField"),
)
}
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