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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.vertex.kotlin

import com.pulumi.core.Output
import com.pulumi.core.Output.of
import com.pulumi.gcp.vertex.AiIndexArgs.builder
import com.pulumi.gcp.vertex.kotlin.inputs.AiIndexMetadataArgs
import com.pulumi.gcp.vertex.kotlin.inputs.AiIndexMetadataArgsBuilder
import com.pulumi.kotlin.ConvertibleToJava
import com.pulumi.kotlin.PulumiTagMarker
import com.pulumi.kotlin.applySuspend
import kotlin.Pair
import kotlin.String
import kotlin.Suppress
import kotlin.Unit
import kotlin.collections.Map
import kotlin.jvm.JvmName

/**
 * A representation of a collection of database items organized in a way that allows for approximate nearest neighbor (a.k.a ANN) algorithms search.
 * To get more information about Index, see:
 * * [API documentation](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.indexes/)
 * ## Example Usage
 * ### Vertex Ai Index
 * 
 * ```typescript
 * import * as pulumi from "@pulumi/pulumi";
 * import * as gcp from "@pulumi/gcp";
 * const bucket = new gcp.storage.Bucket("bucket", {
 *     name: "vertex-ai-index-test",
 *     location: "us-central1",
 *     uniformBucketLevelAccess: true,
 * });
 * // The sample data comes from the following link:
 * // https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 * const data = new gcp.storage.BucketObject("data", {
 *     name: "contents/data.json",
 *     bucket: bucket.name,
 *     content: `{"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
 * {"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
 * `,
 * });
 * const index = new gcp.vertex.AiIndex("index", {
 *     labels: {
 *         foo: "bar",
 *     },
 *     region: "us-central1",
 *     displayName: "test-index",
 *     description: "index for test",
 *     metadata: {
 *         contentsDeltaUri: pulumi.interpolate`gs://${bucket.name}/contents`,
 *         config: {
 *             dimensions: 2,
 *             approximateNeighborsCount: 150,
 *             shardSize: "SHARD_SIZE_SMALL",
 *             distanceMeasureType: "DOT_PRODUCT_DISTANCE",
 *             algorithmConfig: {
 *                 treeAhConfig: {
 *                     leafNodeEmbeddingCount: 500,
 *                     leafNodesToSearchPercent: 7,
 *                 },
 *             },
 *         },
 *     },
 *     indexUpdateMethod: "BATCH_UPDATE",
 * });
 * ```
 * ```python
 * import pulumi
 * import pulumi_gcp as gcp
 * bucket = gcp.storage.Bucket("bucket",
 *     name="vertex-ai-index-test",
 *     location="us-central1",
 *     uniform_bucket_level_access=True)
 * # The sample data comes from the following link:
 * # https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 * data = gcp.storage.BucketObject("data",
 *     name="contents/data.json",
 *     bucket=bucket.name,
 *     content="""{"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
 * {"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
 * """)
 * index = gcp.vertex.AiIndex("index",
 *     labels={
 *         "foo": "bar",
 *     },
 *     region="us-central1",
 *     display_name="test-index",
 *     description="index for test",
 *     metadata=gcp.vertex.AiIndexMetadataArgs(
 *         contents_delta_uri=bucket.name.apply(lambda name: f"gs://{name}/contents"),
 *         config=gcp.vertex.AiIndexMetadataConfigArgs(
 *             dimensions=2,
 *             approximate_neighbors_count=150,
 *             shard_size="SHARD_SIZE_SMALL",
 *             distance_measure_type="DOT_PRODUCT_DISTANCE",
 *             algorithm_config=gcp.vertex.AiIndexMetadataConfigAlgorithmConfigArgs(
 *                 tree_ah_config=gcp.vertex.AiIndexMetadataConfigAlgorithmConfigTreeAhConfigArgs(
 *                     leaf_node_embedding_count=500,
 *                     leaf_nodes_to_search_percent=7,
 *                 ),
 *             ),
 *         ),
 *     ),
 *     index_update_method="BATCH_UPDATE")
 * ```
 * ```csharp
 * using System.Collections.Generic;
 * using System.Linq;
 * using Pulumi;
 * using Gcp = Pulumi.Gcp;
 * return await Deployment.RunAsync(() =>
 * {
 *     var bucket = new Gcp.Storage.Bucket("bucket", new()
 *     {
 *         Name = "vertex-ai-index-test",
 *         Location = "us-central1",
 *         UniformBucketLevelAccess = true,
 *     });
 *     // The sample data comes from the following link:
 *     // https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 *     var data = new Gcp.Storage.BucketObject("data", new()
 *     {
 *         Name = "contents/data.json",
 *         Bucket = bucket.Name,
 *         Content = @"{""id"": ""42"", ""embedding"": [0.5, 1.0], ""restricts"": [{""namespace"": ""class"", ""allow"": [""cat"", ""pet""]},{""namespace"": ""category"", ""allow"": [""feline""]}]}
 * {""id"": ""43"", ""embedding"": [0.6, 1.0], ""restricts"": [{""namespace"": ""class"", ""allow"": [""dog"", ""pet""]},{""namespace"": ""category"", ""allow"": [""canine""]}]}
 * ",
 *     });
 *     var index = new Gcp.Vertex.AiIndex("index", new()
 *     {
 *         Labels =
 *         {
 *             { "foo", "bar" },
 *         },
 *         Region = "us-central1",
 *         DisplayName = "test-index",
 *         Description = "index for test",
 *         Metadata = new Gcp.Vertex.Inputs.AiIndexMetadataArgs
 *         {
 *             ContentsDeltaUri = bucket.Name.Apply(name => $"gs://{name}/contents"),
 *             Config = new Gcp.Vertex.Inputs.AiIndexMetadataConfigArgs
 *             {
 *                 Dimensions = 2,
 *                 ApproximateNeighborsCount = 150,
 *                 ShardSize = "SHARD_SIZE_SMALL",
 *                 DistanceMeasureType = "DOT_PRODUCT_DISTANCE",
 *                 AlgorithmConfig = new Gcp.Vertex.Inputs.AiIndexMetadataConfigAlgorithmConfigArgs
 *                 {
 *                     TreeAhConfig = new Gcp.Vertex.Inputs.AiIndexMetadataConfigAlgorithmConfigTreeAhConfigArgs
 *                     {
 *                         LeafNodeEmbeddingCount = 500,
 *                         LeafNodesToSearchPercent = 7,
 *                     },
 *                 },
 *             },
 *         },
 *         IndexUpdateMethod = "BATCH_UPDATE",
 *     });
 * });
 * ```
 * ```go
 * package main
 * import (
 * 	"fmt"
 * 	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/storage"
 * 	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/vertex"
 * 	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
 * )
 * func main() {
 * 	pulumi.Run(func(ctx *pulumi.Context) error {
 * 		bucket, err := storage.NewBucket(ctx, "bucket", &storage.BucketArgs{
 * 			Name:                     pulumi.String("vertex-ai-index-test"),
 * 			Location:                 pulumi.String("us-central1"),
 * 			UniformBucketLevelAccess: pulumi.Bool(true),
 * 		})
 * 		if err != nil {
 * 			return err
 * 		}
 * 		// The sample data comes from the following link:
 * 		// https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 * 		_, err = storage.NewBucketObject(ctx, "data", &storage.BucketObjectArgs{
 * 			Name:    pulumi.String("contents/data.json"),
 * 			Bucket:  bucket.Name,
 * 			Content: pulumi.String("{\"id\": \"42\", \"embedding\": [0.5, 1.0], \"restricts\": [{\"namespace\": \"class\", \"allow\": [\"cat\", \"pet\"]},{\"namespace\": \"category\", \"allow\": [\"feline\"]}]}\n{\"id\": \"43\", \"embedding\": [0.6, 1.0], \"restricts\": [{\"namespace\": \"class\", \"allow\": [\"dog\", \"pet\"]},{\"namespace\": \"category\", \"allow\": [\"canine\"]}]}\n"),
 * 		})
 * 		if err != nil {
 * 			return err
 * 		}
 * 		_, err = vertex.NewAiIndex(ctx, "index", &vertex.AiIndexArgs{
 * 			Labels: pulumi.StringMap{
 * 				"foo": pulumi.String("bar"),
 * 			},
 * 			Region:      pulumi.String("us-central1"),
 * 			DisplayName: pulumi.String("test-index"),
 * 			Description: pulumi.String("index for test"),
 * 			Metadata: &vertex.AiIndexMetadataArgs{
 * 				ContentsDeltaUri: bucket.Name.ApplyT(func(name string) (string, error) {
 * 					return fmt.Sprintf("gs://%v/contents", name), nil
 * 				}).(pulumi.StringOutput),
 * 				Config: &vertex.AiIndexMetadataConfigArgs{
 * 					Dimensions:                pulumi.Int(2),
 * 					ApproximateNeighborsCount: pulumi.Int(150),
 * 					ShardSize:                 pulumi.String("SHARD_SIZE_SMALL"),
 * 					DistanceMeasureType:       pulumi.String("DOT_PRODUCT_DISTANCE"),
 * 					AlgorithmConfig: &vertex.AiIndexMetadataConfigAlgorithmConfigArgs{
 * 						TreeAhConfig: &vertex.AiIndexMetadataConfigAlgorithmConfigTreeAhConfigArgs{
 * 							LeafNodeEmbeddingCount:   pulumi.Int(500),
 * 							LeafNodesToSearchPercent: pulumi.Int(7),
 * 						},
 * 					},
 * 				},
 * 			},
 * 			IndexUpdateMethod: pulumi.String("BATCH_UPDATE"),
 * 		})
 * 		if err != nil {
 * 			return err
 * 		}
 * 		return nil
 * 	})
 * }
 * ```
 * ```java
 * package generated_program;
 * import com.pulumi.Context;
 * import com.pulumi.Pulumi;
 * import com.pulumi.core.Output;
 * import com.pulumi.gcp.storage.Bucket;
 * import com.pulumi.gcp.storage.BucketArgs;
 * import com.pulumi.gcp.storage.BucketObject;
 * import com.pulumi.gcp.storage.BucketObjectArgs;
 * import com.pulumi.gcp.vertex.AiIndex;
 * import com.pulumi.gcp.vertex.AiIndexArgs;
 * import com.pulumi.gcp.vertex.inputs.AiIndexMetadataArgs;
 * import com.pulumi.gcp.vertex.inputs.AiIndexMetadataConfigArgs;
 * import com.pulumi.gcp.vertex.inputs.AiIndexMetadataConfigAlgorithmConfigArgs;
 * import com.pulumi.gcp.vertex.inputs.AiIndexMetadataConfigAlgorithmConfigTreeAhConfigArgs;
 * import java.util.List;
 * import java.util.ArrayList;
 * import java.util.Map;
 * import java.io.File;
 * import java.nio.file.Files;
 * import java.nio.file.Paths;
 * public class App {
 *     public static void main(String[] args) {
 *         Pulumi.run(App::stack);
 *     }
 *     public static void stack(Context ctx) {
 *         var bucket = new Bucket("bucket", BucketArgs.builder()
 *             .name("vertex-ai-index-test")
 *             .location("us-central1")
 *             .uniformBucketLevelAccess(true)
 *             .build());
 *         // The sample data comes from the following link:
 *         // https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 *         var data = new BucketObject("data", BucketObjectArgs.builder()
 *             .name("contents/data.json")
 *             .bucket(bucket.name())
 *             .content("""
 * {"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
 * {"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
 *             """)
 *             .build());
 *         var index = new AiIndex("index", AiIndexArgs.builder()
 *             .labels(Map.of("foo", "bar"))
 *             .region("us-central1")
 *             .displayName("test-index")
 *             .description("index for test")
 *             .metadata(AiIndexMetadataArgs.builder()
 *                 .contentsDeltaUri(bucket.name().applyValue(name -> String.format("gs://%s/contents", name)))
 *                 .config(AiIndexMetadataConfigArgs.builder()
 *                     .dimensions(2)
 *                     .approximateNeighborsCount(150)
 *                     .shardSize("SHARD_SIZE_SMALL")
 *                     .distanceMeasureType("DOT_PRODUCT_DISTANCE")
 *                     .algorithmConfig(AiIndexMetadataConfigAlgorithmConfigArgs.builder()
 *                         .treeAhConfig(AiIndexMetadataConfigAlgorithmConfigTreeAhConfigArgs.builder()
 *                             .leafNodeEmbeddingCount(500)
 *                             .leafNodesToSearchPercent(7)
 *                             .build())
 *                         .build())
 *                     .build())
 *                 .build())
 *             .indexUpdateMethod("BATCH_UPDATE")
 *             .build());
 *     }
 * }
 * ```
 * ```yaml
 * resources:
 *   bucket:
 *     type: gcp:storage:Bucket
 *     properties:
 *       name: vertex-ai-index-test
 *       location: us-central1
 *       uniformBucketLevelAccess: true
 *   # The sample data comes from the following link:
 *   # https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 *   data:
 *     type: gcp:storage:BucketObject
 *     properties:
 *       name: contents/data.json
 *       bucket: ${bucket.name}
 *       content: |
 *         {"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
 *         {"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
 *   index:
 *     type: gcp:vertex:AiIndex
 *     properties:
 *       labels:
 *         foo: bar
 *       region: us-central1
 *       displayName: test-index
 *       description: index for test
 *       metadata:
 *         contentsDeltaUri: gs://${bucket.name}/contents
 *         config:
 *           dimensions: 2
 *           approximateNeighborsCount: 150
 *           shardSize: SHARD_SIZE_SMALL
 *           distanceMeasureType: DOT_PRODUCT_DISTANCE
 *           algorithmConfig:
 *             treeAhConfig:
 *               leafNodeEmbeddingCount: 500
 *               leafNodesToSearchPercent: 7
 *       indexUpdateMethod: BATCH_UPDATE
 * ```
 * 
 * ### Vertex Ai Index Streaming
 * 
 * ```typescript
 * import * as pulumi from "@pulumi/pulumi";
 * import * as gcp from "@pulumi/gcp";
 * const bucket = new gcp.storage.Bucket("bucket", {
 *     name: "vertex-ai-index-test",
 *     location: "us-central1",
 *     uniformBucketLevelAccess: true,
 * });
 * // The sample data comes from the following link:
 * // https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 * const data = new gcp.storage.BucketObject("data", {
 *     name: "contents/data.json",
 *     bucket: bucket.name,
 *     content: `{"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
 * {"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
 * `,
 * });
 * const index = new gcp.vertex.AiIndex("index", {
 *     labels: {
 *         foo: "bar",
 *     },
 *     region: "us-central1",
 *     displayName: "test-index",
 *     description: "index for test",
 *     metadata: {
 *         contentsDeltaUri: pulumi.interpolate`gs://${bucket.name}/contents`,
 *         config: {
 *             dimensions: 2,
 *             shardSize: "SHARD_SIZE_LARGE",
 *             distanceMeasureType: "COSINE_DISTANCE",
 *             featureNormType: "UNIT_L2_NORM",
 *             algorithmConfig: {
 *                 bruteForceConfig: {},
 *             },
 *         },
 *     },
 *     indexUpdateMethod: "STREAM_UPDATE",
 * });
 * ```
 * ```python
 * import pulumi
 * import pulumi_gcp as gcp
 * bucket = gcp.storage.Bucket("bucket",
 *     name="vertex-ai-index-test",
 *     location="us-central1",
 *     uniform_bucket_level_access=True)
 * # The sample data comes from the following link:
 * # https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 * data = gcp.storage.BucketObject("data",
 *     name="contents/data.json",
 *     bucket=bucket.name,
 *     content="""{"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
 * {"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
 * """)
 * index = gcp.vertex.AiIndex("index",
 *     labels={
 *         "foo": "bar",
 *     },
 *     region="us-central1",
 *     display_name="test-index",
 *     description="index for test",
 *     metadata=gcp.vertex.AiIndexMetadataArgs(
 *         contents_delta_uri=bucket.name.apply(lambda name: f"gs://{name}/contents"),
 *         config=gcp.vertex.AiIndexMetadataConfigArgs(
 *             dimensions=2,
 *             shard_size="SHARD_SIZE_LARGE",
 *             distance_measure_type="COSINE_DISTANCE",
 *             feature_norm_type="UNIT_L2_NORM",
 *             algorithm_config=gcp.vertex.AiIndexMetadataConfigAlgorithmConfigArgs(
 *                 brute_force_config=gcp.vertex.AiIndexMetadataConfigAlgorithmConfigBruteForceConfigArgs(),
 *             ),
 *         ),
 *     ),
 *     index_update_method="STREAM_UPDATE")
 * ```
 * ```csharp
 * using System.Collections.Generic;
 * using System.Linq;
 * using Pulumi;
 * using Gcp = Pulumi.Gcp;
 * return await Deployment.RunAsync(() =>
 * {
 *     var bucket = new Gcp.Storage.Bucket("bucket", new()
 *     {
 *         Name = "vertex-ai-index-test",
 *         Location = "us-central1",
 *         UniformBucketLevelAccess = true,
 *     });
 *     // The sample data comes from the following link:
 *     // https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 *     var data = new Gcp.Storage.BucketObject("data", new()
 *     {
 *         Name = "contents/data.json",
 *         Bucket = bucket.Name,
 *         Content = @"{""id"": ""42"", ""embedding"": [0.5, 1.0], ""restricts"": [{""namespace"": ""class"", ""allow"": [""cat"", ""pet""]},{""namespace"": ""category"", ""allow"": [""feline""]}]}
 * {""id"": ""43"", ""embedding"": [0.6, 1.0], ""restricts"": [{""namespace"": ""class"", ""allow"": [""dog"", ""pet""]},{""namespace"": ""category"", ""allow"": [""canine""]}]}
 * ",
 *     });
 *     var index = new Gcp.Vertex.AiIndex("index", new()
 *     {
 *         Labels =
 *         {
 *             { "foo", "bar" },
 *         },
 *         Region = "us-central1",
 *         DisplayName = "test-index",
 *         Description = "index for test",
 *         Metadata = new Gcp.Vertex.Inputs.AiIndexMetadataArgs
 *         {
 *             ContentsDeltaUri = bucket.Name.Apply(name => $"gs://{name}/contents"),
 *             Config = new Gcp.Vertex.Inputs.AiIndexMetadataConfigArgs
 *             {
 *                 Dimensions = 2,
 *                 ShardSize = "SHARD_SIZE_LARGE",
 *                 DistanceMeasureType = "COSINE_DISTANCE",
 *                 FeatureNormType = "UNIT_L2_NORM",
 *                 AlgorithmConfig = new Gcp.Vertex.Inputs.AiIndexMetadataConfigAlgorithmConfigArgs
 *                 {
 *                     BruteForceConfig = null,
 *                 },
 *             },
 *         },
 *         IndexUpdateMethod = "STREAM_UPDATE",
 *     });
 * });
 * ```
 * ```go
 * package main
 * import (
 * 	"fmt"
 * 	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/storage"
 * 	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/vertex"
 * 	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
 * )
 * func main() {
 * 	pulumi.Run(func(ctx *pulumi.Context) error {
 * 		bucket, err := storage.NewBucket(ctx, "bucket", &storage.BucketArgs{
 * 			Name:                     pulumi.String("vertex-ai-index-test"),
 * 			Location:                 pulumi.String("us-central1"),
 * 			UniformBucketLevelAccess: pulumi.Bool(true),
 * 		})
 * 		if err != nil {
 * 			return err
 * 		}
 * 		// The sample data comes from the following link:
 * 		// https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 * 		_, err = storage.NewBucketObject(ctx, "data", &storage.BucketObjectArgs{
 * 			Name:    pulumi.String("contents/data.json"),
 * 			Bucket:  bucket.Name,
 * 			Content: pulumi.String("{\"id\": \"42\", \"embedding\": [0.5, 1.0], \"restricts\": [{\"namespace\": \"class\", \"allow\": [\"cat\", \"pet\"]},{\"namespace\": \"category\", \"allow\": [\"feline\"]}]}\n{\"id\": \"43\", \"embedding\": [0.6, 1.0], \"restricts\": [{\"namespace\": \"class\", \"allow\": [\"dog\", \"pet\"]},{\"namespace\": \"category\", \"allow\": [\"canine\"]}]}\n"),
 * 		})
 * 		if err != nil {
 * 			return err
 * 		}
 * 		_, err = vertex.NewAiIndex(ctx, "index", &vertex.AiIndexArgs{
 * 			Labels: pulumi.StringMap{
 * 				"foo": pulumi.String("bar"),
 * 			},
 * 			Region:      pulumi.String("us-central1"),
 * 			DisplayName: pulumi.String("test-index"),
 * 			Description: pulumi.String("index for test"),
 * 			Metadata: &vertex.AiIndexMetadataArgs{
 * 				ContentsDeltaUri: bucket.Name.ApplyT(func(name string) (string, error) {
 * 					return fmt.Sprintf("gs://%v/contents", name), nil
 * 				}).(pulumi.StringOutput),
 * 				Config: &vertex.AiIndexMetadataConfigArgs{
 * 					Dimensions:          pulumi.Int(2),
 * 					ShardSize:           pulumi.String("SHARD_SIZE_LARGE"),
 * 					DistanceMeasureType: pulumi.String("COSINE_DISTANCE"),
 * 					FeatureNormType:     pulumi.String("UNIT_L2_NORM"),
 * 					AlgorithmConfig: &vertex.AiIndexMetadataConfigAlgorithmConfigArgs{
 * 						BruteForceConfig: nil,
 * 					},
 * 				},
 * 			},
 * 			IndexUpdateMethod: pulumi.String("STREAM_UPDATE"),
 * 		})
 * 		if err != nil {
 * 			return err
 * 		}
 * 		return nil
 * 	})
 * }
 * ```
 * ```java
 * package generated_program;
 * import com.pulumi.Context;
 * import com.pulumi.Pulumi;
 * import com.pulumi.core.Output;
 * import com.pulumi.gcp.storage.Bucket;
 * import com.pulumi.gcp.storage.BucketArgs;
 * import com.pulumi.gcp.storage.BucketObject;
 * import com.pulumi.gcp.storage.BucketObjectArgs;
 * import com.pulumi.gcp.vertex.AiIndex;
 * import com.pulumi.gcp.vertex.AiIndexArgs;
 * import com.pulumi.gcp.vertex.inputs.AiIndexMetadataArgs;
 * import com.pulumi.gcp.vertex.inputs.AiIndexMetadataConfigArgs;
 * import com.pulumi.gcp.vertex.inputs.AiIndexMetadataConfigAlgorithmConfigArgs;
 * import com.pulumi.gcp.vertex.inputs.AiIndexMetadataConfigAlgorithmConfigBruteForceConfigArgs;
 * import java.util.List;
 * import java.util.ArrayList;
 * import java.util.Map;
 * import java.io.File;
 * import java.nio.file.Files;
 * import java.nio.file.Paths;
 * public class App {
 *     public static void main(String[] args) {
 *         Pulumi.run(App::stack);
 *     }
 *     public static void stack(Context ctx) {
 *         var bucket = new Bucket("bucket", BucketArgs.builder()
 *             .name("vertex-ai-index-test")
 *             .location("us-central1")
 *             .uniformBucketLevelAccess(true)
 *             .build());
 *         // The sample data comes from the following link:
 *         // https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 *         var data = new BucketObject("data", BucketObjectArgs.builder()
 *             .name("contents/data.json")
 *             .bucket(bucket.name())
 *             .content("""
 * {"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
 * {"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
 *             """)
 *             .build());
 *         var index = new AiIndex("index", AiIndexArgs.builder()
 *             .labels(Map.of("foo", "bar"))
 *             .region("us-central1")
 *             .displayName("test-index")
 *             .description("index for test")
 *             .metadata(AiIndexMetadataArgs.builder()
 *                 .contentsDeltaUri(bucket.name().applyValue(name -> String.format("gs://%s/contents", name)))
 *                 .config(AiIndexMetadataConfigArgs.builder()
 *                     .dimensions(2)
 *                     .shardSize("SHARD_SIZE_LARGE")
 *                     .distanceMeasureType("COSINE_DISTANCE")
 *                     .featureNormType("UNIT_L2_NORM")
 *                     .algorithmConfig(AiIndexMetadataConfigAlgorithmConfigArgs.builder()
 *                         .bruteForceConfig()
 *                         .build())
 *                     .build())
 *                 .build())
 *             .indexUpdateMethod("STREAM_UPDATE")
 *             .build());
 *     }
 * }
 * ```
 * ```yaml
 * resources:
 *   bucket:
 *     type: gcp:storage:Bucket
 *     properties:
 *       name: vertex-ai-index-test
 *       location: us-central1
 *       uniformBucketLevelAccess: true
 *   # The sample data comes from the following link:
 *   # https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
 *   data:
 *     type: gcp:storage:BucketObject
 *     properties:
 *       name: contents/data.json
 *       bucket: ${bucket.name}
 *       content: |
 *         {"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
 *         {"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
 *   index:
 *     type: gcp:vertex:AiIndex
 *     properties:
 *       labels:
 *         foo: bar
 *       region: us-central1
 *       displayName: test-index
 *       description: index for test
 *       metadata:
 *         contentsDeltaUri: gs://${bucket.name}/contents
 *         config:
 *           dimensions: 2
 *           shardSize: SHARD_SIZE_LARGE
 *           distanceMeasureType: COSINE_DISTANCE
 *           featureNormType: UNIT_L2_NORM
 *           algorithmConfig:
 *             bruteForceConfig: {}
 *       indexUpdateMethod: STREAM_UPDATE
 * ```
 * 
 * ## Import
 * Index can be imported using any of these accepted formats:
 * * `projects/{{project}}/locations/{{region}}/indexes/{{name}}`
 * * `{{project}}/{{region}}/{{name}}`
 * * `{{region}}/{{name}}`
 * * `{{name}}`
 * When using the `pulumi import` command, Index can be imported using one of the formats above. For example:
 * ```sh
 * $ pulumi import gcp:vertex/aiIndex:AiIndex default projects/{{project}}/locations/{{region}}/indexes/{{name}}
 * ```
 * ```sh
 * $ pulumi import gcp:vertex/aiIndex:AiIndex default {{project}}/{{region}}/{{name}}
 * ```
 * ```sh
 * $ pulumi import gcp:vertex/aiIndex:AiIndex default {{region}}/{{name}}
 * ```
 * ```sh
 * $ pulumi import gcp:vertex/aiIndex:AiIndex default {{name}}
 * ```
 * @property description The description of the Index.
 * @property displayName The display name of the Index. The name can be up to 128 characters long and can consist of any UTF-8 characters.
 * - - -
 * @property indexUpdateMethod The update method to use with this Index. The value must be the followings. If not set, BATCH_UPDATE will be used by default.
 * * BATCH_UPDATE: user can call indexes.patch with files on Cloud Storage of datapoints to update.
 * * STREAM_UPDATE: user can call indexes.upsertDatapoints/DeleteDatapoints to update the Index and the updates will be applied in corresponding DeployedIndexes in nearly real-time.
 * @property labels The labels with user-defined metadata to organize your Indexes.
 * **Note**: This field is non-authoritative, and will only manage the labels present in your configuration.
 * Please refer to the field `effective_labels` for all of the labels present on the resource.
 * @property metadata An additional information about the Index
 * Structure is documented below.
 * @property project The ID of the project in which the resource belongs.
 * If it is not provided, the provider project is used.
 * @property region The region of the index. eg us-central1
 */
public data class AiIndexArgs(
    public val description: Output? = null,
    public val displayName: Output? = null,
    public val indexUpdateMethod: Output? = null,
    public val labels: Output>? = null,
    public val metadata: Output? = null,
    public val project: Output? = null,
    public val region: Output? = null,
) : ConvertibleToJava {
    override fun toJava(): com.pulumi.gcp.vertex.AiIndexArgs =
        com.pulumi.gcp.vertex.AiIndexArgs.builder()
            .description(description?.applyValue({ args0 -> args0 }))
            .displayName(displayName?.applyValue({ args0 -> args0 }))
            .indexUpdateMethod(indexUpdateMethod?.applyValue({ args0 -> args0 }))
            .labels(labels?.applyValue({ args0 -> args0.map({ args0 -> args0.key.to(args0.value) }).toMap() }))
            .metadata(metadata?.applyValue({ args0 -> args0.let({ args0 -> args0.toJava() }) }))
            .project(project?.applyValue({ args0 -> args0 }))
            .region(region?.applyValue({ args0 -> args0 })).build()
}

/**
 * Builder for [AiIndexArgs].
 */
@PulumiTagMarker
public class AiIndexArgsBuilder internal constructor() {
    private var description: Output? = null

    private var displayName: Output? = null

    private var indexUpdateMethod: Output? = null

    private var labels: Output>? = null

    private var metadata: Output? = null

    private var project: Output? = null

    private var region: Output? = null

    /**
     * @param value The description of the Index.
     */
    @JvmName("woxfbmruhugnxrcf")
    public suspend fun description(`value`: Output) {
        this.description = value
    }

    /**
     * @param value The display name of the Index. The name can be up to 128 characters long and can consist of any UTF-8 characters.
     * - - -
     */
    @JvmName("puynklfanfmlwcot")
    public suspend fun displayName(`value`: Output) {
        this.displayName = value
    }

    /**
     * @param value The update method to use with this Index. The value must be the followings. If not set, BATCH_UPDATE will be used by default.
     * * BATCH_UPDATE: user can call indexes.patch with files on Cloud Storage of datapoints to update.
     * * STREAM_UPDATE: user can call indexes.upsertDatapoints/DeleteDatapoints to update the Index and the updates will be applied in corresponding DeployedIndexes in nearly real-time.
     */
    @JvmName("nhcobtxcjfhvnfpa")
    public suspend fun indexUpdateMethod(`value`: Output) {
        this.indexUpdateMethod = value
    }

    /**
     * @param value The labels with user-defined metadata to organize your Indexes.
     * **Note**: This field is non-authoritative, and will only manage the labels present in your configuration.
     * Please refer to the field `effective_labels` for all of the labels present on the resource.
     */
    @JvmName("dooflowgqfiyokkl")
    public suspend fun labels(`value`: Output>) {
        this.labels = value
    }

    /**
     * @param value An additional information about the Index
     * Structure is documented below.
     */
    @JvmName("yureevppjttmkhsm")
    public suspend fun metadata(`value`: Output) {
        this.metadata = value
    }

    /**
     * @param value The ID of the project in which the resource belongs.
     * If it is not provided, the provider project is used.
     */
    @JvmName("tfsllibjaswdxirk")
    public suspend fun project(`value`: Output) {
        this.project = value
    }

    /**
     * @param value The region of the index. eg us-central1
     */
    @JvmName("bldbxrqoohsfiplc")
    public suspend fun region(`value`: Output) {
        this.region = value
    }

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

    /**
     * @param value The display name of the Index. The name can be up to 128 characters long and can consist of any UTF-8 characters.
     * - - -
     */
    @JvmName("lovyewtisebkchry")
    public suspend fun displayName(`value`: String?) {
        val toBeMapped = value
        val mapped = toBeMapped?.let({ args0 -> of(args0) })
        this.displayName = mapped
    }

    /**
     * @param value The update method to use with this Index. The value must be the followings. If not set, BATCH_UPDATE will be used by default.
     * * BATCH_UPDATE: user can call indexes.patch with files on Cloud Storage of datapoints to update.
     * * STREAM_UPDATE: user can call indexes.upsertDatapoints/DeleteDatapoints to update the Index and the updates will be applied in corresponding DeployedIndexes in nearly real-time.
     */
    @JvmName("etwecixrcuyodfuw")
    public suspend fun indexUpdateMethod(`value`: String?) {
        val toBeMapped = value
        val mapped = toBeMapped?.let({ args0 -> of(args0) })
        this.indexUpdateMethod = mapped
    }

    /**
     * @param value The labels with user-defined metadata to organize your Indexes.
     * **Note**: This field is non-authoritative, and will only manage the labels present in your configuration.
     * Please refer to the field `effective_labels` for all of the labels present on the resource.
     */
    @JvmName("xdnigrpxxyjlipfx")
    public suspend fun labels(`value`: Map?) {
        val toBeMapped = value
        val mapped = toBeMapped?.let({ args0 -> of(args0) })
        this.labels = mapped
    }

    /**
     * @param values The labels with user-defined metadata to organize your Indexes.
     * **Note**: This field is non-authoritative, and will only manage the labels present in your configuration.
     * Please refer to the field `effective_labels` for all of the labels present on the resource.
     */
    @JvmName("sjeefwmcjmyafwhk")
    public fun labels(vararg values: Pair) {
        val toBeMapped = values.toMap()
        val mapped = toBeMapped.let({ args0 -> of(args0) })
        this.labels = mapped
    }

    /**
     * @param value An additional information about the Index
     * Structure is documented below.
     */
    @JvmName("ynvgbqphvkwbucsr")
    public suspend fun metadata(`value`: AiIndexMetadataArgs?) {
        val toBeMapped = value
        val mapped = toBeMapped?.let({ args0 -> of(args0) })
        this.metadata = mapped
    }

    /**
     * @param argument An additional information about the Index
     * Structure is documented below.
     */
    @JvmName("yigvcuvlinqmjpfg")
    public suspend fun metadata(argument: suspend AiIndexMetadataArgsBuilder.() -> Unit) {
        val toBeMapped = AiIndexMetadataArgsBuilder().applySuspend { argument() }.build()
        val mapped = of(toBeMapped)
        this.metadata = mapped
    }

    /**
     * @param value The ID of the project in which the resource belongs.
     * If it is not provided, the provider project is used.
     */
    @JvmName("mrfcxfilvcyxeymn")
    public suspend fun project(`value`: String?) {
        val toBeMapped = value
        val mapped = toBeMapped?.let({ args0 -> of(args0) })
        this.project = mapped
    }

    /**
     * @param value The region of the index. eg us-central1
     */
    @JvmName("fkhthohbrsxjxhjb")
    public suspend fun region(`value`: String?) {
        val toBeMapped = value
        val mapped = toBeMapped?.let({ args0 -> of(args0) })
        this.region = mapped
    }

    internal fun build(): AiIndexArgs = AiIndexArgs(
        description = description,
        displayName = displayName,
        indexUpdateMethod = indexUpdateMethod,
        labels = labels,
        metadata = metadata,
        project = project,
        region = region,
    )
}




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