pulumi-gcp-kotlin/com.pulumi.gcp.vertex.kotlin/AiIndex

AiIndex

class AiIndex : KotlinCustomResource

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

Example Usage

Vertex Ai Index

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&#46;5, 1&#46;0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
{"id": "43", "embedding": [0&#46;6, 1&#46;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",
});

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&#46;5, 1&#46;0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
{"id": "43", "embedding": [0&#46;6, 1&#46;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={
        "contents_delta_uri": bucket.name.apply(lambda name: f"gs://{name}/contents"),
        "config": {
            "dimensions": 2,
            "approximate_neighbors_count": 150,
            "shard_size": "SHARD_SIZE_SMALL",
            "distance_measure_type": "DOT_PRODUCT_DISTANCE",
            "algorithm_config": {
                "tree_ah_config": {
                    "leaf_node_embedding_count": 500,
                    "leaf_nodes_to_search_percent": 7,
                },
            },
        },
    },
    index_update_method="BATCH_UPDATE")

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&#46;5, 1&#46;0], ""restricts"": [{""namespace"": ""class"", ""allow"": [""cat"", ""pet""]},{""namespace"": ""category"", ""allow"": [""feline""]}]}
{""id"": ""43"", ""embedding"": [0&#46;6, 1&#46;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",
    });
});

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&#46;5, 1&#46;0], \"restricts\": [{\"namespace\": \"class\", \"allow\": [\"cat\", \"pet\"]},{\"namespace\": \"category\", \"allow\": [\"feline\"]}]}\n{\"id\": \"43\", \"embedding\": [0&#46;6, 1&#46;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
	})
}

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&#46;5, 1&#46;0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
{"id": "43", "embedding": [0&#46;6, 1&#46;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());
    }
}

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: ${bucket.name}
      content: |
        {"id": "42", "embedding": [0&#46;5, 1&#46;0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
        {"id": "43", "embedding": [0&#46;6, 1&#46;0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
  index:
    type: gcp:vertex:AiIndex
    properties:
      labels:
        foo: bar
      region: us-central1
      displayName: test-index
      description: index for test
      metadata:
        contentsDeltaUri: gs://${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

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&#46;5, 1&#46;0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
{"id": "43", "embedding": [0&#46;6, 1&#46;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",
});

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&#46;5, 1&#46;0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
{"id": "43", "embedding": [0&#46;6, 1&#46;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={
        "contents_delta_uri": bucket.name.apply(lambda name: f"gs://{name}/contents"),
        "config": {
            "dimensions": 2,
            "shard_size": "SHARD_SIZE_LARGE",
            "distance_measure_type": "COSINE_DISTANCE",
            "feature_norm_type": "UNIT_L2_NORM",
            "algorithm_config": {
                "brute_force_config": {},
            },
        },
    },
    index_update_method="STREAM_UPDATE")

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&#46;5, 1&#46;0], ""restricts"": [{""namespace"": ""class"", ""allow"": [""cat"", ""pet""]},{""namespace"": ""category"", ""allow"": [""feline""]}]}
{""id"": ""43"", ""embedding"": [0&#46;6, 1&#46;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",
    });
});

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&#46;5, 1&#46;0], \"restricts\": [{\"namespace\": \"class\", \"allow\": [\"cat\", \"pet\"]},{\"namespace\": \"category\", \"allow\": [\"feline\"]}]}\n{\"id\": \"43\", \"embedding\": [0&#46;6, 1&#46;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
	})
}

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&#46;5, 1&#46;0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
{"id": "43", "embedding": [0&#46;6, 1&#46;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());
    }
}

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: ${bucket.name}
      content: |
        {"id": "42", "embedding": [0&#46;5, 1&#46;0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
        {"id": "43", "embedding": [0&#46;6, 1&#46;0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
  index:
    type: gcp:vertex:AiIndex
    properties:
      labels:
        foo: bar
      region: us-central1
      displayName: test-index
      description: index for test
      metadata:
        contentsDeltaUri: gs://${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:

$ pulumi import gcp:vertex/aiIndex:AiIndex default projects/{{project}}/locations/{{region}}/indexes/{{name}}

$ pulumi import gcp:vertex/aiIndex:AiIndex default {{project}}/{{region}}/{{name}}

$ pulumi import gcp:vertex/aiIndex:AiIndex default {{region}}/{{name}}

$ pulumi import gcp:vertex/aiIndex:AiIndex default {{name}}

Properties

createTime

val createTime: Output<String>

The timestamp of when the Index was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

deployedIndexes

val deployedIndexes: Output<List<AiIndexDeployedIndex>>

The pointers to DeployedIndexes created from this Index. An Index can be only deleted if all its DeployedIndexes had been undeployed first. Structure is documented below.

description

val description: Output<String>?

The description of the Index.

displayName

val displayName: Output<String>

The display name of the Index. The name can be up to 128 characters long and can consist of any UTF-8 characters.

effectiveLabels

val effectiveLabels: Output<Map<String, String>>

All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.

etag

val etag: Output<String>

Used to perform consistent read-modify-write updates.

val id: Output<String>

indexStats

val indexStats: Output<List<AiIndexIndexStat>>

Stats of the index resource. Structure is documented below.

indexUpdateMethod

val indexUpdateMethod: Output<String>?

The update method to use with this Index. The value must be the followings. If not set, BATCH_UPDATE will be used by default.

labels

val labels: Output<Map<String, String>>?

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.

metadata

val metadata: Output<AiIndexMetadata>?

An additional information about the Index Structure is documented below.

metadataSchemaUri

val metadataSchemaUri: Output<String>

Points to a YAML file stored on Google Cloud Storage describing additional information about the Index, that is specific to it. Unset if the Index does not have any additional information.

name

val name: Output<String>

The resource name of the Index.

project

val project: Output<String>

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

pulumiChildResources

val pulumiChildResources: Set<KotlinResource>

pulumiLabels