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Databricks v1.44.0 published on Friday, May 31, 2024 by Pulumi
pulumi/pulumi-databricks

databricks.VectorSearchIndex

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Databricks v1.44.0 published on Friday, May 31, 2024 by Pulumi
pulumi/pulumi-databricks

    Note This resource could be only used on Unity Catalog-enabled workspace!

    This resource allows you to create Vector Search Index in Databricks. Vector Search is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. The Vector Search Index provides the ability to search data in the linked Delta Table.

    Example Usage

    import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";

const sync = new databricks.VectorSearchIndex("sync", {
    name: "main.default.vector_search_index",
    endpointName: thisDatabricksVectorSearchEndpoint.name,
    primaryKey: "id",
    indexType: "DELTA_SYNC",
    deltaSyncIndexSpec: {
        sourceTable: "main.default.source_table",
        pipelineType: "TRIGGERED",
        embeddingSourceColumns: [{
            name: "text",
            embeddingModelEndpointName: _this.name,
        }],
    },
});

    import pulumi
import pulumi_databricks as databricks

sync = databricks.VectorSearchIndex("sync",
    name="main.default.vector_search_index",
    endpoint_name=this_databricks_vector_search_endpoint["name"],
    primary_key="id",
    index_type="DELTA_SYNC",
    delta_sync_index_spec=databricks.VectorSearchIndexDeltaSyncIndexSpecArgs(
        source_table="main.default.source_table",
        pipeline_type="TRIGGERED",
        embedding_source_columns=[databricks.VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumnArgs(
            name="text",
            embedding_model_endpoint_name=this["name"],
        )],
    ))

    package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewVectorSearchIndex(ctx, "sync", &databricks.VectorSearchIndexArgs{
			Name:         pulumi.String("main.default.vector_search_index"),
			EndpointName: pulumi.Any(thisDatabricksVectorSearchEndpoint.Name),
			PrimaryKey:   pulumi.String("id"),
			IndexType:    pulumi.String("DELTA_SYNC"),
			DeltaSyncIndexSpec: &databricks.VectorSearchIndexDeltaSyncIndexSpecArgs{
				SourceTable:  pulumi.String("main.default.source_table"),
				PipelineType: pulumi.String("TRIGGERED"),
				EmbeddingSourceColumns: databricks.VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumnArray{
					&databricks.VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumnArgs{
						Name:                       pulumi.String("text"),
						EmbeddingModelEndpointName: pulumi.Any(this.Name),
					},
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

    using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var sync = new Databricks.VectorSearchIndex("sync", new()
    {
        Name = "main.default.vector_search_index",
        EndpointName = thisDatabricksVectorSearchEndpoint.Name,
        PrimaryKey = "id",
        IndexType = "DELTA_SYNC",
        DeltaSyncIndexSpec = new Databricks.Inputs.VectorSearchIndexDeltaSyncIndexSpecArgs
        {
            SourceTable = "main.default.source_table",
            PipelineType = "TRIGGERED",
            EmbeddingSourceColumns = new[]
            {
                new Databricks.Inputs.VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumnArgs
                {
                    Name = "text",
                    EmbeddingModelEndpointName = @this.Name,
                },
            },
        },
    });

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.VectorSearchIndex;
import com.pulumi.databricks.VectorSearchIndexArgs;
import com.pulumi.databricks.inputs.VectorSearchIndexDeltaSyncIndexSpecArgs;
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 sync = new VectorSearchIndex("sync", VectorSearchIndexArgs.builder()
            .name("main.default.vector_search_index")
            .endpointName(thisDatabricksVectorSearchEndpoint.name())
            .primaryKey("id")
            .indexType("DELTA_SYNC")
            .deltaSyncIndexSpec(VectorSearchIndexDeltaSyncIndexSpecArgs.builder()
                .sourceTable("main.default.source_table")
                .pipelineType("TRIGGERED")
                .embeddingSourceColumns(VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumnArgs.builder()
                    .name("text")
                    .embeddingModelEndpointName(this_.name())
                    .build())
                .build())
            .build());

    }
}

    resources:
  sync:
    type: databricks:VectorSearchIndex
    properties:
      name: main.default.vector_search_index
      endpointName: ${thisDatabricksVectorSearchEndpoint.name}
      primaryKey: id
      indexType: DELTA_SYNC
      deltaSyncIndexSpec:
        sourceTable: main.default.source_table
        pipelineType: TRIGGERED
        embeddingSourceColumns:
          - name: text
            embeddingModelEndpointName: ${this.name}

    Create VectorSearchIndex Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new VectorSearchIndex(name: string, args: VectorSearchIndexArgs, opts?: CustomResourceOptions);
    @overload
def VectorSearchIndex(resource_name: str,
                      args: VectorSearchIndexArgs,
                      opts: Optional[ResourceOptions] = None)

@overload
def VectorSearchIndex(resource_name: str,
                      opts: Optional[ResourceOptions] = None,
                      endpoint_name: Optional[str] = None,
                      index_type: Optional[str] = None,
                      primary_key: Optional[str] = None,
                      delta_sync_index_spec: Optional[VectorSearchIndexDeltaSyncIndexSpecArgs] = None,
                      direct_access_index_spec: Optional[VectorSearchIndexDirectAccessIndexSpecArgs] = None,
                      name: Optional[str] = None)
    func NewVectorSearchIndex(ctx *Context, name string, args VectorSearchIndexArgs, opts ...ResourceOption) (*VectorSearchIndex, error)
    public VectorSearchIndex(string name, VectorSearchIndexArgs args, CustomResourceOptions? opts = null)
    public VectorSearchIndex(String name, VectorSearchIndexArgs args)
public VectorSearchIndex(String name, VectorSearchIndexArgs args, CustomResourceOptions options)

    type: databricks:VectorSearchIndex
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args VectorSearchIndexArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args VectorSearchIndexArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args VectorSearchIndexArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args VectorSearchIndexArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args VectorSearchIndexArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Example

    The following reference example uses placeholder values for all input properties.

    var vectorSearchIndexResource = new Databricks.VectorSearchIndex("vectorSearchIndexResource", new()
{
    EndpointName = "string",
    IndexType = "string",
    PrimaryKey = "string",
    DeltaSyncIndexSpec = new Databricks.Inputs.VectorSearchIndexDeltaSyncIndexSpecArgs
    {
        EmbeddingSourceColumns = new[]
        {
            new Databricks.Inputs.VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumnArgs
            {
                EmbeddingModelEndpointName = "string",
                Name = "string",
            },
        },
        EmbeddingVectorColumns = new[]
        {
            new Databricks.Inputs.VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumnArgs
            {
                EmbeddingDimension = 0,
                Name = "string",
            },
        },
        EmbeddingWritebackTable = "string",
        PipelineId = "string",
        PipelineType = "string",
        SourceTable = "string",
    },
    DirectAccessIndexSpec = new Databricks.Inputs.VectorSearchIndexDirectAccessIndexSpecArgs
    {
        EmbeddingSourceColumns = new[]
        {
            new Databricks.Inputs.VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumnArgs
            {
                EmbeddingModelEndpointName = "string",
                Name = "string",
            },
        },
        EmbeddingVectorColumns = new[]
        {
            new Databricks.Inputs.VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumnArgs
            {
                EmbeddingDimension = 0,
                Name = "string",
            },
        },
        SchemaJson = "string",
    },
    Name = "string",
});

    example, err := databricks.NewVectorSearchIndex(ctx, "vectorSearchIndexResource", &databricks.VectorSearchIndexArgs{
	EndpointName: pulumi.String("string"),
	IndexType:    pulumi.String("string"),
	PrimaryKey:   pulumi.String("string"),
	DeltaSyncIndexSpec: &databricks.VectorSearchIndexDeltaSyncIndexSpecArgs{
		EmbeddingSourceColumns: databricks.VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumnArray{
			&databricks.VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumnArgs{
				EmbeddingModelEndpointName: pulumi.String("string"),
				Name:                       pulumi.String("string"),
			},
		},
		EmbeddingVectorColumns: databricks.VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumnArray{
			&databricks.VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumnArgs{
				EmbeddingDimension: pulumi.Int(0),
				Name:               pulumi.String("string"),
			},
		},
		EmbeddingWritebackTable: pulumi.String("string"),
		PipelineId:              pulumi.String("string"),
		PipelineType:            pulumi.String("string"),
		SourceTable:             pulumi.String("string"),
	},
	DirectAccessIndexSpec: &databricks.VectorSearchIndexDirectAccessIndexSpecArgs{
		EmbeddingSourceColumns: databricks.VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumnArray{
			&databricks.VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumnArgs{
				EmbeddingModelEndpointName: pulumi.String("string"),
				Name:                       pulumi.String("string"),
			},
		},
		EmbeddingVectorColumns: databricks.VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumnArray{
			&databricks.VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumnArgs{
				EmbeddingDimension: pulumi.Int(0),
				Name:               pulumi.String("string"),
			},
		},
		SchemaJson: pulumi.String("string"),
	},
	Name: pulumi.String("string"),
})

    var vectorSearchIndexResource = new VectorSearchIndex("vectorSearchIndexResource", VectorSearchIndexArgs.builder()
    .endpointName("string")
    .indexType("string")
    .primaryKey("string")
    .deltaSyncIndexSpec(VectorSearchIndexDeltaSyncIndexSpecArgs.builder()
        .embeddingSourceColumns(VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumnArgs.builder()
            .embeddingModelEndpointName("string")
            .name("string")
            .build())
        .embeddingVectorColumns(VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumnArgs.builder()
            .embeddingDimension(0)
            .name("string")
            .build())
        .embeddingWritebackTable("string")
        .pipelineId("string")
        .pipelineType("string")
        .sourceTable("string")
        .build())
    .directAccessIndexSpec(VectorSearchIndexDirectAccessIndexSpecArgs.builder()
        .embeddingSourceColumns(VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumnArgs.builder()
            .embeddingModelEndpointName("string")
            .name("string")
            .build())
        .embeddingVectorColumns(VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumnArgs.builder()
            .embeddingDimension(0)
            .name("string")
            .build())
        .schemaJson("string")
        .build())
    .name("string")
    .build());

    vector_search_index_resource = databricks.VectorSearchIndex("vectorSearchIndexResource",
    endpoint_name="string",
    index_type="string",
    primary_key="string",
    delta_sync_index_spec=databricks.VectorSearchIndexDeltaSyncIndexSpecArgs(
        embedding_source_columns=[databricks.VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumnArgs(
            embedding_model_endpoint_name="string",
            name="string",
        )],
        embedding_vector_columns=[databricks.VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumnArgs(
            embedding_dimension=0,
            name="string",
        )],
        embedding_writeback_table="string",
        pipeline_id="string",
        pipeline_type="string",
        source_table="string",
    ),
    direct_access_index_spec=databricks.VectorSearchIndexDirectAccessIndexSpecArgs(
        embedding_source_columns=[databricks.VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumnArgs(
            embedding_model_endpoint_name="string",
            name="string",
        )],
        embedding_vector_columns=[databricks.VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumnArgs(
            embedding_dimension=0,
            name="string",
        )],
        schema_json="string",
    ),
    name="string")

    const vectorSearchIndexResource = new databricks.VectorSearchIndex("vectorSearchIndexResource", {
    endpointName: "string",
    indexType: "string",
    primaryKey: "string",
    deltaSyncIndexSpec: {
        embeddingSourceColumns: [{
            embeddingModelEndpointName: "string",
            name: "string",
        }],
        embeddingVectorColumns: [{
            embeddingDimension: 0,
            name: "string",
        }],
        embeddingWritebackTable: "string",
        pipelineId: "string",
        pipelineType: "string",
        sourceTable: "string",
    },
    directAccessIndexSpec: {
        embeddingSourceColumns: [{
            embeddingModelEndpointName: "string",
            name: "string",
        }],
        embeddingVectorColumns: [{
            embeddingDimension: 0,
            name: "string",
        }],
        schemaJson: "string",
    },
    name: "string",
});

    type: databricks:VectorSearchIndex
properties:
    deltaSyncIndexSpec:
        embeddingSourceColumns:
            - embeddingModelEndpointName: string
              name: string
        embeddingVectorColumns:
            - embeddingDimension: 0
              name: string
        embeddingWritebackTable: string
        pipelineId: string
        pipelineType: string
        sourceTable: string
    directAccessIndexSpec:
        embeddingSourceColumns:
            - embeddingModelEndpointName: string
              name: string
        embeddingVectorColumns:
            - embeddingDimension: 0
              name: string
        schemaJson: string
    endpointName: string
    indexType: string
    name: string
    primaryKey: string

    VectorSearchIndex Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    The VectorSearchIndex resource accepts the following input properties:

    EndpointName string
    The name of the Vector Search Endpoint that will be used for indexing the data.
    IndexType string
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    PrimaryKey string
    The column name that will be used as a primary key.
    DeltaSyncIndexSpec VectorSearchIndexDeltaSyncIndexSpec
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    DirectAccessIndexSpec VectorSearchIndexDirectAccessIndexSpec
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    EndpointName string
    The name of the Vector Search Endpoint that will be used for indexing the data.
    IndexType string
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    PrimaryKey string
    The column name that will be used as a primary key.
    DeltaSyncIndexSpec VectorSearchIndexDeltaSyncIndexSpecArgs
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    DirectAccessIndexSpec VectorSearchIndexDirectAccessIndexSpecArgs
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    endpointName String
    The name of the Vector Search Endpoint that will be used for indexing the data.
    indexType String
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    primaryKey String
    The column name that will be used as a primary key.
    deltaSyncIndexSpec VectorSearchIndexDeltaSyncIndexSpec
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    directAccessIndexSpec VectorSearchIndexDirectAccessIndexSpec
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    endpointName string
    The name of the Vector Search Endpoint that will be used for indexing the data.
    indexType string
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    primaryKey string
    The column name that will be used as a primary key.
    deltaSyncIndexSpec VectorSearchIndexDeltaSyncIndexSpec
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    directAccessIndexSpec VectorSearchIndexDirectAccessIndexSpec
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    endpoint_name str
    The name of the Vector Search Endpoint that will be used for indexing the data.
    index_type str
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    primary_key str
    The column name that will be used as a primary key.
    delta_sync_index_spec VectorSearchIndexDeltaSyncIndexSpecArgs
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    direct_access_index_spec VectorSearchIndexDirectAccessIndexSpecArgs
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    name str
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    endpointName String
    The name of the Vector Search Endpoint that will be used for indexing the data.
    indexType String
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    primaryKey String
    The column name that will be used as a primary key.
    deltaSyncIndexSpec Property Map
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    directAccessIndexSpec Property Map
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).

    Outputs

    All input properties are implicitly available as output properties. Additionally, the VectorSearchIndex resource produces the following output properties:

    Creator string
    Creator of the endpoint.
    Id string
    The provider-assigned unique ID for this managed resource.
    Statuses List<VectorSearchIndexStatus>
    Object describing the current status of the index consisting of the following fields:
    Creator string
    Creator of the endpoint.
    Id string
    The provider-assigned unique ID for this managed resource.
    Statuses []VectorSearchIndexStatus
    Object describing the current status of the index consisting of the following fields:
    creator String
    Creator of the endpoint.
    id String
    The provider-assigned unique ID for this managed resource.
    statuses List<VectorSearchIndexStatus>
    Object describing the current status of the index consisting of the following fields:
    creator string
    Creator of the endpoint.
    id string
    The provider-assigned unique ID for this managed resource.
    statuses VectorSearchIndexStatus[]
    Object describing the current status of the index consisting of the following fields:
    creator str
    Creator of the endpoint.
    id str
    The provider-assigned unique ID for this managed resource.
    statuses Sequence[VectorSearchIndexStatus]
    Object describing the current status of the index consisting of the following fields:
    creator String
    Creator of the endpoint.
    id String
    The provider-assigned unique ID for this managed resource.
    statuses List<Property Map>
    Object describing the current status of the index consisting of the following fields:

    Look up Existing VectorSearchIndex Resource

    Get an existing VectorSearchIndex resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

    public static get(name: string, id: Input<ID>, state?: VectorSearchIndexState, opts?: CustomResourceOptions): VectorSearchIndex
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        creator: Optional[str] = None,
        delta_sync_index_spec: Optional[VectorSearchIndexDeltaSyncIndexSpecArgs] = None,
        direct_access_index_spec: Optional[VectorSearchIndexDirectAccessIndexSpecArgs] = None,
        endpoint_name: Optional[str] = None,
        index_type: Optional[str] = None,
        name: Optional[str] = None,
        primary_key: Optional[str] = None,
        statuses: Optional[Sequence[VectorSearchIndexStatusArgs]] = None) -> VectorSearchIndex
    func GetVectorSearchIndex(ctx *Context, name string, id IDInput, state *VectorSearchIndexState, opts ...ResourceOption) (*VectorSearchIndex, error)
    public static VectorSearchIndex Get(string name, Input<string> id, VectorSearchIndexState? state, CustomResourceOptions? opts = null)
    public static VectorSearchIndex get(String name, Output<String> id, VectorSearchIndexState state, CustomResourceOptions options)
    Resource lookup is not supported in YAML
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    resource_name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    The following state arguments are supported:
    Creator string
    Creator of the endpoint.
    DeltaSyncIndexSpec VectorSearchIndexDeltaSyncIndexSpec
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    DirectAccessIndexSpec VectorSearchIndexDirectAccessIndexSpec
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    EndpointName string
    The name of the Vector Search Endpoint that will be used for indexing the data.
    IndexType string
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    PrimaryKey string
    The column name that will be used as a primary key.
    Statuses List<VectorSearchIndexStatus>
    Object describing the current status of the index consisting of the following fields:
    Creator string
    Creator of the endpoint.
    DeltaSyncIndexSpec VectorSearchIndexDeltaSyncIndexSpecArgs
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    DirectAccessIndexSpec VectorSearchIndexDirectAccessIndexSpecArgs
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    EndpointName string
    The name of the Vector Search Endpoint that will be used for indexing the data.
    IndexType string
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    PrimaryKey string
    The column name that will be used as a primary key.
    Statuses []VectorSearchIndexStatusArgs
    Object describing the current status of the index consisting of the following fields:
    creator String
    Creator of the endpoint.
    deltaSyncIndexSpec VectorSearchIndexDeltaSyncIndexSpec
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    directAccessIndexSpec VectorSearchIndexDirectAccessIndexSpec
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    endpointName String
    The name of the Vector Search Endpoint that will be used for indexing the data.
    indexType String
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    primaryKey String
    The column name that will be used as a primary key.
    statuses List<VectorSearchIndexStatus>
    Object describing the current status of the index consisting of the following fields:
    creator string
    Creator of the endpoint.
    deltaSyncIndexSpec VectorSearchIndexDeltaSyncIndexSpec
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    directAccessIndexSpec VectorSearchIndexDirectAccessIndexSpec
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    endpointName string
    The name of the Vector Search Endpoint that will be used for indexing the data.
    indexType string
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    primaryKey string
    The column name that will be used as a primary key.
    statuses VectorSearchIndexStatus[]
    Object describing the current status of the index consisting of the following fields:
    creator str
    Creator of the endpoint.
    delta_sync_index_spec VectorSearchIndexDeltaSyncIndexSpecArgs
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    direct_access_index_spec VectorSearchIndexDirectAccessIndexSpecArgs
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    endpoint_name str
    The name of the Vector Search Endpoint that will be used for indexing the data.
    index_type str
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    name str
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    primary_key str
    The column name that will be used as a primary key.
    statuses Sequence[VectorSearchIndexStatusArgs]
    Object describing the current status of the index consisting of the following fields:
    creator String
    Creator of the endpoint.
    deltaSyncIndexSpec Property Map
    Specification for Delta Sync Index. Required if index_type is DELTA_SYNC.
    directAccessIndexSpec Property Map
    Specification for Direct Vector Access Index. Required if index_type is DIRECT_ACCESS.
    endpointName String
    The name of the Vector Search Endpoint that will be used for indexing the data.
    indexType String
    Vector Search index type. Currently supported values are:

    • DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes.
    • DIRECT_ACCESS: An index that supports the direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    primaryKey String
    The column name that will be used as a primary key.
    statuses List<Property Map>
    Object describing the current status of the index consisting of the following fields:

    Supporting Types

    VectorSearchIndexDeltaSyncIndexSpec, VectorSearchIndexDeltaSyncIndexSpecArgs

    EmbeddingSourceColumns List<VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumn>
    array of objects representing columns that contain the embedding source. Each entry consists of:
    EmbeddingVectorColumns List<VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumn>
    EmbeddingWritebackTable string
    PipelineId string
    ID of the associated Delta Live Table pipeline.
    PipelineType string
    SourceTable string
    The name of the source table.
    EmbeddingSourceColumns []VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumn
    array of objects representing columns that contain the embedding source. Each entry consists of:
    EmbeddingVectorColumns []VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumn
    EmbeddingWritebackTable string
    PipelineId string
    ID of the associated Delta Live Table pipeline.
    PipelineType string
    SourceTable string
    The name of the source table.
    embeddingSourceColumns List<VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumn>
    array of objects representing columns that contain the embedding source. Each entry consists of:
    embeddingVectorColumns List<VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumn>
    embeddingWritebackTable String
    pipelineId String
    ID of the associated Delta Live Table pipeline.
    pipelineType String
    sourceTable String
    The name of the source table.
    embeddingSourceColumns VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumn[]
    array of objects representing columns that contain the embedding source. Each entry consists of:
    embeddingVectorColumns VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumn[]
    embeddingWritebackTable string
    pipelineId string
    ID of the associated Delta Live Table pipeline.
    pipelineType string
    sourceTable string
    The name of the source table.
    embedding_source_columns Sequence[VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumn]
    array of objects representing columns that contain the embedding source. Each entry consists of:
    embedding_vector_columns Sequence[VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumn]
    embedding_writeback_table str
    pipeline_id str
    ID of the associated Delta Live Table pipeline.
    pipeline_type str
    source_table str
    The name of the source table.
    embeddingSourceColumns List<Property Map>
    array of objects representing columns that contain the embedding source. Each entry consists of:
    embeddingVectorColumns List<Property Map>
    embeddingWritebackTable String
    pipelineId String
    ID of the associated Delta Live Table pipeline.
    pipelineType String
    sourceTable String
    The name of the source table.

    VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumn, VectorSearchIndexDeltaSyncIndexSpecEmbeddingSourceColumnArgs

    EmbeddingModelEndpointName string
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    EmbeddingModelEndpointName string
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingModelEndpointName String
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingModelEndpointName string
    name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embedding_model_endpoint_name str
    name str
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingModelEndpointName String
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).

    VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumn, VectorSearchIndexDeltaSyncIndexSpecEmbeddingVectorColumnArgs

    EmbeddingDimension int
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    EmbeddingDimension int
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingDimension Integer
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingDimension number
    name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embedding_dimension int
    name str
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingDimension Number
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).

    VectorSearchIndexDirectAccessIndexSpec, VectorSearchIndexDirectAccessIndexSpecArgs

    EmbeddingSourceColumns List<VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumn>
    array of objects representing columns that contain the embedding source. Each entry consists of:
    EmbeddingVectorColumns List<VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumn>
    SchemaJson string
    The schema of the index in JSON format. Check the API documentation for a list of supported data types.
    EmbeddingSourceColumns []VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumn
    array of objects representing columns that contain the embedding source. Each entry consists of:
    EmbeddingVectorColumns []VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumn
    SchemaJson string
    The schema of the index in JSON format. Check the API documentation for a list of supported data types.
    embeddingSourceColumns List<VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumn>
    array of objects representing columns that contain the embedding source. Each entry consists of:
    embeddingVectorColumns List<VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumn>
    schemaJson String
    The schema of the index in JSON format. Check the API documentation for a list of supported data types.
    embeddingSourceColumns VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumn[]
    array of objects representing columns that contain the embedding source. Each entry consists of:
    embeddingVectorColumns VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumn[]
    schemaJson string
    The schema of the index in JSON format. Check the API documentation for a list of supported data types.
    embedding_source_columns Sequence[VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumn]
    array of objects representing columns that contain the embedding source. Each entry consists of:
    embedding_vector_columns Sequence[VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumn]
    schema_json str
    The schema of the index in JSON format. Check the API documentation for a list of supported data types.
    embeddingSourceColumns List<Property Map>
    array of objects representing columns that contain the embedding source. Each entry consists of:
    embeddingVectorColumns List<Property Map>
    schemaJson String
    The schema of the index in JSON format. Check the API documentation for a list of supported data types.

    VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumn, VectorSearchIndexDirectAccessIndexSpecEmbeddingSourceColumnArgs

    EmbeddingModelEndpointName string
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    EmbeddingModelEndpointName string
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingModelEndpointName String
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingModelEndpointName string
    name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embedding_model_endpoint_name str
    name str
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingModelEndpointName String
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).

    VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumn, VectorSearchIndexDirectAccessIndexSpecEmbeddingVectorColumnArgs

    EmbeddingDimension int
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    EmbeddingDimension int
    Name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingDimension Integer
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingDimension number
    name string
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embedding_dimension int
    name str
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).
    embeddingDimension Number
    name String
    Three-level name of the Vector Search Index to create (catalog.schema.index_name).

    VectorSearchIndexStatus, VectorSearchIndexStatusArgs

    IndexUrl string
    Index API Url to be used to perform operations on the index
    IndexedRowCount int
    Number of rows indexed
    Message string
    Message associated with the index status
    Ready bool
    Whether the index is ready for search
    IndexUrl string
    Index API Url to be used to perform operations on the index
    IndexedRowCount int
    Number of rows indexed
    Message string
    Message associated with the index status
    Ready bool
    Whether the index is ready for search
    indexUrl String
    Index API Url to be used to perform operations on the index
    indexedRowCount Integer
    Number of rows indexed
    message String
    Message associated with the index status
    ready Boolean
    Whether the index is ready for search
    indexUrl string
    Index API Url to be used to perform operations on the index
    indexedRowCount number
    Number of rows indexed
    message string
    Message associated with the index status
    ready boolean
    Whether the index is ready for search
    index_url str
    Index API Url to be used to perform operations on the index
    indexed_row_count int
    Number of rows indexed
    message str
    Message associated with the index status
    ready bool
    Whether the index is ready for search
    indexUrl String
    Index API Url to be used to perform operations on the index
    indexedRowCount Number
    Number of rows indexed
    message String
    Message associated with the index status
    ready Boolean
    Whether the index is ready for search

    Import

    The resource can be imported using the name of the Vector Search Index

    bash

    $ pulumi import databricks:index/vectorSearchIndex:VectorSearchIndex this <index-name>

    To learn more about importing existing cloud resources, see Importing resources.

    Package Details

    Repository
    databricks pulumi/pulumi-databricks
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the databricks Terraform Provider.
    databricks logo
    Databricks v1.44.0 published on Friday, May 31, 2024 by Pulumi
    pulumi/pulumi-databricks