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Pinecone v2.0.2 published on Wednesday, Nov 5, 2025 by pinecone-io
pinecone-io/pulumi-pinecone

pinecone.Index

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Explain and create a pinecone.Index resource
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Pinecone v2.0.2 published on Wednesday, Nov 5, 2025 by pinecone-io
pinecone-io/pulumi-pinecone

    The pinecone.Index resource lets you create and manage indexes in Pinecone. Learn more about indexes in the docs.

    Example Usage

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

const test = new pinecone.Index("test", {
    name: "tftestindex",
    dimension: 10,
    spec: {
        serverless: {
            cloud: "aws",
            region: "us-west-2",
        },
    },
});

    import pulumi
import pulumi_pinecone as pinecone

test = pinecone.Index("test",
    name="tftestindex",
    dimension=10,
    spec={
        "serverless": {
            "cloud": "aws",
            "region": "us-west-2",
        },
    })

    package main

import (
	"github.com/pulumi/pulumi-terraform-provider/sdks/go/pinecone/v2/pinecone"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := pinecone.NewIndex(ctx, "test", &pinecone.IndexArgs{
			Name:      pulumi.String("tftestindex"),
			Dimension: pulumi.Float64(10),
			Spec: &pinecone.IndexSpecArgs{
				Serverless: &pinecone.IndexSpecServerlessArgs{
					Cloud:  pulumi.String("aws"),
					Region: pulumi.String("us-west-2"),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

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

return await Deployment.RunAsync(() => 
{
    var test = new Pinecone.Index("test", new()
    {
        Name = "tftestindex",
        Dimension = 10,
        Spec = new Pinecone.Inputs.IndexSpecArgs
        {
            Serverless = new Pinecone.Inputs.IndexSpecServerlessArgs
            {
                Cloud = "aws",
                Region = "us-west-2",
            },
        },
    });

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.pinecone.Index;
import com.pulumi.pinecone.IndexArgs;
import com.pulumi.pinecone.inputs.IndexSpecArgs;
import com.pulumi.pinecone.inputs.IndexSpecServerlessArgs;
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 test = new Index("test", IndexArgs.builder()
            .name("tftestindex")
            .dimension(10.0)
            .spec(IndexSpecArgs.builder()
                .serverless(IndexSpecServerlessArgs.builder()
                    .cloud("aws")
                    .region("us-west-2")
                    .build())
                .build())
            .build());

    }
}

    resources:
  test:
    type: pinecone:Index
    properties:
      name: tftestindex
      dimension: 10
      spec:
        serverless:
          cloud: aws
          region: us-west-2

    Create Index Resource

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

    Constructor syntax

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

@overload
def Index(resource_name: str,
          opts: Optional[ResourceOptions] = None,
          deletion_protection: Optional[str] = None,
          dimension: Optional[int] = None,
          embed: Optional[IndexEmbedArgs] = None,
          metric: Optional[str] = None,
          name: Optional[str] = None,
          spec: Optional[IndexSpecArgs] = None,
          tags: Optional[Mapping[str, str]] = None,
          timeouts: Optional[IndexTimeoutsArgs] = None,
          vector_type: Optional[str] = None)
    func NewIndex(ctx *Context, name string, args *IndexArgs, opts ...ResourceOption) (*Index, error)
    public Index(string name, IndexArgs? args = null, CustomResourceOptions? opts = null)
    public Index(String name, IndexArgs args)
public Index(String name, IndexArgs args, CustomResourceOptions options)

    type: pinecone:Index
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 IndexArgs
    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 IndexArgs
    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 IndexArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args IndexArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args IndexArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

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

    var indexResource = new Pinecone.Index("indexResource", new()
{
    DeletionProtection = "string",
    Dimension = 0,
    Embed = new Pinecone.Inputs.IndexEmbedArgs
    {
        Dimension = 0,
        FieldMap = 
        {
            { "string", "string" },
        },
        Metric = "string",
        Model = "string",
        ReadParameters = 
        {
            { "string", "string" },
        },
        VectorType = "string",
        WriteParameters = 
        {
            { "string", "string" },
        },
    },
    Metric = "string",
    Name = "string",
    Spec = new Pinecone.Inputs.IndexSpecArgs
    {
        Pod = new Pinecone.Inputs.IndexSpecPodArgs
        {
            Environment = "string",
            PodType = "string",
            MetadataConfig = new Pinecone.Inputs.IndexSpecPodMetadataConfigArgs
            {
                Indexeds = new[]
                {
                    "string",
                },
            },
            Pods = 0,
            Replicas = 0,
            Shards = 0,
            SourceCollection = "string",
        },
        Serverless = new Pinecone.Inputs.IndexSpecServerlessArgs
        {
            Cloud = "string",
            Region = "string",
        },
    },
    Tags = 
    {
        { "string", "string" },
    },
    Timeouts = new Pinecone.Inputs.IndexTimeoutsArgs
    {
        Create = "string",
        Delete = "string",
    },
    VectorType = "string",
});

    example, err := pinecone.NewIndex(ctx, "indexResource", &pinecone.IndexArgs{
	DeletionProtection: pulumi.String("string"),
	Dimension:          pulumi.Int(0),
	Embed: &pinecone.IndexEmbedArgs{
		Dimension: pulumi.Int(0),
		FieldMap: pulumi.StringMap{
			"string": pulumi.String("string"),
		},
		Metric: pulumi.String("string"),
		Model:  pulumi.String("string"),
		ReadParameters: pulumi.StringMap{
			"string": pulumi.String("string"),
		},
		VectorType: pulumi.String("string"),
		WriteParameters: pulumi.StringMap{
			"string": pulumi.String("string"),
		},
	},
	Metric: pulumi.String("string"),
	Name:   pulumi.String("string"),
	Spec: &pinecone.IndexSpecArgs{
		Pod: &pinecone.IndexSpecPodArgs{
			Environment: pulumi.String("string"),
			PodType:     pulumi.String("string"),
			MetadataConfig: &pinecone.IndexSpecPodMetadataConfigArgs{
				Indexeds: pulumi.StringArray{
					pulumi.String("string"),
				},
			},
			Pods:             pulumi.Int(0),
			Replicas:         pulumi.Int(0),
			Shards:           pulumi.Int(0),
			SourceCollection: pulumi.String("string"),
		},
		Serverless: &pinecone.IndexSpecServerlessArgs{
			Cloud:  pulumi.String("string"),
			Region: pulumi.String("string"),
		},
	},
	Tags: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	Timeouts: &pinecone.IndexTimeoutsArgs{
		Create: pulumi.String("string"),
		Delete: pulumi.String("string"),
	},
	VectorType: pulumi.String("string"),
})

    var indexResource = new Index("indexResource", IndexArgs.builder()
    .deletionProtection("string")
    .dimension(0)
    .embed(IndexEmbedArgs.builder()
        .dimension(0)
        .fieldMap(Map.of("string", "string"))
        .metric("string")
        .model("string")
        .readParameters(Map.of("string", "string"))
        .vectorType("string")
        .writeParameters(Map.of("string", "string"))
        .build())
    .metric("string")
    .name("string")
    .spec(IndexSpecArgs.builder()
        .pod(IndexSpecPodArgs.builder()
            .environment("string")
            .podType("string")
            .metadataConfig(IndexSpecPodMetadataConfigArgs.builder()
                .indexeds("string")
                .build())
            .pods(0)
            .replicas(0)
            .shards(0)
            .sourceCollection("string")
            .build())
        .serverless(IndexSpecServerlessArgs.builder()
            .cloud("string")
            .region("string")
            .build())
        .build())
    .tags(Map.of("string", "string"))
    .timeouts(IndexTimeoutsArgs.builder()
        .create("string")
        .delete("string")
        .build())
    .vectorType("string")
    .build());

    index_resource = pinecone.Index("indexResource",
    deletion_protection="string",
    dimension=0,
    embed={
        "dimension": 0,
        "field_map": {
            "string": "string",
        },
        "metric": "string",
        "model": "string",
        "read_parameters": {
            "string": "string",
        },
        "vector_type": "string",
        "write_parameters": {
            "string": "string",
        },
    },
    metric="string",
    name="string",
    spec={
        "pod": {
            "environment": "string",
            "pod_type": "string",
            "metadata_config": {
                "indexeds": ["string"],
            },
            "pods": 0,
            "replicas": 0,
            "shards": 0,
            "source_collection": "string",
        },
        "serverless": {
            "cloud": "string",
            "region": "string",
        },
    },
    tags={
        "string": "string",
    },
    timeouts={
        "create": "string",
        "delete": "string",
    },
    vector_type="string")

    const indexResource = new pinecone.Index("indexResource", {
    deletionProtection: "string",
    dimension: 0,
    embed: {
        dimension: 0,
        fieldMap: {
            string: "string",
        },
        metric: "string",
        model: "string",
        readParameters: {
            string: "string",
        },
        vectorType: "string",
        writeParameters: {
            string: "string",
        },
    },
    metric: "string",
    name: "string",
    spec: {
        pod: {
            environment: "string",
            podType: "string",
            metadataConfig: {
                indexeds: ["string"],
            },
            pods: 0,
            replicas: 0,
            shards: 0,
            sourceCollection: "string",
        },
        serverless: {
            cloud: "string",
            region: "string",
        },
    },
    tags: {
        string: "string",
    },
    timeouts: {
        create: "string",
        "delete": "string",
    },
    vectorType: "string",
});

    type: pinecone:Index
properties:
    deletionProtection: string
    dimension: 0
    embed:
        dimension: 0
        fieldMap:
            string: string
        metric: string
        model: string
        readParameters:
            string: string
        vectorType: string
        writeParameters:
            string: string
    metric: string
    name: string
    spec:
        pod:
            environment: string
            metadataConfig:
                indexeds:
                    - string
            podType: string
            pods: 0
            replicas: 0
            shards: 0
            sourceCollection: string
        serverless:
            cloud: string
            region: string
    tags:
        string: string
    timeouts:
        create: string
        delete: string
    vectorType: string

    Index Resource Properties

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

    Inputs

    In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

    The Index resource accepts the following input properties:

    DeletionProtection string
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    Dimension int
    The dimensions of the vectors to be inserted in the index
    Embed PineconeDatabase.Pinecone.Inputs.IndexEmbed
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    Metric string
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    Name string
    The name of the index to be created. The maximum length is 45 characters.
    Spec PineconeDatabase.Pinecone.Inputs.IndexSpec
    Spec
    Tags Dictionary<string, string>
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    Timeouts PineconeDatabase.Pinecone.Inputs.IndexTimeouts
    VectorType string
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
    DeletionProtection string
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    Dimension int
    The dimensions of the vectors to be inserted in the index
    Embed IndexEmbedArgs
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    Metric string
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    Name string
    The name of the index to be created. The maximum length is 45 characters.
    Spec IndexSpecArgs
    Spec
    Tags map[string]string
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    Timeouts IndexTimeoutsArgs
    VectorType string
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
    deletionProtection String
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    dimension Integer
    The dimensions of the vectors to be inserted in the index
    embed IndexEmbed
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    metric String
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    name String
    The name of the index to be created. The maximum length is 45 characters.
    spec IndexSpec
    Spec
    tags Map<String,String>
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    timeouts IndexTimeouts
    vectorType String
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
    deletionProtection string
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    dimension number
    The dimensions of the vectors to be inserted in the index
    embed IndexEmbed
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    metric string
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    name string
    The name of the index to be created. The maximum length is 45 characters.
    spec IndexSpec
    Spec
    tags {[key: string]: string}
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    timeouts IndexTimeouts
    vectorType string
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
    deletion_protection str
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    dimension int
    The dimensions of the vectors to be inserted in the index
    embed IndexEmbedArgs
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    metric str
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    name str
    The name of the index to be created. The maximum length is 45 characters.
    spec IndexSpecArgs
    Spec
    tags Mapping[str, str]
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    timeouts IndexTimeoutsArgs
    vector_type str
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
    deletionProtection String
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    dimension Number
    The dimensions of the vectors to be inserted in the index
    embed Property Map
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    metric String
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    name String
    The name of the index to be created. The maximum length is 45 characters.
    spec Property Map
    Spec
    tags Map<String>
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    timeouts Property Map
    vectorType String
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.

    Outputs

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

    Host string
    The URL address where the index is hosted.
    Id string
    The provider-assigned unique ID for this managed resource.
    Status PineconeDatabase.Pinecone.Outputs.IndexStatus
    Status
    Host string
    The URL address where the index is hosted.
    Id string
    The provider-assigned unique ID for this managed resource.
    Status IndexStatus
    Status
    host String
    The URL address where the index is hosted.
    id String
    The provider-assigned unique ID for this managed resource.
    status IndexStatus
    Status
    host string
    The URL address where the index is hosted.
    id string
    The provider-assigned unique ID for this managed resource.
    status IndexStatus
    Status
    host str
    The URL address where the index is hosted.
    id str
    The provider-assigned unique ID for this managed resource.
    status IndexStatus
    Status
    host String
    The URL address where the index is hosted.
    id String
    The provider-assigned unique ID for this managed resource.
    status Property Map
    Status

    Look up Existing Index Resource

    Get an existing Index 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?: IndexState, opts?: CustomResourceOptions): Index
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        deletion_protection: Optional[str] = None,
        dimension: Optional[int] = None,
        embed: Optional[IndexEmbedArgs] = None,
        host: Optional[str] = None,
        metric: Optional[str] = None,
        name: Optional[str] = None,
        spec: Optional[IndexSpecArgs] = None,
        status: Optional[IndexStatusArgs] = None,
        tags: Optional[Mapping[str, str]] = None,
        timeouts: Optional[IndexTimeoutsArgs] = None,
        vector_type: Optional[str] = None) -> Index
    func GetIndex(ctx *Context, name string, id IDInput, state *IndexState, opts ...ResourceOption) (*Index, error)
    public static Index Get(string name, Input<string> id, IndexState? state, CustomResourceOptions? opts = null)
    public static Index get(String name, Output<String> id, IndexState state, CustomResourceOptions options)
    resources:  _:    type: pinecone:Index    get:      id: ${id}
    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:
    DeletionProtection string
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    Dimension int
    The dimensions of the vectors to be inserted in the index
    Embed PineconeDatabase.Pinecone.Inputs.IndexEmbed
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    Host string
    The URL address where the index is hosted.
    Metric string
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    Name string
    The name of the index to be created. The maximum length is 45 characters.
    Spec PineconeDatabase.Pinecone.Inputs.IndexSpec
    Spec
    Status PineconeDatabase.Pinecone.Inputs.IndexStatus
    Status
    Tags Dictionary<string, string>
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    Timeouts PineconeDatabase.Pinecone.Inputs.IndexTimeouts
    VectorType string
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
    DeletionProtection string
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    Dimension int
    The dimensions of the vectors to be inserted in the index
    Embed IndexEmbedArgs
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    Host string
    The URL address where the index is hosted.
    Metric string
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    Name string
    The name of the index to be created. The maximum length is 45 characters.
    Spec IndexSpecArgs
    Spec
    Status IndexStatusArgs
    Status
    Tags map[string]string
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    Timeouts IndexTimeoutsArgs
    VectorType string
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
    deletionProtection String
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    dimension Integer
    The dimensions of the vectors to be inserted in the index
    embed IndexEmbed
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    host String
    The URL address where the index is hosted.
    metric String
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    name String
    The name of the index to be created. The maximum length is 45 characters.
    spec IndexSpec
    Spec
    status IndexStatus
    Status
    tags Map<String,String>
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    timeouts IndexTimeouts
    vectorType String
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
    deletionProtection string
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    dimension number
    The dimensions of the vectors to be inserted in the index
    embed IndexEmbed
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    host string
    The URL address where the index is hosted.
    metric string
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    name string
    The name of the index to be created. The maximum length is 45 characters.
    spec IndexSpec
    Spec
    status IndexStatus
    Status
    tags {[key: string]: string}
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    timeouts IndexTimeouts
    vectorType string
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
    deletion_protection str
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    dimension int
    The dimensions of the vectors to be inserted in the index
    embed IndexEmbedArgs
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    host str
    The URL address where the index is hosted.
    metric str
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    name str
    The name of the index to be created. The maximum length is 45 characters.
    spec IndexSpecArgs
    Spec
    status IndexStatusArgs
    Status
    tags Mapping[str, str]
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    timeouts IndexTimeoutsArgs
    vector_type str
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
    deletionProtection String
    Whether deletion protection for the index is enabled. You can use 'enabled', or 'disabled'.
    dimension Number
    The dimensions of the vectors to be inserted in the index
    embed Property Map
    Specify the integrated inference embedding configuration for the index. Once set, the model cannot be changed. However, you can later update the embedding configuration—including field map, read parameters, and write parameters.
    host String
    The URL address where the index is hosted.
    metric String
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
    name String
    The name of the index to be created. The maximum length is 45 characters.
    spec Property Map
    Spec
    status Property Map
    Status
    tags Map<String>
    Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
    timeouts Property Map
    vectorType String
    The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.

    Supporting Types

    IndexEmbed, IndexEmbedArgs

    Dimension int
    The dimension of the embedding model, specifying the size of the output vector.
    FieldMap Dictionary<string, string>
    Identifies the name of the text field from your document model that will be embedded.
    Metric string
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vectortype' is 'sparse', the metric must be 'dotproduct'. If the vectortype is dense, the metric defaults to 'cosine'.
    Model string
    the name of the embedding model to use for the index.
    ReadParameters Dictionary<string, string>
    The read parameters for the embedding model.
    VectorType string
    The index vector type associated with the model. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension will be nil.
    WriteParameters Dictionary<string, string>
    The write parameters for the embedding model.
    Dimension int
    The dimension of the embedding model, specifying the size of the output vector.
    FieldMap map[string]string
    Identifies the name of the text field from your document model that will be embedded.
    Metric string
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vectortype' is 'sparse', the metric must be 'dotproduct'. If the vectortype is dense, the metric defaults to 'cosine'.
    Model string
    the name of the embedding model to use for the index.
    ReadParameters map[string]string
    The read parameters for the embedding model.
    VectorType string
    The index vector type associated with the model. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension will be nil.
    WriteParameters map[string]string
    The write parameters for the embedding model.
    dimension Integer
    The dimension of the embedding model, specifying the size of the output vector.
    fieldMap Map<String,String>
    Identifies the name of the text field from your document model that will be embedded.
    metric String
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vectortype' is 'sparse', the metric must be 'dotproduct'. If the vectortype is dense, the metric defaults to 'cosine'.
    model String
    the name of the embedding model to use for the index.
    readParameters Map<String,String>
    The read parameters for the embedding model.
    vectorType String
    The index vector type associated with the model. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension will be nil.
    writeParameters Map<String,String>
    The write parameters for the embedding model.
    dimension number
    The dimension of the embedding model, specifying the size of the output vector.
    fieldMap {[key: string]: string}
    Identifies the name of the text field from your document model that will be embedded.
    metric string
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vectortype' is 'sparse', the metric must be 'dotproduct'. If the vectortype is dense, the metric defaults to 'cosine'.
    model string
    the name of the embedding model to use for the index.
    readParameters {[key: string]: string}
    The read parameters for the embedding model.
    vectorType string
    The index vector type associated with the model. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension will be nil.
    writeParameters {[key: string]: string}
    The write parameters for the embedding model.
    dimension int
    The dimension of the embedding model, specifying the size of the output vector.
    field_map Mapping[str, str]
    Identifies the name of the text field from your document model that will be embedded.
    metric str
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vectortype' is 'sparse', the metric must be 'dotproduct'. If the vectortype is dense, the metric defaults to 'cosine'.
    model str
    the name of the embedding model to use for the index.
    read_parameters Mapping[str, str]
    The read parameters for the embedding model.
    vector_type str
    The index vector type associated with the model. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension will be nil.
    write_parameters Mapping[str, str]
    The write parameters for the embedding model.
    dimension Number
    The dimension of the embedding model, specifying the size of the output vector.
    fieldMap Map<String>
    Identifies the name of the text field from your document model that will be embedded.
    metric String
    The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vectortype' is 'sparse', the metric must be 'dotproduct'. If the vectortype is dense, the metric defaults to 'cosine'.
    model String
    the name of the embedding model to use for the index.
    readParameters Map<String>
    The read parameters for the embedding model.
    vectorType String
    The index vector type associated with the model. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension will be nil.
    writeParameters Map<String>
    The write parameters for the embedding model.

    IndexSpec, IndexSpecArgs

    Pod PineconeDatabase.Pinecone.Inputs.IndexSpecPod
    Configuration needed to deploy a pod-based index.
    Serverless PineconeDatabase.Pinecone.Inputs.IndexSpecServerless
    Configuration needed to deploy a serverless index.
    Pod IndexSpecPod
    Configuration needed to deploy a pod-based index.
    Serverless IndexSpecServerless
    Configuration needed to deploy a serverless index.
    pod IndexSpecPod
    Configuration needed to deploy a pod-based index.
    serverless IndexSpecServerless
    Configuration needed to deploy a serverless index.
    pod IndexSpecPod
    Configuration needed to deploy a pod-based index.
    serverless IndexSpecServerless
    Configuration needed to deploy a serverless index.
    pod IndexSpecPod
    Configuration needed to deploy a pod-based index.
    serverless IndexSpecServerless
    Configuration needed to deploy a serverless index.
    pod Property Map
    Configuration needed to deploy a pod-based index.
    serverless Property Map
    Configuration needed to deploy a serverless index.

    IndexSpecPod, IndexSpecPodArgs

    Environment string
    The environment where the index is hosted.
    PodType string
    The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
    MetadataConfig PineconeDatabase.Pinecone.Inputs.IndexSpecPodMetadataConfig
    Configuration for the behavior of Pinecone's internal metadata index. By default, all metadata is indexed; when metadata*config is present, only specified metadata fields are indexed. These configurations are only valid for use with pod-based indexes.
    Pods int
    The number of pods to be used in the index. This should be equal to shards x replicas.'
    Replicas int
    The number of replicas. Replicas duplicate your index. They provide higher availability and throughput. Replicas can be scaled up or down as your needs change.
    Shards int
    The number of shards. Shards split your data across multiple pods so you can fit more data into an index.
    SourceCollection string
    The name of the collection to create an index from.
    Environment string
    The environment where the index is hosted.
    PodType string
    The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
    MetadataConfig IndexSpecPodMetadataConfig
    Configuration for the behavior of Pinecone's internal metadata index. By default, all metadata is indexed; when metadata*config is present, only specified metadata fields are indexed. These configurations are only valid for use with pod-based indexes.
    Pods int
    The number of pods to be used in the index. This should be equal to shards x replicas.'
    Replicas int
    The number of replicas. Replicas duplicate your index. They provide higher availability and throughput. Replicas can be scaled up or down as your needs change.
    Shards int
    The number of shards. Shards split your data across multiple pods so you can fit more data into an index.
    SourceCollection string
    The name of the collection to create an index from.
    environment String
    The environment where the index is hosted.
    podType String
    The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
    metadataConfig IndexSpecPodMetadataConfig
    Configuration for the behavior of Pinecone's internal metadata index. By default, all metadata is indexed; when metadata*config is present, only specified metadata fields are indexed. These configurations are only valid for use with pod-based indexes.
    pods Integer
    The number of pods to be used in the index. This should be equal to shards x replicas.'
    replicas Integer
    The number of replicas. Replicas duplicate your index. They provide higher availability and throughput. Replicas can be scaled up or down as your needs change.
    shards Integer
    The number of shards. Shards split your data across multiple pods so you can fit more data into an index.
    sourceCollection String
    The name of the collection to create an index from.
    environment string
    The environment where the index is hosted.
    podType string
    The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
    metadataConfig IndexSpecPodMetadataConfig
    Configuration for the behavior of Pinecone's internal metadata index. By default, all metadata is indexed; when metadata*config is present, only specified metadata fields are indexed. These configurations are only valid for use with pod-based indexes.
    pods number
    The number of pods to be used in the index. This should be equal to shards x replicas.'
    replicas number
    The number of replicas. Replicas duplicate your index. They provide higher availability and throughput. Replicas can be scaled up or down as your needs change.
    shards number
    The number of shards. Shards split your data across multiple pods so you can fit more data into an index.
    sourceCollection string
    The name of the collection to create an index from.
    environment str
    The environment where the index is hosted.
    pod_type str
    The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
    metadata_config IndexSpecPodMetadataConfig
    Configuration for the behavior of Pinecone's internal metadata index. By default, all metadata is indexed; when metadata*config is present, only specified metadata fields are indexed. These configurations are only valid for use with pod-based indexes.
    pods int
    The number of pods to be used in the index. This should be equal to shards x replicas.'
    replicas int
    The number of replicas. Replicas duplicate your index. They provide higher availability and throughput. Replicas can be scaled up or down as your needs change.
    shards int
    The number of shards. Shards split your data across multiple pods so you can fit more data into an index.
    source_collection str
    The name of the collection to create an index from.
    environment String
    The environment where the index is hosted.
    podType String
    The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
    metadataConfig Property Map
    Configuration for the behavior of Pinecone's internal metadata index. By default, all metadata is indexed; when metadata*config is present, only specified metadata fields are indexed. These configurations are only valid for use with pod-based indexes.
    pods Number
    The number of pods to be used in the index. This should be equal to shards x replicas.'
    replicas Number
    The number of replicas. Replicas duplicate your index. They provide higher availability and throughput. Replicas can be scaled up or down as your needs change.
    shards Number
    The number of shards. Shards split your data across multiple pods so you can fit more data into an index.
    sourceCollection String
    The name of the collection to create an index from.

    IndexSpecPodMetadataConfig, IndexSpecPodMetadataConfigArgs

    Indexeds List<string>
    The indexed fields.
    Indexeds []string
    The indexed fields.
    indexeds List<String>
    The indexed fields.
    indexeds string[]
    The indexed fields.
    indexeds Sequence[str]
    The indexed fields.
    indexeds List<String>
    The indexed fields.

    IndexSpecServerless, IndexSpecServerlessArgs

    Cloud string
    The public cloud where you would like your index hosted. [gcp|aws|azure]
    Region string
    The region where you would like your index to be created.
    Cloud string
    The public cloud where you would like your index hosted. [gcp|aws|azure]
    Region string
    The region where you would like your index to be created.
    cloud String
    The public cloud where you would like your index hosted. [gcp|aws|azure]
    region String
    The region where you would like your index to be created.
    cloud string
    The public cloud where you would like your index hosted. [gcp|aws|azure]
    region string
    The region where you would like your index to be created.
    cloud str
    The public cloud where you would like your index hosted. [gcp|aws|azure]
    region str
    The region where you would like your index to be created.
    cloud String
    The public cloud where you would like your index hosted. [gcp|aws|azure]
    region String
    The region where you would like your index to be created.

    IndexStatus, IndexStatusArgs

    Ready bool
    Ready.
    State string
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready
    Ready bool
    Ready.
    State string
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready
    ready Boolean
    Ready.
    state String
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready
    ready boolean
    Ready.
    state string
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready
    ready bool
    Ready.
    state str
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready
    ready Boolean
    Ready.
    state String
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready

    IndexTimeouts, IndexTimeoutsArgs

    Create string
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).
    Delete string
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).
    Create string
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).
    Delete string
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).
    create String
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).
    delete String
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).
    create string
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).
    delete string
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).
    create str
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).
    delete str
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).
    create String
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).
    delete String
    Timeout defaults to 5 mins. Accepts a string that can be parsed as a duration consisting of numbers and unit suffixes, such as "30s" or "2h45m". Valid time units are "s" (seconds), "m" (minutes), "h" (hours).

    Package Details

    Repository
    pinecone pinecone-io/pulumi-pinecone
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the pinecone Terraform Provider.
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    Pinecone v2.0.2 published on Wednesday, Nov 5, 2025 by pinecone-io
    pinecone-io/pulumi-pinecone
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