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

pinecone.get

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

    Index data source

    Using get

    Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

    function get(args: GetArgs, opts?: InvokeOptions): Promise<GetResult>
function getOutput(args: GetOutputArgs, opts?: InvokeOptions): Output<GetResult>
    def get(embed: Optional[GetEmbed] = None,
        name: Optional[str] = None,
        spec: Optional[GetSpec] = None,
        status: Optional[GetStatus] = None,
        opts: Optional[InvokeOptions] = None) -> GetResult
def get_output(embed: Optional[pulumi.Input[GetEmbedArgs]] = None,
        name: Optional[pulumi.Input[str]] = None,
        spec: Optional[pulumi.Input[GetSpecArgs]] = None,
        status: Optional[pulumi.Input[GetStatusArgs]] = None,
        opts: Optional[InvokeOptions] = None) -> Output[GetResult]
    func Get(ctx *Context, args *GetArgs, opts ...InvokeOption) (*GetResult, error)
func GetOutput(ctx *Context, args *GetOutputArgs, opts ...InvokeOption) GetResultOutput

    > Note: This function is named Get in the Go SDK.

    public static class Get 
{
    public static Task<GetResult> InvokeAsync(GetArgs args, InvokeOptions? opts = null)
    public static Output<GetResult> Invoke(GetInvokeArgs args, InvokeOptions? opts = null)
}
    public static CompletableFuture<GetResult> get(GetArgs args, InvokeOptions options)
public static Output<GetResult> get(GetArgs args, InvokeOptions options)

    fn::invoke:
  function: pinecone:index/get:get
  arguments:
    # arguments dictionary

    The following arguments are supported:

    Name string
    Index name
    Embed PineconeDatabase.Pinecone.Inputs.GetEmbed
    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.
    Spec PineconeDatabase.Pinecone.Inputs.GetSpec
    Status PineconeDatabase.Pinecone.Inputs.GetStatus
    Name string
    Index name
    Embed GetEmbed
    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.
    Spec GetSpec
    Status GetStatus
    name String
    Index name
    embed GetEmbed
    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.
    spec GetSpec
    status GetStatus
    name string
    Index name
    embed GetEmbed
    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.
    spec GetSpec
    status GetStatus
    name str
    Index name
    embed GetEmbed
    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.
    spec GetSpec
    status GetStatus
    name String
    Index name
    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.
    spec Property Map
    status Property Map

    get Result

    The following output properties are available:

    DeletionProtection string
    Dimension int
    Embed PineconeDatabase.Pinecone.Outputs.GetEmbed
    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
    Id string
    Metric string
    Name string
    Index name
    Spec PineconeDatabase.Pinecone.Outputs.GetSpec
    Status PineconeDatabase.Pinecone.Outputs.GetStatus
    Tags Dictionary<string, string>
    VectorType string
    DeletionProtection string
    Dimension int
    Embed GetEmbed
    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
    Id string
    Metric string
    Name string
    Index name
    Spec GetSpec
    Status GetStatus
    Tags map[string]string
    VectorType string
    deletionProtection String
    dimension Integer
    embed GetEmbed
    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
    id String
    metric String
    name String
    Index name
    spec GetSpec
    status GetStatus
    tags Map<String,String>
    vectorType String
    deletionProtection string
    dimension number
    embed GetEmbed
    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
    id string
    metric string
    name string
    Index name
    spec GetSpec
    status GetStatus
    tags {[key: string]: string}
    vectorType string
    deletion_protection str
    dimension int
    embed GetEmbed
    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
    id str
    metric str
    name str
    Index name
    spec GetSpec
    status GetStatus
    tags Mapping[str, str]
    vector_type str
    deletionProtection String
    dimension Number
    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
    id String
    metric String
    name String
    Index name
    spec Property Map
    status Property Map
    tags Map<String>
    vectorType String

    Supporting Types

    GetEmbed

    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.

    GetSpec

    Pod PineconeDatabase.Pinecone.Inputs.GetSpecPod
    Configuration needed to deploy a pod-based index.
    Serverless PineconeDatabase.Pinecone.Inputs.GetSpecServerless
    Configuration needed to deploy a serverless index.
    Pod GetSpecPod
    Configuration needed to deploy a pod-based index.
    Serverless GetSpecServerless
    Configuration needed to deploy a serverless index.
    pod GetSpecPod
    Configuration needed to deploy a pod-based index.
    serverless GetSpecServerless
    Configuration needed to deploy a serverless index.
    pod GetSpecPod
    Configuration needed to deploy a pod-based index.
    serverless GetSpecServerless
    Configuration needed to deploy a serverless index.
    pod GetSpecPod
    Configuration needed to deploy a pod-based index.
    serverless GetSpecServerless
    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.

    GetSpecPod

    Environment string
    The environment where the index is hosted.
    MetadataConfig PineconeDatabase.Pinecone.Inputs.GetSpecPodMetadataConfig
    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.
    PodType string
    The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
    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.
    MetadataConfig GetSpecPodMetadataConfig
    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.
    PodType string
    The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
    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.
    metadataConfig GetSpecPodMetadataConfig
    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.
    podType String
    The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
    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.
    metadataConfig GetSpecPodMetadataConfig
    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.
    podType string
    The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
    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.
    metadata_config GetSpecPodMetadataConfig
    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.
    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.
    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.
    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.
    podType String
    The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
    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.

    GetSpecPodMetadataConfig

    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.

    GetSpecServerless

    Cloud string
    Ready.
    Region string
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready
    Cloud string
    Ready.
    Region string
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready
    cloud String
    Ready.
    region String
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready
    cloud string
    Ready.
    region string
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready
    cloud str
    Ready.
    region str
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready
    cloud String
    Ready.
    region String
    Initializing InitializationFailed ScalingUp ScalingDown ScalingUpPodSize ScalingDownPodSize Upgrading Terminating Ready

    GetStatus

    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

    Package Details

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