Pinecone v2.0.2, Nov 5 25

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

pinecone-io/pulumi-pinecone

pinecone.getEs

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

Schema (JSON)

pinecone-io/pulumi-pinecone

Using getEs

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 getEs(opts?: InvokeOptions): Promise<GetEsResult>
function getEsOutput(opts?: InvokeOptions): Output<GetEsResult>

def get_es(opts: Optional[InvokeOptions] = None) -> GetEsResult
def get_es_output(opts: Optional[InvokeOptions] = None) -> Output[GetEsResult]

func GetEs(ctx *Context, opts ...InvokeOption) (*GetEsResult, error)
func GetEsOutput(ctx *Context, opts ...InvokeOption) GetEsResultOutput

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

public static class GetEs 
{
    public static Task<GetEsResult> InvokeAsync(InvokeOptions? opts = null)
    public static Output<GetEsResult> Invoke(InvokeOptions? opts = null)
}

public static CompletableFuture<GetEsResult> getEs(InvokeOptions options)
public static Output<GetEsResult> getEs(InvokeOptions options)

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

getEs Result

The following output properties are available:

Id string: Indexes identifier
Indexes List<PineconeDatabase.Pinecone.Outputs.GetEsIndex>: List of the indexes in your project

Id string: Indexes identifier
Indexes []GetEsIndex: List of the indexes in your project

id String: Indexes identifier
indexes List<GetEsIndex>: List of the indexes in your project

id string: Indexes identifier
indexes GetEsIndex[]: List of the indexes in your project

id str: Indexes identifier
indexes Sequence[GetEsIndex]: List of the indexes in your project

id String: Indexes identifier
indexes List<Property Map>: List of the indexes in your project

Supporting Types

GetEsIndex

DeletionProtection string: Index deletion protection configuration
Dimension int: Index dimension
Embed PineconeDatabase.Pinecone.Inputs.GetEsIndexEmbed: 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: Index metric
Name string: Index name
Spec PineconeDatabase.Pinecone.Inputs.GetEsIndexSpec: Spec
Status PineconeDatabase.Pinecone.Inputs.GetEsIndexStatus: 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. To specify metadata fields to index, provide an array of the following form: [example_metadata_field]
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.
VectorType string: Index vector type

DeletionProtection string: Index deletion protection configuration
Dimension int: Index dimension
Embed GetEsIndexEmbed: 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: Index metric
Name string: Index name
Spec GetEsIndexSpec: Spec
Status GetEsIndexStatus: 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. To specify metadata fields to index, provide an array of the following form: [example_metadata_field]
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.
VectorType string: Index vector type

deletionProtection String: Index deletion protection configuration
dimension Integer: Index dimension
embed GetEsIndexEmbed: 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: Index metric
name String: Index name
spec GetEsIndexSpec: Spec
status GetEsIndexStatus: 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. To specify metadata fields to index, provide an array of the following form: [example_metadata_field]
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.
vectorType String: Index vector type

deletionProtection string: Index deletion protection configuration
dimension number: Index dimension
embed GetEsIndexEmbed: 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: Index metric
name string: Index name
spec GetEsIndexSpec: Spec
status GetEsIndexStatus: 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. To specify metadata fields to index, provide an array of the following form: [example_metadata_field]
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.
vectorType string: Index vector type

deletion_protection str: Index deletion protection configuration
dimension int: Index dimension
embed GetEsIndexEmbed: 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: Index metric
name str: Index name
spec GetEsIndexSpec: Spec
status GetEsIndexStatus: 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. To specify metadata fields to index, provide an array of the following form: [example_metadata_field]
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.
vector_type str: Index vector type

deletionProtection String: Index deletion protection configuration
dimension Number: Index dimension
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: Index metric
name String: Index name
spec Property Map: Spec
status 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. To specify metadata fields to index, provide an array of the following form: [example_metadata_field]
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.
vectorType String: Index vector type

GetEsIndexEmbed

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.

GetEsIndexSpec

Pod PineconeDatabase.Pinecone.Inputs.GetEsIndexSpecPod: Configuration needed to deploy a pod-based index.
Serverless PineconeDatabase.Pinecone.Inputs.GetEsIndexSpecServerless: Configuration needed to deploy a serverless index.

Pod GetEsIndexSpecPod: Configuration needed to deploy a pod-based index.
Serverless GetEsIndexSpecServerless: Configuration needed to deploy a serverless index.

pod GetEsIndexSpecPod: Configuration needed to deploy a pod-based index.
serverless GetEsIndexSpecServerless: Configuration needed to deploy a serverless index.

pod GetEsIndexSpecPod: Configuration needed to deploy a pod-based index.
serverless GetEsIndexSpecServerless: Configuration needed to deploy a serverless index.

pod GetEsIndexSpecPod: Configuration needed to deploy a pod-based index.
serverless GetEsIndexSpecServerless: 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.

GetEsIndexSpecPod

Environment string: The environment where the index is hosted.
MetadataConfig PineconeDatabase.Pinecone.Inputs.GetEsIndexSpecPodMetadataConfig: 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 GetEsIndexSpecPodMetadataConfig: 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 GetEsIndexSpecPodMetadataConfig: 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 GetEsIndexSpecPodMetadataConfig: 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 GetEsIndexSpecPodMetadataConfig: 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.

GetEsIndexSpecPodMetadataConfig

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.

GetEsIndexSpecServerless

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

GetEsIndexStatus

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

Schema (JSON)

pinecone-io/pulumi-pinecone

pinecone.getEs

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Using getEs

getEs Result

Supporting Types

GetEsIndex

GetEsIndexEmbed

GetEsIndexSpec

GetEsIndexSpecPod

GetEsIndexSpecPodMetadataConfig

GetEsIndexSpecServerless

GetEsIndexStatus

Package Details

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