List indexes

PINECONE_API_KEY="YOUR_API_KEY"

curl -i -X GET "https://api.pinecone.io/indexes" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "X-Pinecone-Api-Version: 2025-10"

{
  "indexes": [
    {
      "name": "example-serverless-dedicated-index",
      "vector_type": "dense",
      "metric": "cosine",
      "dimension": 1536,
      "status": {
        "ready": true,
        "state": "Ready"
      },
      "host": "example-serverless-dedicated-index-bhnyigt.svc.aped-4627-b74a.pinecone.io",
      "spec": {
        "serverless": {
          "region": "us-east-1",
          "cloud": "aws",
          "read_capacity": {
            "mode": "Dedicated",
            "dedicated": {
              "node_type": "b1",
              "scaling": "Manual",
              "manual": {
                "shards": 1,
                "replicas": 2
              }
            },
            "status": {
              "state": "Scaling",
              "current_shards": 1,
              "current_replicas": 1
            }
          }
        }
      },
      "deletion_protection": "enabled",
      "tags": {
        "tag0": "value0",
        "tag1": "value1"
      }
    },
    {
      "name": "example-serverless-ondemand-index",
      "vector_type": "dense",
      "metric": "cosine",
      "dimension": 1024,
      "status": {
        "ready": true,
        "state": "Ready"
      },
      "host": "example-serverless-ondemand-index-bhnyigt.svc.aped-4627-b74a.pinecone.io",
      "spec": {
        "serverless": {
          "region": "us-east-1",
          "cloud": "aws",
          "read_capacity": {
            "mode": "OnDemand",
            "status": {
              "state": "Ready",
              "current_shards": null,
              "current_replicas": null
            }
          }
        }
      },
      "deletion_protection": "enabled",
      "tags": {
        "tag1": "value1",
        "tag2": "value2"
      },
      "embed": {
        "model": "llama-text-embed-v2",
        "field_map": {
          "text": "text"
        },
        "dimension": 1024,
        "metric": "cosine",
        "write_parameters": {
          "dimension": 1024,
          "input_type": "passage",
          "truncate": "END"
        },
        "read_parameters": {
          "dimension": 1024,
          "input_type": "query",
          "truncate": "END"
        },
        "vector_type": "dense"
      }
    },
    {
      "name": "example-pod-index",
      "vector_type": "dense",
      "metric": "cosine",
      "dimension": 768,
      "status": {
        "ready": true,
        "state": "Ready"
      },
      "host": "example-pod-index-bhnyigt.svc.us-east-1-aws.pinecone.io",
      "spec": {
        "pod": {
          "replicas": 1,
          "shards": 1,
          "pods": 1,
          "pod_type": "s1.x1",
          "environment": "us-east-1-aws"
        }
      },
      "deletion_protection": "disabled",
      "tags": null
    }
  ]
}

Authorizations

Api-Key

string

header

required

An API Key is required to call Pinecone APIs. Get yours from the console.

Headers

X-Pinecone-Api-Version

string

default:2025-10

required

Required date-based version header

Response

This operation returns a list of all the indexes that you have previously created, and which are associated with the given project

The list of indexes that exist in the project.

indexes

object[]

List of indexes in the project

Show child attributes

indexes.name

string

required

The name of the index. Resource name must be 1-45 characters long, start and end with an alphanumeric character, and consist only of lower case alphanumeric characters or '-'.

Required string length: 1 - 45

Example:

"example-index"

indexes.metric

string

required

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'. Possible values: cosine, euclidean, or dotproduct.

indexes.host

string

required

The URL address where the index is hosted.

Example:

"semantic-search-c01b5b5.svc.us-west1-gcp.pinecone.io"

indexes.spec

Serverless · object

required

The spec object defines how the index should be deployed.

Serverless
Pod-based
BYOC

Show child attributes

indexes.spec.serverless

object

required

Configuration needed to deploy a serverless index.

Show child attributes

indexes.spec.serverless.cloud

string

required

The public cloud where you would like your index hosted. Possible values: gcp, aws, or azure.

Example:

"aws"

indexes.spec.serverless.region

string

required

The region where you would like your index to be created.

Example:

"us-east-1"

indexes.spec.serverless.read_capacity

On-demand · object

required

Response containing read capacity configuration

On-demand
Dedicated

Show child attributes

indexes.spec.serverless.read_capacity.mode

string

required

The mode of the index. Possible values: OnDemand or Dedicated. Defaults to OnDemand. If set to Dedicated, dedicated.node_type, and dedicated.scaling must be specified.

indexes.spec.serverless.read_capacity.status

object

required

The current status of factors affecting the read capacity of a serverless index

Show child attributes

indexes.spec.serverless.read_capacity.status.state

string

required

The state describes the overall status of factors relating to the read capacity of an index.

Available values:

Ready is the state most of the time
Scaling if the number of replicas or shards has been recently updated by calling the configure index endpoint
Migrating if the index is being migrated to a new node_type
Error if there is an error with the read capacity configuration. In that case, see error_message for more details.

indexes.spec.serverless.read_capacity.status.current_replicas

integer<int32>

The number of replicas. Each replica has dedicated compute resources and data storage. Increasing this number will increase the total throughput of the index.

indexes.spec.serverless.read_capacity.status.current_shards

integer<int32>

The number of shards. Each shard has dedicated storage. Increasing shards alleiviates index fullness.

indexes.spec.serverless.read_capacity.status.error_message

string

An optional error message indicating any issues with your read capacity configuration

Example:

{
  "mode": "OnDemand",
  "status": { "state": "Ready" }
}

indexes.spec.serverless.source_collection

string

The name of the collection to be used as the source for the index.

Example:

"movie-embeddings"

indexes.spec.serverless.schema

object

Schema for the behavior of Pinecone's internal metadata index. By default, all metadata is indexed; when schema is present, only fields which are present in the fields object with a filterable: true are indexed. Note that filterable: false is not currently supported.

Show child attributes

indexes.spec.serverless.schema.fields

object

required

A map of metadata field names to their configuration. The field name must be a valid metadata field name. The field name must be unique.

Show child attributes

indexes.spec.serverless.schema.fields.{key}

object

Show child attributes

indexes.spec.serverless.schema.fields.{key}.filterable

boolean

Whether the field is filterable. If true, the field is indexed and can be used in filters. Only true values are allowed.

Example:

{
  "fields": {
    "description": { "filterable": true },
    "genre": { "filterable": true },
    "year": { "filterable": true }
  }
}

Example:

{
  "pod": {
    "environment": "us-east-1-aws",
    "metadata_config": {
      "indexed": ["genre", "title", "imdb_rating"]
    },
    "pod_type": "p1.x1",
    "pods": 1,
    "replicas": 1,
    "shards": 1
  }
}

indexes.status

object

required

The current status of the index

Show child attributes

indexes.status.ready

boolean

required

Whether the index is ready for use

indexes.status.state

string

required

The state of the index. Possible values: Initializing, InitializationFailed, ScalingUp, ScalingDown, ScalingUpPodSize, ScalingDownPodSize, Terminating, Ready, or Disabled.

Example:

{
  "ready": true,
  "state": "ScalingUpPodSize"
}

indexes.vector_type

string

default:dense

required

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.

indexes.dimension

integer<int32>

The dimensions of the vectors to be inserted in the index.

Required range: 1 <= x <= 20000

Example:

1536

indexes.private_host

string

The private endpoint URL of an index.

Example:

"semantic-search-c01b5b5.svc.private.us-west1-gcp.pinecone.io"

indexes.deletion_protection

string

default:disabled

Whether deletion protection is enabled/disabled for the index. Possible values: disabled or enabled.

indexes.tags

object

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.

Show child attributes

indexes.tags.{key}

string

Example:

{ "tag0": "val0", "tag1": "val1" }

indexes.embed

object

The embedding model and document fields mapped to embedding inputs.

Show child attributes

indexes.embed.model

string

required

The name of the embedding model used to create the index.

Example:

"multilingual-e5-large"

indexes.embed.metric

string

The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If not specified, the metric will be defaulted according to the model. Cannot be updated once set. Possible values: cosine, euclidean, or dotproduct.

indexes.embed.dimension

integer<int32>

The dimensions of the vectors to be inserted in the index.

Required range: 1 <= x <= 20000

Example:

1536

indexes.embed.vector_type

string

default:dense

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.

indexes.embed.field_map

object

Identifies the name of the text field from your document model that is embedded.

Example:

{ "text": "your-text-field" }

indexes.embed.read_parameters

object

The read parameters for the embedding model.

indexes.embed.write_parameters

object

The write parameters for the embedding model.

Example:

{
  "field_map": { "text": "your-text-field" },
  "metric": "cosine",
  "model": "multilingual-e5-large",
  "read_parameters": { "input_type": "query", "truncate": "NONE" },
  "write_parameters": { "input_type": "passage" }
}

APIs

Database

Inference

Admin

SDKs

CLI

Tools

Authorizations

Headers

Response