from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

index_model = pc.create_index_for_model(
    name="docs-example",
    cloud="aws",
    region="us-east-1",
    embed={
        "model": "multilingual-e5-large",
        "field_map": {"text": "chunk_text"}
    }
)

PINECONE_API_KEY="YOUR_API_KEY"

curl "https://api.pinecone.io/indexes/create-for-model" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "name": "docs-example",
    "cloud": "aws",
    "region": "us-east-1",
    "embed": {
      "model": "multilingual-e5-large",
      "field_map": {"text": "chunk_text"}
    }
  }'

{
  "deletion_protection": "disabled",
  "deployment": {
    "cloud": "aws",
    "deployment_type": "managed",
    "region": "us-east-1"
  },
  "host": "my-index-abc123.svc.pinecone.io",
  "name": "my-index",
  "read_capacity": {
    "mode": "OnDemand",
    "status": {
      "state": "Ready"
    }
  },
  "schema": {
    "fields": {
      "embedding": {
        "dimension": 1536,
        "metric": "cosine",
        "type": "dense_vector"
      },
      "title": {
        "full_text_search": {
          "language": "en",
          "stemming": false,
          "stop_words": false
        },
        "type": "string"
      }
    }
  },
  "status": {
    "ready": true,
    "state": "Ready"
  }
}

{
  "error": {
    "code": "INVALID_ARGUMENT",
    "message": "Bad request. The request body included invalid request parameters."
  },
  "status": 400
}

"Invalid API key"

{
  "error": {
    "code": "PAYMENT_REQUIRED",
    "message": "Request failed. Pay all past due invoices to lift restrictions on your account."
  },
  "status": 402
}

{
  "error": {
    "code": "FORBIDDEN",
    "message": "Increase your quota or upgrade to create more indexes."
  },
  "status": 403
}

{
  "error": {
    "code": "NOT_FOUND",
    "message": "Resource cloud: aws region: us-west1 not found."
  },
  "status": 404
}

{
  "error": {
    "code": "ALREADY_EXISTS",
    "message": "Resource already exists."
  },
  "status": 409
}

{
  "error": {
    "code": "UNPROCESSABLE_ENTITY",
    "message": "Failed to deserialize the JSON body into the target type: missing field `metric` at line 1 column 16"
  },
  "status": 422
}

{
  "error": {
    "code": "UNKNOWN",
    "message": "Internal server error"
  },
  "status": 500
}

Indexes

Create an index with integrated embedding

Create an index with integrated embedding. With this type of index, you provide source text, and Pinecone uses a hosted embedding model to convert the text automatically during upsert and search. The response is this version’s index model, with the embedding configuration surfaced as a semantic_text field in the index schema, named after the field_map text entry. Read and write the index through the records API. For guidance and examples, see Create an index.

POST

indexes

create-for-model

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

index_model = pc.create_index_for_model(
    name="docs-example",
    cloud="aws",
    region="us-east-1",
    embed={
        "model": "multilingual-e5-large",
        "field_map": {"text": "chunk_text"}
    }
)

PINECONE_API_KEY="YOUR_API_KEY"

curl "https://api.pinecone.io/indexes/create-for-model" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "name": "docs-example",
    "cloud": "aws",
    "region": "us-east-1",
    "embed": {
      "model": "multilingual-e5-large",
      "field_map": {"text": "chunk_text"}
    }
  }'

{
  "deletion_protection": "disabled",
  "deployment": {
    "cloud": "aws",
    "deployment_type": "managed",
    "region": "us-east-1"
  },
  "host": "my-index-abc123.svc.pinecone.io",
  "name": "my-index",
  "read_capacity": {
    "mode": "OnDemand",
    "status": {
      "state": "Ready"
    }
  },
  "schema": {
    "fields": {
      "embedding": {
        "dimension": 1536,
        "metric": "cosine",
        "type": "dense_vector"
      },
      "title": {
        "full_text_search": {
          "language": "en",
          "stemming": false,
          "stop_words": false
        },
        "type": "string"
      }
    }
  },
  "status": {
    "ready": true,
    "state": "Ready"
  }
}

{
  "error": {
    "code": "INVALID_ARGUMENT",
    "message": "Bad request. The request body included invalid request parameters."
  },
  "status": 400
}

"Invalid API key"

{
  "error": {
    "code": "PAYMENT_REQUIRED",
    "message": "Request failed. Pay all past due invoices to lift restrictions on your account."
  },
  "status": 402
}

{
  "error": {
    "code": "FORBIDDEN",
    "message": "Increase your quota or upgrade to create more indexes."
  },
  "status": 403
}

{
  "error": {
    "code": "NOT_FOUND",
    "message": "Resource cloud: aws region: us-west1 not found."
  },
  "status": 404
}

{
  "error": {
    "code": "ALREADY_EXISTS",
    "message": "Resource already exists."
  },
  "status": 409
}

{
  "error": {
    "code": "UNPROCESSABLE_ENTITY",
    "message": "Failed to deserialize the JSON body into the target type: missing field `metric` at line 1 column 16"
  },
  "status": 422
}

{
  "error": {
    "code": "UNKNOWN",
    "message": "Internal server error"
  },
  "status": 500
}

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

index_model = pc.create_index_for_model(
    name="docs-example",
    cloud="aws",
    region="us-east-1",
    embed={
        "model": "multilingual-e5-large",
        "field_map": {"text": "chunk_text"}
    }
)

PINECONE_API_KEY="YOUR_API_KEY"

curl "https://api.pinecone.io/indexes/create-for-model" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "name": "docs-example",
    "cloud": "aws",
    "region": "us-east-1",
    "embed": {
      "model": "multilingual-e5-large",
      "field_map": {"text": "chunk_text"}
    }
  }'

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:2026-07

required

Required date-based version header

Body

application/json

The desired configuration for the index and associated embedding model.

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"

cloud

string

required

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

Example:

"aws"

region

string

required

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

Example:

"us-east-1"

embed

object

required

Specify the integrated inference embedding configuration for the index.

Once set the model cannot be changed, but you can later update the embedding configuration for an integrated inference index including field map, read parameters, or write parameters.

Refer to the model guide for available models and model details.

Show child attributes

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" }
}

deletion_protection

string

default:disabled

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

Response

The index has successfully been created for the embedding model.

The IndexModel describes the configuration and status of a Pinecone index.

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"

host

string

required

The URL address where the index is hosted.

Example:

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

status

object

required

The current status of the index.

Show child attributes

Example:

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

deployment

Pod-based · object

required

The deployment configuration of a Pinecone index. The deployment_type field indicates which infrastructure model the index uses.

pod: Dedicated pod-based infrastructure. Suitable for workloads that

require predictable performance.
managed: Serverless infrastructure managed by Pinecone, including

full-text search indexes. Scales automatically; billed per usage.
byoc: Bring-your-own-compute. Runs in customer-managed infrastructure.

Pod-based
Serverless
BYOC

Show child attributes

Example:

{
  "deployment_type": "pod",
  "environment": "us-east1-gcp",
  "pod_type": "p1.x1",
  "replicas": 1,
  "shards": 1
}

schema

object

required

The schema of a Pinecone index. The schema defines the typed fields that documents in the index can contain, including vector fields, semantic text fields, and metadata fields.

Show child attributes

Example:

{
  "fields": {
    "embedding": {
      "dimension": 1536,
      "metric": "cosine",
      "type": "dense_vector"
    },
    "title": {
      "full_text_search": {
        "language": "en",
        "stemming": false,
        "stop_words": false
      },
      "type": "string"
    }
  }
}

deletion_protection

string

default:disabled

required

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

private_host

string

The private endpoint URL of an index.

Example:

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

read_capacity

On-demand · object

Response containing read capacity configuration

On-demand
Dedicated

Show child attributes

Example:

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

source_collection

string

The name of the collection this index was created from, if any.

Example:

"movie-embeddings"

source_backup_id

string

The ID of the backup this index was restored from, if any.

Example:

"670e8400-e29b-41d4-a716-446655440000"

cmek_id

string

The ID of the customer-managed encryption key (CMEK) used to encrypt this index, if any.

Example:

"arn:aws:kms:us-east-1:123456789012:key/mrk-abc123"

APIs

Database

Inference

Admin

SDKs

CLI

Tools

Create an index with integrated embedding

Authorizations

Headers

Body

Response