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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.

Python
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"}
    }
)
curl
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"}
    }
  }'

POST /indexes/create-for-model

201
{
  "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"
  }
}
400
{
  "error": {
    "code": "INVALID_ARGUMENT",
    "message": "Bad request. The request body included invalid request parameters."
  },
  "status": 400
}
402
{
  "error": {
    "code": "PAYMENT_REQUIRED",
    "message": "Request failed. Pay all past due invoices to lift restrictions on your account."
  },
  "status": 402
}
403
{
  "error": {
    "code": "FORBIDDEN",
    "message": "Increase your quota or upgrade to create more indexes."
  },
  "status": 403
}
404
{
  "error": {
    "code": "NOT_FOUND",
    "message": "Resource cloud: aws region: us-west1 not found."
  },
  "status": 404
}
409
{
  "error": {
    "code": "ALREADY_EXISTS",
    "message": "Resource already exists."
  },
  "status": 409
}
422
{
  "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
}

Authorizations

Section titled “Authorizations”
Api-Keystringrequired

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

Headers

Section titled “Headers”
X-Pinecone-Api-Versionstringrequired

Required date-based version header

Typestring
Default2026-07

Body

Section titled “Body”
namestringrequired

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

Typestring
cloudstringrequired

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

Example: aws

Typestring
regionstringrequired

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

Example: us-east-1

Typestring
deletion_protection?string
Typestring
Defaultdisabled

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

tags?object | null

Custom user tags added to an index, at most 20 per index. Keys must be 80 characters or less and alphanumeric, '', or '-'. Values must be 120 characters or less and consist of printable ASCII characters or spaces. To unset a key, set the value to be an empty string. null in responses when the index has no tags.

Typeobject | null
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.

Typeobject
Show child attributes
fields?object

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. At most 50 fields may be declared. Field names may not begin with $, which introduces a filter operator. When declared at index creation, names beginning with _ are also rejected (reserved for internal use).

Typeobject
read_capacity?object
Show child attributes
modestringrequired

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.

Typestring
embedobjectrequired

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
modelstringrequired

The name of the embedding model to use for the index.

Example: multilingual-e5-large

Typestring
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.

Typestring
field_mapobjectrequired

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

Typeobject
dimension?integer

The dimension of embedding vectors produced for the index.

Typeinteger
read_parameters?object

The read parameters for the embedding model.

Typeobject
write_parameters?object

The write parameters for the embedding model.

Typeobject

Response

Section titled “Response”

201 — The index has successfully been created for the embedding model.

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

namestringrequired

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

Typestring
hoststringrequired

The URL address where the index is hosted.

Example: semantic-search-c01b5b5.svc.us-west1-gcp.pinecone.io

Typestring
private_host?string

The private endpoint URL of an index.

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

Typestring
statusobjectrequired

The current status of the index.

Typeobject
Show child attributes
readybooleanrequired

Whether the index is ready for use.

Typeboolean
statestringrequired

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

Typestring
deploymentobjectrequired

Deployment configuration for a pod-based index.

Typeobject
Show child attributes
deployment_typeenum<string>required

Identifies this as a pod-based deployment. Must be pod.

Available options: pod

Typeenum<string>
environmentstringrequired

The environment where the index is hosted.

Example: us-east1-gcp

Typestring
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.

Required range: 1 <= x

Typeinteger
Default1
shards?integer

The number of shards. Shards split your data across multiple pods so you can fit more data into an index.

Required range: 1 <= x

Typeinteger
Default1
pod_typestringrequired

The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.

Typestring
Defaultp1.x1
read_capacity?object
Show child attributes
modestringrequired

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.

Typestring
statusobjectrequired

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

Typeobject
Show child attributes
statestringrequired

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.

current_replicas?integer | null

The number of replicas. Each replica has dedicated compute resources and data storage. Increasing this number will increase the total throughput of the index. null for OnDemand indexes and for a Dedicated index that has not finished provisioning.

Typeinteger | null
current_shards?integer | null

The number of shards. Each shard has dedicated storage. Increasing shards alleviates index fullness. null for OnDemand indexes and for a Dedicated index that has not finished provisioning.

Typeinteger | null
error_message?string

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

Typestring
source_collection?string

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

Example: movie-embeddings

Typestring
source_backup_id?string

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

Example: 670e8400-e29b-41d4-a716-446655440000

Typestring
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

Typestring
schemaobjectrequired

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.

Typeobject
Show child attributes
fieldsobjectrequired

A map of field names to their configurations. Indexes that are served by the vectors API — created with the reserved _values / _sparse_values schema, or created by an earlier API version from dimension, metric, and vector_type — report their vector fields under the reserved names _values (dense) and _sparse_values (sparse), and any metadata fields configured for filtering as legacy fields carrying only filterable. Dense vectors-API indexes always report both _values and _sparse_values, regardless of how they were created.

Typeobject
tags?object | null

Custom user tags added to an index, at most 20 per index. Keys must be 80 characters or less and alphanumeric, '', or '-'. Values must be 120 characters or less and consist of printable ASCII characters or spaces. To unset a key, set the value to be an empty string. null in responses when the index has no tags.

Typeobject | null
deletion_protectionstringrequired
Typestring
Defaultdisabled

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

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