POST /indexes/create-for-model

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.
For guidance and examples, see Create an index.

Servers

https://api.pinecone.io

Request headers

Name Type Required Description

Name	Type	Required	Description
`Content-Type`	String	Yes	The media type of the request body. Default value: "application/json"
`X-Pinecone-Api-Version`	String	Yes	Required date-based version header Default value: "2025-10"

Content-Type

String

Yes

The media type of the request body.

Default value: "application/json"

X-Pinecone-Api-Version

String

Yes

Required date-based version header

Default value: "2025-10"

Request body fields

Name	Type	Required	Description
`region`	String	Yes	The region where you would like your index to be created.
`name`	String	Yes	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 '-'.
`deletion_protection`	String	No	Whether deletion protection is enabled/disabled for the index. Possible values: `disabled` or `enabled`. Default value: "disabled"
`schema`	Object	No	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.
`schema.fields`	Object	Yes	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.
`schema.fields.name`	Object	No
`schema.fields.name.filterable`	Boolean	No	Whether the field is filterable. If true, the field is indexed and can be used in filters. Only true values are allowed.
`tags`	Object	No	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.
`read_capacity`		No	By default the index will be created with read capacity mode `OnDemand`. If you prefer to allocate dedicated read nodes for your workload, you must specify mode `Dedicated` and additional configurations for `node_type` and `scaling`.
`embed`	Object	Yes	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.
`embed.metric`	String	No	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`.
`embed.dimension`	Integer	No	The dimension of embedding vectors produced for the index.
`embed.field_map`	Object	Yes	Identifies the name of the text field from your document model that will be embedded.
`embed.write_parameters`	Object	No	The write parameters for the embedding model.
`embed.model`	String	Yes	The name of the embedding model to use for the index.
`embed.read_parameters`	Object	No	The read parameters for the embedding model.
`cloud`	String	Yes	The public cloud where you would like your index hosted. Possible values: `gcp`, `aws`, or `azure`.

How to start integrating

Add HTTP Task to your workflow definition.
Search for the API you want to integrate with and click on the name.
- This loads the API reference documentation and prepares the Http request settings.
Click Test request to test run your request to the API and see the API's response.