> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt
> Use this file to discover all available pages before exploring further.
# 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](https://docs.pinecone.io/guides/index-data/create-an-index#embedding-models) to convert the text automatically during [upsert](https://docs.pinecone.io/reference/api/2025-01/data-plane/upsert_records) and [search](https://docs.pinecone.io/reference/api/2025-01/data-plane/search_records).
For guidance and examples, see [Create an index](https://docs.pinecone.io/guides/index-data/create-an-index#integrated-embedding).
```python Python theme={null}
# pip install --upgrade pinecone
from pinecone import Pinecone
pc = Pinecone(api_key="YOUR_API_KEY")
index_name = "integrated-dense-py"
index_model = pc.create_index_for_model(
name=index_name,
cloud="aws",
region="us-east-1",
embed={
"model":"llama-text-embed-v2",
"field_map":{"text": "chunk_text"}
}
)
# Import specific classes to get type hints and autocompletions
from pinecone import CloudProvider, AwsRegion, IndexEmbed, EmbedModel
index_model = pc.create_index_for_model(
name=index_name,
cloud=CloudProvider.AWS,
region=AwsRegion.US_EAST_1,
embed=IndexEmbed(
model=EmbedModel.Multilingual_E5_Large,
field_map={"text": "chunk_text"},
metric='cosine'
)
)
```
```javascript JavaScript theme={null}
// npm install @pinecone-database/pinecone
import { Pinecone } from '@pinecone-database/pinecone'
const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });
await pc.createIndexForModel({
name: 'integrated-dense-js',
cloud: 'aws',
region: 'us-east-1',
embed: {
model: 'llama-text-embed-v2',
fieldMap: { text: 'chunk_text' },
},
waitUntilReady: true,
});
```
```java Java theme={null}
import io.pinecone.clients.Pinecone;
import org.openapitools.db_control.client.ApiException;
import org.openapitools.db_control.client.model.CreateIndexForModelRequest;
import org.openapitools.db_control.client.model.CreateIndexForModelRequestEmbed;
import org.openapitools.db_control.client.model.DeletionProtection;
import org.openapitools.db_control.client.model.IndexModel;
import java.util.HashMap;
import java.util.Map;
public class CreateIntegratedIndex {
public static void main(String[] args) throws ApiException {
Pinecone pc = new Pinecone.Builder("YOUR_API_KEY").build();
String indexName = "integrated-dense-java";
String region = "us-east-1";
HashMap fieldMap = new HashMap<>();
fieldMap.put("text", "chunk_text");
CreateIndexForModelRequestEmbed embed = new CreateIndexForModelRequestEmbed()
.model("llama-text-embed-v2")
.fieldMap(fieldMap);
Map tags = new HashMap<>();
tags.put("environment", "development");
IndexModel index = pc.createIndexForModel(
indexName,
CreateIndexForModelRequest.CloudEnum.AWS,
region,
embed,
DeletionProtection.DISABLED,
tags
);
System.out.println(index);
}
}
```
```go Go theme={null}
package main
import (
"context"
"fmt"
"log"
"github.com/pinecone-io/go-pinecone/v4/pinecone"
)
func main() {
ctx := context.Background()
pc, err := pinecone.NewClient(pinecone.NewClientParams{
ApiKey: "YOUR_API_KEY",
})
if err != nil {
log.Fatalf("Failed to create Client: %v", err)
}
indexName := "integrated-dense-go"
DeletionProtection: &deletionProtection,
index, err := pc.CreateIndexForModel(ctx, &pinecone.CreateIndexForModelRequest{
Name: indexName,
Cloud: pinecone.Aws,
Region: "us-east-1",
Embed: pinecone.CreateIndexForModelEmbed{
Model: "llama-text-embed-v2",
FieldMap: map[string]interface{}{"text": "chunk_text"},
},
DeletionProtection: &deletionProtection,
Tags: &pinecone.IndexTags{ "environment": "development" },
})
if err != nil {
log.Fatalf("Failed to create serverless integrated index: %v", idx.Name)
} else {
fmt.Printf("Successfully created serverless integrated index: %v", idx.Name)
}
}
```
```csharp C# theme={null}
using Pinecone;
var pinecone = new PineconeClient("YOUR_API_KEY");
var createIndexRequest = await pinecone.CreateIndexForModelAsync(
new CreateIndexForModelRequest
{
Name = "integrated-dense-dotnet",
Cloud = CreateIndexForModelRequestCloud.Aws,
Region = "us-east-1",
Embed = new CreateIndexForModelRequestEmbed
{
Model = "llama-text-embed-v2",
FieldMap = new Dictionary() { { "text", "chunk_text" } },
},
DeletionProtection = DeletionProtection.Disabled,
Tags = new Dictionary
{
{ "environment", "development" }
}
}
);
```
```shell curl theme={null}
PINECONE_API_KEY="YOUR_API_KEY"
curl -X POST https://api.pinecone.io/indexes/create-for-model \
-H "Content-Type: application/json" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "X-Pinecone-Api-Version: 2025-04" \
-d '{
"name": "integrated-dense-curl",
"cloud": "aws",
"region": "us-east-1",
"embed": {
"model": "llama-text-embed-v2",
"metric": "cosine",
"field_map": {
"text": "chunk_text"
},
"write_parameters": {
"input_type": "passage",
"truncate": "END"
},
"read_parameters": {
"input_type": "query",
"truncate": "END"
}
}
}'
```
```bash CLI theme={null}
# Target the project where you want to create the index.
pc target -o "example-org" -p "example-project"
# Create the index.
pc index create \
--name "integrated-dense-cli" \
--dimension 1024 \
--metric "cosine" \
--cloud "aws" \
--region "us-east-1" \
--model "llama-text-embed-v2" \
--field_map "text=chunk_text"
```
```python Python theme={null}
{'deletion_protection': 'disabled',
'dimension': 1024,
'embed': {'dimension': 1024,
'field_map': {'text': 'chunk_text'},
'metric': 'cosine',
'model': 'llama-text-embed-v2',
'read_parameters': {'input_type': 'query', 'truncate': 'END'},
'write_parameters': {'input_type': 'passage', 'truncate': 'END'}},
'host': 'integrated-dense-py-govk0nt.svc.aped-4627-b74a.pinecone.io',
'id': '9dabb7cb-ec0a-4e2e-b79e-c7c997e592ce',
'metric': 'cosine',
'name': 'integrated-dense-py',
'spec': {'serverless': {'cloud': 'aws', 'region': 'us-east-1'}},
'status': {'ready': True, 'state': 'Ready'},
'tags': None}
```
```javascript JavaScript theme={null}
{
"name": "integrated-dense-js",
"dimension": 1024,
"metric": "cosine",
"host": "integrated-dense-js-govk0nt.svc.aped-4627-b74a.pinecone.io",
"deletionProtection": "disabled",
"tags": undefined,
"embed": {
"model": "llama-text-embed-v2",
metric: 'cosine',
dimension: 1024,
vectorType: 'dense',
fieldMap: { text: 'chunk_text' },
readParameters: { input_type: 'query', truncate: 'END' },
writeParameters: { input_type: 'passage', truncate: 'END' }
},
spec: { pod: undefined, serverless: { cloud: 'aws', region: 'us-east-1' } },
status: { ready: true, state: 'Ready' },
vectorType: 'dense'
}
```
```java Java theme={null}
class IndexModel {
name: integrated-dense-java
dimension: 1024
metric: cosine
host: integrated-dense-java-govk0nt.svc.aped-4627-b74a.pinecone.io
deletionProtection: disabled
tags: {environment=development}
embed: class ModelIndexEmbed {
model: llama-text-embed-v2
metric: cosine
dimension: 1024
vectorType: dense
fieldMap: {text=chunk_text}
readParameters: {dimension=1024.0, input_type=query, truncate=END}
writeParameters: {dimension=1024.0, input_type=passage, truncate=END}
additionalProperties: null
}
spec: class IndexModelSpec {
byoc: null
pod: null
serverless: class ServerlessSpec {
cloud: aws
region: us-east-1
additionalProperties: null
}
additionalProperties: null
}
status: class IndexModelStatus {
ready: false
state: Initializing
additionalProperties: null
}
vectorType: dense
additionalProperties: null
}
```
```go Go theme={null}
{
"name": "integrated-dense-go",
"host": "integrated-dense-go-govk0nt.svc.aped-4627-b74a.pinecone.io",
"metric": "cosine",
"vector_type": "dense",
"deletion_protection": "disabled",
"dimension": 1024,
"spec": {
"serverless": {
"cloud": "aws",
"region": "us-east-1"
}
},
"status": {
"ready": true,
"state": "Ready"
},
"embed": {
"model": "llama-text-embed-v2",
"dimension": 1024,
"metric": "cosine",
"vector_type": "dense",
"field_map": {
"text": "chunk_text"
},
"read_parameters": {
"input_type": "query",
"truncate": "END"
},
"write_parameters": {
"input_type": "passage",
"truncate": "END"
}
}
}
```
```csharp C# theme={null}
{
"name": "integrated-dense-dotnet",
"dimension": 1024,
"metric": "cosine",
"host": "integrated-dense-dotnet-govk0nt.svc.aped-4627-b74a.pinecone.io",
"deletion_protection": "disabled",
"tags": {
"environment": "development"
},
"embed": {
"model": "llama-text-embed-v2",
"metric": "cosine",
"dimension": 1024,
"vector_type": "dense",
"field_map": {
"text": "chunk_text"
},
"read_parameters": {
"dimension": 1024,
"input_type": "query",
"truncate": "END"
},
"write_parameters": {
"dimension": 1024,
"input_type": "passage",
"truncate": "END"
}
},
"spec": {
"serverless": {
"cloud": "aws",
"region": "us-east-1"
}
},
"status": {
"ready": true,
"state": "Ready"
},
"vector_type": "dense"
}
```
```json curl theme={null}
{
"id": "9dabb7cb-ec0a-4e2e-b79e-c7c997e592ce",
"name": "integrated-dense-curl",
"metric": "cosine",
"dimension": 1024,
"status": {
"ready": false,
"state": "Initializing"
},
"host": "integrated-dense-curl-govk0nt.svc.aped-4627-b74a.pinecone.io",
"spec": {
"serverless": {
"region": "us-east-1",
"cloud": "aws"
}
},
"deletion_protection": "disabled",
"tags": null,
"embed": {
"model": "llama-text-embed-v2",
"field_map": {
"text": "chunk_text"
},
"dimension": 1024,
"metric": "cosine",
"write_parameters": {
"input_type": "passage",
"truncate": "END"
},
"read_parameters": {
"input_type": "query",
"truncate": "END"
}
}
}
```
```text CLI theme={null}
[SUCCESS] Index integrated-dense-cli created successfully. Run pc index describe --name integrated-dense-cli to check status.
ATTRIBUTE VALUE
Name integrated-dense-cli
Dimension 1024
Metric cosine
Deletion Protection disabled
Vector Type dense
State Initializing
Ready false
Host integrated-dense-cli-1c6ab6aa.svc.aped-4627-b74a.pinecone.io
Private Host
Spec serverless
Cloud aws
Region us-east-1
Source Collection
Model llama-text-embed-v2
Field Map {"text":"chunk_text"}
Read Parameters {"dimension":1024,"input_type":"query","truncate":"END"}
Write Parameters {"dimension":1024,"input_type":"passage","truncate":"END"}
```
## OpenAPI
````yaml https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-04/db_control_2025-04.oas.yaml post /indexes/create-for-model
openapi: 3.0.3
info:
title: Pinecone Control Plane API
description: >-
Pinecone is a vector database that makes it easy to search and retrieve
billions of high-dimensional vectors.
contact:
name: Pinecone Support
url: https://support.pinecone.io
email: support@pinecone.io
license:
name: Apache 2.0
url: https://www.apache.org/licenses/LICENSE-2.0
version: 2025-04
servers:
- url: https://api.pinecone.io
description: Production API endpoints
security:
- ApiKeyAuth: []
tags:
- name: Manage Indexes
description: Actions that manage indexes
externalDocs:
description: More Pinecone.io API docs
url: https://docs.pinecone.io/introduction
paths:
/indexes/create-for-model:
post:
tags:
- Manage Indexes
summary: Create an index with integrated embedding
description: >-
Create an index with integrated embedding.
With this type of index, you provide source text, and Pinecone uses a
[hosted embedding
model](https://docs.pinecone.io/guides/index-data/create-an-index#embedding-models)
to convert the text automatically during
[upsert](https://docs.pinecone.io/reference/api/2025-01/data-plane/upsert_records)
and
[search](https://docs.pinecone.io/reference/api/2025-01/data-plane/search_records).
For guidance and examples, see [Create an
index](https://docs.pinecone.io/guides/index-data/create-an-index#integrated-embedding).
operationId: create_index_for_model
requestBody:
description: >-
The desired configuration for the index and associated embedding
model.
content:
application/json:
schema:
$ref: '#/components/schemas/CreateIndexForModelRequest'
examples:
index-for-model:
summary: Creating a serverless index for the specified model
value:
cloud: gcp
deletion_protection: enabled
embed:
field_map:
text: your-text-field
metric: cosine
model: multilingual-e5-large
name: multilingual-e5-large-index
region: us-east1
required: true
responses:
'201':
description: The index has successfully been created for the embedding model.
content:
application/json:
schema:
$ref: '#/components/schemas/IndexModel'
'400':
description: Bad request. The request body included invalid request parameters.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
examples:
index-metric-validation-error:
summary: Validation error
value:
error:
code: INVALID_ARGUMENT
message: >-
Bad request. The request body included invalid request
parameters.
status: 400
'401':
description: 'Unauthorized. Possible causes: Invalid API key.'
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
examples:
unauthorized:
summary: Unauthorized
value:
error:
code: UNAUTHENTICATED
message: Invalid API key.
status: 401
'404':
description: Unknown cloud or region when creating a serverless index.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
examples:
serverless-spec-cloud-not-found:
summary: Cannot create serverless index with invalid spec.
value:
error:
code: NOT_FOUND
message: 'Resource cloud: aws region: us-west1 not found.'
status: 404
'409':
description: Index of given name already exists.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
examples:
index-name-already-exists:
summary: Index name needs to be unique
value:
error:
code: ALREADY_EXISTS
message: Resource already exists.
status: 409
'422':
description: Unprocessable entity. The request body could not be deserialized.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
examples:
missing-field:
summary: Unprocessable entity
value:
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
'500':
description: Internal server error.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
examples:
internal-server-error:
summary: Internal server error
value:
error:
code: UNKNOWN
message: Internal server error
status: 500
components:
schemas:
CreateIndexForModelRequest:
description: The desired configuration for the index and associated embedding model.
type: object
properties:
name:
example: example-index
description: >
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 '-'.
type: string
minLength: 1
maxLength: 45
cloud:
example: aws
description: The public cloud where you would like your index hosted.
type: string
enum:
- gcp
- aws
- azure
region:
example: us-east-1
description: The region where you would like your index to be created.
type: string
deletion_protection:
$ref: '#/components/schemas/DeletionProtection'
tags:
$ref: '#/components/schemas/IndexTags'
embed:
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
description: >-
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](https://docs.pinecone.io/guides/index-data/create-an-index#embedding-models)
for available models and model details.
type: object
properties:
model:
example: multilingual-e5-large
description: The name of the embedding model to use for the index.
type: string
metric:
description: >-
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.
type: string
enum:
- cosine
- euclidean
- dotproduct
field_map:
example:
text: your-text-field
description: >-
Identifies the name of the text field from your document model
that will be embedded.
type: object
dimension:
description: The dimension of embedding vectors produced for the index.
type: integer
read_parameters:
description: The read parameters for the embedding model.
type: object
write_parameters:
description: The write parameters for the embedding model.
type: object
required:
- model
- field_map
required:
- name
- cloud
- region
- embed
IndexModel:
description: >-
The IndexModel describes the configuration and status of a Pinecone
index.
type: object
properties:
name:
example: example-index
description: >
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 '-'.
type: string
minLength: 1
maxLength: 45
dimension:
example: 1536
description: The dimensions of the vectors to be inserted in the index.
type: integer
format: int32
minimum: 1
maximum: 20000
metric:
description: >-
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'.
type: string
enum:
- cosine
- euclidean
- dotproduct
host:
example: semantic-search-c01b5b5.svc.us-west1-gcp.pinecone.io
description: The URL address where the index is hosted.
type: string
private_host:
example: semantic-search-c01b5b5.svc.private.us-west1-gcp.pinecone.io
description: The private endpoint URL of an index.
type: string
deletion_protection:
$ref: '#/components/schemas/DeletionProtection'
tags:
$ref: '#/components/schemas/IndexTags'
embed:
$ref: '#/components/schemas/ModelIndexEmbed'
spec:
example:
pod:
environment: us-east-1-aws
metadata_config:
indexed:
- genre
- title
- imdb_rating
pod_type: p1.x1
pods: 1
replicas: 1
shards: 1
type: object
properties:
byoc:
$ref: '#/components/schemas/ByocSpec'
pod:
$ref: '#/components/schemas/PodSpec'
serverless:
$ref: '#/components/schemas/ServerlessSpec'
status:
example:
ready: true
state: ScalingUpPodSize
type: object
properties:
ready:
type: boolean
state:
type: string
enum:
- Initializing
- InitializationFailed
- ScalingUp
- ScalingDown
- ScalingUpPodSize
- ScalingDownPodSize
- Terminating
- Ready
- Disabled
required:
- ready
- state
vector_type:
description: >-
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.
default: dense
type: string
required:
- name
- metric
- status
- spec
- host
- vector_type
ErrorResponse:
example:
error:
code: QUOTA_EXCEEDED
message: >-
The index exceeds the project quota of 5 pods by 2 pods. Upgrade
your account or change the project settings to increase the quota.
status: 429
description: The response shape used for all error responses.
type: object
properties:
status:
example: 500
description: The HTTP status code of the error.
type: integer
error:
example:
code: INVALID_ARGUMENT
message: >-
Index name must contain only lowercase alphanumeric characters or
hyphens, and must not begin or end with a hyphen.
description: Detailed information about the error that occurred.
type: object
properties:
code:
type: string
enum:
- OK
- UNKNOWN
- INVALID_ARGUMENT
- DEADLINE_EXCEEDED
- QUOTA_EXCEEDED
- NOT_FOUND
- ALREADY_EXISTS
- PERMISSION_DENIED
- UNAUTHENTICATED
- RESOURCE_EXHAUSTED
- FAILED_PRECONDITION
- ABORTED
- OUT_OF_RANGE
- UNIMPLEMENTED
- INTERNAL
- UNAVAILABLE
- DATA_LOSS
- FORBIDDEN
- UNPROCESSABLE_ENTITY
- PAYMENT_REQUIRED
message:
example: >-
Index name must contain only lowercase alphanumeric characters
or hyphens, and must not begin or end with a hyphen.
type: string
details:
description: >-
Additional information about the error. This field is not
guaranteed to be present.
type: object
required:
- code
- message
required:
- status
- error
DeletionProtection:
description: >
Whether [deletion
protection](http://docs.pinecone.io/guides/manage-data/manage-indexes#configure-deletion-protection)
is enabled/disabled for the index.
default: disabled
type: string
enum:
- disabled
- enabled
IndexTags:
example:
tag0: val0
tag1: val1
description: >-
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.
type: object
additionalProperties:
type: string
ModelIndexEmbed:
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
description: The embedding model and document fields mapped to embedding inputs.
type: object
properties:
model:
example: multilingual-e5-large
description: The name of the embedding model used to create the index.
type: string
metric:
description: >-
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.
type: string
enum:
- cosine
- euclidean
- dotproduct
dimension:
example: 1536
description: The dimensions of the vectors to be inserted in the index.
type: integer
format: int32
minimum: 1
maximum: 20000
vector_type:
description: >-
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.
default: dense
type: string
field_map:
example:
text: your-text-field
description: >-
Identifies the name of the text field from your document model that
is embedded.
type: object
read_parameters:
description: The read parameters for the embedding model.
type: object
write_parameters:
description: The write parameters for the embedding model.
type: object
required:
- model
ByocSpec:
example:
environment: aws-us-east-1-b921
description: Configuration needed to deploy an index in a BYOC environment.
type: object
properties:
environment:
example: aws-us-east-1-b921
description: The environment where the index is hosted.
type: string
required:
- environment
PodSpec:
example:
environment: us-east1-gcp
metadata_config:
indexed:
- genre
- title
- imdb_rating
pod_type: p1.x1
pods: 1
replicas: 1
shards: 1
source_collection: movie-embeddings
description: Configuration needed to deploy a pod-based index.
type: object
properties:
environment:
example: us-east1-gcp
description: The environment where the index is hosted.
type: string
replicas:
description: >-
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.
default: 1
type: integer
format: int32
minimum: 1
shards:
description: >-
The number of shards. Shards split your data across multiple pods so
you can fit more data into an index.
default: 1
type: integer
format: int32
minimum: 1
pod_type:
description: >-
The type of pod to use. One of `s1`, `p1`, or `p2` appended with `.`
and one of `x1`, `x2`, `x4`, or `x8`.
default: p1.x1
type: string
pods:
example: 1
description: >-
The number of pods to be used in the index. This should be equal to
`shards` x `replicas`.'
default: 1
type: integer
minimum: 1
metadata_config:
example:
indexed:
- genre
- title
- imdb_rating
description: >-
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.
type: object
properties:
indexed:
description: >-
By default, all metadata is indexed; to change this behavior,
use this property to specify an array of metadata fields that
should be indexed.
type: array
items:
type: string
source_collection:
example: movie-embeddings
description: The name of the collection to be used as the source for the index.
type: string
required:
- environment
- pod_type
ServerlessSpec:
description: Configuration needed to deploy a serverless index.
type: object
properties:
cloud:
example: aws
description: The public cloud where you would like your index hosted.
type: string
enum:
- gcp
- aws
- azure
region:
example: us-east-1
description: The region where you would like your index to be created.
type: string
source_collection:
type: string
description: The name of the collection to be used as the source for the index.
required:
- cloud
- region
securitySchemes:
ApiKeyAuth:
type: apiKey
in: header
name: Api-Key
description: >-
An API Key is required to call Pinecone APIs. Get yours from the
[console](https://app.pinecone.io/).
````