Create an index
For guidance and examples, see Create an index.
# pip install "pinecone[grpc]"
from pinecone.grpc import PineconeGRPC as Pinecone
from pinecone import ServerlessSpec
pc = Pinecone(api_key="YOUR_API_KEY")
pc.create_index(
name="docs-example1",
dimension=1536,
metric="cosine",
spec=ServerlessSpec(
cloud="aws",
region="us-east-1",
),
deletion_protection="disabled"
)// npm install @pinecone-database/pinecone
import { Pinecone } from '@pinecone-database/pinecone'
const pc = new Pinecone({
apiKey: 'YOUR_API_KEY'
});
await pc.createIndex({
name: 'docs-example1',
dimension: 1536,
metric: 'cosine',
spec: {
serverless: {
cloud: 'aws',
region: 'us-east-1'
}
},
deletionProtection: 'disabled',
});import io.pinecone.clients.Pinecone;
public class CreateServerlessIndexExample {
public static void main(String[] args) {
Pinecone pc = new Pinecone.Builder("YOUR_API_KEY").build();
pc.createServerlessIndex("docs-example1", "cosine", 1536, "aws", "us-east-1", DeletionProtection.disabled);
}
}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)
}
deletionProtection := pinecone.DeletionProtectionDisabled
idx, err := pc.CreateServerlessIndex(ctx, &pinecone.CreateServerlessIndexRequest{
Name: "docs-example1",
Dimension: 1536,
Metric: pinecone.Cosine,
Cloud: pinecone.Aws,
Region: "us-east-1",
DeletionProtection: &deletionProtection,
})
if err != nil {
log.Fatalf("Failed to create serverless index: %v", err)
} else {
fmt.Printf("Successfully created serverless index: %v", idx.Name)
}
}using Pinecone;
var pinecone = new PineconeClient("YOUR_API_KEY");
var createIndexRequest = await pinecone.CreateIndexAsync(new CreateIndexRequest
{
Name = "docs-example1",
Dimension = 1536,
Metric = MetricType.Cosine,
Spec = new ServerlessIndexSpec
{
Serverless = new ServerlessSpec
{
Cloud = ServerlessSpecCloud.Aws,
Region = "us-east-1",
}
},
DeletionProtection = DeletionProtection.Disabled
});PINECONE_API_KEY="YOUR_API_KEY"
# Serverless index
curl -X POST "https://api.pinecone.io/indexes" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "X-Pinecone-Api-Version: 2025-04" \
-d '{
"name": "docs-example1",
"vector_type": "dense",
"dimension": 1536,
"metric": "cosine",
"spec": {
"serverless": {
"cloud": "aws",
"region": "us-east-1"
}
},
"tags": {
"example": "tag"
},
"deletion_protection": "disabled"
}'
# BYOC index
curl -X POST"https://api.pinecone.io/indexes" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "X-Pinecone-Api-Version: 2025-04" \
-d '{
"name": "example-byoc-index",
"vector_type": "dense",
"dimension": 1536,
"metric": "cosine",
"spec": {
"byoc": {
"environment": "aws-us-east-1-b921"
}
},
"tags": {
"example": "tag"
},
"deletion_protection": "disabled"
}'# Target the project where you'd like to create the index.
pc target -o "example-org" -p "example-project"
# Create the index.
pc index create \
--name "docs-example1" \
--dimension 1536 \
--metric "cosine" \
--cloud "aws" \
--region "us-east-1" \
--deletion_protection "disabled" \
--tags "example=tag,example2=tag2"# Serverless index
{
"name": "docs-example1",
"vector_type": "dense",
"metric": "cosine",
"dimension": 1536,
"status": {
"ready": true,
"state": "Ready"
},
"host": "example-serverless-index-govk0nt.svc.aped-4627-b74a.pinecone.io",
"spec": {
"serverless": {
"region": "us-east-1",
"cloud": "aws"
}
},
"deletion_protection": "disabled",
"tags": {
"example": "tag"
}
}
# BYOC index
{
"name": "example-byoc-index",
"vector_type": "dense",
"metric": "cosine",
"dimension": 1536,
"status": {
"ready": true,
"state": "Ready"
},
"host": "example-byoc-index-govk0nt.svc.private.aped-4627-b74a.pinecone.io",
"spec": {
"byoc": {
"environment": "aws-us-east-1-b921"
}
},
"deletion_protection": "disabled",
"tags": {
"example": "tag"
}
}POST /indexes
Authorizations
Section titled “Authorizations”Api-KeystringrequiredAn API Key is required to call Pinecone APIs. Get yours from the console.
The desired configuration for the index.
namestringrequiredThe 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
dimension?integerThe dimensions of the vectors to be inserted in the index.
Required range: 1 <= x <= 20000. Example: 1536
metric?enum<string>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'.
Available options: cosine, euclidean, dotproduct
deletion_protection?enum<string>Available options: disabled, enabled
Whether deletion protection is enabled/disabled for the index.
tags?objectCustom 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.
specobjectrequiredThe spec object defines how the index should be deployed. For serverless indexes, you set only the cloud and region where the index should be hosted. For pod-based indexes, you set the environment where the index should be hosted, the pod type and size to use, and other index characteristics. For BYOC indexes, you set the environment name provided to you during onboarding.
Show child attributes
serverless?objectConfiguration needed to deploy a serverless index.
Show child attributes
cloudenum<string>requiredThe public cloud where you would like your index hosted.
Available options: gcp, aws, azure. Example: aws
regionstringrequiredThe region where you would like your index to be created.
Example: us-east-1
source_collection?stringThe name of the collection to be used as the source for the index.
pod?objectConfiguration needed to deploy a pod-based index.
Show child attributes
environmentstringrequiredThe environment where the index is hosted.
Example: us-east1-gcp
replicas?integerThe 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
shards?integerThe number of shards. Shards split your data across multiple pods so you can fit more data into an index.
Required range: 1 <= x
pod_typestringrequiredThe type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
pods?integerThe number of pods to be used in the index. This should be equal to shards x replicas.'
Required range: 1 <= x. Example: 1
metadata_config?objectConfiguration 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.
Show child attributes
indexed?string[]By default, all metadata is indexed; to change this behavior, use this property to specify an array of metadata fields that should be indexed.
source_collection?stringThe name of the collection to be used as the source for the index.
Example: movie-embeddings
byoc?objectConfiguration needed to deploy an index in a BYOC environment.
Show child attributes
environmentstringrequiredThe environment where the index is hosted.
Example: aws-us-east-1-b921
vector_type?stringThe 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.
Response
Section titled “Response”201 — The index has been successfully created.
The IndexModel describes the configuration and status of a Pinecone index.
namestringrequiredThe 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
dimension?integerThe dimensions of the vectors to be inserted in the index.
Required range: 1 <= x <= 20000. Example: 1536
metricenum<string>requiredThe 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'.
Available options: cosine, euclidean, dotproduct
hoststringrequiredThe URL address where the index is hosted.
Example: semantic-search-c01b5b5.svc.us-west1-gcp.pinecone.io
private_host?stringThe private endpoint URL of an index.
Example: semantic-search-c01b5b5.svc.private.us-west1-gcp.pinecone.io
deletion_protection?enum<string>Available options: disabled, enabled
Whether deletion protection is enabled/disabled for the index.
tags?objectCustom 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.
embed?objectThe embedding model and document fields mapped to embedding inputs.
Show child attributes
modelstringrequiredThe name of the embedding model used to create the index.
Example: multilingual-e5-large
metric?enum<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.
Available options: cosine, euclidean, dotproduct
dimension?integerThe dimensions of the vectors to be inserted in the index.
Required range: 1 <= x <= 20000. Example: 1536
vector_type?stringThe 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.
field_map?objectIdentifies the name of the text field from your document model that is embedded.
read_parameters?objectThe read parameters for the embedding model.
write_parameters?objectThe write parameters for the embedding model.
specobjectrequiredShow child attributes
byoc?objectConfiguration needed to deploy an index in a BYOC environment.
Show child attributes
environmentstringrequiredThe environment where the index is hosted.
Example: aws-us-east-1-b921
pod?objectConfiguration needed to deploy a pod-based index.
Show child attributes
environmentstringrequiredThe environment where the index is hosted.
Example: us-east1-gcp
replicas?integerThe 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
shards?integerThe number of shards. Shards split your data across multiple pods so you can fit more data into an index.
Required range: 1 <= x
pod_typestringrequiredThe type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
pods?integerThe number of pods to be used in the index. This should be equal to shards x replicas.'
Required range: 1 <= x. Example: 1
metadata_config?objectConfiguration 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.
Show child attributes
indexed?string[]By default, all metadata is indexed; to change this behavior, use this property to specify an array of metadata fields that should be indexed.
source_collection?stringThe name of the collection to be used as the source for the index.
Example: movie-embeddings
serverless?objectConfiguration needed to deploy a serverless index.
Show child attributes
cloudenum<string>requiredThe public cloud where you would like your index hosted.
Available options: gcp, aws, azure. Example: aws
regionstringrequiredThe region where you would like your index to be created.
Example: us-east-1
source_collection?stringThe name of the collection to be used as the source for the index.
statusobjectrequiredShow child attributes
readybooleanrequiredstateenum<string>requiredAvailable options: Initializing, InitializationFailed, ScalingUp, ScalingDown, ScalingUpPodSize, ScalingDownPodSize, Terminating, Ready, Disabled
vector_typestringrequiredThe 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.