POST
/
indexes
curl --request POST \
  --url https://api.pinecone.io/indexes \
  --header 'Api-Key: <api-key>' \
  --header 'Content-Type: application/json' \
  --data '{
  "name": "movie-recommendations",
  "dimension": 1536,
  "metric": "cosine",
  "spec": {
    "serverless": {
      "cloud": "gcp",
      "region": "us-east1"
    }
  }
}'

Authorizations

Api-Key
string
headerrequired

Body

application/json

The configuration needed to create 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
dimension
integer
required

The dimensions of the vectors to be inserted in the index.

Required range: 1 < x < 20000
spec
object
required

The spec object defines how the index should be deployed.

For serverless indexes, you define only the cloud and region where the index should be hosted. For pod-based indexes, you define the environment where the index should be hosted, the pod type and size to use, and other index characteristics.

Serverless indexes are in public preview and are available only on AWS in the us-west-2 and us-east-1 regions. Test thoroughly before using serverless indexes in production.

metric
enum<string>
default: cosine

The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'.

Available options:
cosine,
euclidean,
dotproduct

Response

201 - application/json

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
dimension
integer
required

The dimensions of the vectors to be inserted in the index.

Required range: 1 < x < 20000
metric
enum<string>
default: cosinerequired

The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'.

Available options:
cosine,
euclidean,
dotproduct
spec
object
required
status
object
required
host
string

The URL address where the index is hosted.

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