Serverless Indexes

For introductory information on indexes, please see Understanding indexes

Sparse vs Dense embedding vectors

When you are working with dense embedding vectors, you must specify the dimension of the vectors you expect to store at the time your index is created. For sparse vectors, used to represent vectors where most values are zero, you omit dimension and must specify vector_type="sparse".

from pinecone import (
    Pinecone,
    ServerlessSpec,
    CloudProvider,
    AwsRegion,
    Metric,
    VectorType
)

pc = Pinecone(api_key='<<PINECONE_API_KEY>>')

pc.create_index(
    name='index-for-dense-vectors',
    dimension=1536,
    metric=Metric.COSINE,
    # vector_type="dense" is the default value, so it can be omitted if you prefer
    vector_type=VectorType.DENSE,
    spec=ServerlessSpec(
        cloud=CloudProvider.AWS,
        region=AwsRegion.US_WEST_2
    ),
)

pc.create_index(
    name='index-for-sparse-vectors',
    metric=Metric.DOTPRODUCT,
    vector_type=VectorType.SPARSE,
    spec=ServerlessSpec(
        cloud=CloudProvider.AWS,
        region=AwsRegion.US_WEST_2
    ),
)

Available clouds

See the available cloud regions page for the most up-to-date information one which cloud regions are available.

Create a serverless index on Amazon Web Services (AWS)

The following example creates a serverless index in the us-west-2 region of AWS. For more information on serverless and regional availability, see Understanding indexes.

from pinecone import (
    Pinecone,
    ServerlessSpec,
    CloudProvider,
    AwsRegion,
    Metric,
    VectorType
)

pc = Pinecone(api_key='<<PINECONE_API_KEY>>')
pc.create_index(
    name='my-index',
    dimension=1536,
    metric=Metric.COSINE,
    spec=ServerlessSpec(
        cloud=CloudProvider.AWS,
        region=AwsRegion.US_WEST_2
    ),
    vector_type=VectorType.DENSE
)

Create a serverless index on Google Cloud Platform

The following example creates a serverless index in the us-central1 region of GCP. For more information on serverless and regional availability, see Understanding indexes.

from pinecone import (
    Pinecone,
    ServerlessSpec,
    CloudProvider,
    GcpRegion,
    Metric
)

pc = Pinecone(api_key='<<PINECONE_API_KEY>>')

pc.create_index(
    name='my-index',
    dimension=1536,
    metric=Metric.COSINE,
    spec=ServerlessSpec(
        cloud=CloudProvider.GCP,
        region=GcpRegion.US_CENTRAL1
    )
)

Create a serverless index on Azure

The following example creates a serverless index on Azure. For more information on serverless and regional availability, see Understanding indexes.

from pinecone import (
    Pinecone,
    ServerlessSpec,
    CloudProvider,
    AzureRegion,
    Metric
)

pc = Pinecone(api_key='<<PINECONE_API_KEY>>')

pc.create_index(
    name='my-index',
    dimension=1536,
    metric=Metric.COSINE,
    spec=ServerlessSpec(
        cloud=CloudProvider.AZURE,
        region=AzureRegion.EASTUS2
    )
)

Configuring, listing, describing, and deleting

See shared index actions to learn about how to manage the lifecycle of your index after it is created.