What is a Cluster?

• enVector Cluster is a fully managed environment where you can store and search encrypted vector data without dealing with infrastructure details.
• Every cluster has a dedicated endpoint and data space—so you can connect directly with enVector SDK and handle your requests smoothly.

Notes

• You can view your cluster details and the indexes created within them.
• Indexes are available only when the cluster is running.

How to connect (enVector SDK)

1. Install the enVector SDK version that matches the cluster version.

   pip install pyenvector=={envector_version}

2. Connect to the cluster with your API Key.

   import pyenvector as ev
   
   ev.init(
       address="<YOUR_CLUSTER_ENDPOINT>",
       access_token="<YOUR_API_KEY>",
       key_path="./path/to/sdk-keys",
       key_id="name-of-sdk-key",
   )

   • address: cluster endpoint
   • access_token: API Key value
   • key_path: path to load SDK keys for encryption/operations
   • key_id: name of the SDK keys

Features - Cluster Info

• Cluster ID: Unique identifier of the cluster.
• Created At: Cluster creation time.
• Cloud: CSP selected at the time of cluster creation.
• Cloud Region: CSP region selected the time of cluster creation.
• enVector Version: The latest available enVector SDK version. The cluster you create is compatible with this SDK version.
• Endpoint: The endpoint address to connect to this cluster.
• Cluster Status

  Running	The cluster is activate and ready for use. You can view the indexes created within the cluster while it is in the running status.
  Provisioning	The cluster is in a provisioning state.
  Starting	The cluster is restarting from a stopped state.
  Deleting	The cluster is being deleted.
  Stopping	The cluster is stopping from a running state.
  Stopped	The cluster is stopped. You won't be able to use the cluster until it is started again.
  Error	An error occurred in the cluster.
  pending	The cluster is in a status preparing for provisioning.

Features - Indexes

• Index Name: The name assigned when the index was created.
• Created At: Index creation time.
• Index Type: The method used to store and search vector data.
• Dim: Vector dimension.
• Row Count: Total number of vector rows stored.