Set global configs#

Experimental feature

This is an experimental feature, and may change at any time. Learn more.

Promptflow supports setting global configs to avoid passing the same parameters to each command. The global configs are stored in a yaml file, which is located at ~/.promptflow/pf.yaml by default.

The config file is shared between promptflow extension and sdk/cli. Promptflow extension controls each config through UI, so the following sections will show how to set global configs using promptflow cli.

Set config#

pf config set <config_name>=<config_value>

For example:

pf config set connection.provider="azureml://subscriptions/<your-subscription>/resourceGroups/<your-resourcegroup>/providers/Microsoft.MachineLearningServices/workspaces/<your-workspace>"

Show config#

The following command will get all configs and show them as json format:

pf config show

After running the above config set command, show command will return the following result:

  "connection": {
    "provider": "azureml://subscriptions/<your-subscription>/resourceGroups/<your-resourcegroup>/providers/Microsoft.MachineLearningServices/workspaces/<your-workspace>"

Supported configs#


The connection provider, default to “local”. There are 3 possible provider values.


Set connection provider to local with connection.provider=local.

Connections will be saved locally. PFClient(or pf connection commands) will manage local connections. Consequently, the flow will be executed using these local connections.

full azure machine learning workspace resource id#

Set connection provider to a specific workspace with:


When get or list connections, PFClient(or pf connection commands) will return workspace connections, and flow will be executed using these workspace connections. Secrets for workspace connection will not be shown by those commands, which means you may see empty dict {} for custom connections.


Command create, update and delete are not supported for workspace connections, please manage it in workspace portal, Azure AI Studio, az ml cli or azure-ai-ml sdk.


In addition to the full resource id, you can designate the connection provider as “azureml” with connection.provider=azureml. In this case, promptflow will attempt to retrieve the workspace configuration by searching .azureml/config.json from the current directory, then progressively from its parent folders. So it’s possible to set the workspace configuration for different flow by placing the config file in the project folder.

The expected format of the config file is as follows:

  "workspace_name": "<your-workspace-name>",
  "resource_group": "<your-resource-group>",
  "subscription_id": "<your-subscription-id>"

💡 Tips In addition to the CLI command line setting approach, we also support setting this connection provider through the VS Code extension UI. Click here to learn more.