Rai Policy Args
Azure OpenAI Content Filters resource. Uses Azure REST API version 2025-01-01-preview. In version 2.x of the Azure Native provider, it used API version 2024-04-01-preview. Other available API versions: 2024-04-01-preview, 2024-07-01-preview, 2024-10-01-preview. These can be accessed by generating a local SDK package using the CLI command pulumi package add azure-native machinelearningservices [ApiVersion]
. See the ../../../version-guide/#accessing-any-api-version-via-local-packages for details.
Example Usage
Create Rai policy
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
var raiPolicy = new AzureNative.MachineLearningServices.RaiPolicy("raiPolicy", new()
{
EndpointName = "Azure.OpenAI",
Properties = new AzureNative.MachineLearningServices.Inputs.RaiPolicyPropertiesArgs
{
BasePolicyName = "112",
CompletionBlocklists = new[]
{
new AzureNative.MachineLearningServices.Inputs.RaiBlocklistConfigArgs
{
Blocking = false,
BlocklistName = "blocklistName",
},
},
ContentFilters = new[]
{
new AzureNative.MachineLearningServices.Inputs.RaiPolicyContentFilterArgs
{
AllowedContentLevel = AzureNative.MachineLearningServices.AllowedContentLevel.Low,
Blocking = false,
Enabled = false,
Name = "policyName",
Source = AzureNative.MachineLearningServices.RaiPolicyContentSource.Prompt,
},
},
Mode = AzureNative.MachineLearningServices.RaiPolicyMode.Blocking,
PromptBlocklists = new[]
{
new AzureNative.MachineLearningServices.Inputs.RaiBlocklistConfigArgs
{
Blocking = false,
BlocklistName = "blocklistName",
},
},
Type = AzureNative.MachineLearningServices.RaiPolicyType.SystemManaged,
},
RaiPolicyName = "raiPolicyName",
ResourceGroupName = "test-rg",
WorkspaceName = "aml-workspace-name",
});
});
package main
import (
machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v3"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := machinelearningservices.NewRaiPolicy(ctx, "raiPolicy", &machinelearningservices.RaiPolicyArgs{
EndpointName: pulumi.String("Azure.OpenAI"),
Properties: &machinelearningservices.RaiPolicyPropertiesArgs{
BasePolicyName: pulumi.String("112"),
CompletionBlocklists: machinelearningservices.RaiBlocklistConfigArray{
&machinelearningservices.RaiBlocklistConfigArgs{
Blocking: pulumi.Bool(false),
BlocklistName: pulumi.String("blocklistName"),
},
},
ContentFilters: machinelearningservices.RaiPolicyContentFilterArray{
&machinelearningservices.RaiPolicyContentFilterArgs{
AllowedContentLevel: pulumi.String(machinelearningservices.AllowedContentLevelLow),
Blocking: pulumi.Bool(false),
Enabled: pulumi.Bool(false),
Name: pulumi.String("policyName"),
Source: pulumi.String(machinelearningservices.RaiPolicyContentSourcePrompt),
},
},
Mode: pulumi.String(machinelearningservices.RaiPolicyModeBlocking),
PromptBlocklists: machinelearningservices.RaiBlocklistConfigArray{
&machinelearningservices.RaiBlocklistConfigArgs{
Blocking: pulumi.Bool(false),
BlocklistName: pulumi.String("blocklistName"),
},
},
Type: pulumi.String(machinelearningservices.RaiPolicyTypeSystemManaged),
},
RaiPolicyName: pulumi.String("raiPolicyName"),
ResourceGroupName: pulumi.String("test-rg"),
WorkspaceName: pulumi.String("aml-workspace-name"),
})
if err != nil {
return err
}
return nil
})
}
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.RaiPolicy;
import com.pulumi.azurenative.machinelearningservices.RaiPolicyArgs;
import com.pulumi.azurenative.machinelearningservices.inputs.RaiPolicyPropertiesArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
var raiPolicy = new RaiPolicy("raiPolicy", RaiPolicyArgs.builder()
.endpointName("Azure.OpenAI")
.properties(RaiPolicyPropertiesArgs.builder()
.basePolicyName("112")
.completionBlocklists(RaiBlocklistConfigArgs.builder()
.blocking(false)
.blocklistName("blocklistName")
.build())
.contentFilters(RaiPolicyContentFilterArgs.builder()
.allowedContentLevel("Low")
.blocking(false)
.enabled(false)
.name("policyName")
.source("Prompt")
.build())
.mode("Blocking")
.promptBlocklists(RaiBlocklistConfigArgs.builder()
.blocking(false)
.blocklistName("blocklistName")
.build())
.type("SystemManaged")
.build())
.raiPolicyName("raiPolicyName")
.resourceGroupName("test-rg")
.workspaceName("aml-workspace-name")
.build());
}
}
Import
An existing resource can be imported using its type token, name, and identifier, e.g.
$ pulumi import azure-native:machinelearningservices:RaiPolicy raiPolicyName /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/endpoints/{endpointName}/raiPolicies/{raiPolicyName}
Properties
Name of the endpoint resource.
Azure OpenAI Content Filters properties.
Api version used by proxy call
Name of the Rai Policy.
The name of the resource group. The name is case insensitive.
Azure Machine Learning Workspace Name