Create an AI custom ruleset

// Create an AI custom ruleset returns "Successfully created" response

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"os"

	"github.com/DataDog/datadog-api-client-go/v2/api/datadog"
	"github.com/DataDog/datadog-api-client-go/v2/api/datadogV2"
)

func main() {
	body := datadogV2.AiCustomRulesetRequest{
		Data: &datadogV2.AiCustomRulesetRequestData{
			Attributes: &datadogV2.AiCustomRulesetRequestAttributes{
				Description:      "Ruleset description",
				Name:             "my-ai-ruleset",
				ShortDescription: "Ruleset short description",
			},
			Id:   datadog.PtrString("my-ai-ruleset"),
			Type: datadogV2.AICUSTOMRULESETDATATYPE_AI_RULESET.Ptr(),
		},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN"))
	configuration := datadog.NewConfiguration()
	configuration.SetUnstableOperationEnabled("v2.CreateAiCustomRuleset", true)
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV2.NewStaticAnalysisApi(apiClient)
	resp, r, err := api.CreateAiCustomRuleset(ctx, body)

	if err != nil {
		fmt.Fprintf(os.Stderr, "Error when calling `StaticAnalysisApi.CreateAiCustomRuleset`: %v\n", err)
		fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r)
	}

	responseContent, _ := json.MarshalIndent(resp, "", "  ")
	fmt.Fprintf(os.Stdout, "Response from `StaticAnalysisApi.CreateAiCustomRuleset`:\n%s\n", responseContent)
}

// Create an AI custom ruleset returns "Successfully created" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v2.api.StaticAnalysisApi;
import com.datadog.api.client.v2.model.AiCustomRulesetDataType;
import com.datadog.api.client.v2.model.AiCustomRulesetRequest;
import com.datadog.api.client.v2.model.AiCustomRulesetRequestAttributes;
import com.datadog.api.client.v2.model.AiCustomRulesetRequestData;
import com.datadog.api.client.v2.model.AiCustomRulesetResponse;

public class Example {
  public static void main(String[] args) {
    ApiClient defaultClient = ApiClient.getDefaultApiClient();
    defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN"));
    defaultClient.setUnstableOperationEnabled("v2.createAiCustomRuleset", true);
    StaticAnalysisApi apiInstance = new StaticAnalysisApi(defaultClient);

    AiCustomRulesetRequest body =
        new AiCustomRulesetRequest()
            .data(
                new AiCustomRulesetRequestData()
                    .attributes(
                        new AiCustomRulesetRequestAttributes()
                            .description("Ruleset description")
                            .name("my-ai-ruleset")
                            .shortDescription("Ruleset short description"))
                    .id("my-ai-ruleset")
                    .type(AiCustomRulesetDataType.AI_RULESET));

    try {
      AiCustomRulesetResponse result = apiInstance.createAiCustomRuleset(body);
      System.out.println(result);
    } catch (ApiException e) {
      System.err.println("Exception when calling StaticAnalysisApi#createAiCustomRuleset");
      System.err.println("Status code: " + e.getCode());
      System.err.println("Reason: " + e.getResponseBody());
      System.err.println("Response headers: " + e.getResponseHeaders());
      e.printStackTrace();
    }
  }
}

"""
Create an AI custom ruleset returns "Successfully created" response
"""

from os import environ
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.static_analysis_api import StaticAnalysisApi
from datadog_api_client.v2.model.ai_custom_ruleset_data_type import AiCustomRulesetDataType
from datadog_api_client.v2.model.ai_custom_ruleset_request import AiCustomRulesetRequest
from datadog_api_client.v2.model.ai_custom_ruleset_request_attributes import AiCustomRulesetRequestAttributes
from datadog_api_client.v2.model.ai_custom_ruleset_request_data import AiCustomRulesetRequestData

body = AiCustomRulesetRequest(
    data=AiCustomRulesetRequestData(
        attributes=AiCustomRulesetRequestAttributes(
            description="Ruleset description",
            name="my-ai-ruleset",
            short_description="Ruleset short description",
        ),
        id="my-ai-ruleset",
        type=AiCustomRulesetDataType.AI_RULESET,
    ),
)

configuration = Configuration()
configuration.access_token = environ["DD_BEARER_TOKEN"]
configuration.unstable_operations["create_ai_custom_ruleset"] = True
with ApiClient(configuration) as api_client:
    api_instance = StaticAnalysisApi(api_client)
    response = api_instance.create_ai_custom_ruleset(body=body)

    print(response)

# Create an AI custom ruleset returns "Successfully created" response

require "datadog_api_client"
DatadogAPIClient.configure do |config|
  config.access_token = ENV["DD_BEARER_TOKEN"]
  config.unstable_operations["v2.create_ai_custom_ruleset".to_sym] = true
end
api_instance = DatadogAPIClient::V2::StaticAnalysisAPI.new

body = DatadogAPIClient::V2::AiCustomRulesetRequest.new({
  data: DatadogAPIClient::V2::AiCustomRulesetRequestData.new({
    attributes: DatadogAPIClient::V2::AiCustomRulesetRequestAttributes.new({
      description: "Ruleset description",
      name: "my-ai-ruleset",
      short_description: "Ruleset short description",
    }),
    id: "my-ai-ruleset",
    type: DatadogAPIClient::V2::AiCustomRulesetDataType::AI_RULESET,
  }),
})
p api_instance.create_ai_custom_ruleset(body)

// Create an AI custom ruleset returns "Successfully created" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_static_analysis::StaticAnalysisAPI;
use datadog_api_client::datadogV2::model::AiCustomRulesetDataType;
use datadog_api_client::datadogV2::model::AiCustomRulesetRequest;
use datadog_api_client::datadogV2::model::AiCustomRulesetRequestAttributes;
use datadog_api_client::datadogV2::model::AiCustomRulesetRequestData;

#[tokio::main]
async fn main() {
    let body = AiCustomRulesetRequest::new().data(
        AiCustomRulesetRequestData::new()
            .attributes(AiCustomRulesetRequestAttributes::new(
                "Ruleset description".to_string(),
                "my-ai-ruleset".to_string(),
                "Ruleset short description".to_string(),
            ))
            .id("my-ai-ruleset".to_string())
            .type_(AiCustomRulesetDataType::AI_RULESET),
    );
    let mut configuration = datadog::Configuration::new();
    configuration.set_unstable_operation_enabled("v2.create_ai_custom_ruleset", true);
    let api = StaticAnalysisAPI::with_config(configuration);
    let resp = api.create_ai_custom_ruleset(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

/**
 * Create an AI custom ruleset returns "Successfully created" response
 */

import { client, v2 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration({
  authMethods: {
    AuthZ: {
      accessToken: process.env.DD_BEARER_TOKEN as string,
    },
  },
});
configuration.unstableOperations["v2.createAiCustomRuleset"] = true;
const apiInstance = new v2.StaticAnalysisApi(configuration);

const params: v2.StaticAnalysisApiCreateAiCustomRulesetRequest = {
  body: {
    data: {
      attributes: {
        description: "Ruleset description",
        name: "my-ai-ruleset",
        shortDescription: "Ruleset short description",
      },
      id: "my-ai-ruleset",
      type: "ai_ruleset",
    },
  },
};

apiInstance
  .createAiCustomRuleset(params)
  .then((data: v2.AiCustomRulesetResponse) => {
    console.log(
      "API called successfully. Returned data: " + JSON.stringify(data)
    );
  })
  .catch((error: any) => console.error(error));