AI 커스텀 규칙 세트 업데이트

// Update an AI custom ruleset returns "Successfully updated" response

package main

import (
	"context"
	"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.AiCustomRulesetUpdateRequest{
		Data: &datadogV2.AiCustomRulesetUpdateData{
			Attributes: &datadogV2.AiCustomRulesetUpdateAttributes{
				Description:      datadog.PtrString("Ruleset description"),
				Name:             datadog.PtrString("my-ai-ruleset"),
				ShortDescription: datadog.PtrString("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.UpdateAiCustomRuleset", true)
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV2.NewStaticAnalysisApi(apiClient)
	r, err := api.UpdateAiCustomRuleset(ctx, "my-ai-ruleset", body)

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

// Update an AI custom ruleset returns "Successfully updated" 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.AiCustomRulesetUpdateAttributes;
import com.datadog.api.client.v2.model.AiCustomRulesetUpdateData;
import com.datadog.api.client.v2.model.AiCustomRulesetUpdateRequest;

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

    AiCustomRulesetUpdateRequest body =
        new AiCustomRulesetUpdateRequest()
            .data(
                new AiCustomRulesetUpdateData()
                    .attributes(
                        new AiCustomRulesetUpdateAttributes()
                            .description("Ruleset description")
                            .name("my-ai-ruleset")
                            .shortDescription("Ruleset short description"))
                    .id("my-ai-ruleset")
                    .type(AiCustomRulesetDataType.AI_RULESET));

    try {
      apiInstance.updateAiCustomRuleset("my-ai-ruleset", body);
    } catch (ApiException e) {
      System.err.println("Exception when calling StaticAnalysisApi#updateAiCustomRuleset");
      System.err.println("Status code: " + e.getCode());
      System.err.println("Reason: " + e.getResponseBody());
      System.err.println("Response headers: " + e.getResponseHeaders());
      e.printStackTrace();
    }
  }
}

"""
Update an AI custom ruleset returns "Successfully updated" 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_update_attributes import AiCustomRulesetUpdateAttributes
from datadog_api_client.v2.model.ai_custom_ruleset_update_data import AiCustomRulesetUpdateData
from datadog_api_client.v2.model.ai_custom_ruleset_update_request import AiCustomRulesetUpdateRequest

body = AiCustomRulesetUpdateRequest(
    data=AiCustomRulesetUpdateData(
        attributes=AiCustomRulesetUpdateAttributes(
            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["update_ai_custom_ruleset"] = True
with ApiClient(configuration) as api_client:
    api_instance = StaticAnalysisApi(api_client)
    api_instance.update_ai_custom_ruleset(ruleset_name="my-ai-ruleset", body=body)

# Update an AI custom ruleset returns "Successfully updated" response

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

body = DatadogAPIClient::V2::AiCustomRulesetUpdateRequest.new({
  data: DatadogAPIClient::V2::AiCustomRulesetUpdateData.new({
    attributes: DatadogAPIClient::V2::AiCustomRulesetUpdateAttributes.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.update_ai_custom_ruleset("my-ai-ruleset", body)

// Update an AI custom ruleset returns "Successfully updated" 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::AiCustomRulesetUpdateAttributes;
use datadog_api_client::datadogV2::model::AiCustomRulesetUpdateData;
use datadog_api_client::datadogV2::model::AiCustomRulesetUpdateRequest;

#[tokio::main]
async fn main() {
    let body = AiCustomRulesetUpdateRequest::new().data(
        AiCustomRulesetUpdateData::new()
            .attributes(
                AiCustomRulesetUpdateAttributes::new()
                    .description("Ruleset description".to_string())
                    .name("my-ai-ruleset".to_string())
                    .short_description("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.update_ai_custom_ruleset", true);
    let api = StaticAnalysisAPI::with_config(configuration);
    let resp = api
        .update_ai_custom_ruleset("my-ai-ruleset".to_string(), body)
        .await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

/**
 * Update an AI custom ruleset returns "Successfully updated" 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.updateAiCustomRuleset"] = true;
const apiInstance = new v2.StaticAnalysisApi(configuration);

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

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