Create or update a patterns configuration

// Create or update a patterns configuration returns "OK" 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.LLMObsPatternsConfigUpsertRequest{
		Data: datadogV2.LLMObsPatternsConfigUpsertRequestData{
			Attributes: datadogV2.LLMObsPatternsConfigUpsertRequestAttributes{
				AccountId:           datadog.PtrString("1000000001"),
				ConfigId:            datadog.PtrString("a7c8d9e0-1234-5678-9abc-def012345678"),
				EvpQuery:            "@ml_app:support-bot",
				HierarchyDepth:      2,
				IntegrationProvider: datadog.PtrString("openai"),
				ModelName:           datadog.PtrString("gpt-4o"),
				Name:                "Support chatbot topics",
				NumRecords:          1000,
				SamplingRatio:       0.1,
				Scope:               datadog.PtrString(""),
				Template:            datadog.PtrString(""),
			},
			Type: datadogV2.LLMOBSPATTERNSCONFIGTYPE_TOPIC_DISCOVERY_CONFIGS,
		},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN"))
	configuration := datadog.NewConfiguration()
	configuration.SetUnstableOperationEnabled("v2.UpsertLLMObsPatternsConfig", true)
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV2.NewAgentObservabilityApi(apiClient)
	resp, r, err := api.UpsertLLMObsPatternsConfig(ctx, body)

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

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

// Create or update a patterns configuration returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v2.api.AgentObservabilityApi;
import com.datadog.api.client.v2.model.LLMObsPatternsConfigResponse;
import com.datadog.api.client.v2.model.LLMObsPatternsConfigType;
import com.datadog.api.client.v2.model.LLMObsPatternsConfigUpsertRequest;
import com.datadog.api.client.v2.model.LLMObsPatternsConfigUpsertRequestAttributes;
import com.datadog.api.client.v2.model.LLMObsPatternsConfigUpsertRequestData;

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

    LLMObsPatternsConfigUpsertRequest body =
        new LLMObsPatternsConfigUpsertRequest()
            .data(
                new LLMObsPatternsConfigUpsertRequestData()
                    .attributes(
                        new LLMObsPatternsConfigUpsertRequestAttributes()
                            .accountId("1000000001")
                            .configId("a7c8d9e0-1234-5678-9abc-def012345678")
                            .evpQuery("@ml_app:support-bot")
                            .hierarchyDepth(2)
                            .integrationProvider("openai")
                            .modelName("gpt-4o")
                            .name("Support chatbot topics")
                            .numRecords(1000)
                            .samplingRatio(0.1)
                            .scope("")
                            .template(""))
                    .type(LLMObsPatternsConfigType.TOPIC_DISCOVERY_CONFIGS));

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

"""
Create or update a patterns configuration returns "OK" response
"""

from os import environ
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.agent_observability_api import AgentObservabilityApi
from datadog_api_client.v2.model.llm_obs_patterns_config_type import LLMObsPatternsConfigType
from datadog_api_client.v2.model.llm_obs_patterns_config_upsert_request import LLMObsPatternsConfigUpsertRequest
from datadog_api_client.v2.model.llm_obs_patterns_config_upsert_request_attributes import (
    LLMObsPatternsConfigUpsertRequestAttributes,
)
from datadog_api_client.v2.model.llm_obs_patterns_config_upsert_request_data import (
    LLMObsPatternsConfigUpsertRequestData,
)

body = LLMObsPatternsConfigUpsertRequest(
    data=LLMObsPatternsConfigUpsertRequestData(
        attributes=LLMObsPatternsConfigUpsertRequestAttributes(
            account_id="1000000001",
            config_id="a7c8d9e0-1234-5678-9abc-def012345678",
            evp_query="@ml_app:support-bot",
            hierarchy_depth=2,
            integration_provider="openai",
            model_name="gpt-4o",
            name="Support chatbot topics",
            num_records=1000,
            sampling_ratio=0.1,
            scope="",
            template="",
        ),
        type=LLMObsPatternsConfigType.TOPIC_DISCOVERY_CONFIGS,
    ),
)

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

    print(response)

# Create or update a patterns configuration returns "OK" response

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

body = DatadogAPIClient::V2::LLMObsPatternsConfigUpsertRequest.new({
  data: DatadogAPIClient::V2::LLMObsPatternsConfigUpsertRequestData.new({
    attributes: DatadogAPIClient::V2::LLMObsPatternsConfigUpsertRequestAttributes.new({
      account_id: "1000000001",
      config_id: "a7c8d9e0-1234-5678-9abc-def012345678",
      evp_query: "@ml_app:support-bot",
      hierarchy_depth: 2,
      integration_provider: "openai",
      model_name: "gpt-4o",
      name: "Support chatbot topics",
      num_records: 1000,
      sampling_ratio: 0.1,
      scope: "",
      template: "",
    }),
    type: DatadogAPIClient::V2::LLMObsPatternsConfigType::TOPIC_DISCOVERY_CONFIGS,
  }),
})
p api_instance.upsert_llm_obs_patterns_config(body)

// Create or update a patterns configuration returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_agent_observability::AgentObservabilityAPI;
use datadog_api_client::datadogV2::model::LLMObsPatternsConfigType;
use datadog_api_client::datadogV2::model::LLMObsPatternsConfigUpsertRequest;
use datadog_api_client::datadogV2::model::LLMObsPatternsConfigUpsertRequestAttributes;
use datadog_api_client::datadogV2::model::LLMObsPatternsConfigUpsertRequestData;

#[tokio::main]
async fn main() {
    let body = LLMObsPatternsConfigUpsertRequest::new(LLMObsPatternsConfigUpsertRequestData::new(
        LLMObsPatternsConfigUpsertRequestAttributes::new(
            "@ml_app:support-bot".to_string(),
            2,
            "Support chatbot topics".to_string(),
            1000,
            0.1,
        )
        .account_id("1000000001".to_string())
        .config_id("a7c8d9e0-1234-5678-9abc-def012345678".to_string())
        .integration_provider("openai".to_string())
        .model_name("gpt-4o".to_string())
        .scope("".to_string())
        .template("".to_string()),
        LLMObsPatternsConfigType::TOPIC_DISCOVERY_CONFIGS,
    ));
    let mut configuration = datadog::Configuration::new();
    configuration.set_unstable_operation_enabled("v2.upsert_llm_obs_patterns_config", true);
    let api = AgentObservabilityAPI::with_config(configuration);
    let resp = api.upsert_llm_obs_patterns_config(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

/**
 * Create or update a patterns configuration returns "OK" 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.upsertLLMObsPatternsConfig"] = true;
const apiInstance = new v2.AgentObservabilityApi(configuration);

const params: v2.AgentObservabilityApiUpsertLLMObsPatternsConfigRequest = {
  body: {
    data: {
      attributes: {
        accountId: "1000000001",
        configId: "a7c8d9e0-1234-5678-9abc-def012345678",
        evpQuery: "@ml_app:support-bot",
        hierarchyDepth: 2,
        integrationProvider: "openai",
        modelName: "gpt-4o",
        name: "Support chatbot topics",
        numRecords: 1000,
        samplingRatio: 0.1,
        scope: "",
        template: "",
      },
      type: "topic_discovery_configs",
    },
  },
};

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