Create an LLM Observability prompt

// Create an LLM Observability prompt 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.LLMObsCreatePromptRequest{
		Data: datadogV2.LLMObsCreatePromptData{
			Attributes: datadogV2.LLMObsCreatePromptDataAttributes{
				PromptId: "Example-LLM-Observability",
				Title:    datadog.PtrString("Customer Support Assistant"),
				Template: datadogV2.LLMObsPromptTemplate{
					LLMObsPromptChatTemplate: &datadogV2.LLMObsPromptChatTemplate{Items: []datadogV2.LLMObsPromptChatMessage{
						{
							Content: "You are a helpful customer support assistant for {{company_name}}.",
							Role:    "system",
						},
						{
							Content: "Help {{customer_name}} with this question: {{question}}",
							Role:    "user",
						},
					}}},
			},
			Type: datadogV2.LLMOBSPROMPTTYPE_PROMPT_TEMPLATES,
		},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	configuration := datadog.NewConfiguration()
	configuration.SetUnstableOperationEnabled("v2.CreateLLMObsPrompt", true)
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV2.NewLLMObservabilityApi(apiClient)
	resp, r, err := api.CreateLLMObsPrompt(ctx, body)

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

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

// Create an LLM Observability prompt returns "OK" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v2.api.LlmObservabilityApi;
import com.datadog.api.client.v2.model.LLMObsCreatePromptData;
import com.datadog.api.client.v2.model.LLMObsCreatePromptDataAttributes;
import com.datadog.api.client.v2.model.LLMObsCreatePromptRequest;
import com.datadog.api.client.v2.model.LLMObsPromptChatMessage;
import com.datadog.api.client.v2.model.LLMObsPromptChatTemplate;
import com.datadog.api.client.v2.model.LLMObsPromptResponse;
import com.datadog.api.client.v2.model.LLMObsPromptTemplate;
import com.datadog.api.client.v2.model.LLMObsPromptType;
import java.util.Arrays;

public class Example {
  public static void main(String[] args) {
    ApiClient defaultClient = ApiClient.getDefaultApiClient();
    defaultClient.setUnstableOperationEnabled("v2.createLLMObsPrompt", true);
    LlmObservabilityApi apiInstance = new LlmObservabilityApi(defaultClient);

    LLMObsCreatePromptRequest body =
        new LLMObsCreatePromptRequest()
            .data(
                new LLMObsCreatePromptData()
                    .attributes(
                        new LLMObsCreatePromptDataAttributes()
                            .promptId("Example-LLM-Observability")
                            .title("Customer Support Assistant")
                            .template(
                                new LLMObsPromptTemplate(
                                    new LLMObsPromptChatTemplate(
                                        Arrays.asList(
                                            new LLMObsPromptChatMessage()
                                                .content(
                                                    "You are a helpful customer support assistant"
                                                        + " for {{company_name}}.")
                                                .role("system"),
                                            new LLMObsPromptChatMessage()
                                                .content(
                                                    "Help {{customer_name}} with this question:"
                                                        + " {{question}}")
                                                .role("user"))))))
                    .type(LLMObsPromptType.PROMPT_TEMPLATES));

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

"""
Create an LLM Observability prompt returns "OK" response
"""

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.llm_observability_api import LLMObservabilityApi
from datadog_api_client.v2.model.llm_obs_create_prompt_data import LLMObsCreatePromptData
from datadog_api_client.v2.model.llm_obs_create_prompt_data_attributes import LLMObsCreatePromptDataAttributes
from datadog_api_client.v2.model.llm_obs_create_prompt_request import LLMObsCreatePromptRequest
from datadog_api_client.v2.model.llm_obs_prompt_chat_message import LLMObsPromptChatMessage
from datadog_api_client.v2.model.llm_obs_prompt_type import LLMObsPromptType

body = LLMObsCreatePromptRequest(
    data=LLMObsCreatePromptData(
        attributes=LLMObsCreatePromptDataAttributes(
            prompt_id="Example-LLM-Observability",
            title="Customer Support Assistant",
            template=[
                LLMObsPromptChatMessage(
                    content="You are a helpful customer support assistant for {{company_name}}.",
                    role="system",
                ),
                LLMObsPromptChatMessage(
                    content="Help {{customer_name}} with this question: {{question}}",
                    role="user",
                ),
            ],
        ),
        type=LLMObsPromptType.PROMPT_TEMPLATES,
    ),
)

configuration = Configuration()
configuration.unstable_operations["create_llm_obs_prompt"] = True
with ApiClient(configuration) as api_client:
    api_instance = LLMObservabilityApi(api_client)
    response = api_instance.create_llm_obs_prompt(body=body)

    print(response)

# Create an LLM Observability prompt returns "OK" response

require "datadog_api_client"
DatadogAPIClient.configure do |config|
  config.unstable_operations["v2.create_llm_obs_prompt".to_sym] = true
end
api_instance = DatadogAPIClient::V2::LLMObservabilityAPI.new

body = DatadogAPIClient::V2::LLMObsCreatePromptRequest.new({
  data: DatadogAPIClient::V2::LLMObsCreatePromptData.new({
    attributes: DatadogAPIClient::V2::LLMObsCreatePromptDataAttributes.new({
      prompt_id: "Example-LLM-Observability",
      title: "Customer Support Assistant",
      template: [
        DatadogAPIClient::V2::LLMObsPromptChatMessage.new({
          content: "You are a helpful customer support assistant for {{company_name}}.",
          role: "system",
        }),
        DatadogAPIClient::V2::LLMObsPromptChatMessage.new({
          content: "Help {{customer_name}} with this question: {{question}}",
          role: "user",
        }),
      ],
    }),
    type: DatadogAPIClient::V2::LLMObsPromptType::PROMPT_TEMPLATES,
  }),
})
p api_instance.create_llm_obs_prompt(body)

// Create an LLM Observability prompt returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_llm_observability::LLMObservabilityAPI;
use datadog_api_client::datadogV2::model::LLMObsCreatePromptData;
use datadog_api_client::datadogV2::model::LLMObsCreatePromptDataAttributes;
use datadog_api_client::datadogV2::model::LLMObsCreatePromptRequest;
use datadog_api_client::datadogV2::model::LLMObsPromptChatMessage;
use datadog_api_client::datadogV2::model::LLMObsPromptTemplate;
use datadog_api_client::datadogV2::model::LLMObsPromptType;

#[tokio::main]
async fn main() {
    let body = LLMObsCreatePromptRequest::new(LLMObsCreatePromptData::new(
        LLMObsCreatePromptDataAttributes::new(
            "Example-LLM-Observability".to_string(),
            LLMObsPromptTemplate::LLMObsPromptChatTemplate(vec![
                LLMObsPromptChatMessage::new(
                    "You are a helpful customer support assistant for {{company_name}}."
                        .to_string(),
                    "system".to_string(),
                ),
                LLMObsPromptChatMessage::new(
                    "Help {{customer_name}} with this question: {{question}}".to_string(),
                    "user".to_string(),
                ),
            ]),
        )
        .title("Customer Support Assistant".to_string()),
        LLMObsPromptType::PROMPT_TEMPLATES,
    ));
    let mut configuration = datadog::Configuration::new();
    configuration.set_unstable_operation_enabled("v2.CreateLLMObsPrompt", true);
    let api = LLMObservabilityAPI::with_config(configuration);
    let resp = api.create_llm_obs_prompt(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

/**
 * Create an LLM Observability prompt returns "OK" response
 */

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

const configuration = client.createConfiguration();
configuration.unstableOperations["v2.createLLMObsPrompt"] = true;
const apiInstance = new v2.LLMObservabilityApi(configuration);

const params: v2.LLMObservabilityApiCreateLLMObsPromptRequest = {
  body: {
    data: {
      attributes: {
        promptId: "Example-LLM-Observability",
        title: "Customer Support Assistant",
        template: [
          {
            content:
              "You are a helpful customer support assistant for {{company_name}}.",
            role: "system",
          },
          {
            content: "Help {{customer_name}} with this question: {{question}}",
            role: "user",
          },
        ],
      },
      type: "prompt-templates",
    },
  },
};

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