curl --request POST \
  --url https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
[
  {
    "system_message": "communicate as if we are in a business meeting",
    "message_chain": [
      {
        "role": "user",
        "message": "Hello, what’s up?"
      },
      {
        "role": "ai",
        "message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?"
      }
    ],
    "model_name": "gpt-4.1-mini",
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]
'

import requests

url = "https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post"

payload = [
    {
        "system_message": "communicate as if we are in a business meeting",
        "message_chain": [
            {
                "role": "user",
                "message": "Hello, what’s up?"
            },
            {
                "role": "ai",
                "message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?"
            }
        ],
        "model_name": "gpt-4.1-mini",
        "user_prompt": "provide information on how relevant the amusement park business is in France now"
    }
]
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify([
    {
      system_message: 'communicate as if we are in a business meeting',
      message_chain: [
        {role: 'user', message: 'Hello, what’s up?'},
        {
          role: 'ai',
          message: 'Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?'
        }
      ],
      model_name: 'gpt-4.1-mini',
      user_prompt: 'provide information on how relevant the amusement park business is in France now'
    }
  ])
};

fetch('https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    [
        'system_message' => 'communicate as if we are in a business meeting',
        'message_chain' => [
                [
                                'role' => 'user',
                                'message' => 'Hello, what’s up?'
                ],
                [
                                'role' => 'ai',
                                'message' => 'Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?'
                ]
        ],
        'model_name' => 'gpt-4.1-mini',
        'user_prompt' => 'provide information on how relevant the amusement park business is in France now'
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post"

	payload := strings.NewReader("[\n  {\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"message_chain\": [\n      {\n        \"role\": \"user\",\n        \"message\": \"Hello, what’s up?\"\n      },\n      {\n        \"role\": \"ai\",\n        \"message\": \"Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?\"\n      }\n    ],\n    \"model_name\": \"gpt-4.1-mini\",\n    \"user_prompt\": \"provide information on how relevant the amusement park business is in France now\"\n  }\n]")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("[\n  {\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"message_chain\": [\n      {\n        \"role\": \"user\",\n        \"message\": \"Hello, what’s up?\"\n      },\n      {\n        \"role\": \"ai\",\n        \"message\": \"Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?\"\n      }\n    ],\n    \"model_name\": \"gpt-4.1-mini\",\n    \"user_prompt\": \"provide information on how relevant the amusement park business is in France now\"\n  }\n]")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "[\n  {\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"message_chain\": [\n      {\n        \"role\": \"user\",\n        \"message\": \"Hello, what’s up?\"\n      },\n      {\n        \"role\": \"ai\",\n        \"message\": \"Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?\"\n      }\n    ],\n    \"model_name\": \"gpt-4.1-mini\",\n    \"user_prompt\": \"provide information on how relevant the amusement park business is in France now\"\n  }\n]"

response = http.request(request)
puts response.read_body

{
  "version": "<string>",
  "status_code": 123,
  "status_message": "<string>",
  "time": "<string>",
  "cost": 123,
  "tasks_count": 123,
  "tasks_error": 123,
  "tasks": [
    {
      "id": "<string>",
      "status_code": 123,
      "status_message": "<string>",
      "time": "<string>",
      "cost": 123,
      "result_count": 123,
      "path": [
        "<string>"
      ],
      "data": {},
      "result": {}
    }
  ]
}