Instead of ‘login’ and ‘password’ use your credentials from https://app.dataforseo.com/api-access

# Instead of 'login' and 'password' use your credentials from https://app.dataforseo.com/api-access
login="login"
password="password"
cred="$(printf ${login}:${password} | base64)"
curl --location --request POST "https://api.dataforseo.com/v3/ai_optimization/gemini/llm_responses/live" \
--header "Authorization: Basic ${cred}" \
--header "Content-Type: application/json" \
--data-raw '[
  {
    "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?"
      }
    ],
    "max_output_tokens": 200,
    "temperature": 0.3,
    "top_p": 0.5,
    "model_name": "gemini-2.5-flash",
    "web_search": true,
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]'

<?php
// You can download this file from here https://cdn.dataforseo.com/v3/examples/php/php_RestClient.zip
require('RestClient.php');
$api_url = 'https://api.dataforseo.com/';
try {
   // Instead of 'login' and 'password' use your credentials from https://app.dataforseo.com/api-access
   $client = new RestClient($api_url, null, 'login', 'password');
} catch (RestClientException $e) {
   echo "n";
   print "HTTP code: {$e->getHttpCode()}n";
   print "Error code: {$e->getCode()}n";
   print "Message: {$e->getMessage()}n";
   print  $e->getTraceAsString();
   echo "n";
   exit();
}
$post_array = array();
// You can set only one task at a time
$post_array[] = array(
        "system_message" => "communicate as if we are in a business meeting",
        "message_chain" => [
            [
                "role"    => "user",
                "message" => "Hello, what's up?"
            ],
            [
                "role"    => "ai",
                "message" => "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?"
            ]
        ],
        "max_output_tokens" => 200,
        "temperature" => 0.3,
        "top_p" => 0.5,
        "model_name" => "gemini-2.5-flash",
        "web_search" => true,
        "user_prompt" => "provide information on how relevant the amusement park business is in France now"
);
if (count($post_array) > 0) {
try {
    // POST /v3/ai_optimization/gemini/llm_responses/live
    // in addition to 'google' and 'ai_mode' you can also set other search engine and type parameters
    // the full list of possible parameters is available in documentation
    $result = $client->post('/v3/ai_optimization/gemini/llm_responses/live', $post_array);
    print_r($result);
    // do something with post result
} catch (RestClientException $e) {
    echo "n";
    print "HTTP code: {$e->getHttpCode()}n";
    print "Error code: {$e->getCode()}n";
    print "Message: {$e->getMessage()}n";
    print  $e->getTraceAsString();
    echo "n";
}
$client = null;
?>

"""
Method: POST
Endpoint: https://api.dataforseo.com/v3/ai_optimization/gemini/llm_responses/live
@see https://docs.dataforseo.com/v3/ai_optimization/gemini/llm_responses/live
"""

import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../../../')))
from lib.client import RestClient
from lib.config import username, password
client = RestClient(username, password)

post_data = []
post_data.append({
        '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?'
            }
        ],
        'max_output_tokens': 200,
        'temperature': 0.3,
        'top_p': 0.5,
        'model_name': 'gemini-2.5-flash',
        'web_search': True,
        'user_prompt': 'provide information on how relevant the amusement park business is in France now'
    })
try:
    response = client.post('/v3/ai_optimization/gemini/llm_responses/live', post_data)
    print(response)
    # do something with post result
except Exception as e:
    print(f'An error occurred: {e}')

const axios = require('axios');

axios({
    method: 'post',
    url: 'https://api.dataforseo.com/v3/ai_optimization/gemini/llm_responses/live',
    auth: {
        username: 'login',
        password: 'password'
    },
    data: [{
    system_message: encodeURI("communicate as if we are in a business meeting"),
    message_chain: [
      {
        role: "user",
        message: "Hello, what’s up?"
      },
      {
        role: "ai",
        message: encodeURI("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?")
      }
    ],
    max_output_tokens: 200,
    temperature: 0.3,
    top_p: 0.5,
    model_name: "gemini-2.5-flash",
    web_search: true,
    user_prompt: encodeURI("provide information on how relevant the amusement park business is in France now")
    }],
    headers: {
        'content-type': 'application/json'
    }
}).then(function (response) {
    var result = response['data']['tasks'];
    // Result data
    console.log(result);
}).catch(function (error) {
    console.log(error);
});

using System;
using System.Linq;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Text;
using System.Collections.Generic;
using System.Threading.Tasks;
using Newtonsoft.Json;
namespace DataForSeoSdk;

public class AiOptimization
{

    private static readonly HttpClient _httpClient;
    
    static AiOptimization()
    {
        _httpClient = new HttpClient
        {
            BaseAddress = new Uri("https://api.dataforseo.com/")
        };
        _httpClient.DefaultRequestHeaders.Authorization =
            new AuthenticationHeaderValue("Basic", ApiConfig.Base64Auth);
    }

    /// <summary>
    /// Method: POST
    /// Endpoint: https://api.dataforseo.com/v3/ai_optimization/gemini/llm_responses/live
    /// </summary>
    /// <see href="https://docs.dataforseo.com/v3/ai_optimization/gemini/llm_responses/live"/>
    
    public static async Task GeminiLlmResponsesLive()
    {
        var postData = new List<object>();
        // a simple way to set a task, the full list of possible parameters is available in documentation
        postData.Add(new
        {
            system_message = "communicate as if we are in a business meeting",
            message_chain = new object[]
            {
                new
                {
                    role = "user",
                    message = "Hello, what's up?"
                },
                new
                {
                    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?"
                }
            },
            max_output_tokens = 200,
            temperature = 0.3,
            top_p = 0.5,
            model_name = "gemini-2.5-flash",
            web_search = true,
            user_prompt = "provide information on how relevant the amusement park business is in France now"
        });

        var content = new StringContent(JsonConvert.SerializeObject(postData), Encoding.UTF8, "application/json");
        using var response = await _httpClient.PostAsync("/v3/ai_optimization/gemini/llm_responses/live", content);
        var result = JsonConvert.DeserializeObject<dynamic>(await response.Content.ReadAsStringAsync());
        // you can find the full list of the response codes here https://docs.dataforseo.com/v3/appendix/errors
        if (result.status_code == 20000)
        {
            // do something with result
            Console.WriteLine(result);
        }
        else
            Console.WriteLine($"error. Code: {result.status_code} Message: {result.status_message}");
    }

The above command returns JSON structured like this:

{
  "version": "0.1.20250526",
  "status_code": 20000,
  "status_message": "Ok.",
  "time": "14.6761 sec.",
  "cost": 0.0357548,
  "tasks_count": 1,
  "tasks_error": 0,
  "tasks": [
    {
      "id": "07021706-1535-0612-0000-e8acb319f072",
      "status_code": 20000,
      "status_message": "Ok.",
      "time": "14.3327 sec.",
      "cost": 0.0357548,
      "result_count": 1,
      "path": [
        "v3",
        "ai_optimization",
        "gemini",
        "llm_responses",
        "live"
      ],
      "data": {
        "api": "ai_optimization",
        "function": "llm_responses",
        "se": "gemini",
        "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?"
          }
        ],
        "max_output_tokens": 200,
        "temperature": 0.3,
        "top_p": 0.5,
        "model_name": "gemini-2.5-flash",
        "user_prompt": "provide information on how relevant the amusement park business is in France now"
      },
      "result": [
        {
          "model_name": "gemini-2.5-flash",
          "input_tokens": 68,
          "output_tokens": 241,
          "web_search": true,
          "money_spent": 0.0351548,
          "datetime": "2025-07-02 14:06:32 +00:00",
          "items": [
            {
              "type": "message",
              "sections": [
                {
                  "type": "text",
                  "text": "The amusement park business in France is highly relevant and a significant part of the country's tourism and leisure industry. Here's a breakdown of its current relevance:nn**1. Market Size and Growth:**n*   The French amusement parks market generated a revenue of USD 3,249.3 million in 2024.n*   It is projected to reach USD 4,274.3 million by 2030, demonstrating a compound annual growth rate (CAGR) of 4.4% from 2025 to 2030.n*   France accounted for 3.2% of the global amusement parks market in 2024.n*   In Europe, the French amusement parks market is expected to lead in terms of revenue by 2030 and is projected to be the fastest-growing regional market.nn**",
                  "annotations": [
                    {
                      "title": "grandviewresearch.com",
                      "url": "https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQE4kVSlqaUzZmAm6xWUASs0ppDa88LJ3WrthsLwppW3uzY6ROF9gQAeT1Q85e5W4etkjCovvSU8ygGEPgCcs0eC46cdz8IOjbyGJXbAvC5UPmsL2MWW5nCMa7JNk7rsMimpbiBDyXzpO_YZCecF-egFpoGFq3UN-GQ8wlYKgpZ7Z7kP8uHLWc2eOw=="
                    }
                  ]
                }
              ]
            }
          ]
        }
      ]
    }
  ]
}