ai_optimization/claude/llm_responses/live

Live Claude LLM Responses

‌‌
Live Claude LLM Responses endpoint allows you to retrieve structured responses from a specific Claude model, based on the input parameters.

POST

The cost of the task can be calculated on the Pricing page.

All POST data should be sent in the JSON format (UTF-8 encoding). The task setting is done using the POST method. When setting a task, you should send all task parameters in the task array of the generic POST array. You can send up to 2000 API calls per minute, each Live Claude LLM Responses call can contain only one task.

The number of concurrent Live tasks is currently limited to 30 per account for each platform in the LLM Responses.

Execution time for tasks set with the Live Claude LLM Responses endpoint is currently up to 120 seconds.

Below you will find a detailed description of the fields you can use for setting a task.

Description of the fields for setting a task:

Field name	Type	Description
`user_prompt`	string	prompt for the AI model required field the question or task you want to send to the AI model; you can specify up to 500 characters in the `user_prompt` field
`model_name`	string	name of the AI model required field `model_name`consists of the actual model name and version name; if the basic model name is specified, its latest version will be set by default; for example, if `claude-opus-4-0` is specified, the `claude-opus-4-20250514` will be set as `model_name` automatically; you can receive the list of available LLM models by making a separate request to the `https://api.dataforseo.com/v3/ai_optimization/claude/llm_responses/models`
`max_output_tokens`	integer	maximum number of tokens in the AI response optional field minimum value: `1`; maximum value: `4096`; default value: `2048`; Note: if `web_search` is set to `true` or the reasoning model is specified in the request, the output token count may exceed the specified `max_output_tokens` limit Note #2: if `use_reasoning` is set to `true`, the minimum value for `max_output_tokens` is `1025`
`temperature`	float	randomness of the AI response optional field higher values make output more diverse; lower values make output more focused; minimum value: `0` maximum value: `1` default value: `0.7` Note: `temperature` cannot be used together with `top_p` in the same request
`top_p`	float	diversity of the AI response optional field controls diversity of the response by limiting token selection; minimum value: `0` maximum value: `1` default value: `null` Note: `top_p` cannot be used together with `temperature` in the same request
`web_search`	boolean	enable web search for current information optional field when enabled, the AI model can access and cite current web information; Note: refer to the Models endpoint for a list of models that support `web_search`; default value: `false`; The cost of the parameter can be calculated on the Pricing page
`force_web_search`	boolean	force AI agent to use web search optional field to enable this parameter, `web_search` must also be enabled; when enabled, the AI model is forced to access and cite current web information; default value: `false`; Note: even if the parameter is set to `true`, there is no guarantee web sources will be cited in the response
`web_search_country_iso_code`	string	ISO country code of the location optional field possible values: `'AR','AT','AU','BE','BR','CA','CH','CL','CN','DE','DK','ES','FI','FR','GB','HK','ID','IN','IT','JP','KR','MX','MY','NL','NO','NZ','PH','PL','PT','RU','SA','SE','TR','TW','US','ZA'`
`web_search_city`	string	city name of the location optional field Note: specify `web_search_country_iso_code` to use this parameter
`system_message`	string	instructions for the AI behaviour optional field defines the AI's role, tone, or specific behavior; you can specify up to 500 characters in the `system_message` field
`message_chain`	array	conversation history optional field array of message objects representing previous conversation turns; each object must contain `role` and `message` parameters: `role` string with either `user` or `ai` role; `message` string with message content (max 500 characters); you can specify the maximum of 10 message objects in the array; example: `"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?"}]`
`use_reasoning`	boolean	enable reasoning for the AI model optional field when enabled, the model will perform reasoning before generating a response refer to the Models endpoint for a list of models that support `reasoning` default value: `false` Note: if set to `true`, the minimum value for `max_output_tokens` is `1025` Note #2: if set to `true`, `force_web_search` must be set to `false` Note #3: if set to `true`, the `temperature` and `top_p` cannot be used
`tag`	string	user-defined task identifier optional field the character limit is 255 you can use this parameter to identify the task and match it with the result you will find the specified `tag` value in the `data` object of the response

‌
‌‌As a response of the API server, you will receive JSON-encoded data containing a tasks array with the information specific to the set tasks.
‌
Description of the fields in the results array:

Field name	Type	Description
`version`	string	the current version of the API
`status_code`	integer	general status code you can find the full list of the response codes here Note: we strongly recommend designing a necessary system for handling related exceptional or error conditions
`status_message`	string	general informational message you can find the full list of general informational messages here
`time`	string	execution time, seconds
`cost`	float	total tasks cost, USD
`tasks_count`	integer	the number of tasks in the `tasks`* array*
`tasks_error`	integer	the number of tasks in the `tasks`* array returned with an error*
`tasks`	array	array of tasks
`id`	string	task identifier unique task identifier in our system in the UUID format
`status_code`	integer	status code of the task generated by DataForSEO; can be within the following range: 10000-60000 you can find the full list of the response codes here
`status_message`	string	informational message of the task you can find the full list of general informational messages here
`time`	string	execution time, seconds
`cost`	float	cost of the task, USD includes the base task price plus the `money_spent` value
`result_count`	integer	number of elements in the `result` array
`path`	array	URL path
`data`	object	contains the same parameters that you specified in the POST request
`result`	array	array of results
`model_name`	string	name of the AI model used
`input_tokens`	integer	number of tokens in the input total count of tokens processed
`output_tokens`	integer	number of tokens in the output total count of tokens generated in the AI response
`reasoning_tokens`	integer	number of reasoning tokens total count of tokens used to generate reasoning content
`web_search`	boolean	indicates if web search was used
`money_spent`	float	cost of AI tokens, USD the price charged by the third-party AI model provider for according to its Pricing
`datetime`	string	date and time when the result was received in the UTC format: “yyyy-mm-dd hh-mm-ss +00:00” example: `2019-11-15 12:57:46 +00:00`
`items`	array	array of response items contains structured AI response data
`reasoning`	object	element in the response
`type`	string	type of the element = 'reasoning' Note: this element is supported only in reasoning models and is not guaranteed to be returned
`sections`	array	reasoning chain sections array of objects containing the reasoning chain sections generated by the LLM
`type`	string	type of element='summary_text'
`text`	string	text of the reasoning chain section text of the reasoning chain section summarizing the model's thought process
`message`	object	element in the response
`type`	string	type of the element = 'message'
`sections`	array	array of content sections contains different parts of the AI response
`type`	string	type of element='text'
`text`	string	AI-generated text content
`annotations`	array	array of references used to generate the response equals `null` if the `web_search` parameter is not set to `true` Note: `annotations` may return empty even when `web_search` is `true`, as the AI will attempt to retrieve web information but may not find relevant results
`title`	string	the domain name or title of the quoted source
`url`	string	URL of the quoted source
`fan_out_queries`	array	array of fan-out queries contains related search queries derived from the main query to provide a more comprehensive response

‌‌

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/claude/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,
    "model_name": "claude-opus-4-0",
    "temperature": 0.3,
    "web_search": true,
    "web_search_country_iso_code": "FR",
    "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
$post_array[] = array(
        "system_message" => "communicate as if we are in a business meeting",
        "message_chain" => [
            [
                "role"    => "user",
                "message" => "Hello, what's up?"
            ],
            [
                "role"    => "ai",
                "message" => "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?"
            ]
        ],
        "max_output_tokens" => 200,
        "model_name" => "claude-opus-4-0",
        "temperature" => 0.3,
        "web_search" => true,
        "web_search_country_iso_code" => "FR",
        "user_prompt" => "provide information on how relevant the amusement park business is in France now"
);
if (count($post_array) > 0) {
try {
    // POST /v3/serp/google/ai_mode/live/advanced
    // 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/claude/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;
?>

const axios = require('axios');

axios({
    method: 'post',
    url: 'https://api.dataforseo.com/v3/ai_optimization/claude/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,
    model_name: "claude-opus-4-0",
    temperature: 0.3,
    web_search: true,
    web_search_country_iso_code: "FR",
    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);
});

"""
Method: POST
Endpoint: https://api.dataforseo.com/v3/ai_optimization/claude/llm_responses/live
@see https://docs.dataforseo.com/v3/ai_optimization/claude/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': 1024,
        'temperature': 0.3,
        'web_search_country_iso_code': 'FR',
        'model_name': 'claude-opus-4-0',
        '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/claude/llm_responses/live', post_data)
    print(response)
    # do something with post result
except Exception as e:
    print(f'An error occurred: {e}')

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/claude/llm_responses/live
    /// </summary>
    /// <see href="https://docs.dataforseo.com/v3/ai_optimization/claude/llm_responses/live"/>
    
    public static async Task ClaudeLlmResponsesLive()
    {
        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 = 1024,
            temperature = 0.3,
            web_search_country_iso_code = "FR",
            model_name = "claude-opus-4-0",
            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/claude/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": "0.1.20251208",
  "status_code": 20000,
  "status_message": "Ok.",
  "time": "30.3333 sec.",
  "cost": 0.55051,
  "tasks_count": 1,
  "tasks_error": 0,
  "tasks": [
    {
      "id": "12111428-1535-0612-0000-dca69722c6fd",
      "status_code": 20000,
      "status_message": "Ok.",
      "time": "30.2569 sec.",
      "cost": 0.55051,
      "result_count": 1,
      "path": [
        "v3",
        "ai_optimization",
        "claude",
        "llm_responses",
        "live"
      ],
      "data": {
        "api": "ai_optimization",
        "function": "llm_responses",
        "se": "claude",
        "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?"
          }
        ],
        "temperature": 0.3,
        "web_search_country_iso_code": "FR",
        "model_name": "claude-opus-4-0",
        "web_search": true,
        "user_prompt": "provide information on how relevant the amusement park business is in France now"
      },
      "result": [
        {
          "model_name": "claude-opus-4-20250514",
          "input_tokens": 31304,
          "output_tokens": 938,
          "reasoning_tokens": 576,
          "web_search": true,
          "money_spent": 0.54991,
          "datetime": "2025-12-11 14:29:13 +00:00",
          "items": [
            {
              "type": "reasoning",
              "sections": [
                {
                  "type": "summary_text",
                  "text": "**Exploring a riddle**nnThis likely refers to a riddle or joke. The classic answer seems to be a cup that's closed at the top and bottom, making it essentially useless. Or, is it a trophy cup or even a cupcake? I need to think through this riddle: I have a cup with no bottom and a closed top. How can I drink from it? The punchline might simply be that you can't drink from it. Hm, I wonder if there might be other interpretations too!"
                },
                {
                  "type": "summary_text",
                  "text": "**Pondering a riddle’s meaning**nnI’m considering if this cup could also mean something like hiccup — closed at the top and bottom? But if there’s no bottom, anything liquid just falls out. A closed top means you can’t pour anything in. So it may not be a traditional drinking cup. Maybe it’s an acorn cup, although it has an open top. The joke could suggest inverting it, but that still leaves it open. I’m not sure how it all ties back to drinking from it!"
                },
                {
                  "type": "summary_text",
                  "text": "**Clarifying the riddle’s punchline**nnSo, the answer seems to be that you're meant to drink from the rim, but since there’s no bottom and the top is closed, it's impossible to do that. The correct response points to a thimble instead, which has an open bottom. The punchline is clear: You can't drink; it's a thimble. The riddle plays on the expectation of a witty response. To keep it light, I could say, You don't! That's a thimble! and add a playful tone."
                }
              ]
            },
            {
              "type": "message",
              "sections": [
                {
                  "type": "text",
                  "text": "I'll search for current information about the amusement park business in France.",
                  "annotations": null
                }
              ]
            },
            {
              "type": "message",
              "sections": [
                {
                  "type": "text",
                  "text": "Let me search for more specific information about the French amusement park market's current performance and trends.",
                  "annotations": null
                }
              ]
            },
            {
              "type": "message",
              "sections": [
                {
                  "type": "text",
                  "text": "Based on my research, I can provide you with comprehensive information about the current relevance of the amusement park business in France:nn## Current State of the French Amusement Park IndustrynnThe amusement park business remains highly relevant and robust in France. Here are the key findings:nn### Market Size and Performancenn",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "France's amusement park industry generates approximately €4 billion in annual revenue and welcomes over 70 million visitors each year",
                  "annotations": [
                    {
                      "title": "Le marché des parcs de loisirs : étude, tendances, classements",
                      "url": "https://www.xerfi.com/presentationetude/le-marche-des-parcs-de-loisirs_SME12"
                    },
                    {
                      "title": "Le marché des parcs de loisirs : étude, tendances, classements",
                      "url": "https://www.xerfi.com/presentationetude/le-marche-des-parcs-de-loisirs_SME12"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": ". ",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "The country has approximately 650 leisure parks distributed across its territory",
                  "annotations": [
                    {
                      "title": "Le marché des parcs de loisirs : étude, tendances, classements",
                      "url": "https://www.xerfi.com/presentationetude/le-marche-des-parcs-de-loisirs_SME12"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": ".nn",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "The recreational activities sector in France, which includes amusement parks, is projected to grow from €29.5 billion in 2023 to €34.3 billion by 2028",
                  "annotations": [
                    {
                      "title": "France Recreational Activity Industry Outlook 2024 - 2028",
                      "url": "https://www.reportlinker.com/clp/country/508831/726362"
                    },
                    {
                      "title": "France Recreational Activity Industry Outlook 2024 - 2028",
                      "url": "https://www.reportlinker.com/clp/country/508831/726362"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": ", representing ",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "a compound annual growth rate (CAGR) of 2.4%",
                  "annotations": [
                    {
                      "title": "France Recreational Activity Industry Outlook 2024 - 2028",
                      "url": "https://www.reportlinker.com/clp/country/508831/726362"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": ".nn### Key Players and RankingsnnThe French market is dominated by several major operators:nn1. **Disneyland Paris** - ",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "Attracts 15 million annual visitors and generates €6.2 billion in economic impact",
                  "annotations": [
                    {
                      "title": "Amusement Parks Market Size, Share & Growth Report [2024-2034]",
                      "url": "https://www.emergenresearch.com/industry-report/amusement-parks-market"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": "n2. **Parc Astérix** - One of the top three parks in Francen3. **Puy du Fou** - ",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "The third most visited theme park in France with 2.8 million annual visitors in 2024",
                  "annotations": [
                    {
                      "title": "Le Puy du Fou - Wikipedia",
                      "url": "https://en.wikipedia.org/wiki/Le_Puy_du_Fou"
                    },
                    {
                      "title": "Le Puy du Fou - Wikipedia",
                      "url": "https://en.wikipedia.org/wiki/Le_Puy_du_Fou"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": "nn### Industry Structurenn",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "While the majority of establishments are small, often family-managed with local roots, the bulk of attendance is concentrated in about fifteen major parks with national or international reach",
                  "annotations": [
                    {
                      "title": "Le marché des parcs de loisirs : étude, tendances, classements",
                      "url": "https://www.xerfi.com/presentationetude/le-marche-des-parcs-de-loisirs_SME12"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": ".nn### European Contextnn",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "France and the United Kingdom represent mature markets",
                  "annotations": [
                    {
                      "title": "Europe Amusement Parks Market Size & Share Analysis - Industry Research Report - Growth Trends",
                      "url": "https://www.mordorintelligence.com/industry-reports/europe-amusement-parks-market"
                    },
                    {
                      "title": "Europe Amusement Parks Market Size & Share Analysis - Industry Research Report - Growth Trends",
                      "url": "https://www.mordorintelligence.com/industry-reports/europe-amusement-parks-market"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": " within Europe. ",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "Europe accounts for 28.5% of the global amusement parks market in 2024",
                  "annotations": [
                    {
                      "title": "Amusement Parks Market Size, Share & Growth Report [2024-2034]",
                      "url": "https://www.emergenresearch.com/industry-report/amusement-parks-market"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": ", with ",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "the European amusement park market expected to reach USD 27.09 billion in 2025",
                  "annotations": [
                    {
                      "title": "Europe Amusement Parks Market Size & Share Analysis - Industry Research Report - Growth Trends",
                      "url": "https://www.mordorintelligence.com/industry-reports/europe-amusement-parks-market"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": ".nn### Industry Strengthsnn1. **Government Support**: ",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "France's theme park sector benefits from government tourism promotion efforts",
                  "annotations": [
                    {
                      "title": "Amusement Parks Market Size, Share & Growth Report [2024-2034]",
                      "url": "https://www.emergenresearch.com/industry-report/amusement-parks-market"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": "n2. **Employment**: ",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "The industry employs more than 50,000 people",
                  "annotations": [
                    {
                      "title": "No. 1 amusement park in France | Puy du Fou",
                      "url": "https://www.puydufou.com/france/en/no-1-amusement-park-france"
                    },
                    {
                      "title": "No. 1 amusement park in France | Puy du Fou",
                      "url": "https://www.puydufou.com/france/en/no-1-amusement-park-france"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": "n3. **Innovation**: Parks continue to invest heavily in new attractions and experiencesn4. **International Recognition**: ",
                  "annotations": null
                },
                {
                  "type": "text",
                  "text": "Puy du Fou has won 9 international awards in just 7 years",
                  "annotations": [
                    {
                      "title": "No. 1 amusement park in France | Puy du Fou",
                      "url": "https://www.puydufou.com/france/en/no-1-amusement-park-france"
                    }
                  ]
                },
                {
                  "type": "text",
                  "text": "nn### Current Trendsnn- Focus on experiential entertainment and immersive experiencesn- Development of on-site accommodations to transform parks into multi-day destinationsn- Integration of new technologiesn- Emphasis on themed experiences and storytellingnnThe French amusement park industry demonstrates strong resilience and continued growth potential, making it a highly relevant business sector in the country's economy and tourism landscape.",
                  "annotations": null
                }
              ]
            }
          ],
          "fan_out_queries": [
            "France amusement park industry 2024",
            "France theme park attendance 2024 recovery"
          ]
        }
      ]
    }
  ]
}