Live Perplexity LLM Responses endpoint allows you to retrieve structured responses from a specific Perplexity AI model, based on the input parameters.
Note: Perplexity uses web_search in all sonar-family models by default, but it’s not guaranteed to work with every request.
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/perplexity/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,
"web_search_country_iso_code": "FR",
"model_name": "sonar-reasoning",
"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
$post_array[] = array(
"system_message" => "communicate as if we are in a business meeting",
"message_chain" => [
[
"role" => "user",
"message" => "Hello, what’s up?"
],
[
"role" => "ai",
"message" => "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?"
]
],
"max_output_tokens" => 200,
"temperature" => 0.3,
"top_p" => 0.5,
"model_name" => "sonar-reasoning",
"web_search_country_iso_code" => "FR",
"user_prompt" => "provide information on how relevant the amusement park business is in France now"
);
if (count($post_array) > 0) {
try {
// POST /v3/ai_optimization/perplexity/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/perplexity/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/perplexity/llm_responses/live
@see https://docs.dataforseo.com/v3/ai_optimization/perplexity/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,
'web_search_country_iso_code': 'FR',
'model_name': 'sonar-reasoning',
'user_prompt': 'provide information on how relevant the amusement park business is in France now'
})
try:
response = client.post('/v3/ai_optimization/perplexity/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/perplexity/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: "sonar-reasoning",
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);
});
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/perplexity/llm_responses/live
/// </summary>
/// <see href="https://docs.dataforseo.com/v3/ai_optimization/perplexity/llm_responses/live"/>
public static async Task PerplexityLlmResponsesLive()
{
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,
web_search_country_iso_code = "FR",
model_name = "sonar-reasoning",
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/perplexity/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": "4.0657 sec.",
"cost": 0.006662,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "07231850-1535-0612-0000-0fece0a0a9e9",
"status_code": 20000,
"status_message": "Ok.",
"time": "3.9703 sec.",
"cost": 0.006662,
"result_count": 1,
"path": [
"v3",
"ai_optimization",
"perplexity",
"llm_responses",
"live"
],
"data": {
"api": "ai_optimization",
"function": "llm_responses",
"se": "perplexity",
"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,
"web_search_country_iso_code": "FR",
"model_name": "sonar-reasoning",
"user_prompt": "provide information on how relevant the amusement park business is in France now"
},
"result": [
{
"model_name": "sonar-reasoning",
"input_tokens": 62,
"output_tokens": 200,
"web_search": true,
"money_spent": 0.006062,
"datetime": "2025-07-23 18:50:27 +00:00",
"items": [
{
"type": "message",
"sections": [
{
"type": "text",
"text": "\nOkay, let's tackle this query. The user wants to know how relevant the amusement park business is in France right now, and they want the answer presented as if we're in a business meeting. So, I need to structure the information clearly and concisely, using the search results provided.\n\nFirst, I'll look at the search results. Result [1] talks about the France Amusement Parks Market, giving specific numbers: 2.35 USD Billion in 2024, projected to reach 3.41 USD Billion by 2032 with a 6.10% CAGR. That's a solid growth rate. Also mentions the largest segment is female visitors with 1.39 USD Billion revenue, and tickets are the fastest-growing revenue source at 7.36% CAGR. \n\nResult [2] is about the Europe market, where France is expected to have the highest CAGR from 2025-2030. The Europe market was 12.",
"annotations": [
{
"title": "France Amusement Parks Market Size, Trends and Forecast to 2032",
"url": "https://www.databridgemarketresearch.com/nucleus/france-amusement-parks-market"
},
{
"title": "Europe Amusement Parks Market Size & Outlook, 2024-2030",
"url": "https://www.grandviewresearch.com/horizon/outlook/amusement-parks-market/europe"
},
{
"title": "Amusement Parks Market Report 2025",
"url": "https://www.researchandmarkets.com/reports/5939563/amusement-parks-market-report"
},
{
"title": "France Amusement Parks Market Size & Outlook, 2024-2030",
"url": "https://www.grandviewresearch.com/horizon/outlook/amusement-parks-market/france"
},
{
"title": "Entertainment - France | Statista Market Forecast",
"url": "https://www.statista.com/outlook/amo/entertainment/france?currency=USD"
}
]
}
]
}
]
}
]
}
]
}
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 Perplexity LLM Responses call can contain only one task.
Execution time for tasks set with the Live Chat GPT 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_nameconsists of the actual model name and version name;
if the basic model name is specified, its latest version will be set by default;
you can receive the list of available LLM models by making a separate request to the https://api.dataforseo.com/v3/ai_optimization/gemini/llm_responses/models
max_output_tokens
integer
maximum number of tokens in the AI response
optional field
minimum value: 1
maximum value: 2048
default value: 2048
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: 1.9
default value: 0.77
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: 0.9
web_search_country_iso_code
string
country code for web search localization
optional field
specify the country ISO code to get localized web search results Note: available only for Perplexity Sonar models
example: US
system_message
string
instructions for the AI behavior
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 string with either user or ai role; message string with message content (max 500 characters);
you can specify maximum of 10 message objects in the array; Note: for Perplexity models, messages must strictly alternate between user and AI roles (user → ai);
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?"}]
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
web_search
boolean
indicates if web search was used Note: web search is enabled by default in Perplexity Sonar models
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
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