Setting ‘LLM Responses Gemini’ Tasks

Reference example

# Fixed reference example. Playground inputs do not update this sample.
curl --request POST \
  --url https://api.aisa.one/apis/v1/dataforseo/ai_optimization/gemini/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": "gemini-2.5-flash",
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]'

curl --request POST \
  --url https://api.aisa.one/apis/v1/dataforseo/ai_optimization/gemini/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": "gemini-2.5-flash",
    "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/gemini/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": "gemini-2.5-flash",
        "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: 'gemini-2.5-flash',
      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/gemini/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/gemini/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' => 'gemini-2.5-flash',
        '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/gemini/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\": \"gemini-2.5-flash\",\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/gemini/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\": \"gemini-2.5-flash\",\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/gemini/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\": \"gemini-2.5-flash\",\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": {}
    }
  ]
}

POST

https://api.aisa.one

apis

dataforseo

ai_optimization

gemini

llm_responses

task_post

Reference example

# Fixed reference example. Playground inputs do not update this sample.
curl --request POST \
  --url https://api.aisa.one/apis/v1/dataforseo/ai_optimization/gemini/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": "gemini-2.5-flash",
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]'

curl --request POST \
  --url https://api.aisa.one/apis/v1/dataforseo/ai_optimization/gemini/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": "gemini-2.5-flash",
    "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/gemini/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": "gemini-2.5-flash",
        "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: 'gemini-2.5-flash',
      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/gemini/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/gemini/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' => 'gemini-2.5-flash',
        '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/gemini/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\": \"gemini-2.5-flash\",\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/gemini/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\": \"gemini-2.5-flash\",\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/gemini/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\": \"gemini-2.5-flash\",\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": {}
    }
  ]
}

Queues a Gemini prompt instead of waiting, returning a task id. Wrapped in DataForSEO’s envelope: data in tasks[0].result, outcome in tasks[0].status_code - a rejected request still returns HTTP 200. Check task_post_supported in get_dataforseo_ai_gemini_llm_responses_models, and retrieve with get_dataforseo_ai_gemini_llm_responses_fetch.

Example

curl -X POST "https://api.aisa.one/apis/v1/dataforseo/ai_optimization/gemini/llm_responses/task_post" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '[{"user_prompt": "...", "model_name": "...", "max_output_tokens": "..."}]'

Cost and request rules

Published reference values are not an account-specific quote or a settled charge. For endpoints supporting X-AISA-Cost-Mode: quote, obtain a free quote for your exact request before execution; reconcile the settled cost with response metadata and Usage Logs. USD amounts use 1,000,000 integer micro-USD per dollar.

Default request reference estimate (USD): 0.701
Cost contract: response_usd
Published reference multiplier: 1.0
Settlement evidence source: response_body

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

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;
for example, if gemini-1.5-pro is specified, the gemini-1.5-pro-002 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/gemini/llm_responses/models

max_output_tokens

integer | null

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 1024

temperature

number | null

randomness of the AI response
optional field
higher values make output more diverse
lower values make output more focused
minimum value: 0
maximum value: 2
default value: 1.3

top_p

number | null

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

boolean | null

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

system_message

string | null

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

(object | null)[] | null

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?"}]

Show child attributes

use_reasoning

boolean | null

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 1024
Note #2: for Gemini Pro models, the use_reasoning will automatically be set to true

tag

string | null

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

postback_url

string | null

URL for sending task results
optional field
once the task is completed, we will send a POST request with its results compressed in the gzip format to the postback_url you specified
you can use the ‘$id’ string as a $id variable and ‘$tag’ as urlencoded $tag variable. We will set the necessary values before sending the request.
example:
http://your-server.com/postbackscript?id=$id
http://your-server.com/postbackscript?id=$id&tag=$tag
Note: special character in postback_url will be urlencoded;
i.a., the # character will be encoded into %23

learn more on our Help Center

pingback_url

string | null

notification URL of a completed task
optional field
when a task is completed we will notify you by GET request sent to the URL you have specified
you can use the ‘$id’ string as a $id variable and ‘$tag’ as urlencoded $tag variable. We will set the necessary values before sending the request
example:
http://your-server.com/pingscript?id=$id
http://your-server.com/pingscript?id=$id&tag=$tag
Note: special character in pingback_url will be urlencoded;
i.a., the # character will be encoded into %23

learn more on our Help Center

Example:

[
  {
    "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": "gemini-2.5-flash",
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]

Response

object | null

Successful operation

version

string | null

the current version of the API

status_code

integer | null

general status code you can find the full list of the response codes here

status_message

string | null

general informational message you can find the full list of general informational messages here

time

string | null

total execution time, seconds

cost

number<double> | null

total tasks cost, USD

tasks_count

integer<int64> | null

the number of tasks in the tasks array

tasks_error

integer<int64> | null

the number of tasks in the tasks array returned with an error

tasks

(object | null)[] | null

array of tasks

Show child attributes

​Example

​Cost and request rules

Authorizations

Body

Response

Example

Cost and request rules