Image metadata enrichment use case

import requests

url = "https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}"

payload = { "batch": [{ "imageLink": "https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png" }] }
headers = {
    "Authorization": "<authorization>",
    "Content-Type": "application/json"
}

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

print(response.text)

HttpResponse<String> response = Unirest.post("https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}")
  .header("Authorization", "<authorization>")
  .header("Content-Type", "application/json")
  .body("{\n  \"batch\": [\n    {\n      \"imageLink\": \"https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png\"\n    }\n  ]\n}")
  .asString();

const options = {
  method: 'POST',
  headers: {Authorization: '<authorization>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    batch: [{imageLink: 'https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png'}]
  })
};

fetch('https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

require 'uri'
require 'net/http'

url = URI("https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}")

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = '<authorization>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"batch\": [\n    {\n      \"imageLink\": \"https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png\"\n    }\n  ]\n}"

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

package main

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

func main() {

	url := "https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}"

	payload := strings.NewReader("{\n  \"batch\": [\n    {\n      \"imageLink\": \"https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png\"\n    }\n  ]\n}")

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

	req.Header.Add("Authorization", "<authorization>")
	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))

}

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}",
  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([
    'batch' => [
        [
                'imageLink' => 'https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png'
        ]
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: <authorization>",
    "Content-Type: application/json"
  ],
]);

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

curl_close($curl);

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

curl --request POST \
  --url https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID} \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "batch": [
    {
      "imageLink": "https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png"
    }
  ]
}
'

{
  "predictionId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "status": "SUBMITTED"
}

{
  "predictionId": "fd110486-f168-47c0-a419-1518a4840589",
  "status": "READY",
  "message": "<string>"
}

Image metadata enrichment use case

In the image metadata enrichment use case, the LLM ingests a text containing either a base64-encoded image or a public HTTP/HTTPS or Google Cloud Storage (gs://) image link and routes the image to a multimodal model. A JSON response is returned that contains an imageMetadata dictionary with a list of keywords, subcategories, and keyword synonyms.

The POST request obtains and indexes prediction information related to the specified use case, and returns a unique predictionId and status of the request. The predictionId can be used later in the GET request to retrieve the results.

POST

async-prediction

image-metadata-enrichment

{MODEL_ID}

Image metadata enrichment use case

import requests

url = "https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}"

payload = { "batch": [{ "imageLink": "https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png" }] }
headers = {
    "Authorization": "<authorization>",
    "Content-Type": "application/json"
}

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

print(response.text)

HttpResponse<String> response = Unirest.post("https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}")
  .header("Authorization", "<authorization>")
  .header("Content-Type", "application/json")
  .body("{\n  \"batch\": [\n    {\n      \"imageLink\": \"https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png\"\n    }\n  ]\n}")
  .asString();

const options = {
  method: 'POST',
  headers: {Authorization: '<authorization>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    batch: [{imageLink: 'https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png'}]
  })
};

fetch('https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

require 'uri'
require 'net/http'

url = URI("https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}")

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = '<authorization>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"batch\": [\n    {\n      \"imageLink\": \"https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png\"\n    }\n  ]\n}"

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

package main

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

func main() {

	url := "https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}"

	payload := strings.NewReader("{\n  \"batch\": [\n    {\n      \"imageLink\": \"https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png\"\n    }\n  ]\n}")

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

	req.Header.Add("Authorization", "<authorization>")
	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))

}

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID}",
  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([
    'batch' => [
        [
                'imageLink' => 'https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png'
        ]
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: <authorization>",
    "Content-Type: application/json"
  ],
]);

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

curl_close($curl);

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

curl --request POST \
  --url https://application_id.applications.lucidworks.com/ai/async-prediction/image-metadata-enrichment/{MODEL_ID} \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "batch": [
    {
      "imageLink": "https://i.postimg.cc/T3Gggmrx/blushing-happymushroom.png"
    }
  ]
}
'

{
  "predictionId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "status": "SUBMITTED"
}

{
  "predictionId": "fd110486-f168-47c0-a419-1518a4840589",
  "status": "READY",
  "message": "<string>"
}

Headers

Authorization

string

required

Bearer token used for authentication. Format: Authorization: Bearer ACCESS_TOKEN.

Content-Type

string

application/json

Example:

"application/json"

Path Parameters

MODEL_ID

string

required

Unique identifier for the model. Must be a multimodal model capable of processing images, such as gemini-2.5-flash-lite.

Body

application/json

batch

object[]

required

An array of request items. Each item must contain exactly one of text, image, or imageLink.

Show child attributes

useCaseConfig

UseCaseConfigImageMetadataEnrichment · object

Optional object to control metadata generation. The image must be provided in the batch item as useCaseConfig only tunes the output.

Show child attributes

modelConfig

ModelConfig · object

Provides fields and values that specify ranges for tokens. Fields used for specific use cases and models are specified. The default values are used if other values are not specified.

Show child attributes

Response

This is the response to the POST prediction request submitted for a specific useCase and modelId.

predictionId

string<uuid>

The universal unique identifier (UUID) returned in the POST request. This UUID is required in the GET request to retrieve results.

status

string

The current status of the prediction. Allowed values are:

SUBMITTED - The POST request was successful and the response has returned the predictionId and status that is used by the GET request.
ERROR - An error was generated when the GET request was sent.
READY - The results associated with the predictionId are available and ready to be retrieved.
RETRIEVED - The results associated with the predictionId are returned successfully when the GET request was sent.

Example:

"SUBMITTED"

Authentication

Async Chunking

Async Prediction

Commerce Studio Rules & Rewrites

Models

Platform Signals

Prediction

Prompt Preview

Tokenization

Use Case

Typeahead

Image metadata enrichment use case

Headers

Path Parameters

Body

Response