Get Model

Get model details.

GET /models/{model_id}

Get Model

cURL

curl --request GET \
  --url https://api.woodwide.ai/models/{model_id} \
  --header 'Authorization: Bearer <token>'

import requests

url = "https://api.woodwide.ai/models/{model_id}"

headers = {"Authorization": "Bearer <token>"}

response = requests.get(url, headers=headers)

print(response.text)

const options = {method: 'GET', headers: {Authorization: 'Bearer <token>'}};

fetch('https://api.woodwide.ai/models/{model_id}', 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.woodwide.ai/models/{model_id}",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "GET",
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>"
  ],
]);

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

curl_close($curl);

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

package main

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

func main() {

	url := "https://api.woodwide.ai/models/{model_id}"

	req, _ := http.NewRequest("GET", url, nil)

	req.Header.Add("Authorization", "Bearer <token>")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.get("https://api.woodwide.ai/models/{model_id}")
  .header("Authorization", "Bearer <token>")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.woodwide.ai/models/{model_id}")

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

request = Net::HTTP::Get.new(url)
request["Authorization"] = 'Bearer <token>'

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

200

{
  "model_id": "b2c3d4e5-0000-0000-0000-000000000001",
  "model_type": "prediction",
  "status": "ready",
  "current_metrics": {
    "accuracy": 0.98
  },
  "current_version": {
    "version_id": "b2c3d4e5-0000-0000-0000-000000000002",
    "version": 1,
    "status": "ready",
    "metrics": {
      "accuracy": 0.98
    }
  },
  "training_dataset_id": "a1b2c3d4-0000-0000-0000-000000000001",
  "training_dataset_name": "retention_customers",
  "label_column": "churnReason",
  "created_at": "2025-01-15T11:00:00Z",
  "updated_at": "2025-01-15T11:05:00Z"
}

{
  "detail": "Not authenticated"
}

{
  "detail": "Forbidden"
}

{
  "detail": "Not found"
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

{
  "detail": "Rate limit exceeded. Retry after a short delay."
}

Authorizations

Authorization · string · header · required

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

Path Parameters

model_id · string<uuid> · required

Response

200

application/json

Successful Response

Model response model.

model_id · string<uuid> · required

model_type · enum<string> · required

Canonical model types supported by the platform.

• prediction -- Supervised prediction (classification or regression) on a target column.
• anomaly -- Unsupervised anomaly detection. Returns anomaly scores and anomalous row IDs.
• embedding -- Generate dense vector embeddings for each row.
• clustering -- Unsupervised clustering. Returns cluster labels and descriptions.
• factors -- Factor analysis / dimensionality reduction.
• search -- Semantic nearest-neighbor search over the training dataset.

Available options: prediction, anomaly, embedding, clustering, factors, search

created_at · string<date-time> · required

updated_at · string<date-time> · required

status · string · default: processing

Aggregated from the current version: ready, processing, or failed.

current_metrics · Current Metrics · object | null

Validation metrics from the current (latest) version, promoted for convenience. Keys depend on model type: prediction (classification): accuracy (0–1). prediction (regression): r2 (R-squared). clustering: n_clusters, silhouette_score (−1 to 1). Other model types do not produce metrics.

Show child attributes

current_version · CurrentVersion · object | null

Current version summary embedded in the Model response.

Show child attributes

training_dataset_id · string<uuid> | null

Dataset used to train the current version (if known).

training_dataset_name · string | null

Display name of the training dataset for the current version.

label_column · string | null

Target column used to train the current version (prediction models only). Omitted for other model types.