Update Model Metadata

curl --request PATCH \
  --url https://api.woodwide.ai/models/{model_id} \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "name": "<string>",
  "description": "<string>"
}
'

import requests

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

payload = {
    "name": "<string>",
    "description": "<string>"
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'PATCH',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({name: '<string>', description: '<string>'})
};

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 => "PATCH",
  CURLOPT_POSTFIELDS => json_encode([
    'name' => '<string>',
    'description' => '<string>'
  ]),
  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.woodwide.ai/models/{model_id}"

	payload := strings.NewReader("{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\"\n}")

	req, _ := http.NewRequest("PATCH", 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.patch("https://api.woodwide.ai/models/{model_id}")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\"\n}")
  .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::Patch.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\"\n}"

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

{
  "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>",
      "input": "<unknown>",
      "ctx": {}
    }
  ]
}

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

PATCH

models

{model_id}

Update Model Metadata

curl --request PATCH \
  --url https://api.woodwide.ai/models/{model_id} \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "name": "<string>",
  "description": "<string>"
}
'

import requests

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

payload = {
    "name": "<string>",
    "description": "<string>"
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'PATCH',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({name: '<string>', description: '<string>'})
};

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 => "PATCH",
  CURLOPT_POSTFIELDS => json_encode([
    'name' => '<string>',
    'description' => '<string>'
  ]),
  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.woodwide.ai/models/{model_id}"

	payload := strings.NewReader("{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\"\n}")

	req, _ := http.NewRequest("PATCH", 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.patch("https://api.woodwide.ai/models/{model_id}")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\"\n}")
  .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::Patch.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"name\": \"<string>\",\n  \"description\": \"<string>\"\n}"

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

{
  "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>",
      "input": "<unknown>",
      "ctx": {}
    }
  ]
}

{
  "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

Body

application/json

Request to update model metadata.

name

string

description

string | null

Response

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.
causal -- Causal effect estimation. Train with the outcome as label_column (the treatment is named via params.action_column or inferred at training time), then estimate effects with POST /models/{model_id}/estimate-causal-effect.

Available options:

prediction,

anomaly,

embedding,

clustering,

factors,

search,

causal

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.

Getting Started

Endpoints

Details

Update Model Metadata

Authorizations

Path Parameters

Body

Response