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PATCH
/
v2.0
/
largelanguagemodels
/
{largeLanguageModelId}
Update a large language model
curl --request PATCH \
  --url https://api-trial.cognigy.ai/new/v2.0/largelanguagemodels/{largeLanguageModelId} \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "name": "My large language model",
  "description": "Large language model to be used for customer-facing AI Agents.",
  "connectionId": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "isDefault": false
}
'
import requests

url = "https://api-trial.cognigy.ai/new/v2.0/largelanguagemodels/{largeLanguageModelId}"

payload = {
    "name": "My large language model",
    "description": "Large language model to be used for customer-facing AI Agents.",
    "connectionId": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
    "isDefault": False
}
headers = {
    "X-API-Key": "<api-key>",
    "Content-Type": "application/json"
}

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

print(response.text)
const options = {
  method: 'PATCH',
  headers: {'X-API-Key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    name: 'My large language model',
    description: 'Large language model to be used for customer-facing AI Agents.',
    connectionId: '3c90c3cc-0d44-4b50-8888-8dd25736052a',
    isDefault: false
  })
};

fetch('https://api-trial.cognigy.ai/new/v2.0/largelanguagemodels/{largeLanguageModelId}', 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-trial.cognigy.ai/new/v2.0/largelanguagemodels/{largeLanguageModelId}",
  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' => 'My large language model',
    'description' => 'Large language model to be used for customer-facing AI Agents.',
    'connectionId' => '3c90c3cc-0d44-4b50-8888-8dd25736052a',
    'isDefault' => false
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "X-API-Key: <api-key>"
  ],
]);

$response = curl_exec($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-trial.cognigy.ai/new/v2.0/largelanguagemodels/{largeLanguageModelId}"

	payload := strings.NewReader("{\n  \"name\": \"My large language model\",\n  \"description\": \"Large language model to be used for customer-facing AI Agents.\",\n  \"connectionId\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n  \"isDefault\": false\n}")

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

	req.Header.Add("X-API-Key", "<api-key>")
	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-trial.cognigy.ai/new/v2.0/largelanguagemodels/{largeLanguageModelId}")
  .header("X-API-Key", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"name\": \"My large language model\",\n  \"description\": \"Large language model to be used for customer-facing AI Agents.\",\n  \"connectionId\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n  \"isDefault\": false\n}")
  .asString();
require 'uri'
require 'net/http'

url = URI("https://api-trial.cognigy.ai/new/v2.0/largelanguagemodels/{largeLanguageModelId}")

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

request = Net::HTTP::Patch.new(url)
request["X-API-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"name\": \"My large language model\",\n  \"description\": \"Large language model to be used for customer-facing AI Agents.\",\n  \"connectionId\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n  \"isDefault\": false\n}"

response = http.request(request)
puts response.read_body
{
  "type": "Bad Request",
  "title": "Bad Request Error",
  "status": 400,
  "detail": "Validation failed. Missing payload.",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 1000,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}
{
  "type": "Unauthorized",
  "title": "Unauthorized Error",
  "status": 401,
  "detail": "<string>",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 401,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}
{
  "type": "Payment Required",
  "title": "Payment Required Error",
  "status": 402,
  "detail": "Validation failed. Missing payload.",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 402,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}
{
  "type": "Forbidden",
  "title": "Forbidden Error",
  "status": 403,
  "detail": "<string>",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 1000,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}
{
  "type": "Not Found",
  "title": "Not Found Error",
  "status": 404,
  "detail": "<string>",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 1000,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {},
  "logLevel": "error"
}
{
  "type": "Method Not Allowed",
  "title": "Method Not Allowed Error",
  "status": 405,
  "detail": "<string>",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 1000,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}
{
  "type": "Conflict",
  "title": "Conflict Error",
  "status": 409,
  "detail": "<string>",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 1004,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}
{
  "type": "Payload Too Large",
  "title": "Payload Too Large Error",
  "status": 413,
  "detail": "<string>",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 1000,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}
{
  "type": "Internal Server Error",
  "title": "Internal Server Error",
  "status": 500,
  "detail": "<string>",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 1000,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}
{
  "type": "Not Implemented",
  "title": "Not Implemented Error",
  "status": 501,
  "detail": "<string>",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 1009,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}
{
  "type": "Bad Gateway",
  "title": "Bad Gateway Error",
  "status": 502,
  "detail": "<string>",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 1000,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}
{
  "type": "Service Unavailable",
  "title": "Service Unavailable Error",
  "status": 503,
  "detail": "<string>",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 503,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}
{
  "type": "Gateway Timeout",
  "title": "Gateway Timeout Error",
  "status": 504,
  "detail": "<string>",
  "instance": "/v2.0/flows/5ce7c2d833ea1e04d7e6c432",
  "code": 1000,
  "traceId": "api--f84324f4-98eb-4f02-abdd-375a2e6c3c1f",
  "details": {}
}

Authorizations

​
X-API-Key
string
header
required

Supply the API Key in the HTTP-Header

Headers

​
Accept
enum<string>

The Accept header specifies the media type that the client expects in the response. Available options: application/json, application/hal+json, application/xml, text/xml, text/csv. The default value is application/json.

Available options:
application/json,
application/hal+json,
application/xml,
text/xml,
text/csv

Path Parameters

​
largeLanguageModelId
string
required

The unique identifier for the large language model.

Required string length: 24
Pattern: ^[a-z0-9]{24}$

Body

application/json
​
name
string

The display name of the large language model.

Example:

"My large language model"

​
description
string

A meaningful description of the large language model.

Example:

"Large language model to be used for customer-facing AI Agents."

​
provider
enum<string>
Available options:
azureOpenAI,
openAI,
anthropic,
googleVertexAI,
googleGemini,
googleGenAI,
alephAlpha,
awsBedrock,
mistral
​
connectionId
string<uuid>

The identifier for the large language model connection.

Required string length: 36
​
isDefault
boolean

If set to true, the large language model is used as the default large language model when no other large language model is set.

Example:

false

Response

Successfully updated large language model.

Last modified on July 8, 2026