> ## Documentation Index
>
> Fetch the complete documentation index at: [/llms.txt](https://docs.cognigy.com/llms.txt)
>
> Use this file to discover all available pages before exploring further.

[Skip to main content](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#content-area)

POST

/

v2.0

/

largelanguagemodels

Try it

Create a large language model

cURL

```
curl --request POST \
  --url https://api-trial.cognigy.ai/new/v2.0/largelanguagemodels \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "name": "Large language model for customer service",
  "connectionId": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "resourceLevel": "organisation",
  "description": "Large language model for customer-facing AI Agents.",
  "isCustomModel": true,
  "openAI": {
    "customModel": "gpt-4-32k-0613"
  },
  "anthropic": {
    "customModel": "claude-sonnet-4-6"
  },
  "azureOpenAI": {
    "resourceName": "<string>",
    "deploymentName": "<string>",
    "apiVersion": "<string>",
    "baseCustomUrl": [\
      "https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/chat/completions?api-version=<apiVersion>",\
      "https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/completions?api-version=<apiVersion>",\
      "https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/embeddings?api-version=<apiVersion>"\
    ]
  },
  "googleVertexAI": {
    "location": "<string>",
    "apiEndpoint": "<string>",
    "publisher": "<string>"
  },
  "googleGemini": {
    "location": "<string>"
  },
  "googleGenAI": {
    "location": "<string>"
  },
  "alephAlpha": {
    "customModel": "luminous-003",
    "baseCustomUrl": "https://api.aleph-alpha.com"
  },
  "openAICompatible": {
    "customModel": "luminous-003",
    "baseCustomUrl": "https://own-llm-deployment.company.com/openai/v1",
    "customAuthHeader": "Ocp-Apim-Subscription-Key"
  },
  "assignedToProjects": [\
    "68edf5dd4c931f68d31111",\
    "690b02fc100e454245adde111",\
    "68e6eda61ff68d2111"\
  ]
}
'
```

```
import requests

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

payload = {
    "name": "Large language model for customer service",
    "connectionId": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
    "resourceLevel": "organisation",
    "description": "Large language model for customer-facing AI Agents.",
    "isCustomModel": True,
    "openAI": { "customModel": "gpt-4-32k-0613" },
    "anthropic": { "customModel": "claude-sonnet-4-6" },
    "azureOpenAI": {
        "resourceName": "<string>",
        "deploymentName": "<string>",
        "apiVersion": "<string>",
        "baseCustomUrl": ["https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/chat/completions?api-version=<apiVersion>", "https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/completions?api-version=<apiVersion>", "https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/embeddings?api-version=<apiVersion>"]
    },
    "googleVertexAI": {
        "location": "<string>",
        "apiEndpoint": "<string>",
        "publisher": "<string>"
    },
    "googleGemini": { "location": "<string>" },
    "googleGenAI": { "location": "<string>" },
    "alephAlpha": {
        "customModel": "luminous-003",
        "baseCustomUrl": "https://api.aleph-alpha.com"
    },
    "openAICompatible": {
        "customModel": "luminous-003",
        "baseCustomUrl": "https://own-llm-deployment.company.com/openai/v1",
        "customAuthHeader": "Ocp-Apim-Subscription-Key"
    },
    "assignedToProjects": ["68edf5dd4c931f68d31111", "690b02fc100e454245adde111", "68e6eda61ff68d2111"]
}
headers = {
    "X-API-Key": "<api-key>",
    "Content-Type": "application/json"
}

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

print(response.text)
```

```
const options = {
  method: 'POST',
  headers: {'X-API-Key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    name: 'Large language model for customer service',
    connectionId: '3c90c3cc-0d44-4b50-8888-8dd25736052a',
    resourceLevel: 'organisation',
    description: 'Large language model for customer-facing AI Agents.',
    isCustomModel: true,
    openAI: {customModel: 'gpt-4-32k-0613'},
    anthropic: {customModel: 'claude-sonnet-4-6'},
    azureOpenAI: {
      resourceName: '<string>',
      deploymentName: '<string>',
      apiVersion: '<string>',
      baseCustomUrl: [\
        'https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/chat/completions?api-version=<apiVersion>',\
        'https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/completions?api-version=<apiVersion>',\
        'https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/embeddings?api-version=<apiVersion>'\
      ]
    },
    googleVertexAI: {location: '<string>', apiEndpoint: '<string>', publisher: '<string>'},
    googleGemini: {location: '<string>'},
    googleGenAI: {location: '<string>'},
    alephAlpha: {customModel: 'luminous-003', baseCustomUrl: 'https://api.aleph-alpha.com'},
    openAICompatible: {
      customModel: 'luminous-003',
      baseCustomUrl: 'https://own-llm-deployment.company.com/openai/v1',
      customAuthHeader: 'Ocp-Apim-Subscription-Key'
    },
    assignedToProjects: ['68edf5dd4c931f68d31111', '690b02fc100e454245adde111', '68e6eda61ff68d2111']
  })
};

fetch('https://api-trial.cognigy.ai/new/v2.0/largelanguagemodels', 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",\
  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([\
    'name' => 'Large language model for customer service',\
    'connectionId' => '3c90c3cc-0d44-4b50-8888-8dd25736052a',\
    'resourceLevel' => 'organisation',\
    'description' => 'Large language model for customer-facing AI Agents.',\
    'isCustomModel' => true,\
    'openAI' => [\
        'customModel' => 'gpt-4-32k-0613'\
    ],\
    'anthropic' => [\
        'customModel' => 'claude-sonnet-4-6'\
    ],\
    'azureOpenAI' => [\
        'resourceName' => '<string>',\
        'deploymentName' => '<string>',\
        'apiVersion' => '<string>',\
        'baseCustomUrl' => [\
                'https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/chat/completions?api-version=<apiVersion>',\
                'https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/completions?api-version=<apiVersion>',\
                'https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/embeddings?api-version=<apiVersion>'\
        ]\
    ],\
    'googleVertexAI' => [\
        'location' => '<string>',\
        'apiEndpoint' => '<string>',\
        'publisher' => '<string>'\
    ],\
    'googleGemini' => [\
        'location' => '<string>'\
    ],\
    'googleGenAI' => [\
        'location' => '<string>'\
    ],\
    'alephAlpha' => [\
        'customModel' => 'luminous-003',\
        'baseCustomUrl' => 'https://api.aleph-alpha.com'\
    ],\
    'openAICompatible' => [\
        'customModel' => 'luminous-003',\
        'baseCustomUrl' => 'https://own-llm-deployment.company.com/openai/v1',\
        'customAuthHeader' => 'Ocp-Apim-Subscription-Key'\
    ],\
    'assignedToProjects' => [\
        '68edf5dd4c931f68d31111',\
        '690b02fc100e454245adde111',\
        '68e6eda61ff68d2111'\
    ]\
  ]),\
  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"

payload := strings.NewReader("{\n  \"name\": \"Large language model for customer service\",\n  \"connectionId\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n  \"resourceLevel\": \"organisation\",\n  \"description\": \"Large language model for customer-facing AI Agents.\",\n  \"isCustomModel\": true,\n  \"openAI\": {\n    \"customModel\": \"gpt-4-32k-0613\"\n  },\n  \"anthropic\": {\n    \"customModel\": \"claude-sonnet-4-6\"\n  },\n  \"azureOpenAI\": {\n    \"resourceName\": \"<string>\",\n    \"deploymentName\": \"<string>\",\n    \"apiVersion\": \"<string>\",\n    \"baseCustomUrl\": [\n      \"https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/chat/completions?api-version=<apiVersion>\",\n      \"https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/completions?api-version=<apiVersion>\",\n      \"https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/embeddings?api-version=<apiVersion>\"\n    ]\n  },\n  \"googleVertexAI\": {\n    \"location\": \"<string>\",\n    \"apiEndpoint\": \"<string>\",\n    \"publisher\": \"<string>\"\n  },\n  \"googleGemini\": {\n    \"location\": \"<string>\"\n  },\n  \"googleGenAI\": {\n    \"location\": \"<string>\"\n  },\n  \"alephAlpha\": {\n    \"customModel\": \"luminous-003\",\n    \"baseCustomUrl\": \"https://api.aleph-alpha.com\"\n  },\n  \"openAICompatible\": {\n    \"customModel\": \"luminous-003\",\n    \"baseCustomUrl\": \"https://own-llm-deployment.company.com/openai/v1\",\n    \"customAuthHeader\": \"Ocp-Apim-Subscription-Key\"\n  },\n  \"assignedToProjects\": [\n    \"68edf5dd4c931f68d31111\",\n    \"690b02fc100e454245adde111\",\n    \"68e6eda61ff68d2111\"\n  ]\n}")

req, _ := http.NewRequest("POST", 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.post("https://api-trial.cognigy.ai/new/v2.0/largelanguagemodels")
  .header("X-API-Key", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"name\": \"Large language model for customer service\",\n  \"connectionId\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n  \"resourceLevel\": \"organisation\",\n  \"description\": \"Large language model for customer-facing AI Agents.\",\n  \"isCustomModel\": true,\n  \"openAI\": {\n    \"customModel\": \"gpt-4-32k-0613\"\n  },\n  \"anthropic\": {\n    \"customModel\": \"claude-sonnet-4-6\"\n  },\n  \"azureOpenAI\": {\n    \"resourceName\": \"<string>\",\n    \"deploymentName\": \"<string>\",\n    \"apiVersion\": \"<string>\",\n    \"baseCustomUrl\": [\n      \"https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/chat/completions?api-version=<apiVersion>\",\n      \"https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/completions?api-version=<apiVersion>\",\n      \"https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/embeddings?api-version=<apiVersion>\"\n    ]\n  },\n  \"googleVertexAI\": {\n    \"location\": \"<string>\",\n    \"apiEndpoint\": \"<string>\",\n    \"publisher\": \"<string>\"\n  },\n  \"googleGemini\": {\n    \"location\": \"<string>\"\n  },\n  \"googleGenAI\": {\n    \"location\": \"<string>\"\n  },\n  \"alephAlpha\": {\n    \"customModel\": \"luminous-003\",\n    \"baseCustomUrl\": \"https://api.aleph-alpha.com\"\n  },\n  \"openAICompatible\": {\n    \"customModel\": \"luminous-003\",\n    \"baseCustomUrl\": \"https://own-llm-deployment.company.com/openai/v1\",\n    \"customAuthHeader\": \"Ocp-Apim-Subscription-Key\"\n  },\n  \"assignedToProjects\": [\n    \"68edf5dd4c931f68d31111\",\n    \"690b02fc100e454245adde111\",\n    \"68e6eda61ff68d2111\"\n  ]\n}")
  .asString();
```

```
require 'uri'
require 'net/http'

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

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

request = Net::HTTP::Post.new(url)
request["X-API-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"name\": \"Large language model for customer service\",\n  \"connectionId\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n  \"resourceLevel\": \"organisation\",\n  \"description\": \"Large language model for customer-facing AI Agents.\",\n  \"isCustomModel\": true,\n  \"openAI\": {\n    \"customModel\": \"gpt-4-32k-0613\"\n  },\n  \"anthropic\": {\n    \"customModel\": \"claude-sonnet-4-6\"\n  },\n  \"azureOpenAI\": {\n    \"resourceName\": \"<string>\",\n    \"deploymentName\": \"<string>\",\n    \"apiVersion\": \"<string>\",\n    \"baseCustomUrl\": [\n      \"https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/chat/completions?api-version=<apiVersion>\",\n      \"https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/completions?api-version=<apiVersion>\",\n      \"https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/embeddings?api-version=<apiVersion>\"\n    ]\n  },\n  \"googleVertexAI\": {\n    \"location\": \"<string>\",\n    \"apiEndpoint\": \"<string>\",\n    \"publisher\": \"<string>\"\n  },\n  \"googleGemini\": {\n    \"location\": \"<string>\"\n  },\n  \"googleGenAI\": {\n    \"location\": \"<string>\"\n  },\n  \"alephAlpha\": {\n    \"customModel\": \"luminous-003\",\n    \"baseCustomUrl\": \"https://api.aleph-alpha.com\"\n  },\n  \"openAICompatible\": {\n    \"customModel\": \"luminous-003\",\n    \"baseCustomUrl\": \"https://own-llm-deployment.company.com/openai/v1\",\n    \"customAuthHeader\": \"Ocp-Apim-Subscription-Key\"\n  },\n  \"assignedToProjects\": [\n    \"68edf5dd4c931f68d31111\",\n    \"690b02fc100e454245adde111\",\n    \"68e6eda61ff68d2111\"\n  ]\n}"

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

201

400

401

402

403

404

405

409

413

500

501

502

503

504

```
{
  "name": "Large language model for customer service",
  "connectionId": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "resourceLevel": "organisation",
  "description": "Large language model for customer-facing AI Agents.",
  "isCustomModel": true,
  "openAI": {
    "customModel": "gpt-4-32k-0613"
  },
  "anthropic": {
    "customModel": "claude-sonnet-4-6"
  },
  "azureOpenAI": {
    "resourceName": "<string>",
    "deploymentName": "<string>",
    "apiVersion": "<string>",
    "baseCustomUrl": [\
      "https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/chat/completions?api-version=<apiVersion>",\
      "https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/completions?api-version=<apiVersion>",\
      "https://<resourceName>.openai.azure.com/openai/deployments/<deploymentName>/embeddings?api-version=<apiVersion>"\
    ]
  },
  "googleVertexAI": {
    "location": "<string>",
    "apiEndpoint": "<string>",
    "publisher": "<string>"
  },
  "googleGemini": {
    "location": "<string>"
  },
  "googleGenAI": {
    "location": "<string>"
  },
  "alephAlpha": {
    "customModel": "luminous-003",
    "baseCustomUrl": "https://api.aleph-alpha.com"
  },
  "openAICompatible": {
    "customModel": "luminous-003",
    "baseCustomUrl": "https://own-llm-deployment.company.com/openai/v1",
    "customAuthHeader": "Ocp-Apim-Subscription-Key"
  },
  "assignedToProjects": [\
    "68edf5dd4c931f68d31111",\
    "690b02fc100e454245adde111",\
    "68e6eda61ff68d2111"\
  ],
  "referenceId": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "_id": "<string>",
  "createdAt": 1694518620,
  "lastChanged": 1694518620,
  "createdBy": "<string>",
  "lastChangedBy": "<string>"
}
```

```
{
  "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

APIKeyHeaderAPIKeyQueryParamCXoneTokenHeaderOAuth2APIKeyHeaderAPIKeyQueryParamCXoneTokenHeaderOAuth2

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#authorization-x-api-key)

X-API-Key

string

header

required

Supply the API Key in the HTTP-Header

#### Headers

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#parameter-accept)

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`

#### Body

application/json

- Global large language model resource

- Project level large language model resource

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-name)

name

string

required

Example:

`"Large language model for customer service"`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-model-type)

modelType

enum<string>

required

Available options:

`gpt-3.5-turbo`,

`gpt-3.5-turbo-instruct`,

`gpt-4`,

`gpt-4o`,

`gpt-4o-mini`,

`gpt-4.1`,

`gpt-4.1-mini`,

`gpt-4.1-nano`,

`gpt-5`,

`gpt-5-nano`,

`gpt-5-mini`,

`gpt-5.4-mini`,

`gpt-5.4-nano`,

`gpt-5-chat-latest`,

`gpt-5.1`,

`gpt-5.2`,

`gpt-5.4`,

`gpt-5.5`,

`text-embedding-ada-002`,

`luminous-extended-control`,

`luminous-embedding-128`,

`Pharia-1-Embedding-4608`,

`gemini-embedding-001`,

`gemini-embedding-2`,

`claude-3-opus-20240229`,

`claude-sonnet-4-6`,

`custom-model`,

`custom-embedding-model`,

`gemini-2.5-pro`,

`gemini-2.5-flash`,

`gemini-2.5-flash-lite`,

`gemini-3.1-pro-preview`,

`gemini-3-flash-preview`,

`gemini-3.5-flash`,

`gemini-3.1-flash-lite-preview`,

`gemini-3.1-flash-lite`,

`mistral-large-2411`,

`mistral-small-2503`,

`pixtral-large-2411`,

`pixtral-12b-2409`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-provider)

provider

enum<string>

required

Available options:

`azureOpenAI`,

`openAI`,

`anthropic`,

`googleVertexAI`,

`googleGemini`,

`googleGenAI`,

`alephAlpha`,

`awsBedrock`,

`mistral`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-connection-id)

connectionId

string<uuid>

required

The identifier for the large language model connection.

Required string length: `36`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-resource-level)

resourceLevel

enum<string>

required

Scope for globally scoped resources.

Available options:

`organisation`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-description)

description

string

Example:

`"Large language model for customer-facing AI Agents."`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-model-group)

modelGroup

enum<string>

Available options:

`chat`,

`completion`,

`embedding`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-api-type)

apiType

enum<string>

The API type for chat models. Defaults to chatCompletion when not specified. The responses API is only supported for OpenAI, Azure OpenAI, and OpenAI Compatible providers.

Available options:

`chatCompletion`,

`responses`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-is-custom-model)

isCustomModel

boolean

Example:

`true`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-open-ai)

openAI

object

Metadata for OpenAI large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-anthropic)

anthropic

object

Metadata for Anthropic large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-azure-open-ai)

azureOpenAI

object

Metadata for Azure OpenAI large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-google-vertex-ai)

googleVertexAI

object

Metadata for Google Vertex AI large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-google-gemini)

googleGemini

object

Metadata for Google Gemini large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-google-gen-ai)

googleGenAI

object

Google GenAI specific meta data

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-aleph-alpha)

alephAlpha

object

Metadata for Aleph Alpha large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-open-ai-compatible)

openAICompatible

object

Metadata for OpenAI-compatible large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#body-one-of-0-assigned-to-projects)

assignedToProjects

string\[\]

MongoDB ObjectId representing a project

Example:

```
[\
  "68edf5dd4c931f68d31111",\
  "690b02fc100e454245adde111",\
  "68e6eda61ff68d2111"\
]
```

#### Response

201

application/json

Returns large language model metadata object.

- Option 1

- Option 2

- Option 3

The IEntityMeta defines meta information every entity within the system has. These are dates when a resource was created and modified as well as information about the user who initially created a resource and who modified it the last time.

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-name)

name

string

required

Example:

`"Large language model for customer service"`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-model-type)

modelType

enum<string>

required

Available options:

`gpt-3.5-turbo`,

`gpt-3.5-turbo-instruct`,

`gpt-4`,

`gpt-4o`,

`gpt-4o-mini`,

`gpt-4.1`,

`gpt-4.1-mini`,

`gpt-4.1-nano`,

`gpt-5`,

`gpt-5-nano`,

`gpt-5-mini`,

`gpt-5.4-mini`,

`gpt-5.4-nano`,

`gpt-5-chat-latest`,

`gpt-5.1`,

`gpt-5.2`,

`gpt-5.4`,

`gpt-5.5`,

`text-embedding-ada-002`,

`luminous-extended-control`,

`luminous-embedding-128`,

`Pharia-1-Embedding-4608`,

`gemini-embedding-001`,

`gemini-embedding-2`,

`claude-3-opus-20240229`,

`claude-sonnet-4-6`,

`custom-model`,

`custom-embedding-model`,

`gemini-2.5-pro`,

`gemini-2.5-flash`,

`gemini-2.5-flash-lite`,

`gemini-3.1-pro-preview`,

`gemini-3-flash-preview`,

`gemini-3.5-flash`,

`gemini-3.1-flash-lite-preview`,

`gemini-3.1-flash-lite`,

`mistral-large-2411`,

`mistral-small-2503`,

`pixtral-large-2411`,

`pixtral-12b-2409`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-provider)

provider

enum<string>

required

Available options:

`azureOpenAI`,

`openAI`,

`anthropic`,

`googleVertexAI`,

`googleGemini`,

`googleGenAI`,

`alephAlpha`,

`awsBedrock`,

`mistral`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-connection-id)

connectionId

string<uuid>

required

The identifier for the large language model connection.

Required string length: `36`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-resource-level)

resourceLevel

enum<string>

required

Scope for globally scoped resources.

Available options:

`organisation`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-description)

description

string

Example:

`"Large language model for customer-facing AI Agents."`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-model-group)

modelGroup

enum<string>

Available options:

`chat`,

`completion`,

`embedding`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-api-type)

apiType

enum<string>

Available options:

`chatCompletion`,

`responses`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-is-custom-model)

isCustomModel

boolean

Example:

`true`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-open-ai)

openAI

object

Metadata for OpenAI large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-anthropic)

anthropic

object

Metadata for Anthropic large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-azure-open-ai)

azureOpenAI

object

Metadata for Azure OpenAI large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-google-vertex-ai)

googleVertexAI

object

Metadata for Google Vertex AI large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-google-gemini)

googleGemini

object

Metadata for Google Gemini large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-google-gen-ai)

googleGenAI

object

Google GenAI specific meta data

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-aleph-alpha)

alephAlpha

object

Metadata for Aleph Alpha large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-open-ai-compatible)

openAICompatible

object

Metadata for OpenAI-compatible large language models.

Showchild attributes

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-assigned-to-projects)

assignedToProjects

string\[\]

MongoDB ObjectId representing a project

Example:

```
[\
  "68edf5dd4c931f68d31111",\
  "690b02fc100e454245adde111",\
  "68e6eda61ff68d2111"\
]
```

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-reference-id)

referenceId

string<uuid>

The reference ID of the large language model.

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-id)

\_id

string

Required string length: `24`

Pattern: `^[a-z0-9]{24}$`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-created-at)

createdAt

integer

Unix-timestamp

Required range: `0 <= x <= 2147483647`

Example:

`1694518620`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-last-changed)

lastChanged

integer

Unix-timestamp

Required range: `0 <= x <= 2147483647`

Example:

`1694518620`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-created-by)

createdBy

string

Required string length: `24`

Pattern: `^[a-z0-9]{24}$`

[​](https://docs.cognigy.com/api-reference/large-language-models/create-a-large-language-model#response-one-of-0-last-changed-by)

lastChangedBy

string

Required string length: `24`

Pattern: `^[a-z0-9]{24}$`

Last modified onJuly 8, 2026

[Previous](https://docs.cognigy.com/api-reference/large-language-models/get-large-language-models) [Get a large language modelGets data about a \[large language model\](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/llms).\\
\\
Next](https://docs.cognigy.com/api-reference/large-language-models/get-a-large-language-model)

Ctrl+I

Create a large language model

cURL

```
import requests

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

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

print(response.text)
```

```
<?php

$curl = curl_init();

$response = curl_exec($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"

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

}
```

```
require 'uri'
require 'net/http'

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

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

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

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 }

if (lastThemeClass === null && document.documentElement) {
 lastThemeClass = document.documentElement.className;
 }

if (!themeObserver && document.documentElement) {
 themeObserver = new MutationObserver(function(mutations) {
 for (let i = 0; i < mutations.length; i++) {
 const mutation = mutations\[i\];
 if (mutation.type === 'attributes' && mutation.attributeName === 'class') {
 const currentThemeClass = document.documentElement.className;
 if (lastThemeClass !== null && lastThemeClass !== currentThemeClass) {
 themeToggleInProgress = true;

// Optimized: Lock containers synchronously
 const contentArea = getContentArea();
 if (contentArea) {
 const containers = getContainers(true);
 containers.forEach(function(container) {
 if (container.getAttribute('data-position-locked') !== 'true') {
 const rect = container.getBoundingClientRect();
 Object.assign(container.style, {
 position: 'fixed',
 top: rect.top + 'px',
 left: rect.left + 'px',
 width: rect.width + 'px',
 zIndex: '9999',
 margin: '0'
 });
 container.setAttribute('data-position-locked', 'true');
 containerPositions.set(container, {
 top: rect.top,
 left: rect.left,
 width: rect.width,
 height: rect.height
 });
 }
 });
 }

let repositionAttempts = 0;
 const maxRepositionAttempts = 30;

const repositionInterval = setInterval(function() {
 repositionAttempts++;
 repositionContainersAfterThemeToggleCore(); // Use core function for immediate repositioning
 if (repositionAttempts >= maxRepositionAttempts) {
 clearInterval(repositionInterval);
 }
 }, 100);

setTimeout(function() {
 clearInterval(repositionInterval);
 themeToggleInProgress = false;
 repositionContainersAfterThemeToggleCore(); // Use core function for final repositioning
 unlockContainers();
 }, 3000);
 }
 lastThemeClass = currentThemeClass;
 }
 }
 });

themeObserver.observe(document.documentElement, {
 attributes: true,
 attributeFilter: \['class'\]
 });
 }

if (typeof MutationObserver !== 'undefined') {
 setupObserver();
 }

waitForContentArea(function() {
 setTimeout(function() {
 initSinglePageViewers();
 setTimeout(function() {
 const remaining = getUninitializedViewers(true);
 if (remaining.length > 0) {
 initSinglePageViewers();
 }
 }, INIT\_DELAY);
 }, INIT\_DELAY);
 });
 }

// Optimized: Lock/unlock containers
 function lockContainersInPlace() {
 const contentArea = getContentArea();
 if (!contentArea) return;

const containers = getContainers(true);
 containerPositions.clear();

requestAnimationFrame(function() {
 containers.forEach(function(container) {
 if (container.getAttribute('data-position-locked') !== 'true') {
 const rect = container.getBoundingClientRect();
 Object.assign(container.style, {
 position: 'fixed',
 top: rect.top + 'px',
 left: rect.left + 'px',
 width: rect.width + 'px',
 zIndex: '9999',
 margin: '0'
 });
 container.setAttribute('data-position-locked', 'true');
 containerPositions.set(container, {
 top: rect.top,
 left: rect.left,
 width: rect.width,
 height: rect.height
 });
 }
 });
 });
 }

function unlockContainers() {
 const contentArea = getContentArea();
 if (!contentArea) return;

const containers = Array.from(
 contentArea.querySelectorAll('.single-page-viewer-container\[data-position-locked="true"\]')
 );

containers.forEach(function(container) {
 Object.assign(container.style, {
 position: '',
 top: '',
 left: '',
 width: '',
 zIndex: '',
 margin: ''
 });
 container.removeAttribute('data-position-locked');
 // Clean up container position from Map to prevent memory leak
 containerPositions.delete(container);
 });

containerPositions.clear();
 }

// Optimized: Reposition containers with better DOM operations
 // Core repositioning function (not debounced for immediate use)
 function repositionContainersAfterThemeToggleCore() {
 const contentArea = getContentArea();
 if (!contentArea) return;

const containers = getContainers(true);

containers.forEach(function(container) {
 const viewerId = container.getAttribute('data-viewer-id');
 if (!viewerId) return;

let viewer = document.getElementById(viewerId);

if (!viewer) {
 const iframe = container.querySelector('iframe');
 if (iframe && iframe.src) {
 const allViewers = contentArea.querySelectorAll('\[data-single-page-viewer\]');
 for (let i = 0; i < allViewers.length; i++) {
 const v = allViewers\[i\];
 if (v.getAttribute('data-src') === iframe.src) {
 viewer = v;
 viewer.id = viewerId;
 viewer.setAttribute('data-viewer-id', viewerId);
 Object.assign(viewer.style, {
 display: 'none',
 visibility: 'hidden',
 position: 'absolute',
 width: '1px',
 height: '1px',
 overflow: 'hidden',
 opacity: '0',
 pointerEvents: 'none',
 margin: '0',
 padding: '0'
 });
 viewer.setAttribute('data-initialized', 'true');
 viewer.setAttribute('data-viewer-replaced', 'true');
 break;
 }
 }
 }
 }

if (viewer && viewer.parentNode) {
 if (container.getAttribute('data-position-locked') === 'true') {
 unlockContainers();
 }

const viewerIndex = Array.from(viewer.parentNode.children).indexOf(viewer);
 const containerIndex = Array.from(viewer.parentNode.children).indexOf(container);

if (containerIndex !== viewerIndex + 1) {
 // CRITICAL: Check if container is actually a child before removing
 if (container.parentNode && container.parentNode.contains(container)) {
 try {
 container.parentNode.removeChild(container);
 } catch (e) {
 return; // Skip repositioning if removal failed
 }
 }
 if (viewer.nextSibling) {
 viewer.parentNode.insertBefore(container, viewer.nextSibling);
 } else {
 viewer.parentNode.appendChild(container);
 }
 } else if (container.parentNode !== viewer.parentNode) {
 // CRITICAL: Check if container is actually a child before removing
 if (container.parentNode && container.parentNode.contains(container)) {
 try {
 container.parentNode.removeChild(container);
 } catch (e) {
 return; // Skip repositioning if removal failed
 }
 }
 if (viewer.nextSibling) {
 viewer.parentNode.insertBefore(container, viewer.nextSibling);
 } else {
 viewer.parentNode.appendChild(container);
 }
 }
 }
 });

invalidateQueryCache();
 }

// Debounced version for non-critical repositioning
 const repositionContainersAfterThemeToggle = debounce(repositionContainersAfterThemeToggleCore, REPOSITION\_DEBOUNCE);

// Optimized: Observer setup with better filtering
 let observer = null;
 let containerPositionObserver = null;
 let fallbackObserver = null; // Track fallback observer for cleanup

function setupContainerPositionObserver() {
 if (typeof MutationObserver === 'undefined') return;

const contentArea = getContentArea();
 if (!contentArea \|\| !document.contains(contentArea)) {
 setTimeout(setupContainerPositionObserver, 500);
 return;
 }

if (containerPositionObserver) {
 containerPositionObserver.disconnect();
 }

containerPositionObserver = new MutationObserver(function(mutations) {
 if (themeToggleInProgress) {
 // Use core function (not debounced) for immediate repositioning during theme toggle
 requestAnimationFrame(repositionContainersAfterThemeToggleCore);
 return;
 }

let needsReposition = false;
 for (let i = 0; i < mutations.length && !needsReposition; i++) {
 if (mutations\[i\].type === 'childList') {
 needsReposition = true;
 }
 }

if (needsReposition) {
 clearTimeout(containerPositionObserver.\_timeout);
 containerPositionObserver.\_timeout = setTimeout(repositionContainersAfterThemeToggle, REPOSITION\_DEBOUNCE);
 }
 });

containerPositionObserver.observe(contentArea, {
 childList: true,
 subtree: true,
 attributes: false,
 characterData: false
 });

containerPositionObserver.\_timeout = null;
 }

// Optimized: Main observer with better early exits
 function setupObserver() {
 if (typeof MutationObserver === 'undefined') return;

const contentArea = getContentArea();
 if (!contentArea \|\| !document.contains(contentArea)) {
 setTimeout(setupObserver, 500);
 return;
 }

if (contentArea === document.documentElement \|\| contentArea === document.body) {
 return;
 }

if (observer) {
 const currentObserved = observer.\_observedElement;
 if (currentObserved === contentArea && document.contains(contentArea)) {
 return;
 }
 if (currentObserved) {
 observer.disconnect();
 }
 observer = null;
 }

observer = new MutationObserver(function(mutations) {
 if (themeToggleInProgress) return;

let hasNewViewer = false;
 for (let i = 0; i < mutations.length && !hasNewViewer; i++) {
 const mutation = mutations\[i\];
 if (mutation.addedNodes && mutation.addedNodes.length > 0) {
 for (let j = 0; j < mutation.addedNodes.length && !hasNewViewer; j++) {
 const node = mutation.addedNodes\[j\];
 if (node.nodeType === 1 &&
 node.hasAttribute &&
 node.hasAttribute('data-single-page-viewer') &&
 !node.hasAttribute('data-initialized') &&
 !node.hasAttribute('data-viewer-replaced')) {
 hasNewViewer = true;
 }
 }
 }
 }

if (hasNewViewer) {
 // Use core function for immediate repositioning when new viewer detected
 repositionContainersAfterThemeToggleCore();
 setTimeout(repositionContainersAfterThemeToggleCore, 10);
 setTimeout(repositionContainersAfterThemeToggleCore, 50);
 setTimeout(repositionContainersAfterThemeToggleCore, 200);
 }

clearTimeout(observerTimeout);
 observerTimeout = setTimeout(function() {
 if (themeToggleInProgress) return;

let hasNewViewer = false;
 for (let i = 0; i < mutations.length && !hasNewViewer; i++) {
 const mutation = mutations\[i\];

if (mutation.target === document.documentElement \|\| mutation.target === document.body) {
 continue;
 }

if (mutation.type === 'attributes' &&
 mutation.attributeName === 'class' &&
 mutation.target === document.documentElement) {
 continue;
 }

if (mutation.type === 'attributes' && mutation.attributeName === 'data-initialized') {
 const target = mutation.target;
 if (target && target.hasAttribute &&
 target.hasAttribute('data-single-page-viewer') &&
 !target.hasAttribute('data-initialized')) {
 hasNewViewer = true;
 }
 }

if (mutation.addedNodes && mutation.addedNodes.length > 0) {
 for (let j = 0; j < mutation.addedNodes.length && !hasNewViewer; j++) {
 const node = mutation.addedNodes\[j\];
 if (node.nodeType === 1 &&
 node.hasAttribute &&
 node.hasAttribute('data-single-page-viewer') &&
 !node.hasAttribute('data-initialized')) {
 hasNewViewer = true;
 }
 }
 }
 }

if (hasNewViewer) {
 invalidateQueryCache();
 initSinglePageViewers();
 }
 }, OBSERVER\_DEBOUNCE);
 });

observer.observe(contentArea, {
 childList: true,
 subtree: true,
 attributes: true,
 attributeFilter: \['data-initialized'\],
 characterData: false
 });

observer.\_observedElement = contentArea;
 setupContainerPositionObserver();
 }

function start() {
 setupObserver();
 initialize();
 }

function checkAndInitialize() {
 if (isInitializing) return;

const currentUrl = window.location.href;
 if (currentUrl === lastUrl && lastUrl !== null) {
 return;
 }

const contentArea = document.querySelector('#content-area') \|\| document.querySelector('.mdx-content');
 if (contentArea) {
 const viewers = getUninitializedViewers(true);
 if (viewers.length > 0) {
 cachedContentArea = contentArea;
 setupObserver();
 initialize();
 }
 }
 }

if (document.readyState === 'loading') {
 document.addEventListener('DOMContentLoaded', start);
 } else {
 setTimeout(start, 100);
 }

if (typeof window !== 'undefined' && window.next) {
 let initialLoadAttempts = 0;
 const maxInitialAttempts = 4;
 const attemptDelays = \[200, 500, 1000, 2000\];
 const initialUrl = window.location.href;

attemptDelays.forEach(function(delay) {
 setTimeout(function() {
 if (initialLoadAttempts < maxInitialAttempts) {
 initialLoadAttempts++;
 if (window.location.href === initialUrl) {
 checkAndInitialize();
 }
 }
 }, delay);
 });
 }

window.addEventListener('popstate', function(e) {
 const currentUrl = window.location.href;
 if (currentUrl !== lastUrl) {
 handleNavigation();
 }
 });

if (typeof window !== 'undefined') {
 if (window.next && window.next.router && window.next.router.events) {
 window.next.router.events.on('routeChangeComplete', function(url) {
 lastUrl = window.location.href;
 isInitializing = false;
 pendingInitCall = false;
 if (navigationTimeout) {
 clearTimeout(navigationTimeout);
 navigationTimeout = null;
 }
 invalidateContentAreaCache();
 invalidateQueryCache();
 setTimeout(function() {
 setupObserver();
 initialize();
 }, 300);
 });

window.next.router.events.on('routeChangeStart', function(url) {
 isInitializing = false;
 pendingInitCall = false;
 });
 }

if (!window.next \|\| !window.next.router) {
 const originalPushState = history.pushState;
 const originalReplaceState = history.replaceState;

history.pushState = function() {
 originalPushState.apply(history, arguments);
 setTimeout(function() {
 if (window.location.href !== lastUrl) {
 handleNavigation();
 }
 }, 0);
 };

history.replaceState = function() {
 originalReplaceState.apply(history, arguments);
 setTimeout(function() {
 if (window.location.href !== lastUrl) {
 handleNavigation();
 }
 }, 0);
 };
 }
 }

document.addEventListener('visibilitychange', function() {
 if (!document.hidden) {
 isInitializing = false;
 startPeriodicCheck();
 if (!pendingInitCall) {
 pendingInitCall = true;
 setTimeout(function() {
 pendingInitCall = false;
 initSinglePageViewers();
 }, INIT\_DELAY / 2);
 }
 }
 });

function startPeriodicCheck() {
 if (periodicCheckInterval) return;

periodicCheckInterval = setInterval(function() {
 const uninitialized = getUninitializedViewers(true);
 if (uninitialized.length === 0) {
 clearInterval(periodicCheckInterval);
 periodicCheckInterval = null;
 } else if (!isInitializing) {
 initSinglePageViewers();
 }
 }, 2000);
 }

setTimeout(startPeriodicCheck, INIT\_DELAY \* 2);

window.\_\_singlePageViewerInit = function() {
 const currentUrl = window.location.href;
 if (currentUrl === lastUrl) {
 return;
 }

isInitializing = false;
 pendingInitCall = false;
 lastUrl = currentUrl;
 invalidateContentAreaCache();
 invalidateQueryCache();
 setupObserver();
 initialize();
 };

window.\_\_singlePageViewerCheck = function() {
 invalidateQueryCache();
 initSinglePageViewers();
 };
})();

// Optimized: Second IIFE for navigation handling
(function() {
 'use strict';

let lastPathname = window.location.pathname;
 let lastInitializedPathname = window.location.pathname;

function checkAndInit() {
 const currentPathname = window.location.pathname;
 if (currentPathname === lastInitializedPathname) {
 return;
 }

lastInitializedPathname = currentPathname;
 if (typeof window !== 'undefined') {
 window.\_\_lastFullUrl = window.location.href;
 }

if (window.\_\_singlePageViewerInit) {
 window.\_\_singlePageViewerInit();
 }
 }

function runInit() {
 if (window.\_\_singlePageViewerInit) {
 setTimeout(checkAndInit, 100);
 } else {
 let attempts = 0;
 const maxAttempts = 50;
 const checkInterval = setInterval(function() {
 attempts++;
 if (window.\_\_singlePageViewerInit) {
 clearInterval(checkInterval);
 setTimeout(checkAndInit, 100);
 } else if (attempts >= maxAttempts) {
 clearInterval(checkInterval);
 }
 }, 100);
 }
 }

if (document.readyState === 'loading') {
 document.addEventListener('DOMContentLoaded', runInit);
 } else {
 setTimeout(runInit, 100);
 }

if (typeof window !== 'undefined' && window.next && window.next.router && window.next.router.events) {
 window.next.router.events.on('routeChangeComplete', function(url) {
 lastPathname = window.location.pathname;
 setTimeout(checkAndInit, 300);
 });
 }

setInterval(function() {
 const currentPathname = window.location.pathname;
 if (currentPathname !== lastPathname) {
 lastPathname = currentPathname;
 setTimeout(checkAndInit, 300);
 }
 }, 500);

if (typeof MutationObserver !== 'undefined') {
 let fallbackObserverTimeout = null;
 // Store in outer scope for cleanup tracking
 fallbackObserver = new MutationObserver(function(mutations) {
 if (fallbackObserverTimeout) {
 clearTimeout(fallbackObserverTimeout);
 }
 fallbackObserverTimeout = setTimeout(function() {
 const contentArea = document.querySelector('#content-area');
 if (contentArea) {
 const uninitialized = contentArea.querySelectorAll(
 '\[data-single-page-viewer\]:not(\[data-initialized\]):not(.single-page-viewer-container \[data-single-page-viewer\])'
 );
 if (uninitialized.length > 0) {
 const currentPathname = window.location.pathname;
 if (currentPathname !== lastInitializedPathname) {
 setTimeout(checkAndInit, 200);
 }
 }
 }
 }, 500);
 });

if (document.body) {
 fallbackObserver.observe(document.body, { childList: true, subtree: true });
 } else {
 document.addEventListener('DOMContentLoaded', function() {
 if (document.body) {
 fallbackObserver.observe(document.body, { childList: true, subtree: true });
 }
 });
 }
 }
})();