> For the complete documentation index, see [llms.txt](https://docs.umbraco.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.umbraco.com/ai-in-umbraco/17.latest/management-api/connections/models.md).

# Get Models

Retrieve the list of AI models available through a specific connection. This is useful for populating model selection dropdowns in the UI.

## Endpoint

```
GET /umbraco/ai/management/api/v1/connections/{idOrAlias}/models
```

## Path Parameters

| Parameter   | Type   | Description              |
| ----------- | ------ | ------------------------ |
| `idOrAlias` | string | Connection GUID or alias |

## Response

### Success (200 OK)

{% code title="Response" %}

```json
{
    "items": [
        {
            "id": "gpt-4o",
            "name": "GPT-4o",
            "capabilities": ["Chat"]
        },
        {
            "id": "gpt-4o-mini",
            "name": "GPT-4o Mini",
            "capabilities": ["Chat"]
        },
        {
            "id": "text-embedding-3-small",
            "name": "Text Embedding 3 Small",
            "capabilities": ["Embedding"]
        },
        {
            "id": "text-embedding-3-large",
            "name": "Text Embedding 3 Large",
            "capabilities": ["Embedding"]
        }
    ]
}
```

{% endcode %}

### Model Properties

| Property       | Type      | Description                          |
| -------------- | --------- | ------------------------------------ |
| `id`           | string    | Model identifier to use in API calls |
| `name`         | string    | Display name                         |
| `capabilities` | string\[] | Capabilities this model supports     |

### 404 Not Found

```json
{
    "type": "https://tools.ietf.org/html/rfc7231#section-6.5.4",
    "title": "Not Found",
    "status": 404,
    "detail": "Connection not found"
}
```

## Examples

### cURL

{% code title="cURL" %}

```bash
curl -X GET "https://your-site.com/umbraco/ai/management/api/v1/connections/openai-prod/models" \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN"
```

{% endcode %}

### JavaScript

{% code title="JavaScript" %}

```javascript
async function getModels(connectionIdOrAlias) {
    const response = await fetch(`/umbraco/ai/management/api/v1/connections/${connectionIdOrAlias}/models`, {
        credentials: "include",
    });

    if (!response.ok) {
        throw new Error("Failed to fetch models");
    }

    const { items } = await response.json();
    return items;
}

// Usage
const models = await getModels("openai-prod");
console.log(models);
```

{% endcode %}

### Filter by Capability

{% code title="JavaScript" %}

```javascript
async function getChatModels(connectionIdOrAlias) {
    const models = await getModels(connectionIdOrAlias);
    return models.filter((m) => m.capabilities.includes("Chat"));
}

async function getEmbeddingModels(connectionIdOrAlias) {
    const models = await getModels(connectionIdOrAlias);
    return models.filter((m) => m.capabilities.includes("Embedding"));
}
```

{% endcode %}

### Populate Model Dropdown

{% code title="JavaScript" %}

```javascript
async function populateModelSelect(selectElement, connectionId, capability) {
    const models = await getModels(connectionId);
    const filtered = models.filter((m) => m.capabilities.includes(capability));

    selectElement.innerHTML = "";

    for (const model of filtered) {
        const option = document.createElement("option");
        option.value = model.id;
        option.textContent = model.name;
        selectElement.appendChild(option);
    }
}
```

{% endcode %}

## Notes

{% hint style="info" %}
The list of models is fetched from the AI provider in real-time. Results may vary based on your API key's permissions and the provider's available models.
{% endhint %}

{% hint style="warning" %}
This endpoint makes a call to the AI provider's API. Consider caching the results if you need to call it frequently.
{% endhint %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.umbraco.com/ai-in-umbraco/17.latest/management-api/connections/models.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.