> ## Documentation Index
> Fetch the complete documentation index at: https://docs.slai.io/llms.txt
> Use this file to discover all available pages before exploring further.

# REST API

> Deploying a Beam app as a REST API

### Deploying a REST API

Beam apps can be deployed using the `RestAPI` trigger.

Input and output fields are serialized and validated by the [`Beam.Types`](/beam/triggers/webhook#beam-types) module.

Here's an example of what
this might look like:

```python theme={null}
app.Trigger.RestAPI(
	inputs={"x": beam.Types.Float(), "y": beam.Types.Float()},
	outputs={"sum": beam.Types.Float()},
	handler="test.py:add_numbers",
)
```

| Name      | Type     | Description                                               |
| --------- | -------- | --------------------------------------------------------- |
| `inputs`  | *dict*   | A dictionary specifying the interface for the API         |
| `outputs` | *dict*   | A dictionary specifying the outputs returned from the API |
| `handler` | *string* | The function that will be run when the API is invoked     |

### Calling the API

After deploying the API, you'll be able to copy a cURL request to hit the API. In your app dashboard, click **Call API**.

<img src="https://mintcdn.com/slai-docs/0GTBstnyDK-8Z9OV/img/beam/tools/call-api.png?fit=max&auto=format&n=0GTBstnyDK-8Z9OV&q=85&s=d6589ec096d0f13cafcefb7d8c1f2bd5" alt="Call API" width="1592" height="658" data-path="img/beam/tools/call-api.png" />

### Example Request

```curl theme={null}
curl -X POST --compressed "https://beam.slai.io/ahg0v" \
   -H 'Accept: */*' \
   -H 'Accept-Encoding: gzip, deflate' \
   -H 'Authorization: Basic dW5kZWZpbmVkOnVuZGVmaW5lZA==' \
   -H 'Connection: keep-alive' \
   -H 'Content-Type: application/json' \
   -d '{"prompt": "Here's my example prompt"}'
```

### Example Response

```json theme={null}
{
  "result": {
    "prediction": "{'POSITIVE': 0.9988627433776855, 'NEGATIVE': 0.0011372779263183475}"
  },
  "msg": "",
  "error_msg": ""
}
```

### Beam Types

The **beam** library includes several basic data types that can be used to
validate your inputs against.

| Type                                            | Description                                                                                        |
| ----------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| `beam.types.Json()`                             | A JSON object                                                                                      |
| `beam.types.String(max_length=None)`            | A string field, with an optional max\_length parameter                                             |
| `beam.types.Float()`                            | Standard floating point value                                                                      |
| `beam.types.Boolean()`                          | Standard boolean value                                                                             |
| `beam.types.Tensor(shape=None, dtype=None)`     | A torch tensor object, with optional shape and dtype parameters                                    |
| `beam.types.Dataframe()`                        | A pandas dataframe object                                                                          |
| `beam.types.NumpyArray(shape=None, dtype=None)` | A numpy array, with optional shape and dtype parameters                                            |
| `beam.types.Binary()`                           | A binary object, for example a video file or anything that doesn't fit into the other types listed |
| `beam.types.Image(raw=False)`                   | A generic image, automatically serialized into a PIL object for use in the handler                 |

<Tip>
  Types can be labeled optional by passing in `required=false`

  `beam.types.Boolean(required=false)`

  `beam.types.Dataframe(required=false)`
</Tip>