Webhook

Webhooks are used for deploying functions that run asyncronously on Beam.

You can add webhook triggers to the file with your Beam app definition.

Input fields are serialized and validated by the Beam.Types module.

app.Trigger.Webhook(
    inputs={"prompt": beam.Types.String()},
    handler="run.py:my_async_function",
)

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

Calling a webhook will return a Task ID.

Invocation

You can view the status of each invocation in the dashboard, by clicking on your deployment and navigating to the latest task.

Download-Artifacts

If any files were generated during the execution of your task, you can download them by clicking on the task, and clicking Download.

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

Types can be labeled optional by passing in required=false

beam.types.Boolean(required=false)

beam.types.Dataframe(required=false)