Webhook Sink¶
Class: WebhookSinkBlockV1
Source: inference.core.workflows.core_steps.sinks.webhook.v1.WebhookSinkBlockV1
The Webhook Sink block enables sending a data from Workflow into external APIs by sending HTTP requests containing workflow results. It supports multiple HTTP methods (GET, POST, PUT) and can be configured to send:
-
JSON payloads
-
query parameters
-
multipart-encoded files
This block is designed to provide flexibility for integrating workflows with remote systems for data exchange, notifications, or other integrations.
Setting Query Parameters¶
You can easily set query parameters for your request:
query_parameters = {
"api_key": "$inputs.api_key",
}
will send request into the following URL: https://your-host/some/resource?api_key=<API_KEY_VALUE>
Setting headers¶
Setting headers is as easy as setting query parameters:
headers = {
"api_key": "$inputs.api_key",
}
Sending JSON payloads¶
You can set the body of your message to be JSON document that you construct specifying json_payload
and json_payload_operations
properties. json_payload
works similarly to query_parameters
and
headers
, but you can optionally apply UQL operations on top of JSON body elements.
Let's assume that you want to send number of bounding boxes predicted by object detection model
in body field named detections_number
, then you need to specify configuration similar to the
following:
json_payload = {
"detections_number": "$steps.model.predictions",
}
json_payload_operations = {
"detections_number": [{"type": "SequenceLength"}]
}
Multipart-Encoded Files in POST requests¶
Your endpoint may also accept multipart requests. You can form them in a way which is similar to
JSON payloads - setting multi_part_encoded_files
and multi_part_encoded_files_operations
.
Let's assume you want to send the image in the request, then your configuration may be the following:
multi_part_encoded_files = {
"image": "$inputs.image",
}
multi_part_encoded_files_operations = {
"image": [{"type": "ConvertImageToJPEG"}]
}
Cooldown¶
The block accepts cooldown_seconds
(which defaults to 5
seconds) to prevent unintended bursts of
notifications. Please adjust it according to your needs, setting 0
indicate no cooldown.
During cooldown period, consecutive runs of the step will cause throttling_status
output to be set True
and no notification will be sent.
Cooldown limitations
Current implementation of cooldown is limited to video processing - using this block in context of a
Workflow that is run behind HTTP service (Roboflow Hosted API, Dedicated Deployment or self-hosted
inference
server) will have no effect for processing HTTP requests.
Async execution¶
Configure the fire_and_forget
property. Set it to True if you want the request to be sent in the background,
allowing the Workflow to proceed without waiting on data to be sent. In this case you will not be able to rely on
error_status
output which will always be set to False
, so we recommend setting the fire_and_forget=False
for
debugging purposes.
Disabling notifications based on runtime parameter¶
Sometimes it would be convenient to manually disable the Webhook sink block. This is possible
setting disable_sink
flag to hold reference to Workflow input. with such setup, caller would be
able to disable the sink when needed sending agreed input parameter.
Type identifier¶
Use the following identifier in step "type"
field: roboflow_core/webhook_sink@v1
to add the block as
as step in your workflow.
Properties¶
Name | Type | Description | Refs |
---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
url |
str |
URL of the resource to make request. | ✅ |
method |
str |
HTTP method to be used. | ❌ |
query_parameters |
Dict[str, Union[List[Union[bool, float, int, str]], bool, float, int, str]] |
Request query parameters. | ✅ |
headers |
Dict[str, Union[bool, float, int, str]] |
Request headers. | ✅ |
json_payload |
Dict[str, Union[Dict[Any, Any], List[Any], bool, float, int, str]] |
Fields to put into JSON payload. | ✅ |
json_payload_operations |
Dict[str, List[Union[ClassificationPropertyExtract, ConvertDictionaryToJSON, ConvertImageToBase64, ConvertImageToJPEG, DetectionsFilter, DetectionsOffset, DetectionsPropertyExtract, DetectionsRename, DetectionsSelection, DetectionsShift, DetectionsToDictionary, Divide, ExtractDetectionProperty, ExtractFrameMetadata, ExtractImageProperty, LookupTable, Multiply, NumberRound, NumericSequenceAggregate, PickDetectionsByParentClass, RandomNumber, SequenceAggregate, SequenceApply, SequenceElementsCount, SequenceLength, SequenceMap, SortDetections, StringMatches, StringSubSequence, StringToLowerCase, StringToUpperCase, TimestampToISOFormat, ToBoolean, ToNumber, ToString]]] |
UQL definitions of operations to be performed on defined data w.r.t. each value of json_payload parameter. |
❌ |
multi_part_encoded_files |
Dict[str, Union[Dict[Any, Any], List[Any], bool, float, int, str]] |
Data to POST as Multipart-Encoded File. | ✅ |
multi_part_encoded_files_operations |
Dict[str, List[Union[ClassificationPropertyExtract, ConvertDictionaryToJSON, ConvertImageToBase64, ConvertImageToJPEG, DetectionsFilter, DetectionsOffset, DetectionsPropertyExtract, DetectionsRename, DetectionsSelection, DetectionsShift, DetectionsToDictionary, Divide, ExtractDetectionProperty, ExtractFrameMetadata, ExtractImageProperty, LookupTable, Multiply, NumberRound, NumericSequenceAggregate, PickDetectionsByParentClass, RandomNumber, SequenceAggregate, SequenceApply, SequenceElementsCount, SequenceLength, SequenceMap, SortDetections, StringMatches, StringSubSequence, StringToLowerCase, StringToUpperCase, TimestampToISOFormat, ToBoolean, ToNumber, ToString]]] |
UQL definitions of operations to be performed on defined data w.r.t. each value of multi_part_encoded_files parameter. |
❌ |
form_data |
Dict[str, Union[Dict[Any, Any], List[Any], bool, float, int, str]] |
Fields to put into form-data. | ✅ |
form_data_operations |
Dict[str, List[Union[ClassificationPropertyExtract, ConvertDictionaryToJSON, ConvertImageToBase64, ConvertImageToJPEG, DetectionsFilter, DetectionsOffset, DetectionsPropertyExtract, DetectionsRename, DetectionsSelection, DetectionsShift, DetectionsToDictionary, Divide, ExtractDetectionProperty, ExtractFrameMetadata, ExtractImageProperty, LookupTable, Multiply, NumberRound, NumericSequenceAggregate, PickDetectionsByParentClass, RandomNumber, SequenceAggregate, SequenceApply, SequenceElementsCount, SequenceLength, SequenceMap, SortDetections, StringMatches, StringSubSequence, StringToLowerCase, StringToUpperCase, TimestampToISOFormat, ToBoolean, ToNumber, ToString]]] |
UQL definitions of operations to be performed on defined data w.r.t. each value of form_data parameter. |
❌ |
request_timeout |
int |
Number of seconds to wait for remote API response. | ✅ |
fire_and_forget |
bool |
Boolean flag to run the block asynchronously (True) for faster workflows or synchronously (False) for debugging and error handling.. | ✅ |
disable_sink |
bool |
Boolean flag to disable block execution.. | ✅ |
cooldown_seconds |
int |
Number of seconds to wait until follow-up notification can be sent.. | ✅ |
The Refs column marks possibility to parametrise the property with dynamic values available
in workflow
runtime. See Bindings for more info.
Available Connections¶
Compatible Blocks
Check what blocks you can connect to Webhook Sink
in version v1
.
- inputs:
Model Monitoring Inference Aggregator
,Label Visualization
,Florence-2 Model
,Triangle Visualization
,Image Blur
,OCR Model
,Detections Transformation
,Line Counter Visualization
,Circle Visualization
,Relative Static Crop
,Detections Stitch
,Trace Visualization
,Byte Tracker
,Object Detection Model
,Clip Comparison
,Detections Consensus
,Detection Offset
,Velocity
,Dominant Color
,Stitch Images
,Bounding Rectangle
,Detections Filter
,Cosine Similarity
,Segment Anything 2 Model
,Roboflow Custom Metadata
,Single-Label Classification Model
,VLM as Classifier
,Local File Sink
,Crop Visualization
,Color Visualization
,Image Slicer
,Google Gemini
,Dynamic Crop
,Multi-Label Classification Model
,Instance Segmentation Model
,Continue If
,Keypoint Detection Model
,SmolVLM2
,Google Vision OCR
,Corner Visualization
,Overlap Filter
,Polygon Zone Visualization
,Byte Tracker
,Camera Focus
,Grid Visualization
,Perspective Correction
,Stability AI Image Generation
,Cache Get
,Image Slicer
,CSV Formatter
,OpenAI
,Blur Visualization
,Time in Zone
,Slack Notification
,SIFT Comparison
,Pixel Color Count
,Detections Stabilizer
,Anthropic Claude
,QR Code Detection
,Buffer
,Bounding Box Visualization
,Distance Measurement
,Twilio SMS Notification
,Email Notification
,Object Detection Model
,LMM For Classification
,SIFT Comparison
,VLM as Detector
,LMM
,Florence-2 Model
,Depth Estimation
,CogVLM
,Model Comparison Visualization
,Cache Set
,First Non Empty Or Default
,Data Aggregator
,Barcode Detection
,Environment Secrets Store
,Multi-Label Classification Model
,Path Deviation
,Dimension Collapse
,Rate Limiter
,Gaze Detection
,Reference Path Visualization
,Llama 3.2 Vision
,Expression
,Roboflow Dataset Upload
,Identify Outliers
,Detections Merge
,Polygon Visualization
,Time in Zone
,SIFT
,Image Threshold
,Path Deviation
,Property Definition
,CLIP Embedding Model
,Keypoint Visualization
,JSON Parser
,Ellipse Visualization
,OpenAI
,Instance Segmentation Model
,Dot Visualization
,Roboflow Dataset Upload
,Keypoint Detection Model
,Identify Changes
,Line Counter
,Stability AI Inpainting
,Single-Label Classification Model
,Template Matching
,Background Color Visualization
,Delta Filter
,VLM as Detector
,Line Counter
,Qwen2.5-VL
,Clip Comparison
,Dynamic Zone
,Classification Label Visualization
,Image Convert Grayscale
,Image Preprocessing
,Byte Tracker
,OpenAI
,YOLO-World Model
,Stability AI Outpainting
,Perception Encoder Embedding Model
,Size Measurement
,Webhook Sink
,Moondream2
,Camera Calibration
,Mask Visualization
,Pixelate Visualization
,Stitch OCR Detections
,PTZ Tracking (ONVIF)
.md),Image Contours
,Detections Classes Replacement
,Absolute Static Crop
,Halo Visualization
,VLM as Classifier
- outputs:
Florence-2 Model
,Model Monitoring Inference Aggregator
,Label Visualization
,Florence-2 Model
,Triangle Visualization
,CogVLM
,Image Blur
,Model Comparison Visualization
,Cache Set
,Line Counter Visualization
,Circle Visualization
,Detections Stitch
,Trace Visualization
,Multi-Label Classification Model
,Object Detection Model
,Path Deviation
,Detections Consensus
,Gaze Detection
,Reference Path Visualization
,Llama 3.2 Vision
,Polygon Visualization
,Roboflow Dataset Upload
,Segment Anything 2 Model
,Time in Zone
,Roboflow Custom Metadata
,Single-Label Classification Model
,Image Threshold
,Path Deviation
,CLIP Embedding Model
,Keypoint Visualization
,Ellipse Visualization
,Crop Visualization
,Color Visualization
,Local File Sink
,Google Gemini
,Dynamic Crop
,OpenAI
,Instance Segmentation Model
,Multi-Label Classification Model
,Dot Visualization
,Instance Segmentation Model
,Roboflow Dataset Upload
,Keypoint Detection Model
,Keypoint Detection Model
,Stability AI Inpainting
,Line Counter
,Google Vision OCR
,Single-Label Classification Model
,Template Matching
,Corner Visualization
,Background Color Visualization
,Polygon Zone Visualization
,Stability AI Image Generation
,Perspective Correction
,Cache Get
,Line Counter
,OpenAI
,Clip Comparison
,Blur Visualization
,Dynamic Zone
,Classification Label Visualization
,Time in Zone
,Image Preprocessing
,Slack Notification
,SIFT Comparison
,OpenAI
,Pixel Color Count
,YOLO-World Model
,Stability AI Outpainting
,Perception Encoder Embedding Model
,Anthropic Claude
,Size Measurement
,Webhook Sink
,Mask Visualization
,Bounding Box Visualization
,Distance Measurement
,Pixelate Visualization
,Twilio SMS Notification
,Email Notification
,PTZ Tracking (ONVIF)
.md),Object Detection Model
,Detections Classes Replacement
,Halo Visualization
,LMM For Classification
,LMM
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Webhook Sink
in version v1
has.
Bindings
-
input
url
(string
): URL of the resource to make request.query_parameters
(Union[string
,boolean
,roboflow_api_key
,list_of_values
,float_zero_to_one
,integer
,roboflow_project
,roboflow_model_id
,float
,top_class
]): Request query parameters.headers
(Union[string
,boolean
,roboflow_api_key
,float_zero_to_one
,integer
,roboflow_project
,roboflow_model_id
,float
,top_class
]): Request headers.json_payload
(*
): Fields to put into JSON payload.multi_part_encoded_files
(*
): Data to POST as Multipart-Encoded File.form_data
(*
): Fields to put into form-data.request_timeout
(integer
): Number of seconds to wait for remote API response.fire_and_forget
(boolean
): Boolean flag to run the block asynchronously (True) for faster workflows or synchronously (False) for debugging and error handling..disable_sink
(boolean
): Boolean flag to disable block execution..cooldown_seconds
(integer
): Number of seconds to wait until follow-up notification can be sent..
-
output
Example JSON definition of step Webhook Sink
in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/webhook_sink@v1",
"url": "<block_does_not_provide_example>",
"method": "<block_does_not_provide_example>",
"query_parameters": {
"api_key": "$inputs.api_key"
},
"headers": {
"api_key": "$inputs.api_key"
},
"json_payload": {
"field": "$steps.model.predictions"
},
"json_payload_operations": {
"predictions": [
{
"property_name": "class_name",
"type": "DetectionsPropertyExtract"
}
]
},
"multi_part_encoded_files": {
"image": "$steps.visualization.image"
},
"multi_part_encoded_files_operations": {
"predictions": [
{
"property_name": "class_name",
"type": "DetectionsPropertyExtract"
}
]
},
"form_data": {
"field": "$inputs.field_value"
},
"form_data_operations": {
"predictions": [
{
"property_name": "class_name",
"type": "DetectionsPropertyExtract"
}
]
},
"request_timeout": "$inputs.request_timeout",
"fire_and_forget": "$inputs.fire_and_forget",
"disable_sink": false,
"cooldown_seconds": "$inputs.cooldown_seconds"
}