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:
Llama 3.2 Vision
,Twilio SMS Notification
,Bounding Box Visualization
,Stability AI Outpainting
,Overlap Filter
,Model Comparison Visualization
,Image Slicer
,Background Color Visualization
,Mask Visualization
,QR Code Generator
,Classification Label Visualization
,Buffer
,Polygon Visualization
,Florence-2 Model
,Instance Segmentation Model
,Environment Secrets Store
,Local File Sink
,Grid Visualization
,Clip Comparison
,Instance Segmentation Model
,LMM For Classification
,Dot Visualization
,Keypoint Detection Model
,Pixel Color Count
,Halo Visualization
,VLM as Detector
,Icon Visualization
,Crop Visualization
,Slack Notification
,Pixelate Visualization
,CLIP Embedding Model
,Stitch OCR Detections
,Single-Label Classification Model
,SIFT
,Data Aggregator
,QR Code Detection
,JSON Parser
,Cosine Similarity
,Camera Focus
,Florence-2 Model
,Line Counter Visualization
,Dynamic Crop
,Byte Tracker
,Roboflow Dataset Upload
,Segment Anything 2 Model
,Cache Set
,Webhook Sink
,YOLO-World Model
,Identify Outliers
,Byte Tracker
,Image Slicer
,OpenAI
,Image Preprocessing
,Stability AI Inpainting
,Identify Changes
,Keypoint Visualization
,Barcode Detection
,Multi-Label Classification Model
,Stability AI Image Generation
,Detections Filter
,Model Monitoring Inference Aggregator
,Keypoint Detection Model
,Detections Merge
,First Non Empty Or Default
,Moondream2
,Image Threshold
,Detections Stabilizer
,SIFT Comparison
,LMM
,Gaze Detection
,Corner Visualization
,Distance Measurement
,Dimension Collapse
,Image Contours
,Time in Zone
,CogVLM
,Rate Limiter
,Detections Transformation
,Property Definition
,Trace Visualization
,Perspective Correction
,Path Deviation
,Continue If
,SIFT Comparison
,Byte Tracker
,Image Convert Grayscale
,PTZ Tracking (ONVIF)
.md),Line Counter
,Google Gemini
,Relative Static Crop
,Object Detection Model
,Ellipse Visualization
,Dynamic Zone
,Polygon Zone Visualization
,Time in Zone
,Triangle Visualization
,Size Measurement
,Single-Label Classification Model
,CSV Formatter
,Path Deviation
,Stitch Images
,Color Visualization
,Email Notification
,Blur Visualization
,VLM as Classifier
,Expression
,Anthropic Claude
,Absolute Static Crop
,Label Visualization
,Perception Encoder Embedding Model
,Detections Consensus
,Detection Offset
,Cache Get
,Multi-Label Classification Model
,Reference Path Visualization
,Velocity
,Camera Calibration
,Image Blur
,Qwen2.5-VL
,OpenAI
,VLM as Classifier
,Circle Visualization
,Template Matching
,Dominant Color
,Delta Filter
,OCR Model
,Depth Estimation
,Bounding Rectangle
,OpenAI
,Line Counter
,Detections Classes Replacement
,Roboflow Dataset Upload
,Clip Comparison
,Object Detection Model
,VLM as Detector
,Detections Stitch
,SmolVLM2
,Roboflow Custom Metadata
,Google Vision OCR
- outputs:
Model Monitoring Inference Aggregator
,Bounding Box Visualization
,Twilio SMS Notification
,Llama 3.2 Vision
,Keypoint Detection Model
,Stability AI Outpainting
,Moondream2
,Image Threshold
,Model Comparison Visualization
,SIFT Comparison
,LMM
,Gaze Detection
,Corner Visualization
,Background Color Visualization
,Distance Measurement
,Time in Zone
,CogVLM
,Mask Visualization
,QR Code Generator
,Classification Label Visualization
,Trace Visualization
,Polygon Visualization
,Perspective Correction
,Instance Segmentation Model
,Florence-2 Model
,Path Deviation
,Local File Sink
,Clip Comparison
,Instance Segmentation Model
,PTZ Tracking (ONVIF)
.md),LMM For Classification
,Line Counter
,Dot Visualization
,Google Gemini
,Ellipse Visualization
,Object Detection Model
,Keypoint Detection Model
,Pixel Color Count
,Dynamic Zone
,Time in Zone
,Polygon Zone Visualization
,Halo Visualization
,Icon Visualization
,Triangle Visualization
,Crop Visualization
,Size Measurement
,Slack Notification
,Pixelate Visualization
,Single-Label Classification Model
,Path Deviation
,CLIP Embedding Model
,Single-Label Classification Model
,Color Visualization
,Email Notification
,Blur Visualization
,Anthropic Claude
,Florence-2 Model
,Label Visualization
,Line Counter Visualization
,Detections Consensus
,Cache Get
,Multi-Label Classification Model
,Reference Path Visualization
,Image Blur
,Dynamic Crop
,OpenAI
,Roboflow Dataset Upload
,Circle Visualization
,Segment Anything 2 Model
,Template Matching
,Cache Set
,Webhook Sink
,YOLO-World Model
,OpenAI
,OpenAI
,Image Preprocessing
,Stability AI Inpainting
,Line Counter
,Keypoint Visualization
,Multi-Label Classification Model
,Detections Classes Replacement
,Roboflow Dataset Upload
,Object Detection Model
,Stability AI Image Generation
,Detections Stitch
,Perception Encoder Embedding Model
,Roboflow Custom Metadata
,Google Vision OCR
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[integer
,top_class
,roboflow_api_key
,roboflow_project
,boolean
,string
,float
,roboflow_model_id
,list_of_values
,float_zero_to_one
]): Request query parameters.headers
(Union[integer
,top_class
,roboflow_api_key
,roboflow_project
,boolean
,string
,float
,roboflow_model_id
,float_zero_to_one
]): 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"
}