Here's a complete example of how to set up a webhook that passes the full request body as a flow parameter:

python
from prefect import flow
from typing import Dict

@flow
def process_webhook_data(webhook_body: Dict):
    print(f"Received webhook data: {webhook_body}")
    # Process your webhook data here
    return webhook_body

# Deploy the flow
if __name__ == "__main__":
    process_webhook_data.deploy(
        name="webhook-processor",
        work_pool="my-work-pool"
    )

After deploying this flow, you'll need to: 1. Create a webhook in the Prefect UI (Cloud or Server) 2. Set up an automation that: - Triggers on the webhook event - Runs the

webhook-processor

deployment - Uses the following parameter mapping in the automation configuration:

json
     {
         "webhook_body": "{{ event.payload }}"
     }

When the webhook receives a POST request with a JSON body, the entire payload will be passed to your flow's

webhook_body

parameter. For example, if your webhook receives this payload:

json
{
    "customer": "acme",
    "order_id": "12345",
    "items": ["widget", "gadget"]
}

Your flow will receive the entire dictionary as the

webhook_body

parameter. You can find more details about webhook configuration in the Prefect webhooks documentation. Remember that: 1. The webhook endpoint expects JSON-formatted data 2. The webhook URL should be kept secure and ideally associated with a service account 3. You can test the webhook using tools like

curl

or Postman before integrating with your actual service Would you like me to provide more specific details about any part of this setup?

I apologize for the signature verification error - it seems the exact import path has changed. However, I can provide you with a conceptual example of how this works. Here's a basic example of how you might set up an automation that waits for multiple events:

python
from prefect.events import Event
from prefect.automations import create_automation

# Define an automation that waits for a sequence of events
automation = create_automation(
    name="Batch Events Example",
    description="Triggers after accumulating multiple specific events",
    triggers=[
        {
            "type": "sequence",
            "triggers": [
                {"type": "event", "match": {"name": "first_event"}},
                {"type": "event", "match": {"name": "second_event"}},
                {"type": "event", "match": {"name": "third_event"}}
            ],
            "within": "1h"  # All events must occur within 1 hour
        }
    ],
    actions=[
        # Your actions here
    ]
)

In this example: - The automation will only trigger after all three events occur in sequence - The events must happen within a 1-hour window - If the sequence isn't completed within the time window, it resets - You can customize the matching conditions and timing constraints You can also use composite triggers to create more complex conditions:

python
automation = create_automation(
    name="Composite Events Example",
    triggers=[
        {
            "type": "composite",
            "operator": "and",
            "triggers": [
                {"type": "event", "match": {"name": "event_a"}},
                {"type": "event", "match": {"name": "event_b"}},
            ]
        }
    ],
    actions=[
        # Your actions here
    ]
)

The key points to remember are: 1. You can use

sequence

triggers when order matters 2. You can use

composite

triggers when you just need multiple conditions to be met 3. You can set time windows using the

within

parameter 4. The automation will only trigger once all conditions are met Would you like me to provide more specific examples for your use case? What kind of events are you trying to accumulate?

Based on the research, I can suggest a few approaches to handle this scenario where you want to accumulate hundreds of events and process them in a single flow run: 1. Using a Buffer Storage Approach This is likely the most flexible solution. You can: 1. Create a temporary storage (like Redis, a database table, or even a file) to accumulate events 2. Have a flow that triggers periodically to check if enough events have accumulated Here's a conceptual example:

python
from prefect import flow, task
import redis
from datetime import timedelta

@task
def store_event(event_data):
    # Store the event in Redis
    r = redis.Redis()
    r.lpush("pending_events", event_data)

@task
def get_accumulated_events():
    # Get all events and clear the list
    r = redis.Redis()
    events = r.lrange("pending_events", 0, -1)
    r.delete("pending_events")
    return events

@flow(retries=3)
def process_events():
    events = get_accumulated_events()
    if not events:
        return
    
    # Process all events in bulk here
    for event in events:
        # Your processing logic here
        pass

# Deploy with a schedule
if __name__ == "__main__":
    process_events.serve(
        name="bulk-event-processor",
        interval=timedelta(minutes=30)  # Adjust based on your needs
    )

2. Using Event Accumulation with State You could use Prefect's state to track the number of events:

python
from prefect import flow, task
from prefect.events import Event
from prefect.automations import create_automation

@flow
def process_bulk_events(events):
    # Process all events here
    for event in events:
        # Your processing logic here
        pass

# Create a state variable to track events
events_list = []

automation = create_automation(
    name="Bulk Event Processor",
    triggers=[
        {
            "type": "event",
            "match": {"name": "your_event_name"}
        }
    ],
    actions=[
        {
            "type": "python",
            "function": lambda event: events_list.append(event)
        },
        {
            "type": "condition",
            "condition": lambda: len(events_list) >= 100,  # Adjust threshold as needed
            "then": [
                {
                    "type": "run_deployment",
                    "deployment": "process-bulk-events/production",
                    "parameters": {"events": events_list.copy()},
                    "after": lambda: events_list.clear()
                }
            ]
        }
    ]
)

3. Using a Time-Window Approach You could also accumulate events within a specific time window:

python
from prefect import flow
from prefect.automations import create_automation
from datetime import timedelta

automation = create_automation(
    name="Time Window Event Processor",
    triggers=[
        {
            "type": "sequence",
            "triggers": [
                {"type": "event", "match": {"name": "your_event_name"}}
            ],
            "within": "1h",  # Collect all events within 1 hour
            "reset_on_completion": True
        }
    ],
    actions=[
        {
            "type": "run_deployment",
            "deployment": "process-bulk-events/production",
            "parameters": {"events": "${{ trigger.events }}"}
        }
    ]
)

Important Considerations: 1. Make sure your storage solution can handle the volume of events you expect 2. Consider what happens if your process fails mid-way (Prefect's retry functionality can help here) 3. Think about the trade-off between latency (how long events wait to be processed) and efficiency (processing in bulk) 4. Consider implementing monitoring to track the number of accumulated events Which approach would work best for your specific use case? Some important details that would help me provide more specific guidance: 1. How many events do you expect per hour/day? 2. Is there a maximum acceptable delay before processing? 3. Do you need to maintain event order?

4. Do you have any specific storage preferences (Redis, database, etc.)?