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

# Workflow Webhook Payload

> Schema of the payload Tensorlake sends to your webhook URL when a Serverless Workflow invocation finishes, including per-function status.

The following payload is sent to your configured webhook URL when all functions of a Serverless Workflow finishes running for a given Input.

```json theme={null}
{
    "workflow_name": "workflow_XXXX",
    "invocation_id": "invocation_XXXX",
    "fn_status": {
        "fn_A": "success",
        "fn_B": "failure"
    }
}
```

The following statuses are possible for each function -

* `success` - The function finished running successfully.
* `failure` - The function failed to finish running.

### Example

Once you have configured the webhook, test it with this [example snippet](https://github.com/tensorlakeai/tensorlake/blob/main/examples/webhook.py) that:

1. Parses a document, and sets `deliver_webhook` to `True`.
2. Once the parsing is complete, you should see the webhook payload delivered to your configured webhook URL.

<Warning>We highly recommend using Svix Play to test whether webhooks are getting delivered.</Warning>

```python theme={null}
import time

from tensorlake.documentai import DocumentAI
from tensorlake.documentai.parse import ParsingOptions, TableParsingStrategy

API_KEY = "tl_api_key_XXXX"

doc_ai = DocumentAI(api_key=API_KEY)

# Skip this if you are passing a pre-signed URL to the `DocumentParser`.
# or pass an external URL
# file_id = doc_ai.upload(path="/path/to/files")


job_id = doc_ai.parse(
    # file_id, # You can pass in a publicly accessible URL instead of a file_id
    "https://pub-157277cc11d64fb1a11f71cc52c688eb.r2.dev/invoice-example.pdf",
    options=ParsingOptions(
        table_parsing_strategy=TableParsingStrategy.VLM,
        deliver_webhook=True,
    ),
)

print(f"job id: {job_id}")
result = doc_ai.get_job(job_id=job_id)
print(f"job status: {result.status}")
while True:
    if result.status in ["pending", "processing"]:
        print("waiting 5s...")
        time.sleep(5)
        result = doc_ai.get_job(job_id)
        print(f"job status: {result.status}")
    else:
        if result.status == "successful":
            # save the result to a file
            with open(f"{job_id}.json", "w", encoding="utf-8") as f:
                f.write(result.model_dump_json())
        break
```