How do I use OCR webhooks and async jobs in a document pipeline?

A good pattern is:

1. Upload a document
2. Start an OCR async job
3. Receive a webhook when OCR finishes
4. Fetch the OCR result
5. Continue downstream processing
   (classification, extraction, indexing, human review, etc.)

Typical flow

1) Client uploads a document

Your app sends the file to your backend, or directly to your OCR provider.

2) Backend creates an OCR job

Instead of waiting synchronously, you create an async OCR job and pass:

• the document/file reference
• a webhook callback URL
• any metadata you need to correlate the result to your internal record

Example conceptual request:

{
  "file_url": "https://storage.example.com/docs/abc.pdf",
  "webhook_url": "https://api.yourapp.com/webhooks/ocr",
  "metadata": {
    "document_id": "doc_123"
  }
}

The OCR service returns something like:

{
  "job_id": "ocr_job_789",
  "status": "queued"
}

3) OCR runs asynchronously

The OCR provider processes the file in the background.

Your app should not block waiting for it. Store the job_id and mark the document as something like:

• uploaded
• ocr_processing
• ocr_completed
• ocr_failed

4) Webhook callback notifies completion

When OCR finishes, the provider sends an HTTP POST to your webhook endpoint.

Example webhook payload:

{
  "job_id": "ocr_job_789",
  "status": "completed",
  "document_id": "doc_123",
  "result_url": "https://api.provider.com/results/ocr_job_789",
  "confidence": 0.98
}

Your webhook handler should:

• verify the request is authentic
• parse the payload
• look up your internal document by document_id or job_id
• update processing state
• enqueue the next step in your pipeline

Webhook handler best practices

Verify authenticity

Use one or more of:

• HMAC signature
• shared secret
• provider-signed JWT
• IP allowlist, if supported

Make handlers idempotent

Webhooks may be retried, so the same event can arrive multiple times.

Use:

• unique event IDs
• deduplication table
• state checks before updating

Respond quickly

Return 200 OK fast, then process the job in the background if needed.

If you need to do heavier work:

• validate request
• persist event
• enqueue internal job
• return success

Downstream pipeline example

After OCR completes, you might run:

1. Text normalization
2. Document classification
3. Entity extraction
4. Search indexing
5. Workflow routing
   for example, send invoices to accounting review

Example architecture

Upload API
   ↓
Storage bucket
   ↓
OCR async job
   ↓
OCR webhook → Webhook endpoint
   ↓
Queue / worker
   ↓
Post-OCR pipeline
   ↓
Database / search index / review UI

What to store in your database

At minimum:

• document_id
• file_location
• ocr_job_id
• ocr_status
• ocr_result_location
• created_at
• updated_at

Optional but useful:

• provider_event_id
• retry_count
• processing_errors
• confidence_scores

Handling failures

Design for these cases:

• webhook never arrives
• OCR job fails
• webhook arrives twice
• result fetch fails temporarily

Mitigations:

• poll job status as a fallback
• retry result retrieval with backoff
• use dead-letter queues for failed downstream jobs
• keep an audit trail of webhook events

Recommended pattern

If you want a robust pipeline, use this sequence:

1. Create internal document record
2. Upload file to storage
3. Start OCR job with document_id in metadata
4. Save job_id
5. Handle webhook and verify signature
6. Update status and store OCR output
7. Enqueue post-processing job
8. Poll as fallback if webhook is delayed

If you want, I can also give you:

• a reference architecture
• a Node.js / Python webhook example
• or a state machine design for the document pipeline