Block 1 - Sticky Note
- Type / Role
- n8n-nodes-base.stickyNote - stickyNote
- Config choices
- Version 1
What This Workflow Does Upload a document (PDF, PNG, or JPEG) via a hosted web form and let the easybits Extractor classify it into one of your defined categories – with a self reported confidence ...
n8n-nodes-base.stickynote, n8n-nodes-base.formtrigger, @easybits/n8n-nodes-extractor.easybitsextractor, n8n-nodes-base.if, n8n-nodes-base.slack, n8n-nodes-base.noop
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Felix.
Original n8n.io sourceUpload a document (PDF, PNG, or JPEG) via a hosted web form and let the easybits Extractor classify it into one of your defined categories – with a self-reported confidence score on every classification. Low-confidence or unclassifiable documents are routed to Slack for manual review. Everything else continues through your pipeline.
Classification without confidence scoring is a black box: you never know whether the model was sure or guessing. By asking the extractor to return both document_class and confidence_score in a single call, you get a clean signal for routing decisions and a trivial error-handling pattern – no separate model call, no extra latency, no extra cost.
document_class (e.g. medical_invoice, hotel_invoice, or null if uncertain) and confidence_score (a decimal between 0.0 and 1.0).document_class – for the classification labelconfidence_score – for the self-reported confidence@easybits/n8n-nodes-extractor.The node reads binary data directly from the previous node. No Base64 conversion or manual HTTP configuration needed.
The default threshold in the IF node is 0.5. Raise it (e.g. 0.7) if your downstream pipeline is sensitive to misclassification, or lower it if you'd rather catch more edge cases manually. Adjust based on your review capacity.
document_class and confidence_score in the response, and the review branch should fire on the ambiguous document.This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
| Workflow | Classify documents and score confidence with easybits Extractor and Slack |
|---|---|
| Complexity | intermediate |
| Nodes | 12 |
| Categories | Document Extraction, AI Summarization |
| Author | Felix |
| Published | 22 Apr 2026 |
Use the JSON export at /data/workflows/15229/15229.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
What This Workflow Does Upload a document (PDF, PNG, or JPEG) via a hosted web form and let the easybits Extractor classify it into one of your defined categories – with a self reported confidence ...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Document Extraction, AI Summarization use case.