DEV Community: Felix

How far can you push document extraction before it breaks? Here's the stress test workflow I built to find out.

Felix — Fri, 10 Apr 2026 08:00:50 +0000

👋 Hey everyone,

As I shared in my last post, the easybits Extractor just got released as a verified community node on n8n. With the setup now being faster than ever (auto-mapping + community node = about 2 minutes from zero to working extraction), I figured this is the perfect time to actually stress test the whole thing properly.

A few of you also asked me after my last posts about extraction accuracy – how well does it really hold up when the document quality drops? Clean PDFs are easy. Every solution handles those. But what about scanned copies, coffee-stained paper, or documents covered in pen scribbles? I wanted to answer that with actual numbers instead of guessing.

So I built a stress test workflow and I'm sharing it here so anyone can use it to benchmark their own extraction solution.

⚙️ What the workflow does:

You upload a document through a web form. The workflow extracts the data, compares every single field against the known correct values (ground truth), and shows you a results page with a per-field pass/fail breakdown and an overall accuracy percentage. Upload, wait a few seconds, see the score. That's the whole loop.

No Code node needed – the entire validation is built with native n8n nodes and expressions.

📄 The test documents:

I spent some time thinking about what actually makes a good stress test. Just degrading quality isn't enough – you also need to test whether the extraction actually reads the document or just memorises where fields tend to appear. So I put together 11 test versions of the same invoice:

Original – clean digital PDF. The baseline. Should be 100%.
Versions 1–7 – progressive degradation. It starts mild with v1 (slightly aged scan, barely noticeable) and gets worse step by step. By v4 you're looking at aged paper, coffee stains, and handwritten "Rec'd & OK" annotations. By v6, heavy coffee ring stains are sitting right on top of key fields. And v7 – "The Survivor" – has burn marks, pen scribbles ("WRONG ADDRESS? check billing!"), the amount due field circled and scribbled over, and half the document barely readable. If anything can extract data from that one, I'll be impressed.
2 Layout Variants – same data, completely different visual structure. One uses a card-based layout with grouped sections, the other rearranges everything into a three-column format. These test whether the extraction actually understands the content or is just relying on positional patterns.
1 Handwritten Version – this one came from community feedback. Someone asked how extraction handles handwriting, so I added a fully handwritten version of the same invoice to the test set.

All test documents are available in my GitHub repo (link below), so you can use the exact same set to benchmark your own solution and compare results.

🚀 How I set it up:

The extraction side took about 2 minutes – created a pipeline on easybits, used the auto-mapping feature to detect the fields, dropped the verified community node into the workflow, connected credentials, done. The rest is native n8n: a Set node holding the ground truth values, a Merge node to combine extracted and expected data, a Validation node with expressions comparing each field, and a Form completion screen that displays the results directly in the browser.

I've already done a first test run with the original invoice – 100% accuracy across all 10 fields, as expected. I'll add a screenshot so you can see what the results page looks like.

r/n8n - How far can you push document extraction before it breaks? Here's the stress test workflow I built to find out.

🔄 Want to test a different extraction solution?

The workflow is designed to be solution-agnostic. You can swap out the easybits Extractor node for an HTTP Request node pointing at any other extraction API. As long as your response returns the same field names under json.data, the entire validation chain – ground truth comparison, per-field flagging, accuracy percentage, results page – works identically. So if you're evaluating multiple tools, you can benchmark them all using the exact same workflow and test documents.

✨ What's coming next week:

I'm going to run all 11 documents through the workflow and share a full results breakdown here – accuracy percentages for every single version, from the clean original all the way down to the destroyed one and the handwritten version. I'll also put together a short video walkthrough showing the workflow in action and how the results look across the different quality levels.

Links:

Workflow JSON: GitHub link
Test documents: GitHub repo
easybits Extractor community node: Integration guide

Would love to hear if anyone runs the test with a different extraction solution – curious how the results compare. And if you have ideas for even nastier test documents, I'm all ears.

Best,
Felix

[Verified n8n Community Node] easybits Extractor – The easiest way to set up data extraction in n8n (no code, set up in minutes)

Felix — Wed, 08 Apr 2026 16:24:57 +0000

👋 Hey Everyone!

If you've ever built a document processing pipeline in n8n, you probably know the exact headache we've been dealing with for the last year:

You use a standard OCR node, it dumps a massive wall of raw text, and you spend hours writing custom Code nodes filled with fragile Regex just to find the "Total Amount" on an invoice. Or, you try to use heavy cloud tools like AWS Textract, and you get bogged down in complex setups and unpredictable token pricing.

We got tired of our workflows breaking every time a vendor changed their invoice layout, so we built a dedicated data extraction platform specifically for n8n workflows and just released our official verified community node.

🛠️ What it does

It essentially acts as a pre-processor for your document pipelines. You feed it a binary file (PDFs, invoices, receipts, etc.), and it returns reliable, structured JSON output matching the exact schema you define. No AI hallucinations breaking your downstream nodes, and zero post-processing required.

✨ Key Features for Builders

Simple Setup: If you're on n8n Cloud, just search for "easybits Extractor" in the node panel – it's available out of the box, no installation needed. Self-hosted users: go to Settings → Community Nodes → Install and enter @easybits/n8n-nodes-extractor
Binary In, Clean JSON Out: Replaces massive, fragile Code nodes. The node reads binary file attachments directly from any upstream node – a Telegram Trigger, Gmail Trigger, n8n Form with file upload, or an HTTP Request that fetches a document. You define your schema once on our platform, and we return the exact structured data you need under json.data.
Per-Field Prompting: Instead of one generic prompt for the whole document, you write a specific description for each data point you want to extract. This gives the model precise instructions per field – like telling it exactly what "billing period" means on your invoices versus guessing from context. The more specific your field descriptions, the more accurate your results.
Transparent Pricing: 1 request = 1 request. No complex token conversion math, and no guessing what your pipeline will cost. Our free plan includes 50 API requests per month – enough to build and test your workflows before committing.
Zero Maintenance: We handle all the AI model updates in the background. If a vendor completely changes their invoice layout, your n8n workflow keeps running without you needing to fix anything.

🚀 How to get started

n8n Cloud: Search for "easybits Extractor" in the node panel – it's ready to use immediately.
Self-hosted: Go to Settings → Community Nodes → Install → enter @easybits/n8n-nodes-extractor.
Set up your extraction schema for free at easybits.tech.
Drop the node into your canvas and connect it to any node that outputs a file!

For more details on configuration options and input types, check out our n8n integration guide.

🛑 Put your current setup to the test

We've been running this in production for a while now, and we're confident it holds up – but we'd love for you to challenge it. If you have a gnarly, multi-page document that keeps breaking your current extraction setup, run it through easybits and see how it compares.

Let us know what you think, what features you'd like to see next, or if you run into any edge cases.

Happy building!
Felix

Document Classification in n8n Made Easy: Upload, Classify, Route – Workflow Template Included

Felix — Wed, 25 Mar 2026 10:56:42 +0000

👋 Hey everyone,

A few weeks ago I shared how I built an automation to help my friend catch duplicate invoices. That workflow saved him so much time that he came back with a new request: "Can you also sort my invoices by category? My tax lawyer needs them in separate folders and I'm doing it all by hand."

His situation is pretty common – he receives invoices from doctors, restaurants, hotels, tradespeople, you name it. Every month he manually drags them into the right folders before handing everything off to his tax lawyer. Tedious, error-prone, exactly the kind of thing that should be automated.

Now, my team and I at easybits have been building a data extraction solution (easybits Extractor) – it's designed to pull structured fields out of documents. But classification? That wasn't really what we built it for. Still, I was curious, so I sat down and tested whether I could push it beyond extraction and into document classification territory.

Turns out it works perfectly.

The trick is simple: instead of defining extraction fields like "invoice_number" or "total_amount," you create a single field called document_class and give it a detailed classification prompt. You describe your categories, what signals to look for in each one, and the decision rules. The Extractor analyzes the full document and returns exactly one label – or null if it's unsure.

How the workflow works:

The n8n workflow is four nodes:

Form Upload – User uploads a PDF, PNG, or JPEG through a hosted web form
Extract to Base64 – The binary file gets converted to a base64 string
Build Data URI – The MIME type is read from the upload and prepended to create a proper data URI
Send to easybits – The data URI is POSTed to the Extractor API, which returns the classification result

That's it for the base workflow. From there you can extend it however you want – route files to different Google Drive folders based on the label, send a Slack message when something comes back as null, log everything to a spreadsheet, whatever fits your setup.

Setting up the pipeline in easybits Extractor:

Go to https://go.easybits.tech/class and create a new pipeline
Add one field to the mapping: document_class
In the field description, paste your classification prompt – this is where you define your categories and how the model should identify each one
The prompt tells the model to return exactly one category label (like medical_invoice, restaurant_invoice, hotel_invoice) or null if it can't confidently classify the document

I've included a full example prompt as a sticky note inside the workflow so you can just copy it and adjust the categories to your own use case. The example covers three invoice types, but you can add or remove categories as needed.

The workflow JSON is attached below – just import it into n8n, swap in your own Pipeline ID and API Key, and you're good to go.

{
  "name": "easybits' Extractor Workflow (Classification only)",
  "nodes": [
    {
      "parameters": {
        "operation": "binaryToPropery",
        "binaryPropertyName": "image",
        "options": {}
      },
      "type": "n8n-nodes-base.extractFromFile",
      "typeVersion": 1.1,
      "position": [
        224,
        16
      ],
      "id": "a2f5cd2a-213a-4493-9eda-a8a8d52b96e1",
      "name": "Extract from File"
    },
    {
      "parameters": {
        "formTitle": "Image Upload",
        "formFields": {
          "values": [
            {
              "fieldLabel": "image",
              "fieldType": "file"
            }
          ]
        },
        "options": {}
      },
      "type": "n8n-nodes-base.formTrigger",
      "typeVersion": 2.5,
      "position": [
        -64,
        16
      ],
      "id": "7c9c10ab-a710-42dd-95db-4ac838241e28",
      "name": "On form submission",
      "webhookId": ""
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "540141e7-42d3-4011-b681-8335d9105044",
              "name": "data",
              "value": "=data:{{ $('On form submission').first().binary.image.mimeType }};base64,{{ $json.data }}",
              "type": "string"
            }
          ]
        },
        "options": {}
      },
      "type": "n8n-nodes-base.set",
      "typeVersion": 3.4,
      "position": [
        512,
        16
      ],
      "id": "eb6549d6-e40d-4175-bce4-592c3616425c",
      "name": "Edit Fields"
    },
    {
      "parameters": {
        "content": "## 📋 Form Upload\nAccepts a file upload via a **web form**. Supports **PDF, PNG, and JPEG**.",
        "height": 368,
        "width": 256,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -144,
        -160
      ],
      "typeVersion": 1,
      "id": "5e30f508-922e-4be4-953f-c98ed22e4ea5",
      "name": "Sticky Note"
    },
    {
      "parameters": {
        "content": "## 📄 Extract to Base64\nConverts the uploaded **binary file** into a base64-encoded string stored in `data`.",
        "height": 368,
        "width": 256,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        144,
        -160
      ],
      "typeVersion": 1,
      "id": "41f51ddc-2d6e-4667-866c-0fd82ec33a95",
      "name": "Sticky Note1"
    },
    {
      "parameters": {
        "content": "## 🔗 Build Data URI\nDynamically reads the **MIME type** from the uploaded file and prepends it as a base64 data URI.",
        "height": 368,
        "width": 256,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        432,
        -160
      ],
      "typeVersion": 1,
      "id": "ade13468-81de-4883-ab2d-2cd9b3cc2738",
      "name": "Sticky Note2"
    },
    {
      "parameters": {
        "content": "# 📄 easybits' Document Classification\n\n## What This Workflow Does\nUpload a document (PDF, PNG, or JPEG) via a hosted web form and let **easybits Extractor** classify it into one of your defined categories. This workflow handles the upload, file conversion, and API call – you just define the categories.\n\n## How It Works\n1. **Form Upload** – A user uploads a file through the n8n web form\n2. **Base64 Conversion** – The binary file is extracted and converted to a base64 string\n3. **Build Data URI** – The correct MIME type is read from the original upload and prepended to create a proper data URI\n4. **Classification via easybits** – The data URI is POSTed to the easybits Extractor API, which analyzes the document and returns a `document_class` label (e.g. `medical_invoice`, `hotel_invoice`, or `null` if uncertain)\n\nFrom here, you can extend the workflow however you like – route files to Google Drive folders, send Slack alerts for unrecognized documents, log results to a spreadsheet, etc.\n\n---\n\n## Setup Guide\n\n### 1. Create Your easybits Extractor Pipeline\n1. Go to **extractor.easybits.tech** and create a new pipeline\n2. Add a single field to the mapping called **`document_class`**\n3. In that field's description, paste a classification prompt that tells the model which categories exist and how to identify each one (see the \"📊 Document Classification\" sticky note in this workflow for a full example prompt you can copy and adapt)\n4. The prompt should instruct the model to return **exactly one category label** – or `null` if the document doesn't match any category. No explanations, no extra text.\n5. Adjust the categories to fit your use case. The example uses `medical_invoice`, `restaurant_invoice`, and `hotel_invoice` – but you can define whatever classes you need.\n6. Copy your **Pipeline ID** and **API Key**\n\n### 2. Connect the Nodes in n8n\n1. In the **easybits Extractor for Classification** HTTP node, replace the pipeline URL with your own: `https://extractor.easybits.tech/api/pipelines/YOUR_PIPELINE_ID`\n2. Create a **Bearer Auth** credential using your easybits API Key and assign it to that node\n\n### 3. Activate & Test\n1. Click **Active** in the top-right corner of n8n\n2. Open the form URL and upload a test document\n3. Check the execution output – you should see your `document_class` label in the response",
        "height": 1088,
        "width": 656
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -832,
        -496
      ],
      "typeVersion": 1,
      "id": "3ac9e033-0e76-4519-b8df-434844fdbed8",
      "name": "Sticky Note4"
    },
    {
      "parameters": {
        "content": "## 📊 Document Classification\n\nField in easybits:\ndocument_class\n\nField Desciption:\nYou are an invoice classification expert. Your task is to analyze the content of an invoice document and assign it to exactly ONE of the following three categories. Return ONLY the category label – nothing else. No explanation, no punctuation, no additional text.\n\n## Categories\n\n1. medical_invoice\n2. restaurant_invoice\n3. hotel_invoice\n\n## How to Identify Each Category\n\n### 1. medical_invoice\nThis category covers invoices from any healthcare-related provider. Look for the following signals:\n- The issuer is a doctor, dentist, physiotherapist, psychologist, optician, hospital, clinic, laboratory, or any other licensed medical professional or healthcare facility.\n- The document contains medical terminology such as \"diagnosis\", \"treatment\", \"consultation\", \"patient\", \"examination\", \"therapy session\", \"prescription\", \"referral\", \"ICD code\", \"CPT code\", or \"medical procedure\".\n- Line items describe medical services such as blood tests, X-rays, ultrasounds, vaccinations, surgical procedures, dental cleanings, vision tests, or physiotherapy sessions.\n- The invoice may reference health insurance information, patient IDs, policy numbers, or copay amounts.\n- Common keywords: \"Dr.\", \"MD\", \"clinic\", \"practice\", \"patient name\", \"date of service\", \"health insurance\", \"medical\", \"pharmaceutical\", \"lab results\", \"specimen\".\n\n### 2. restaurant_invoice\nThis category covers invoices and receipts from food and beverage service establishments. Look for the following signals:\n- The issuer is a restaurant, café, bar, pub, bistro, fast food chain, food truck, catering company, bakery (if serving prepared meals), or any other food service business.\n- Line items describe food dishes, beverages, appetizers, desserts, or menu items. They often use informal or culinary descriptions such as \"Caesar Salad\", \"Espresso\", \"House Wine\", \"Burger\", or \"Chef's Special\".\n- The document includes a table number, server name, or covers/guests count.\n- There is a tip or gratuity line, a service charge percentage, or a \"thank you for dining with us\" message.\n- Taxes may be broken down into food tax and beverage/alcohol tax separately.\n- The invoice may show a timestamp that corresponds to typical meal times (lunch, dinner).\n- Common keywords: \"table\", \"server\", \"tip\", \"gratuity\", \"covers\", \"dine-in\", \"takeaway\", \"delivery\", \"menu\", \"dish\", \"beverage\", \"appetizer\", \"main course\", \"dessert\", \"bar tab\".\n\n### 3. hotel_invoice\nThis category covers invoices from accommodation and lodging providers. Look for the following signals:\n- The issuer is a hotel, motel, resort, bed & breakfast, guesthouse, hostel, vacation rental management company, or serviced apartment provider.\n- Line items include room charges with check-in and check-out dates, nightly rates, or a total number of nights.\n- Additional charges may include minibar, room service, parking, spa services, laundry, late checkout fees, resort fees, or conference room rental.\n- The document references a reservation number, booking confirmation number, guest name, or room number.\n- Tax breakdowns may include lodging tax, tourism tax, city tax, or occupancy tax — these are highly specific to hotel invoices.\n- Common keywords: \"check-in\", \"check-out\", \"nights\", \"room type\", \"single\", \"double\", \"suite\", \"reservation\", \"booking\", \"guest\", \"front desk\", \"concierge\", \"minibar\", \"room service\", \"lodging tax\", \"folio\".\n\n## Decision Rules\n\n- Analyze the ENTIRE document before making a decision. Do not rely on a single keyword.\n- If multiple signals from different categories appear (e.g., a hotel invoice that includes a restaurant charge), classify based on the PRIMARY issuer of the invoice. A hotel invoice that lists a restaurant meal as a sub-item is still a hotel_invoice.\n- If the invoice does not clearly and confidently fit any of the three categories, return `null`. Do NOT guess. Do NOT pick the closest match if you are uncertain. It is far better to return `null` than to return a wrong classification. A document that is ambiguous, unclear, unreadable, or simply not an invoice must always result in `null`.\n- Only assign a category when you have strong, concrete evidence from multiple signals described above. A single weak keyword match is NOT enough to assign a category.\n- Never return more than one category.\n- Never add explanations, confidence scores, or any other text to your response.\n- Never invent or hallucinate information about the document. Base your decision strictly on what is actually present in the document content. If the document is empty, corrupted, or contains no recognizable invoice data, return `null`.\n\n## Output Format\n\nReturn exactly one of the following strings and nothing else:\nmedical_invoice\nrestaurant_invoice\nhotel_invoice\nnull",
        "height": 1664,
        "width": 1136,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1008,
        -784
      ],
      "typeVersion": 1,
      "id": "eccbbfa5-72cd-47e0-ba4e-2602e3d0b9ae",
      "name": "Sticky Note7"
    },
    {
      "parameters": {
        "content": "## 🚀 Send to easybits\nPOSTs the data URI to the **easybits Extractor API** for classifcation.",
        "height": 368,
        "width": 256,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        720,
        -160
      ],
      "typeVersion": 1,
      "id": "e0bb8883-03b5-4783-9f89-be95bf96c451",
      "name": "Sticky Note3"
    },
    {
      "parameters": {
        "method": "POST",
        "url": "https://extractor.easybits.tech/api/pipelines/YOUR_PIPELINE_ID",
        "authentication": "predefinedCredentialType",
        "nodeCredentialType": "httpBearerAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "={\n  \"files\": [\n    \"{{ $json.data }}\"\n  ]\n} ",
        "options": {}
      },
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.3,
      "position": [
        800,
        16
      ],
      "id": "6871cf9a-b343-404d-9666-becca270b10e",
      "name": "easybits Extractor for Classification"
    }
  ],
  "pinData": {},
  "connections": {
    "Extract from File": {
      "main": [
        [
          {
            "node": "Edit Fields",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "On form submission": {
      "main": [
        [
          {
            "node": "Extract from File",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Edit Fields": {
      "main": [
        [
          {
            "node": "easybits Extractor for Classification",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "easybits Extractor for Classification": {
      "main": [
        []
      ]
    }
  },
  "active": false,
  "settings": {
    "executionOrder": "v1"
  },
  "meta": {
    "templateCredsSetupCompleted": false
  },
  "tags": []
}

Would love to hear if anyone has a similar classification use case or ideas for extending this. Happy to answer questions about the setup.

Best,
Felix

The simplest way to extract document data in your n8n workflows

Felix — Mon, 23 Mar 2026 15:28:35 +0000

Hi DEV.to community 👋

My name is Felix, and this project started out of pure frustration.

A while back I needed a simple way to extract structured data from PDF documents – invoices, contracts, IDs, that kind of thing. I tested tool after tool. Most were either way too complex to set up, required a data science background to configure, or locked you into vendor infrastructure that made GDPR compliance a headache.

So my team and I built what we wish had existed: easybits – the simplest way to extract structured data from documents, without the overhead.

How it works:

Upload an example document, map the fields you want to extract, and easybits generates a pipeline with its own API endpoint. That's it. No model training. No complex configuration. You can connect it to your existing workflows in minutes.

We just shipped auto-mapping – easybits now automatically detects and suggests the fields to extract based on your document, so you don't have to map anything manually. Upload a doc, confirm the suggestions, and your pipeline is ready. It's the fastest way we've found to go from "I have a document" to "I have structured data." If you want to see how auto-mapping works, feel free to check out this short video:

👉 https://youtu.be/BxWhMBgKXto

Why easybits is different:

→ Auto-mapping – fields are detected automatically, no manual setup needed

→ Works with PDFs, PNGs, and JPEGs – including poor quality scans

→ Fully GDPR compliant – your data stays where it should

→ Simple REST API – works with any automation tool (n8n, Make, Zapier, custom code)

→ Per-pipeline API keys – separate pipelines for invoices, contracts, IDs, whatever you need

Connecting it to n8n takes three steps:

Sign up at http://www.easybits.tech
Create your first pipeline – upload a doc (PDF/PNG/JPEG), let auto-mapping do its thing, confirm the fields.
Grab your Pipeline API URL and API Key from the pipeline details page.
Drop an HTTP Request node into n8n, set the method to POST, set up a Bearer Auth credential using your API Key, and send your document as a URL or base64-encoded file.

To make it even easier to get started, here's a ready-to-use n8n workflow you can import directly. It uses an On form submission trigger so you can upload a document and test extraction instantly:

{
  "name": "easybits' Extractor Workflow",
  "nodes": [
    {
      "parameters": {
        "operation": "binaryToPropery",
        "binaryPropertyName": "image",
        "options": {}
      },
      "type": "n8n-nodes-base.extractFromFile",
      "typeVersion": 1.1,
      "position": [
        224,
        16
      ],
      "id": "5c7b1c55-dd54-4574-89c5-fc71605cf9fd",
      "name": "Extract from File"
    },
    {
      "parameters": {
        "formTitle": "Image Upload",
        "formFields": {
          "values": [
            {
              "fieldLabel": "image",
              "fieldType": "file"
            }
          ]
        },
        "options": {}
      },
      "type": "n8n-nodes-base.formTrigger",
      "typeVersion": 2.5,
      "position": [
        -64,
        16
      ],
      "id": "45f05103-ac27-4f18-8ae9-9e7b2a4e807b",
      "name": "On form submission"
    },
    {
      "parameters": {
        "assignments": {
          "assignments": [
            {
              "id": "540141e7-42d3-4011-b681-8335d9105044",
              "name": "data",
              "value": "=data:{{ $('On form submission').first().binary.image.mimeType }};base64,{{ $json.data }}",
              "type": "string"
            }
          ]
        },
        "options": {}
      },
      "type": "n8n-nodes-base.set",
      "typeVersion": 3.4,
      "position": [
        512,
        16
      ],
      "id": "e03f7ddb-5f0a-43dd-8424-b3dac8ac2875",
      "name": "Edit Fields"
    },
    {
      "parameters": {
        "method": "POST",
        "url": "https://extractor.easybits.tech/...",
        "authentication": "predefinedCredentialType",
        "nodeCredentialType": "httpBearerAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "={\n  \"files\": [\n    \"{{ $json.data }}\"\n  ]\n} ",
        "options": {}
      },
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.3,
      "position": [
        800,
        16
      ],
      "id": "e89dbb99-b7bd-401e-9af2-3d9ba868c810",
      "name": "easybits Extractor"
    },
    {
      "parameters": {
        "content": "## 📋 Form Upload\nAccepts a file upload via a **web form**. Supports **PDF, PNG, and JPEG**.",
        "height": 304,
        "width": 256,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -144,
        -128
      ],
      "typeVersion": 1,
      "id": "07b79e87-5d7f-4e32-8560-4584f0c19367",
      "name": "Sticky Note"
    },
    {
      "parameters": {
        "content": "## 📄 Extract to Base64\nConverts the uploaded **binary file** into a base64-encoded string stored in `data`.",
        "height": 304,
        "width": 256,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        144,
        -128
      ],
      "typeVersion": 1,
      "id": "1e15fc8c-dd32-4c7e-a1f3-14a5ebcccc8a",
      "name": "Sticky Note1"
    },
    {
      "parameters": {
        "content": "## 🔗 Build Data URI\nDynamically reads the **MIME type** from the uploaded file and prepends it as a base64 data URI.",
        "height": 304,
        "width": 256,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        432,
        -128
      ],
      "typeVersion": 1,
      "id": "51c1cbfa-9b63-444b-9e65-31fb7752715e",
      "name": "Sticky Note2"
    },
    {
      "parameters": {
        "content": "## 🚀 Send to easybits\nPOSTs the data URI to the **easybits Extractor API** pipeline for processing. Uses **Bearer token** auth.",
        "height": 304,
        "width": 256,
        "color": 7
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        720,
        -128
      ],
      "typeVersion": 1,
      "id": "96a53f26-788e-4e2e-afb5-2d6a2e36acb5",
      "name": "Sticky Note3"
    },
    {
      "parameters": {
        "content": "# 📤 easybits' Extractor Workflow\n\n## How It Works\nThis workflow lets users upload a file (PDF, PNG, JPEG) via a web form. The file is automatically converted to base64, wrapped in a data URI with the correct MIME type, and sent to the **easybits Extractor API** for structured data extraction.\n\n**Flow overview:**\n1. A user uploads a file through the hosted web form\n2. The binary file is extracted and converted to base64\n3. The MIME type is detected automatically and a data URI is built\n4. The data URI is sent to your easybits Extractor pipeline\n\n---\n\n## Setup Guide\n\n### 1. Set Up Your easybits Extractor Pipeline\n1. Go to [extractor.easybits.tech](https://extractor.easybits.tech), sign up and click **\"Create a Pipeline\"** on your dashboard.\n2. Fill in the **Pipeline Name** and **Description** – describe the type of document you're processing.\n3. Upload a **sample document** as your reference.\n4. Click **\"Map Fields\"** and define the fields you want to extract or use our auto-mapping feature to map all fields automatically.\n5. Click **\"Save & Test Pipeline\"** to verify the extraction works correctly.\n6. Go to **Pipeline Details → View Pipeline** and copy your **Pipeline ID** and **API Key**.\n\n### 2. Connect the easybits Node in n8n\n1. Open the **easybits Extractor** node in the workflow.\n2. Replace the Pipeline ID in the URL with your own.\n3. Set up a **Bearer Auth** credential with your API Key.\n\n### 3. Activate the Workflow\n1. Click the **\"Active\"** toggle in the top-right corner of n8n.\n2. Open the form URL and upload a test file.\n3. Check the execution log to verify the extracted data comes back correctly.",
        "height": 896,
        "width": 624
      },
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -800,
        -416
      ],
      "typeVersion": 1,
      "id": "9c78086c-0d4f-4c8d-9487-8ca60629b1dc",
      "name": "Sticky Note4"
    }
  ],
  "pinData": {},
  "connections": {
    "Extract from File": {
      "main": [
        [
          {
            "node": "Edit Fields",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "On form submission": {
      "main": [
        [
          {
            "node": "Extract from File",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Edit Fields": {
      "main": [
        [
          {
            "node": "easybits Extractor",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  },
  "active": false,
  "settings": {
    "executionOrder": "v1",
    "availableInMCP": false,
    "timeSavedMode": "fixed",
    "callerPolicy": "workflowsFromSameOwner"
  },
  "meta": {},
  "tags": []
}

To use it, just import the JSON into n8n, add your Pipeline API URL and API Key, and run it. Submit a document through the form, and the extraction output will appear directly in the workflow – no additional configuration needed.

We've tested this against some genuinely painful documents – crumpled receipts, 36-page contracts with handdrawn marker annotations baked into the scan, you name it. The goal was always to build something robust enough for the real world, not just clean demo PDFs.

If you're spending time manually copying data out of documents, or you've tried other extraction tools and bounced off the complexity – give easybits a shot. We have a free tier and I'm personally happy to help you get your first pipeline running.

Drop any questions in the comments – I'll be here all day. 🙌 Also, I'd love to hear from you: what data extraction tools have you tried before, and what made you move on from them? Always looking to learn from what others have been through.

Best,
Felix