PDF Processor - Apify Actor Deployment Guide

Overview

This PDF Processor provides four main operations via Apify Actor:

1. Extract Text - Extract text content from all PDF pages
2. Merge Pages - Create new PDFs with selected pages only
3. HTML to PDF - Convert HTML content to PDF using Playwright
4. URL to PDF - Convert web pages to PDF using Playwright

Files Structure

pdf-processor-actor/
├── main.py                    # Apify Actor wrapper (main entry point)
├── requirements.txt           # Dependencies for Apify deployment
├── requirements_apify.txt     # Alternative requirements file
├── Dockerfile                 # Docker configuration for Apify
├── actor.json                 # Apify Actor configuration
├── INPUT_SCHEMA.json          # Input schema definition
├── apify_input_schema.json   # Legacy input schema
├── apify_output_schema.json  # Output schema definition
├── sample_inputs.json         # Example inputs for testing
├── test_local.py              # Local testing script
├── n8n_workflow_example.json  # n8n integration example
├── n8n_direct_api_workflow.json # n8n direct API workflow
├── QUICK_START.md            # Quick start guide
├── apify.json                # Apify configuration
├── actor/                    # Actor configuration directory
│   ├── actor.json
│   └── dataset_schema.json
└── README.md                  # This file

Deployment Steps

1. Prepare Your Repository

# Create a new directory for your actor
mkdir pdf-processor-actor
cd pdf-processor-actor

# Copy all the provided files
cp /path/to/main.py .
cp /path/to/app.py .
cp /path/to/requirements_apify.txt .
cp /path/to/Dockerfile .
cp /path/to/actor.json .
cp /path/to/apify_input_schema.json .
cp /path/to/apify_output_schema.json .
cp /path/to/sample_inputs.json .

2. Deploy to Apify

Option A: Using Apify CLI

# Install Apify CLI
npm install -g apify-cli

# Login to your Apify account
apify login

# Initialize the actor
apify init

# Push to Apify platform
apify push

Option B: Using GitHub Integration

1. Push your code to a GitHub repository
2. Go to Apify Console
3. Click "Actors" → "Create new"
4. Choose "From GitHub repository"
5. Connect your GitHub repo
6. Apify will automatically build and deploy

3. Configure the Actor

In Apify Console:

1. Navigate to your actor
2. Go to "Settings" tab
3. Set the following:
   • Build tag: latest
   • Memory: 512 MB (minimum, increase for complex webpages or large PDFs)
   • Timeout: 300 seconds (adjust based on PDF size and webpage complexity)

4. Test Your Actor

1. Go to the "Input" tab
2. Use one of the sample inputs:

Extract Text:

{
  "action": "extract-text",
  "pdfUrl": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
}

Merge Pages:

{
  "action": "merge-pages",
  "pdfUrl": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf",
  "pageNumbers": [0, 2, 4]
}

HTML to PDF:

{
  "action": "html-to-pdf",
  "html": "<html><body><h1>Hello World</h1><p>This is a test PDF.</p></body></html>"
}

URL to PDF:

{
  "action": "url-to-pdf",
  "pdfUrl": "https://example.com"
}

3. Click "Run"
4. Check the output in the "Dataset" tab

Usage Examples

Via Apify API

from apify_client import ApifyClient

client = ApifyClient('YOUR_API_TOKEN')
actor = client.actor('YOUR_USERNAME/pdf-processor')

# Extract text
run = actor.call(run_input={
    "action": "extract-text",
    "pdfUrl": "https://example.com/document.pdf"
})

# HTML to PDF
run = actor.call(run_input={
    "action": "html-to-pdf",
    "html": "<html><body><h1>Invoice</h1><p>Amount: $100</p></body></html>"
})

# URL to PDF
run = actor.call(run_input={
    "action": "url-to-pdf",
    "pdfUrl": "https://example.com"
})

# Get results
dataset = client.dataset(run['defaultDatasetId'])
results = list(dataset.iterate_items())

Via REST API

# Extract text
curl -X POST https://api.apify.com/v2/acts/YOUR_USERNAME~pdf-processor/runs \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -d '{
    "action": "extract-text",
    "pdfUrl": "https://example.com/document.pdf"
  }'

# HTML to PDF
curl -X POST https://api.apify.com/v2/acts/YOUR_USERNAME~pdf-processor/runs \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -d '{
    "action": "html-to-pdf",
    "html": "<html><body><h1>Invoice</h1></body></html>"
  }'

# URL to PDF
curl -X POST https://api.apify.com/v2/acts/YOUR_USERNAME~pdf-processor/runs \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -d '{
    "action": "url-to-pdf", 
    "pdfUrl": "https://example.com"
  }'

Monitoring

• Check logs in the "Runs" tab for debugging
• Monitor performance in the "Analytics" tab
• Set up webhooks for run completion notifications

Cost Estimation

• Compute Units:
  • Text extraction: ~0.001 CU per page
  • Page merging: ~0.002 CU per page
  • HTML/URL to PDF: ~0.005-0.02 CU (depends on complexity and load time)
• Storage: Minimal for text, ~1 MB per 100 pages for generated PDFs
• Bandwidth: Depends on PDF/webpage size (input + output)

Limitations

• Maximum PDF size: 100 MB (configurable)
• Maximum pages to process: 1000 (configurable)
• Timeout: 5 minutes default (configurable)
• HTML/URL to PDF: Requires Playwright/Chrome (included in Docker image)
• Complex JavaScript sites may need additional wait time

Support

For issues or questions:

1. Check the actor logs for error details
2. Verify PDF URL is publicly accessible
3. Ensure page numbers are within valid range

License

MIT