import boto3 from PyPDF2 import PdfMerger def lambda_handler(event, context): s3 = boto3.client('s3') merger = PdfMerger() # Merge files from S3 bucket for file in event['files']: obj = s3.get_object(Bucket='pdf-bucket', Key=file) merger.append(obj['Body']) # Save merged PDF back to S3 with open('/tmp/merged.pdf', 'wb') as f: merger.write(f) s3.upload_file('/tmp/merged.pdf', 'pdf-bucket', 'merged.pdf')
const { BlobServiceClient } = require('@azure/storage-blob'); const pdf = require('pdf-parse'); module.exports = async function (context, myBlob) { const text = await pdf(myBlob); // Save extracted text to Cosmos DB context.bindings.outputDocument = JSON.stringify({ id: context.bindingData.name, content: text.text }); };
from google.cloud import vision_v1 from google.cloud import storage def ocr_pdf(bucket_name, file_name): client = vision_v1.ImageAnnotatorClient() gcs_source = vision_v1.GcsSource(uri=f"gs://{bucket_name}/{file_name}") input_config = vision_v1.InputConfig(gcs_source=gcs_source, mime_type="application/pdf") # Async OCR request response = client.async_batch_annotate_files(requests=[{ 'input_config': input_config, 'features': [{'type_': vision_v1.Feature.Type.DOCUMENT_TEXT_DETECTION}], 'output_config': {'gcs_destination': {'uri': f"gs://{bucket_name}/output/"}} ]) print(f"OCR started: {response}")
from PyPDF2 import PdfWriter import boto3 def encrypt_and_upload(file): writer = PdfWriter() writer.append(file) writer.encrypt("userpass", "ownerpass") with open('/tmp/encrypted.pdf', 'wb') as f: writer.write(f) s3.upload_file('/tmp/encrypted.pdf', 'bucket', 'encrypted.pdf')
Q1: “Can I use free tiers for small-scale PDF processing?”
Q2: “How to handle large PDFs (>500MB) in serverless functions?”
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