name: pdf-processor description: Process PDFs - extract text, tables, and structured data from documents
PDF Processor - Extract Data from PDFs
Extract text, tables, and structured data from PDF documents.
Workflow
Step 1: Fetch PDF Content
Use Linkup to fetch PDF URLs:
orth api run linkup /fetch --body '{"url": "https://example.com/document.pdf"}'
Step 2: Extract with AI
Use ScrapeGraph to extract specific content:
orth api run scrapegraph /api/extract --body '{
"url": "https://example.com/report.pdf",
"prompt": "Extract all financial figures, tables, and key metrics from this document"
}'
Step 3: Extract Tables
Get structured table data:
orth api run riveter /v1/run --body '{
"input": {
"urls": ["https://example.com/report.pdf"]
},
"output": {
"tables": {"prompt": "Extract all tables with titles, headers, and rows", "contexts": ["urls"]}
}
}'
Step 4: Convert to Markdown
Get readable markdown output:
orth api run scrapegraph /api/scrape --body '{"url": "https://example.com/document.pdf", "formats": [{"type": "markdown"}]}'
Example Usage
# Extract data from financial report
orth api run scrapegraph /api/extract --body '{
"url": "https://example.com/annual-report.pdf",
"prompt": "Extract revenue, profit, and key business metrics with their values"
}'
# Extract invoice data
orth api run riveter /v1/run --body '{
"input": {"urls": ["https://example.com/invoice.pdf"]},
"output": {
"vendor": {"prompt": "Vendor name", "contexts": ["urls"]},
"amount": {"prompt": "Total amount", "contexts": ["urls"]},
"date": {"prompt": "Invoice date", "contexts": ["urls"]}
}
}'
Tips
- Specify exact data you need for better extraction
- Use schemas for consistent structured output
- Handle multi-page documents in chunks
- Verify extracted numbers against source
Discover More
List all endpoints, or add a path for parameter details:
orth api show linkup
orth api show riveter
orth api show scrapegraph
Example: orth api show olostep /v1/scrapes for endpoint parameters.