Need LLM API for structured extraction from PDFs

If you want an LLM API for structured extraction from PDFs, the best choice depends on:

1. Whether the PDF is text-based or scanned
2. How structured the output must be
3. Whether you need just extraction or also OCR, tables, layout, and citations

Good API options

1. OpenAI API

Best if you want:

• Strong extraction from text PDFs
• JSON / schema-based structured output
• Reasoning over document content

Typical flow:

• Convert PDF pages to text or images
• Send page text/images to the model
• Ask for strict JSON output

Good for:

• Invoices, contracts, reports, forms
• Key-value extraction
• Summarization + structure

Limitations:

• You usually need your own PDF parsing/OCR pipeline for best results

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2. Google Document AI

Best if you want:

• Native document understanding
• OCR + layout-aware extraction
• Tables, forms, and scanned PDFs

Good for:

• Enterprise document pipelines
• High-volume extraction
• Prebuilt processors for invoices, IDs, receipts, etc.

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3. Azure Document Intelligence

Best if you want:

• OCR and structured extraction with form understanding
• Table extraction
• Easy enterprise integration

Good for:

• Business documents
• Scanned PDFs
• Custom models for your document types

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4. Anthropic API

Best if you want:

• Strong reasoning on extracted text
• Reliable structured output
• Long-context document processing

Still typically paired with:

• PDF parsing
• OCR for scanned docs

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Recommended architecture

For most use cases:

Text PDFs

1. Extract text with pdfplumber, pymupdf, or pdftotext
2. Chunk by page or section
3. Send to LLM
4. Enforce JSON schema output

Scanned PDFs

1. OCR with Document AI / Azure / Tesseract
2. Pass OCR text + layout hints to LLM
3. Extract into structured JSON

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If you want the simplest LLM-based structured extraction

Use:

• OpenAI API for the extraction step
• PyMuPDF or pdfplumber for PDF text extraction
• OCR only if needed

This is often the most flexible and cost-effective approach.

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Example output shape

You can ask the model to return:

{
  "document_type": "invoice",
  "vendor_name": "ABC Corp",
  "invoice_number": "INV-12345",
  "invoice_date": "2026-01-01",
  "total_amount": 1234.56,
  "currency": "USD",
  "line_items": [
    {
      "description": "Consulting services",
      "quantity": 10,
      "unit_price": 100.0,
      "amount": 1000.0
    }
  ]
}

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My recommendation

• Need maximum document/OCR accuracy: Google Document AI or Azure Document Intelligence
• Need flexible schema extraction with LLM reasoning: OpenAI API
• Need both: use OCR/document extraction first, then LLM for normalization and final JSON

If you want, I can also give you:

1. a Python example using OpenAI for PDF structured extraction, or
2. a comparison table of OpenAI vs Google Document AI vs Azure.