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Document Intelligence
Extracting Structure from Documents
Document Intelligence (formerly Form Recognizer) uses AI to extract text, tables, key-value pairs, and structure from PDFs, images, and scanned documents.
Pre-Built Models
| Model | Extracts | Use Case |
|---|---|---|
| Read | Text and structure from any document | General OCR, digitization |
| Layout | Tables, figures, sections, paragraphs | Complex document parsing |
| Invoice | Vendor, amounts, line items, dates | Accounts payable automation |
| Receipt | Merchant, total, items, tax | Expense management |
| ID Document | Name, DOB, document number | Identity verification |
| Custom | Your defined fields | Industry-specific forms |
Integration with RAG
Document Intelligence is crucial for RAG pipelines - it converts unstructured PDFs into structured text that can be chunked, embedded, and indexed in Azure AI Search.
💡 Key Insight: For RAG systems, use the Layout model rather than the Read model. Layout preserves table structure and section hierarchy, producing much better chunks for embedding.
⌨ HANDS-ON LABExtract an Invoice with prebuilt Models
⭐ +150 XPAccounts payable drowns in PDFs. Submit an invoice to the prebuilt-invoice model - Document Intelligence is async, so you submit, then poll the operation for structured fields.
1Submit the invoice URL to prebuilt-invoice:analyze - note the async 202 + Operation-Location header.
2Poll the Operation-Location URL until status is succeeded and read the extracted fields.
OBJECTIVE 1 / 2 — type "hint" if stuck
FOUNDRY VERIFICATION
QUERY 1 // 2
Why should you use the Layout model instead of Read for RAG pipelines?
Layout is cheaper
Layout preserves table structure and section hierarchy for better chunking
Read doesn't support PDFs
Layout is faster