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Extract structured data from documents using Pydantic schemas and LLM models. Extract from single parse results or use vector stores for RAG-enhanced extraction on large documents.

Extract from Parse Result


Extract from Vector Store

For large documents or multi-file analysis, use RAG-enhanced extraction from a vector store. This improves accuracy by retrieving relevant chunks before extraction.

Extract from File (Direct)

Extract directly from an uploaded file, skipping the parse step. When to use: structured extraction from images or small documents (≈1–5 pages) where visual structure is vital for accurate extraction — passports, ID cards, forms, invoices, and similar. For text-dense or large documents, prefer parse-then-extract.
Supports PDF, image (PNG, JPG, WebP), and office (DOCX, DOC, PPTX, PPT) files. Note that, citations are not yet supported in this mode.

Extract with Citations


Filter Low Confidence Results


Batch Extraction

Start extract jobs for multiple documents, then monitor and collect results. This pattern gives you control over job status and lets you handle failures per document.
To wait for all jobs and collect results in one call, use asyncio.gather with get_result:

Appendix: Essential Imports


Next Steps