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gaik-project/gaik-toolkit

v0.1.0MIT

Skills for building document-understanding pipelines with the gaik Python toolkit: choosing a document parser that preserves table structure, and designing extraction schemas that stay inside provider limits.

extracting-structured-data

Extracts structured data — fields, tables, line items — out of documents into a validated schema using the gaik toolkit, and designs schemas that stay inside provider limits and produce checkable evidence. Use when pulling fields from invoices, forms, contracts, reports, or scanned documents into JSON or a Pydantic model; when an extraction request fails with a 400, returns a truncated list, or silently drops its deepest fields; when deciding whether to parse a document first or feed the PDF straight to the model; when adding page numbers, quotes, or confidence scores as evidence; or when extraction accuracy needs to be measured rather than guessed. Covers schema design, property limits, prompt-versus-schema tuning, and citation verification.

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