gaik-project/gaik-toolkit

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.

Converts PDFs, scans, and Word documents into text or markdown with the gaik toolkit's parsers, choosing the parser that will not silently destroy the structure the downstream task depends on. Use when reading a PDF or DOCX into text, pulling tables out of a document, running OCR on scans, feeding documents into a RAG pipeline or an LLM, deciding between PyMuPDF, Docling, and vision-LLM parsing, or when a parse appeared to succeed but the tables, columns, or whole pages came out wrong or empty. Also use when document parsing is costing more time or money than expected. Covers parser selection, per-page verification, and escalation from cheap local parsing to vision models.