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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.

parsing-documents

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.

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