One YAML File for a Town Archive's Condition Calls

Описание и применение

Создан единый YAML‑модель для классификации объектов небольшого архива, устраняющая несогласованность между разными ноутбуками и обеспечивающая единый набор правил для оценки состояния, цифровизации и риска.

Задача

Multiple notebooks for a town archive disagreed on how to classify items, leading to inconsistent treatment, digitization, and risk decisions.

Результат

Consistent, rule‑driven classification of archive items, a 50% treatment rate, seven items flagged for digitization, two pull risks, and a clear, reproducible workflow.

Рабочий процесс

• Define fact grain as Item
• Define dimensions: Collection, Location, Format
• Define properties: condition_score, digitized, pull_count, note
• Define metrics/judgments: NeedsTreatment, Stable, DigitizeNext, PullRisk
• Write rules in YAML with clear boundaries (e.g., score <=2 for treatment)
• Load YAML with PyYAML and validate with ontology.py
• Bind items to dimensions with bind.py
• Score items with metrics.py
• Generate report via build_report in main.py
• Expose report through FastAPI GET /api/brief
• Render chart with Matplotlib
• Run tests with pytest to ensure rule boundaries and error handling

Доказательства

I kept three notebooks for a fake town archive and they would not agree on a single object.

Источник

The semantic model is schema/archive.yaml. The fact grain is Item.

Источник

Python main.py prints that table. FastAPI serves the same build_report function.

Источник