goldencheck-types

Shared canonical field-type registry for the Golden Suite — a single source of truth for "what does the field type email mean?" (name hints, value signals, confidence thresholds) across every suite package and both languages.

What it is

GoldenCheck profiles a dataset and emits inferred field types; downstream packages consume them:

• Producer: goldencheck (data-quality profiling).
• Consumers: goldenpipe (stage I/O contracts), infermap (target schema inference), and the TypeScript mirror goldencheck-types (cross-language, over a JSON wire).

Keeping the type vocabulary in one small, dependency-light package lets the Python and TypeScript sides produce byte-identical types, and lets consumers depend on the vocabulary without pulling in the full profiler.

Install

pip install goldencheck-types

Only requires pyyaml.

Usage

from goldencheck_types import load_field_types, FieldType, InferredSchema

# Load the bundled canonical field types (16 domain packs: generic + 15 verticals)
field_types = load_field_types()

# Or inspect a single type
email = field_types["email"]
print(email.name_hints, email.confidence_threshold)

Public API (goldencheck_types): SchemaVersion, FieldType, InferredSchema, load_field_types, and the supporting Pydantic models in types.py.

Schema versioning

Every emitted type carries a schema_version. Consumers must check it and refuse unknown versions rather than silently degrade. The version bumps when a required field is added to FieldType, an enum gains a value, or the value_signals wire shape changes.

Cross-language parity

The TypeScript sibling at packages/typescript/goldencheck-types ships the same FieldType / InferredSchema interfaces and the same bundled domain packs, synced byte-for-byte from one canonical source. This is the one Golden Suite package where the TypeScript side keeps snake_case field names, so the producer YAML and consumer JSON pass through unchanged.

License

MIT. Part of the Golden Suite; see the monorepo for the full project.