Exporting and reloading configuration

Use export_config() to save a snapshot of the standard analyzer's live configuration. JSON and YAML exports include the complete registered regex pattern set, active entities, detection threshold, requested spaCy model, cache settings, acronym dictionary, processing settings, and anonymizer entity priorities.

from allyanonimiser import create_allyanonimiser

ally = create_allyanonimiser(spacy_model=None)
ally.add_pattern({
    "entity_type": "REFERENCE_CODE",
    "patterns": [r"REF-[A-Z]{4}"],
    "score": 0.8,
})
ally.analyzer.set_active_entity_types(["REFERENCE_CODE"])
ally.analyzer.set_min_score_threshold(0.75)

if not ally.export_config("config.json", include_metadata=False):
    raise RuntimeError("Configuration export failed; see the logged error")

restored = create_allyanonimiser(settings_path="config.json")
assert restored.anonymize("Use REF-ABCD", report=False)["text"] == "Use <REFERENCE_CODE>"

YAML requires PyYAML. include_metadata=False removes the descriptive counts and examples; it does not remove functional configuration or pattern definitions. Exports include custom regexes and acronyms, so review their contents before sharing them.

Defaults and precedence

Without a configuration, factory behavior is unchanged: the small spaCy model, caching enabled, and 10,000 entries per cache. With a configuration, omitted factory arguments inherit the saved model and cache settings. Explicit spacy_model, enable_caching, and max_cache_size arguments override the saved values. Explicit spacy_model=None selects pattern-only mode.

The facade exports current analyzer settings. Editing the settings manager's stored values alone does not reconfigure a running analyzer; use its setters or load a settings file first. SettingsManager.export_config() exports its stored values directly. save_settings() remains the existing stored-settings API; use the facade's export_config() for a live snapshot.

Saved operator maps and score adjustments are preserved as configuration data. They retain their existing per-call semantics: pass them when invoking the relevant method, for example:

results = restored.analyze(
    text="Use REF-ABCD",
    score_adjustment=restored.settings_manager.get_value("entity_types.score_adjustment", {}),
)
output = restored.anonymize(
    text="Use REF-ABCD",
    analysis_results=results,
    operators=restored.settings_manager.get_anonymization_operators(),
    report=False,
)

Per-call options are not captured as persistent state. To reproduce results, use the same per-call arguments, package/dependency versions, and installed spaCy models. Exports contain model names, not model weights, and model fallback behavior still applies.

Schema and validation

New exports have schema_version: 1. Older files without this field still load. The existing version field is retained separately for compatibility.

Facade snapshots use patterns_mode: replace, so loading one restores its complete pattern set without adding the package defaults a second time. Ordinary legacy pattern lists append to the built-in patterns. Repeated loads of the same exported snapshot do not duplicate patterns.

Settings loading validates the schema version, regexes, configuration shapes, operator names, and numeric ranges before updating stored settings. Unsupported versions and malformed settings return False from load_settings() and log a reason. The factory raises ValueError when its requested settings file cannot be loaded, instead of silently continuing with a different configuration.

Export rejects unsupported custom analyzer/anonymizer subclasses, non-string pattern definitions, and nonserializable settings. Serialization validation happens before opening the output file. Arbitrary Python callbacks and third-party components cannot be reproduced by this format.