Scripted, deterministic conversations (fictional Swiss e-learning chats) via a
mock provider, each asserting exact per-turn round-trips + stable entity ids:
login support (attributes attach to PER_1), two learners (applyKnown reuse of a
re-mentioned name), old/new IBAN kept distinct, parent+minor+doctor (3 people),
and HR (manager+org reused, two employees). 46 tests total.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
anonymizeTurn(text, session) threads a serializable AnonymizerSession
({mapping, legend, history}) so one entity keeps one id across a whole chat
(applyKnown reuse + usedIds + de-collision merge). Adds conversation()
in-memory wrapper and an optional LlmProvider.anonymizeInConversation(text, ctx)
for rich cross-turn context; providers without it fall back to the batch path.
openAICompatibleProvider gains includeMappingInContext (default false — only
send real values to a trusted anonymizer endpoint) + historyMaxTurns (default
10). Verified live vs Gemma 4: Nora stays PER_1 across 4 turns (name/email/AVS/
IBAN), Yanis = PER_2; 41 tests, 98% coverage.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
BREAKING CHANGE: regex fallback is now opt-in (patterns no longer defaults
to presets.swiss). With no fallback an LLM failure throws AnonymizationError
(fail-closed) and the pre-filter is bypassed; at least one of llm/patterns is
required. AnonymizationResult gains a required `legend`; anonymizeChunks seed
is now { mapping, legend? } and returns legend.
- prompt: model may coin new UPPERCASE abbreviations and returns a 'legende'
explaining every abbreviation used (French); backfilled by DEFAULT_LEGEND
- PatternDef.meaning surfaces in the legend; swiss/generic presets get meanings
- AnonymizationError (exported) wraps the cause on fail-closed
- README: drop the chatbot provenance line; add 'How it works' + nLPD sections
- 34 tests / 99% coverage; bump to 0.2.0
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
