feat!: optional fail-closed fallback, legend output, richer prompt & docs

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>
2026-06-25 15:28:04 +01:00
parent 08f84ae14a
commit 669794522e
13 changed files with 408 additions and 83 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,11 +4,34 @@ All notable changes to this project are documented here. The format is based on
 [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to
 [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

+## [0.2.0] - Unreleased
+
+### Added
+
+- **`legend`** on every result — abbreviation → French meaning (`PER`→`Personne`, `M`→`Masculin`), safe to
+  forward to a downstream LLM so it understands the placeholder tokens. Backed by a built-in
+  `DEFAULT_LEGEND` so coverage is guaranteed even if the model omits entries.
+- The default prompt now lets the model **coin new uppercase abbreviations** for entities/attributes/
+  context it discovers and return their meanings in `legende`.
+- `PatternDef.meaning` — optional human label for a tag, surfaced in the `legend`. `presets.swiss`/
+  `presets.generic` ship French meanings.
+- `AnonymizationError` (exported) — thrown when anonymization can't complete and no fallback exists;
+  carries the originating error in `.cause`.
+
+### Changed (breaking)
+
+- **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` or `patterns` is required, or the constructor throws.
+- `AnonymizationResult` gained a required `legend` field; `LlmProvider.anonymizeBatch` returns `legend`.
+- `anonymizeChunks(chunks, seed)` — `seed` is now `{ mapping, legend? }` (was the bare mapping) and the
+  return includes `legend`.
+
 ## [0.1.0] - Unreleased

 ### Added

- Initial public release, extracted from Mobiletic's production Swiss-nLPD chatbot.
+- Initial public release.
 - `Anonymizer` — pre-filter → LLM → regex fallback, bidirectional validation, deterministic coreference,
  and de-collision across question and retrieved chunks (`anonymize`, `anonymizeChunks`, `deanonymize`).
 - `makeStreamDeanonymizer` — streaming-safe de-anonymization that never leaks a split placeholder.
--- a/README.md
+++ b/README.md
@@ -11,16 +11,18 @@ It replaces personal data in text with stable placeholders before the text leave
 values afterwards — including across **streamed** tokens.

 - 🔌 **Pluggable LLM detection** — catch free-form PII (names, addresses) via any OpenAI-compatible
-  endpoint, or your own provider. No LLM? It degrades gracefully to regex.
- 🧩 **Deterministic regex fallback** — structured identifiers (email, phone, IBAN, …) via configurable
-  pattern presets (`swiss`, `generic`) or your own.
+  endpoint, or your own provider.
+- 🧩 **Optional regex fallback** — structured identifiers (email, phone, IBAN, …) via configurable
+  presets (`swiss`, `generic`) or your own. Opt in for graceful degradation, or omit it to **fail closed**.
+- 🏷️ **Self-describing tokens** — every result ships a `legend` (`PER`→`Personne`, `M`→`Masculin`) you can
+  hand to the downstream LLM so it understands the placeholders; the model may coin new abbreviations too.
 - 🔁 **Deterministic coreference** — the same person keeps the same id (`[PER_1]`) across a question and
  every retrieved chunk, with automatic de-collision.
 - 🌊 **Streaming-safe** — a placeholder split across two stream chunks (`[PER_` + `1.NOM:M]`) is never
  leaked partially.
 - 🪶 **Zero runtime dependencies**, ESM + CJS, fully typed.

-> Built by [Mobiletic](https://mobiletic.com) and extracted from a production Swiss-nLPD chatbot.
+> Built by [Mobiletic](https://mobiletic.com).

 ## Install

@@ -39,9 +41,10 @@ import { Anonymizer, presets } from '@mobiletic/anonymizer';

 const anonymizer = new Anonymizer({ patterns: presets.swiss });

-const { anon, mapping } = await anonymizer.anonymize('Écris à jean@exemple.ch');
+const { anon, mapping, legend } = await anonymizer.anonymize('Écris à jean@exemple.ch');
 // anon    -> "Écris à [EMAIL_1]"
-// mapping -> { "[EMAIL_1]": "jean@exemple.ch" }
+// mapping -> { "[EMAIL_1]": "jean@exemple.ch" }   (secret — keep on your side)
+// legend  -> { "EMAIL": "Adresse e-mail" }        (safe to share downstream)

 anonymizer.deanonymize(anon, mapping); // -> "Écris à jean@exemple.ch"
 ```
@@ -58,15 +61,19 @@ const anonymizer = new Anonymizer({
    model: process.env.LLM_MODEL!,
    timeoutMs: 3000,
  }),
-  patterns: presets.swiss, // regex fallback if the LLM is down/misbehaves
+  patterns: presets.swiss, // OPTIONAL regex fallback if the LLM is down/misbehaves
 });

-const { anon, mapping } = await anonymizer.anonymize('Le dossier de Alain Jaccard est complet.');
+const { anon, mapping, legend } = await anonymizer.anonymize('Le dossier de Alain Jaccard est complet.');
 // anon   -> "Le dossier de [PER_1.NOM:M] est complet."
+// legend -> { "PER": "Personne", "NOM": "Nom de famille", "M": "Masculin" }
 ```

-If the LLM call fails, times out, or returns an invalid shape, the anonymizer automatically falls back to
-the regex engine — it never throws on a provider failure.
+**Fallback is opt-in (fail-closed).** If a `patterns` fallback is configured, a failed/timed-out/invalid
+LLM call degrades to the regex engine. If you omit `patterns`, there's nothing to degrade to, so the call
+**throws an `AnonymizationError`** (with the underlying error as `.cause`) — it never silently returns
+un-anonymized text. With no fallback the pre-filter is also bypassed, so every non-trivial call consults
+the LLM (more calls, no leaks). At least one of `llm` or `patterns` is required.

 `openAICompatibleProvider` retries **transient** failures (network error, timeout, HTTP 429/5xx) before
 giving up; 4xx and malformed responses are not retried. Tune with `timeoutMs` (per attempt, default 3000),
@@ -86,13 +93,14 @@ process.stdout.write(stream.flush());

 ### Anonymizing retrieved chunks consistently (RAG)

-`anonymizeChunks(chunks, seed)` reuses the question's mapping (`seed`) so the same person gets the same id
-across the question and every chunk, batches the LLM call, and de-collides genuinely new entities:
+`anonymizeChunks(chunks, seed)` reuses the question's `{ mapping, legend }` so the same person gets the
+same id across the question and every chunk, batches the LLM call, and de-collides genuinely new entities:

 ```ts
 const q = await anonymizer.anonymize(question);
-const { anon, mapping } = await anonymizer.anonymizeChunks(retrievedChunks, q.mapping);
-// `mapping` is the full question ∪ chunks table; pass it to deanonymize()/makeStreamDeanonymizer().
+const { anon, mapping, legend } = await anonymizer.anonymizeChunks(retrievedChunks, q);
+// `mapping`/`legend` are the full question ∪ chunks tables;
+// pass `mapping` to deanonymize()/makeStreamDeanonymizer(), and `legend` to the downstream LLM.
 ```

 ## Configuration
@@ -100,10 +108,11 @@ const { anon, mapping } = await anonymizer.anonymizeChunks(retrievedChunks, q.ma
 ```ts
 new Anonymizer({
  llm?,       // LlmProvider — omit for regex-only mode
-  patterns?,  // PatternDef[] — defaults to presets.swiss
+  patterns?,  // PatternDef[] — opt-in regex fallback; omit to fail closed
  nameHint?,  // RegExp flagging likely names so the LLM is consulted (has a default)
  logger?,    // { warn(msg) } — receives fallback warnings; defaults to no-op
 });
+// At least one of `llm` or `patterns` must be provided, or the constructor throws.
 ```

 ### Presets & custom patterns
@@ -142,10 +151,10 @@ import type { LlmProvider } from '@mobiletic/anonymizer';
 const myProvider: LlmProvider = {
  isConfigured: () => true,
  async anonymize(text) {
-    /* return { anon, mapping } */
+    /* return { anon, mapping, legend } */
  },
  async anonymizeBatch(texts, usedIds) {
-    /* return { segments, mapping } */
+    /* return { segments, mapping, legend } */
  },
 };
 ```
@@ -157,7 +166,69 @@ const myProvider: LlmProvider = {
 [EMAIL_1]       structured id from the regex fallback
 ```

-`PLACEHOLDER_RE` is exported if you need to scan text for placeholders.
+`PLACEHOLDER_RE` is exported if you need to scan text for placeholders. The LLM may also **coin new
+abbreviations** (always uppercase `[A-Z_]`) for entities/attributes/context it discovers — every one it
+uses is described in the result `legend`.
+
+## How it works
+
+The library does **query-time pseudonymization**: it rewrites text so that personal data never leaves your
+trust boundary in identifiable form, while keeping the answer fully reversible on your side.
+
+```
+                        ┌───────────────────────────── your trust boundary ──────────────────────────────┐
+  raw text  ─▶  ① pre-filter ─▶ ② LLM detect ─▶ ③ regex fallback ─▶ ④ validate ─▶  anon + mapping + legend
+                  (skip if            (names,         (optional;          (every                │       │
+                   clearly            addresses,      structured ids;     placeholder    ┌───────┘       │
+                   PII-free)          coins abbrevs,  fail closed if      is mapped)      ▼               ▼
+                                      builds legend)  absent)                          mapping         legend
+                                                                                     (SECRET,        (shareable:
+   downstream LLM  ◀── anon + legend ─────────────────────────────────────────────  reversible)     PER=Personne…)
+   answer (with [PER_1.NOM:M] tokens)
+        │
+        ▼
+  ⑤ stream de-anonymize ──▶ real values restored for the end user (placeholders never leak, even if split)
+```
+
+1. **Pre-filter** — a cheap regex/name check skips the LLM round-trip for text that clearly has no PII.
+   (Bypassed when no regex fallback is configured, so nothing slips through.)
+2. **LLM detection** — finds free-form PII a regex can't (names, addresses), keeps **coreference** (the
+   same person is always `[PER_1]`), coins uppercase abbreviations for anything new, and returns a
+   **legend** describing them.
+3. **Regex fallback** — optional, deterministic detection of structured identifiers; used if the LLM is
+   unavailable. Omit it to **fail closed** (raise `AnonymizationError` rather than risk a leak).
+4. **Validation** — bidirectional check that every placeholder has a mapping entry and vice-versa.
+5. **Streaming de-anonymization** — `makeStreamDeanonymizer` restores real values token-by-token, buffering
+   any placeholder split across chunks so a partial `[PER_` is never emitted.
+
+Three distinct outputs, with different sensitivities:
+
+| Output    | Example                                  | Sensitivity                                             |
+| --------- | ---------------------------------------- | ------------------------------------------------------- |
+| `anon`    | `Le dossier de [PER_1.NOM:M]`            | Safe to send onward — no identifiers                    |
+| `mapping` | `{ "[PER_1.NOM:M]": "Alain Jaccard" }`   | **Secret** — re-identifies people; keep it on your side |
+| `legend`  | `{ "PER": "Personne", "M": "Masculin" }` | Safe to share — explains the tokens to a downstream LLM |
+
+## How this helps with the nLPD
+
+Switzerland's [nLPD](https://www.fedlex.admin.ch/eli/cc/2022/491/fr) (and the GDPR) push for **data
+minimisation** and favour **pseudonymisation** when personal data is processed by third parties. This
+library is built around those principles:
+
+- **The third party never sees raw PII.** When you send text to an external LLM (or any external service),
+  it receives only pseudonymised tokens like `[PER_1.NOM:M]` plus the non-identifying `legend` — never the
+  real name, e-mail, AVS number, etc.
+- **Pseudonymisation, not loss of meaning.** The re-identification key (`mapping`) stays in your
+  infrastructure; only you can reverse the tokens. The `legend` lets the downstream model still reason
+  correctly ("a person", "male") without knowing _who_.
+- **Fail-closed option.** Omitting the regex fallback means that if detection can't run, the call errors
+  instead of forwarding data that wasn't pseudonymised — no silent leak.
+- **Coreference & minimisation.** Re-using one id per entity avoids spreading extra distinguishing detail
+  across a prompt, and the corpus itself can stay in clear text — pseudonymisation happens only at the
+  boundary, at query time.
+
+> This is an engineering aid, not legal advice or a certification. You remain the data controller; assess
+> it against your own obligations (see the disclaimer below).

 ## Compliance note

--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
  "name": "@mobiletic/anonymizer",
-  "version": "0.1.0",
+  "version": "0.2.0",
  "description": "Framework-agnostic PII anonymization & pseudonymization: pluggable LLM detection for free-form PII (names, addresses) with a deterministic regex fallback, deterministic coreference, and streaming-safe de-anonymization.",
  "license": "MIT",
  "author": "Mobiletic",
--- a/src/anonymizer.ts
+++ b/src/anonymizer.ts
@@ -1,5 +1,5 @@
 import { RegexFallback } from './fallback.js';
-import { presets } from './presets.js';
+import { AnonymizationError } from './errors.js';
 import {
  type AnonymizationResult,
  type AnonymizerConfig,
@@ -14,6 +14,36 @@ const NOOP_LOGGER: Logger = { warn: () => {} };
 // Likely proper-name heuristic (two capitalized words), in addition to the structured fallback.
 const DEFAULT_NAME_HINT = /\b[A-ZÀ-Ý][a-zà-ÿ]+\s+[A-ZÀ-Ý][a-zà-ÿ]+\b/;

+/**
+ * Built-in French meanings for the standard abbreviations, used to backfill the
+ * legend when the model (or the regex fallback) doesn't supply one for an
+ * abbreviation that actually appears in the output.
+ */
+const DEFAULT_LEGEND: Record<string, string> = {
+  PER: 'Personne',
+  ORG: 'Organisation',
+  LOC: 'Lieu',
+  NOM: 'Nom de famille',
+  PRENOM: 'Prénom',
+  DATE_NAISSANCE: 'Date de naissance',
+  AGE: 'Âge',
+  ADRESSE: 'Adresse',
+  EMAIL: 'Adresse e-mail',
+  TELEPHONE: 'Numéro de téléphone',
+  TEL: 'Numéro de téléphone',
+  AVS: 'Numéro AVS',
+  IBAN: 'IBAN',
+  NSS: 'Numéro de sécurité sociale',
+  DATE: 'Date',
+  IPV4: 'Adresse IP',
+  CREDIT_CARD: 'Carte de crédit',
+  M: 'Masculin',
+  F: 'Féminin',
+  U: 'Inconnu',
+  Mineur: 'Mineur',
+  Adulte: 'Adulte',
+};
+
 /**
 * Return a stateless copy of a regex: a global/sticky (`g`/`y`) regex carries a
 * mutable `lastIndex`, which makes repeated `.test()` calls return alternating
@@ -27,24 +57,31 @@ function stateless(re: RegExp): RegExp {
 * Framework-agnostic anonymization engine.
 *
 * `anonymize()`: pre-filter (skips the LLM when there is no PII) → LLM →
- * regex fallback on failure/timeout → bidirectional validation.
+ * regex fallback on failure/timeout → bidirectional validation. When no fallback
+ * is configured, an LLM failure throws {@link AnonymizationError} (fail-closed).
 * `deanonymize()` restores the real values. `makeStreamDeanonymizer()` handles
 * the critical case of a placeholder fragmented across several streamed tokens.
 *
- * Pass an {@link LlmProvider} to detect free-form PII (names, addresses);
- * omit it for deterministic regex-only mode.
+ * Each result carries a `legend` (abbreviation → meaning) you can forward to a
+ * downstream LLM so it understands the placeholder tokens.
+ *
+ * Pass an {@link LlmProvider} to detect free-form PII (names, addresses) and/or
+ * `patterns` for the regex fallback. At least one of the two is required.
 */
 export class Anonymizer {
  private readonly llm?: LlmProvider;
-  private readonly fallback: RegexFallback;
+  private readonly fallback?: RegexFallback;
  private readonly nameHint: RegExp;
  private readonly logger: Logger;

  constructor(config: AnonymizerConfig = {}) {
    this.llm = config.llm;
-    this.fallback = new RegexFallback(config.patterns ?? presets.swiss);
+    this.fallback = config.patterns ? new RegexFallback(config.patterns) : undefined;
    this.nameHint = stateless(config.nameHint ?? DEFAULT_NAME_HINT);
    this.logger = config.logger ?? NOOP_LOGGER;
+    if (!this.llm && !this.fallback) {
+      throw new AnonymizationError('Anonymizer requires an LlmProvider, regex patterns, or both');
+    }
  }

  private hasLlm(): boolean {
@@ -52,51 +89,67 @@ export class Anonymizer {
  }

  private likelyHasPii(text: string): boolean {
-    return this.fallback.hasPii(text) || this.nameHint.test(text);
+    return (this.fallback?.hasPii(text) ?? false) || this.nameHint.test(text);
  }

  async anonymize(text: string): Promise<AnonymizationResult> {
-    // Pre-filter: most text has no PII → avoid the LLM round-trip (latency + cost)
-    // and return the text unchanged.
-    if (!this.likelyHasPii(text)) return { anon: text, mapping: {} };
+    // Pre-filter: most text has no PII → avoid the LLM round-trip. Only safe when
+    // a regex fallback exists to judge "no PII"; without one we always consult the
+    // LLM so structured PII (e.g. emails) can't slip through unanonymized.
+    if (this.fallback && !this.likelyHasPii(text)) return { anon: text, mapping: {}, legend: {} };

    if (this.hasLlm()) {
      try {
        const result = await this.llm!.anonymize(text);
        this.validate(result); // throws if inconsistent → fall back
-        return result;
+        return {
+          anon: result.anon,
+          mapping: result.mapping,
+          legend: this.buildLegend(result.anon, result.legend),
+        };
      } catch (err) {
+        if (!this.fallback) {
+          throw new AnonymizationError(`anonymize failed: ${(err as Error).message}`, { cause: err });
+        }
        this.logger.warn(`LLM unavailable/invalid → regex fallback: ${(err as Error).message}`);
      }
    }
-    return this.fallback.anonymize(text);
+    return this.fallbackAnonymize(text);
  }

  /**
   * Anonymize retrieved chunks BEFORE sending them on, CONSISTENTLY with the
-   * question's mapping (`seed`). Returns the anonymized chunks + the COMPLETE
-   * mapping (question ∪ chunks). Strategy:
+   * question's `seed` ({ mapping, legend }). Returns the anonymized chunks + the
+   * COMPLETE mapping and legend (question ∪ chunks). Strategy:
   *  1. Deterministic reuse: replace any already-known value (from the question)
   *     by its placeholder → question↔chunk coreference with no LLM call.
   *  2. Pre-filter: if no residual PII remains → return as-is (most corpus chunks
-   *     have no PII → LLM cost avoided).
+   *     have no PII → LLM cost avoided). Skipped when no regex fallback is set.
   *  3. Otherwise: ONE batched LLM call, then de-collision merge (renumber the
-   *     genuinely new entities). Failure/timeout → regex fallback per chunk.
+   *     genuinely new entities). Failure/timeout → regex fallback per chunk, or
+   *     {@link AnonymizationError} when no fallback is configured.
   */
  async anonymizeChunks(
    chunks: string[],
-    seed: Record<string, string>,
-  ): Promise<{ anon: string[]; mapping: Record<string, string> }> {
-    if (!chunks.length) return { anon: [], mapping: { ...seed } };
+    seed: { mapping: Record<string, string>; legend?: Record<string, string> },
+  ): Promise<{ anon: string[]; mapping: Record<string, string>; legend: Record<string, string> }> {
+    const seedLegend = seed.legend ?? {};
+    if (!chunks.length) return { anon: [], mapping: { ...seed.mapping }, legend: { ...seedLegend } };

-    const seeded = chunks.map((c) => this.applyKnown(c, seed));
-    if (!seeded.some((c) => this.likelyHasPii(c))) return { anon: seeded, mapping: { ...seed } };
+    const seeded = chunks.map((c) => this.applyKnown(c, seed.mapping));
+    if (this.fallback && !seeded.some((c) => this.likelyHasPii(c))) {
+      return { anon: seeded, mapping: { ...seed.mapping }, legend: { ...seedLegend } };
+    }

    if (this.hasLlm()) {
      try {
-        const { segments, mapping: gMap } = await this.llm!.anonymizeBatch(seeded, Object.keys(seed));
+        const {
+          segments,
+          mapping: gMap,
+          legend: gLegend,
+        } = await this.llm!.anonymizeBatch(seeded, Object.keys(seed.mapping));
        if (segments.length !== seeded.length) throw new Error('SEGMENT_COUNT_MISMATCH');
-        const { mapping, rename } = this.mergeMappings(seed, gMap);
+        const { mapping, rename } = this.mergeMappings(seed.mapping, gMap);
        const anon = segments.map((s) => this.applyRename(s, rename));
        // Anti-leak: every placeholder present in a chunk must be mapped.
        for (const s of anon) {
@@ -104,12 +157,16 @@ export class Anonymizer {
            if (!(ph in mapping)) throw new Error(`unmapped placeholder ${ph}`);
          }
        }
-        return { anon, mapping };
+        const legend = this.buildLegend(anon.join('\n'), { ...seedLegend, ...gLegend });
+        return { anon, mapping, legend };
      } catch (err) {
+        if (!this.fallback) {
+          throw new AnonymizationError(`anonymizeChunks failed: ${(err as Error).message}`, { cause: err });
+        }
        this.logger.warn(`Chunk anonymization → regex fallback: ${(err as Error).message}`);
      }
    }
-    return this.fallbackChunks(seeded, seed);
+    return this.fallbackChunks(seeded, seed.mapping, seedLegend);
  }

  /** Replace known values (seed) by their placeholder, longest values first. */
@@ -135,6 +192,32 @@ export class Anonymizer {
    return m ? { type: m[1], index: Number(m[2]), suffix: m[3] ?? '' } : null;
  }

+  /** Abbreviations used by a placeholder: entity type, attribute, and all-caps context codes. */
+  private usedAbbreviations(text: string): Set<string> {
+    const out = new Set<string>();
+    for (const ph of text.match(PLACEHOLDER_RE) ?? []) {
+      const m = /^\[([A-Z]+)_\d+(?:\.([A-Z_]+):([^\]]+))?\]$/.exec(ph);
+      if (!m) continue;
+      out.add(m[1]); // entity type
+      if (m[2]) out.add(m[2]); // attribute
+      if (m[3] && /^[A-Z]+$/.test(m[3])) out.add(m[3]); // context, only if it's a code (not a value)
+    }
+    return out;
+  }
+
+  /**
+   * Build a legend covering every abbreviation that appears in `text`, taking the
+   * meaning from `provided` (LLM/fallback) → {@link DEFAULT_LEGEND} → the
+   * abbreviation itself. Guarantees coverage even if the model omits entries.
+   */
+  private buildLegend(text: string, provided: Record<string, string>): Record<string, string> {
+    const legend: Record<string, string> = {};
+    for (const ab of this.usedAbbreviations(text)) {
+      legend[ab] = provided[ab] ?? DEFAULT_LEGEND[ab] ?? ab;
+    }
+    return legend;
+  }
+
  /**
   * Merge the chunks' LLM mapping into the question's (seed).
   * - value already known → reuse the question's placeholder (rename).
@@ -181,20 +264,29 @@ export class Anonymizer {
    return { mapping, rename };
  }

+  /** Single-text regex fallback, routed through the legend builder. */
+  private fallbackAnonymize(text: string): AnonymizationResult {
+    const r = this.fallback!.anonymize(text);
+    return { anon: r.anon, mapping: r.mapping, legend: this.buildLegend(r.anon, r.legend) };
+  }
+
  /** Fallback (no LLM): regex per chunk, de-collision merged into the seed. */
  private fallbackChunks(
    seeded: string[],
    seed: Record<string, string>,
-  ): { anon: string[]; mapping: Record<string, string> } {
+    seedLegend: Record<string, string>,
+  ): { anon: string[]; mapping: Record<string, string>; legend: Record<string, string> } {
    let mapping: Record<string, string> = { ...seed };
+    let provided: Record<string, string> = { ...seedLegend };
    const anon: string[] = [];
    for (const c of seeded) {
-      const r = this.fallback.anonymize(c);
+      const r = this.fallback!.anonymize(c);
      const { mapping: merged, rename } = this.mergeMappings(mapping, r.mapping);
      mapping = merged;
+      provided = { ...provided, ...r.legend };
      anon.push(this.applyRename(r.anon, rename));
    }
-    return { anon, mapping };
+    return { anon, mapping, legend: this.buildLegend(anon.join('\n'), provided) };
  }

  /** Every placeholder in the text must be in the mapping AND vice-versa (anti-leak). */
--- a/src/errors.ts
+++ b/src/errors.ts
@@ -0,0 +1,11 @@
+/**
+ * Thrown when anonymization cannot be completed and there is no fallback to
+ * degrade to — a fail-closed signal so the caller never proceeds with
+ * un-anonymized text. The originating error is preserved in `.cause`.
+ */
+export class AnonymizationError extends Error {
+  constructor(message: string, options?: { cause?: unknown }) {
+    super(message, options);
+    this.name = 'AnonymizationError';
+  }
+}
--- a/src/fallback.ts
+++ b/src/fallback.ts
@@ -13,10 +13,11 @@ export class RegexFallback {

  anonymize(text: string): AnonymizationResult {
    const mapping: Record<string, string> = {};
+    const legend: Record<string, string> = {};
    const counters: Record<string, number> = {};
    let anon = text;

-    for (const { tag, re, validate } of this.patterns) {
+    for (const { tag, re, validate, meaning } of this.patterns) {
      anon = anon.replace(re, (match) => {
        // A validator can reject a regex match (e.g. Luhn) → leave the text as-is.
        if (validate && !validate(match)) return match;
@@ -26,10 +27,11 @@ export class RegexFallback {
        counters[tag] = (counters[tag] ?? 0) + 1;
        const ph = `[${tag}_${counters[tag]}]`;
        mapping[ph] = match;
+        legend[tag] = meaning ?? tag; // document the abbreviation used
        return ph;
      });
    }
-    return { anon, mapping };
+    return { anon, mapping, legend };
  }

  /** Fast pre-check: is there any structured identifier worth anonymizing? */
--- a/src/index.ts
+++ b/src/index.ts
@@ -1,4 +1,5 @@
 export { Anonymizer } from './anonymizer.js';
+export { AnonymizationError } from './errors.js';
 export { RegexFallback } from './fallback.js';
 export { presets } from './presets.js';
 export {
--- a/src/presets.ts
+++ b/src/presets.ts
@@ -5,11 +5,11 @@ import type { PatternDef } from './types.js';
 * Order matters: specific patterns (AVS, IBAN) come before generic ones.
 */
 const swiss: PatternDef[] = [
-  { tag: 'AVS', re: /\b756\.\d{4}\.\d{4}\.\d{2}\b/g }, // Swiss social security (AVS/AHV)
-  { tag: 'IBAN', re: /\bCH\d{2}[0-9A-Z]{17}\b/gi }, // Swiss IBAN
-  { tag: 'EMAIL', re: /\b[\w.+-]+@[\w-]+\.[\w.-]+\b/g },
-  { tag: 'TEL', re: /(?:\+41|0)(?:[\s.-]?\d){9}\b/g }, // Swiss phone number
-  { tag: 'DATE', re: /\b\d{1,2}[./-]\d{1,2}[./-]\d{2,4}\b/g },
+  { tag: 'AVS', re: /\b756\.\d{4}\.\d{4}\.\d{2}\b/g, meaning: 'Numéro AVS' }, // Swiss social security
+  { tag: 'IBAN', re: /\bCH\d{2}[0-9A-Z]{17}\b/gi, meaning: 'IBAN' }, // Swiss IBAN
+  { tag: 'EMAIL', re: /\b[\w.+-]+@[\w-]+\.[\w.-]+\b/g, meaning: 'Adresse e-mail' },
+  { tag: 'TEL', re: /(?:\+41|0)(?:[\s.-]?\d){9}\b/g, meaning: 'Numéro de téléphone' }, // Swiss phone
+  { tag: 'DATE', re: /\b\d{1,2}[./-]\d{1,2}[./-]\d{2,4}\b/g, meaning: 'Date' },
 ];

 /** Luhn check on a credit-card candidate (ignores spaces/dashes); 13–19 digits. */
@@ -39,16 +39,20 @@ function luhnValid(value: string): boolean {
 * claimed before phone numbers so they don't get mis-tagged as `TEL`.
 */
 const generic: PatternDef[] = [
-  { tag: 'EMAIL', re: /\b[\w.+-]+@[\w-]+\.[\w.-]+\b/g },
-  { tag: 'IBAN', re: /\b[A-Z]{2}\d{2}[0-9A-Z]{11,30}\b/g },
-  { tag: 'IPV4', re: /\b(?:\d{1,3}\.){3}\d{1,3}\b/g },
+  { tag: 'EMAIL', re: /\b[\w.+-]+@[\w-]+\.[\w.-]+\b/g, meaning: 'Adresse e-mail' },
+  { tag: 'IBAN', re: /\b[A-Z]{2}\d{2}[0-9A-Z]{11,30}\b/g, meaning: 'IBAN' },
+  { tag: 'IPV4', re: /\b(?:\d{1,3}\.){3}\d{1,3}\b/g, meaning: 'Adresse IP' },
  // 13–19 digits in optional space/dash groups, gated by Luhn to avoid eating
  // arbitrary long digit runs (account numbers, ids).
-  { tag: 'CREDIT_CARD', re: /\b\d(?:[ -]?\d){12,18}\b/g, validate: luhnValid },
-  { tag: 'DATE', re: /\b\d{1,2}[./-]\d{1,2}[./-]\d{2,4}\b/g },
+  { tag: 'CREDIT_CARD', re: /\b\d(?:[ -]?\d){12,18}\b/g, validate: luhnValid, meaning: 'Carte de crédit' },
+  { tag: 'DATE', re: /\b\d{1,2}[./-]\d{1,2}[./-]\d{2,4}\b/g, meaning: 'Date' },
  // Requires grouped/separated digits (or a leading +country) so plain integers
  // aren't mistaken for phone numbers.
-  { tag: 'TEL', re: /(?:\+\d{1,3}[\s.-]?)?(?:\(?\d{2,4}\)?[\s.-]){2,3}\d{2,4}/g },
+  {
+    tag: 'TEL',
+    re: /(?:\+\d{1,3}[\s.-]?)?(?:\(?\d{2,4}\)?[\s.-]){2,3}\d{2,4}/g,
+    meaning: 'Numéro de téléphone',
+  },
 ];

 /** Built-in pattern sets for the regex fallback. */
--- a/src/providers/openai-compatible.ts
+++ b/src/providers/openai-compatible.ts
@@ -13,12 +13,24 @@ export const DEFAULT_SYSTEM_PROMPT = [
  '  NOM:M|F|U · DATE_NAISSANCE:<année> · AGE:Mineur|Adulte',
  '  ADRESSE:Lieu|Rue|Ville|NPA|Pays · ORG:Entreprise|Ecole',
  '',
+  'ABRÉVIATIONS DYNAMIQUES:',
+  '- Si tu rencontres un TYPE d’entité, un ATTRIBUT ou un CONTEXTE non listé ci-dessus,',
+  '  INVENTE une abréviation COURTE en MAJUSCULES (lettres A–Z et "_" uniquement, ex. PER, NOM, M),',
+  '  suivant la même convention, et réutilise-la de façon cohérente partout.',
+  '- N’utilise JAMAIS d’espaces, accents, chiffres ou symboles dans une abréviation.',
+  '',
+  'LÉGENDE:',
+  '- Renvoie une "legende" : un objet associant CHAQUE abréviation utilisée (entité, attribut, contexte)',
+  '  à sa signification complète en français. Ex.: {"PER":"Personne","NOM":"Nom de famille","M":"Masculin"}.',
+  '- La légende sert à expliquer les placeholders à un autre modèle ; elle ne contient AUCUNE donnée réelle.',
+  '',
  'RÈGLES:',
  '1. Coréférence: la MÊME personne garde le MÊME identifiant (PER_1) dans tout le texte.',
  '2. N’anonymise JAMAIS les termes pédagogiques/techniques (langages, concepts, titres de cours, fonctions).',
-  '3. Si AUCUNE donnée personnelle: renvoie le texte original et "mapping": {}.',
+  '3. Si AUCUNE donnée personnelle: renvoie le texte original, "mapping": {} et "legende": {}.',
  '4. N’anonymise pas un placeholder déjà présent (idempotence).',
-  '5. Sortie STRICTEMENT JSON valide: {"texte_anonymise": "...", "mapping": {"[PER_1.NOM:M]": "..."}}.',
+  '5. Sortie STRICTEMENT JSON valide:',
+  '   {"texte_anonymise": "...", "mapping": {"[PER_1.NOM:M]": "..."}, "legende": {"PER": "Personne", "NOM": "Nom de famille", "M": "Masculin"}}.',
 ].join('\n');

 export interface OpenAICompatibleOptions {
@@ -48,6 +60,20 @@ const sleep = (ms: number): Promise<void> => new Promise((resolve) => setTimeout
 /** A transport error that the caller may retry (network / timeout / 429 / 5xx). */
 class TransientError extends Error {}

+/**
+ * Coerce the model's `legende` into a string→string map, lenient by design: a
+ * missing or malformed legend yields `{}` rather than failing the anonymization
+ * (the legend is metadata; the `mapping` is what guarantees no leak).
+ */
+function asLegend(raw: unknown): Record<string, string> {
+  if (!raw || typeof raw !== 'object') return {};
+  const out: Record<string, string> = {};
+  for (const [k, v] of Object.entries(raw as Record<string, unknown>)) {
+    if (typeof v === 'string') out[k] = v;
+  }
+  return out;
+}
+
 const DEFAULT_BATCH_INSTRUCTIONS = (usedIds: string[]): string =>
  '\n\nMODE LOT (segments) :' +
  '\n- ENTRÉE : un objet JSON {"segments": ["…", "…"]}.' +
@@ -55,8 +81,8 @@ const DEFAULT_BATCH_INSTRUCTIONS = (usedIds: string[]): string =>
  (usedIds.length
    ? `\n- Identifiants DÉJÀ attribués (réutilise-les pour les mêmes valeurs, n'en duplique AUCUN pour d'autres valeurs) : ${usedIds.join(', ')}.`
    : '') +
-  '\n- SORTIE STRICTEMENT JSON : {"segments": ["…anonymisé…"], "mapping": {"[PER_1.NOM:M]": "…"}}.' +
-  '\n- "segments" DOIT avoir la même longueur et le même ordre que l’entrée ; "mapping" ne contient que les NOUVELLES entités.';
+  '\n- SORTIE STRICTEMENT JSON : {"segments": ["…anonymisé…"], "mapping": {"[PER_1.NOM:M]": "…"}, "legende": {"PER": "Personne"}}.' +
+  '\n- "segments" DOIT avoir la même longueur et le même ordre que l’entrée ; "mapping" ne contient que les NOUVELLES entités ; "legende" décrit toutes les abréviations utilisées.';

 /**
 * Build an {@link LlmProvider} backed by any OpenAI-compatible Chat Completions
@@ -131,7 +157,11 @@ export function openAICompatibleProvider(opts: OpenAICompatibleOptions): LlmProv

    async anonymize(text: string): Promise<AnonymizationResult> {
      const content = await chat(systemPrompt, text);
-      const parsed = JSON.parse(content) as { texte_anonymise?: string; mapping?: Record<string, string> };
+      const parsed = JSON.parse(content) as {
+        texte_anonymise?: string;
+        mapping?: Record<string, string>;
+        legende?: Record<string, string>;
+      };
      if (
        typeof parsed.texte_anonymise !== 'string' ||
        typeof parsed.mapping !== 'object' ||
@@ -139,20 +169,24 @@ export function openAICompatibleProvider(opts: OpenAICompatibleOptions): LlmProv
      ) {
        throw new Error('LLM_BAD_SHAPE');
      }
-      return { anon: parsed.texte_anonymise, mapping: parsed.mapping };
+      return { anon: parsed.texte_anonymise, mapping: parsed.mapping, legend: asLegend(parsed.legende) };
    },

    async anonymizeBatch(
      texts: string[],
      usedIds: string[],
-    ): Promise<{ segments: string[]; mapping: Record<string, string> }> {
+    ): Promise<{ segments: string[]; mapping: Record<string, string>; legend: Record<string, string> }> {
      const system = systemPrompt + batchInstructions(usedIds);
      const content = await chat(system, JSON.stringify({ segments: texts }));
-      const parsed = JSON.parse(content) as { segments?: string[]; mapping?: Record<string, string> };
+      const parsed = JSON.parse(content) as {
+        segments?: string[];
+        mapping?: Record<string, string>;
+        legende?: Record<string, string>;
+      };
      if (!Array.isArray(parsed.segments) || typeof parsed.mapping !== 'object' || !parsed.mapping) {
        throw new Error('LLM_BATCH_BAD_SHAPE');
      }
-      return { segments: parsed.segments, mapping: parsed.mapping };
+      return { segments: parsed.segments, mapping: parsed.mapping, legend: asLegend(parsed.legende) };
    },
  };
 }
--- a/src/types.ts
+++ b/src/types.ts
@@ -1,9 +1,17 @@
-/** Result of an anonymization: text with placeholders + the reverse-mapping table. */
+/** Result of an anonymization: text with placeholders + the reverse-mapping table + a legend. */
 export interface AnonymizationResult {
  /** The text with every detected PII value replaced by a placeholder. */
  anon: string;
-  /** Placeholder → original value, e.g. `{ "[PER_1.NOM:M]": "Alain JACCARD" }`. */
+  /**
+   * Placeholder → original value, e.g. `{ "[PER_1.NOM:M]": "Alain JACCARD" }`.
+   * SECRET: it re-identifies people. Keep it on your side; never share it downstream.
+   */
  mapping: Record<string, string>;
+  /**
+   * Abbreviation → human meaning, e.g. `{ "PER": "Personne", "NOM": "Nom", "M": "Masculin" }`.
+   * NON-secret: safe to send to a downstream LLM so it understands the placeholder tokens.
+   */
+  legend: Record<string, string>;
 }

 /**
@@ -31,7 +39,7 @@ export interface LlmProvider {
  anonymizeBatch(
    texts: string[],
    usedIds: string[],
-  ): Promise<{ segments: string[]; mapping: Record<string, string> }>;
+  ): Promise<{ segments: string[]; mapping: Record<string, string>; legend: Record<string, string> }>;
 }

 /** A structured-PII detector: a tag (e.g. `EMAIL`) and the global regex that finds it. */
@@ -45,6 +53,11 @@ export interface PatternDef {
   * Use it to cut false positives (e.g. a Luhn check on credit-card candidates).
   */
  validate?: (match: string) => boolean;
+  /**
+   * Optional human meaning for this tag, surfaced in the result `legend`
+   * (e.g. `'Adresse e-mail'` for tag `EMAIL`). Falls back to the tag itself.
+   */
+  meaning?: string;
 }

 /** Minimal logger sink. Defaults to a no-op; pass your own to capture fallback warnings. */
@@ -56,7 +69,12 @@ export interface Logger {
 export interface AnonymizerConfig {
  /** LLM backend for free-form PII (names, addresses…). Omit for regex-only mode. */
  llm?: LlmProvider;
-  /** Structured-PII patterns for the regex fallback. Defaults to {@link presets.swiss}. */
+  /**
+   * Structured-PII patterns for the regex fallback (e.g. {@link presets.swiss}).
+   * Opt-in: omit to disable the fallback — then an LLM failure throws
+   * {@link AnonymizationError} (fail-closed) instead of degrading. At least one of
+   * `llm` or `patterns` must be provided.
+   */
  patterns?: PatternDef[];
  /**
   * Heuristic that flags likely proper names so the LLM is consulted. Has a
--- a/test/anonymizer.test.ts
+++ b/test/anonymizer.test.ts
@@ -1,5 +1,5 @@
 import { describe, it, expect, vi } from 'vitest';
-import { Anonymizer, presets, type LlmProvider } from '../src/index.js';
+import { Anonymizer, AnonymizationError, presets, type LlmProvider } from '../src/index.js';

 /** An LLM provider that is configured but always fails → forces the regex fallback. */
 const failingLlm = (overrides: Partial<LlmProvider> = {}): LlmProvider => ({
@@ -50,7 +50,7 @@ describe('Anonymizer (Swiss preset)', () => {
    it('PII-free chunks → no LLM call, returned as-is', async () => {
      const llm = failingLlm();
      const s = new Anonymizer({ llm, patterns: presets.swiss });
-      const r = await s.anonymizeChunks(['Un INNER JOIN combine deux tables.'], {});
+      const r = await s.anonymizeChunks(['Un INNER JOIN combine deux tables.'], { mapping: {} });
      expect(llm.anonymizeBatch).not.toHaveBeenCalled();
      expect(r.anon[0]).toContain('INNER JOIN');
      expect(r.mapping).toEqual({});
@@ -60,7 +60,7 @@ describe('Anonymizer (Swiss preset)', () => {
      const llm = failingLlm();
      const s = new Anonymizer({ llm, patterns: presets.swiss });
      const seed = { '[PER_1.NOM:M]': 'Alain JACCARD' };
-      const r = await s.anonymizeChunks(['Le dossier de Alain JACCARD est complet.'], seed);
+      const r = await s.anonymizeChunks(['Le dossier de Alain JACCARD est complet.'], { mapping: seed });
      expect(llm.anonymizeBatch).not.toHaveBeenCalled();
      expect(r.anon[0]).toContain('[PER_1.NOM:M]');
      expect(r.anon[0]).not.toContain('Alain JACCARD');
@@ -71,19 +71,22 @@ describe('Anonymizer (Swiss preset)', () => {
      const anonymizeBatch = vi.fn().mockResolvedValue({
        segments: ['[PER_1.NOM:M] a signé.'],
        mapping: { '[PER_1.NOM:M]': 'Bob Martin' },
+        legend: { PER: 'Personne', NOM: 'Nom de famille', M: 'Masculin' },
      });
      const s = new Anonymizer({ llm: failingLlm({ anonymizeBatch }), patterns: presets.swiss });
      const seed = { '[PER_1.NOM:M]': 'Alain JACCARD' };
-      const r = await s.anonymizeChunks(['Bob Martin a signé.'], seed);
+      const r = await s.anonymizeChunks(['Bob Martin a signé.'], { mapping: seed });
      expect(anonymizeBatch).toHaveBeenCalledTimes(1);
      expect(r.anon[0]).toContain('[PER_2.NOM:M]');
      expect(r.mapping['[PER_1.NOM:M]']).toBe('Alain JACCARD');
      expect(r.mapping['[PER_2.NOM:M]']).toBe('Bob Martin');
+      // legend covers the abbreviations used in the anonymized chunk
+      expect(r.legend).toMatchObject({ PER: 'Personne', NOM: 'Nom de famille', M: 'Masculin' });
    });

    it('falls back to regex (structured ids) when the LLM is unavailable', async () => {
      const s = new Anonymizer({ llm: failingLlm(), patterns: presets.swiss });
-      const r = await s.anonymizeChunks(['Contact : jean@exemple.ch'], {});
+      const r = await s.anonymizeChunks(['Contact : jean@exemple.ch'], { mapping: {} });
      expect(r.anon[0]).not.toContain('jean@exemple.ch');
      expect(Object.values(r.mapping)).toContain('jean@exemple.ch');
    });
@@ -121,4 +124,58 @@ describe('Anonymizer (Swiss preset)', () => {
      expect(r.mapping['[TICKET_1]']).toBe('JIRA-123');
    });
  });
+
+  describe('legend', () => {
+    it('the regex fallback produces a French legend from tag meanings', async () => {
+      const s = new Anonymizer({ patterns: presets.swiss });
+      const r = await s.anonymize('Écris à jean@exemple.ch');
+      expect(r.legend).toEqual({ EMAIL: 'Adresse e-mail' });
+    });
+
+    it('backfills a missing legend entry from the built-in default (LLM omitted it)', async () => {
+      const anonymize = vi.fn().mockResolvedValue({
+        anon: 'Dossier de [PER_1.NOM:M]',
+        mapping: { '[PER_1.NOM:M]': 'Alain Jaccard' },
+        legend: {}, // model returned no legend
+      });
+      const s = new Anonymizer({ llm: failingLlm({ anonymize }), patterns: presets.swiss });
+      const r = await s.anonymize('Dossier de Alain Jaccard');
+      expect(r.legend).toEqual({ PER: 'Personne', NOM: 'Nom de famille', M: 'Masculin' });
+    });
+
+    it('a custom tag with a meaning surfaces it in the legend', async () => {
+      const s = new Anonymizer({
+        patterns: [{ tag: 'TICKET', re: /\bJIRA-\d+\b/g, meaning: 'Ticket de suivi' }],
+      });
+      const r = await s.anonymize('Voir JIRA-123');
+      expect(r.legend).toEqual({ TICKET: 'Ticket de suivi' });
+    });
+  });
+
+  describe('fail-closed (optional fallback)', () => {
+    it('throws if neither an LLM nor patterns are provided', () => {
+      expect(() => new Anonymizer({})).toThrowError(AnonymizationError);
+    });
+
+    it('LLM-only: a provider failure throws AnonymizationError with the cause', async () => {
+      const cause = new Error('LLM_HTTP_500');
+      const s = new Anonymizer({ llm: failingLlm({ anonymize: vi.fn().mockRejectedValue(cause) }) });
+      await expect(s.anonymize('Contact: jean@exemple.ch')).rejects.toBeInstanceOf(AnonymizationError);
+      await expect(s.anonymize('Contact: jean@exemple.ch')).rejects.toMatchObject({ cause });
+    });
+
+    it('LLM-only: bypasses the pre-filter so PII-free text still hits the provider', async () => {
+      const anonymize = vi.fn().mockResolvedValue({ anon: 'INNER JOIN', mapping: {}, legend: {} });
+      const s = new Anonymizer({ llm: failingLlm({ anonymize }) });
+      await s.anonymize('Explique INNER JOIN'); // no structured PII, no name hint
+      expect(anonymize).toHaveBeenCalledTimes(1);
+    });
+
+    it('LLM-only: anonymizeChunks rejects with AnonymizationError on provider failure', async () => {
+      const s = new Anonymizer({ llm: failingLlm() }); // anonymizeBatch rejects
+      await expect(s.anonymizeChunks(['Bob Martin a signé.'], { mapping: {} })).rejects.toBeInstanceOf(
+        AnonymizationError,
+      );
+    });
+  });
 });
--- a/test/openai-compatible.test.ts
+++ b/test/openai-compatible.test.ts
@@ -25,7 +25,7 @@ describe('openAICompatibleProvider', () => {
    vi.stubGlobal('fetch', fetchMock);

    const r = await openAICompatibleProvider(opts).anonymize('a@b.ch');
-    expect(r).toEqual({ anon: '[EMAIL_1]', mapping: { '[EMAIL_1]': 'a@b.ch' } });
+    expect(r).toEqual({ anon: '[EMAIL_1]', mapping: { '[EMAIL_1]': 'a@b.ch' }, legend: {} });

    const [url, init] = fetchMock.mock.calls[0];
    expect(url).toBe('https://api.example.com/v1/chat/completions');
@@ -35,6 +35,18 @@ describe('openAICompatibleProvider', () => {
    expect(init.headers.Authorization).toBe('Bearer k');
  });

+  it('parses the model legende into result.legend, keeping only string values', async () => {
+    const fetchMock = mockFetchJson({
+      texte_anonymise: '[PER_1.NOM:M]',
+      mapping: { '[PER_1.NOM:M]': 'Alain Jaccard' },
+      legende: { PER: 'Personne', NOM: 'Nom de famille', M: 'Masculin', BAD: 42 },
+    });
+    vi.stubGlobal('fetch', fetchMock);
+
+    const r = await openAICompatibleProvider(opts).anonymize('Alain Jaccard');
+    expect(r.legend).toEqual({ PER: 'Personne', NOM: 'Nom de famille', M: 'Masculin' });
+  });
+
  it('anonymizeBatch() returns {segments, mapping} and includes used ids in the prompt', async () => {
    const fetchMock = mockFetchJson({ segments: ['[PER_2.NOM:M]'], mapping: { '[PER_2.NOM:M]': 'Bob' } });
    vi.stubGlobal('fetch', fetchMock);
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -1,9 +1,9 @@
 {
  "compilerOptions": {
-    "target": "ES2021",
+    "target": "ES2022",
    "module": "ESNext",
    "moduleResolution": "Bundler",
-    "lib": ["ES2021", "DOM"],
+    "lib": ["ES2022", "DOM"],
    "declaration": true,
    "strict": true,
    "noUnusedLocals": true,