# @mobiletic/anonymizer

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Framework-agnostic **PII anonymization & pseudonymization** for TypeScript/JavaScript.

It replaces personal data in text with stable placeholders before the text leaves your trust boundary
(e.g. before sending it to a third-party LLM, log sink, or analytics pipeline), and restores the real
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.
- 🔁 **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.

## Install

```bash
npm install @mobiletic/anonymizer
```

Requires Node ≥ 18 (uses native `fetch`).

## Quick start

### Regex-only (no LLM, fully deterministic)

```ts
import { Anonymizer, presets } from '@mobiletic/anonymizer';

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

const { anon, mapping } = await anonymizer.anonymize('Écris à jean@exemple.ch');
// anon    -> "Écris à [EMAIL_1]"
// mapping -> { "[EMAIL_1]": "jean@exemple.ch" }

anonymizer.deanonymize(anon, mapping); // -> "Écris à jean@exemple.ch"
```

### With an LLM (also catches names, addresses…)

```ts
import { Anonymizer, openAICompatibleProvider, presets } from '@mobiletic/anonymizer';

const anonymizer = new Anonymizer({
  llm: openAICompatibleProvider({
    baseUrl: process.env.LLM_BASE_URL!, // OpenAI, Infomaniak, vLLM, Ollama, …
    apiKey: process.env.LLM_API_KEY!,
    model: process.env.LLM_MODEL!,
    timeoutMs: 3000,
  }),
  patterns: presets.swiss, // regex fallback if the LLM is down/misbehaves
});

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

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.

`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),
`retries` (default 1 → 2 attempts), and `retryDelayMs` (linear backoff, default 250). Worst-case latency
is `(retries + 1) × timeoutMs`, so keep `retries` low on latency-sensitive paths.

### Streaming de-anonymization

When you stream an LLM answer back to a user, restore real values without ever emitting a half-written
placeholder:

```ts
const stream = anonymizer.makeStreamDeanonymizer(mapping);
for await (const token of llmTokens) process.stdout.write(stream.push(token));
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:

```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().
```

## Configuration

```ts
new Anonymizer({
  llm?,       // LlmProvider — omit for regex-only mode
  patterns?,  // PatternDef[] — defaults to presets.swiss
  nameHint?,  // RegExp flagging likely names so the LLM is consulted (has a default)
  logger?,    // { warn(msg) } — receives fallback warnings; defaults to no-op
});
```

### Presets & custom patterns

```ts
import { presets } from '@mobiletic/anonymizer';

presets.swiss; // AVS, IBAN CH, EMAIL, Swiss phone, DATE
presets.generic; // EMAIL, IBAN, credit card, IPv4, phone, DATE

// Compose / extend:
const patterns = [
  ...presets.generic,
  { tag: 'TICKET', re: /\bJIRA-\d+\b/g }, // patterns must use the global flag
];
```

Each pattern may carry an optional `validate(match) => boolean` second stage — a match is only redacted
if it passes. `presets.generic` uses it for a [Luhn](https://en.wikipedia.org/wiki/Luhn_algorithm) check
so arbitrary long digit runs aren't mistaken for credit cards:

```ts
{ tag: 'CREDIT_CARD', re: /\b\d(?:[ -]?\d){12,18}\b/g, validate: luhnValid }
```

> The `generic` preset is a **best-effort** starting point — broad patterns (phone, date, card) can
> overlap. For production use, prefer a locale-specific preset (`presets.swiss`) or your own patterns.

### Custom LLM provider

Implement `LlmProvider` to use any backend (Anthropic, a local model, a rules engine…):

```ts
import type { LlmProvider } from '@mobiletic/anonymizer';

const myProvider: LlmProvider = {
  isConfigured: () => true,
  async anonymize(text) {
    /* return { anon, mapping } */
  },
  async anonymizeBatch(texts, usedIds) {
    /* return { segments, mapping } */
  },
};
```

## Placeholder format

```
[PER_1.NOM:M]   entity PER #1, attribute NOM, context M (rich, from the LLM)
[EMAIL_1]       structured id from the regex fallback
```

`PLACEHOLDER_RE` is exported if you need to scan text for placeholders.

## Compliance note

This library is a **best-effort** pseudonymization aid, not a guarantee of legal compliance. LLM and regex
detection can miss or mis-classify data. Validate against your own requirements (nLPD, GDPR, HIPAA, …)
before relying on it for regulated data.

## License

[MIT](./LICENSE) © Mobiletic