Pag-serve ng Custom Method bilang isang API

Ang api method ng i18n-rosetta ay nagbibigay-daan sa iyo na i-point ang anumang translation pair sa isang external HTTP endpoint. Ganito po ninyo mai-integrate ang mga pipelines na masyadong complex para sa isang LLM prompt lang — mga morphological analyzers, finite-state transducers (FSTs), multi-step LLM chains, o anumang custom research method na na-build ninyo.

Bakit isang API Service?

May mga translation pipelines na hindi pwedeng mag-run sa loob ng isang simpleng prompt-response cycle:

Pipeline step	Halimbawa
Morphological decomposition	I-split ang mga polysynthetic words sa mga morphemes bago ang translation
FST validation	I-reject ang mga outputs na nagvi-violate ng mga phonological o morphological rules
Multi-step LLM chains	Generate → verify → correct cycles gamit ang iba't ibang models
Dictionary lookup	Mag-cross-reference sa isang curated bilingual dictionary mid-pipeline
Human-in-the-loop	I-queue ang mga uncertain translations para sa expert review

Tinatrato ng api method ang inyong pipeline bilang isang black box — nagse-send ang i18n-rosetta ng mga source strings, at nagre-return naman ang inyong service ng mga translations. Kung ano ang nangyayari sa loob ay nakadepende na po nang buo sa inyo.

Architecture

Pag-set Up ng Inyong Service

Kailangan pong mag-implement ang inyong API service ng isang endpoint na nag-a-accept at nagre-return ng JSON:

Request Format

Ise-send ng rosetta ang eksaktong JSON body na ito (tingnan ang api.js):

POST /translate
Content-Type: application/json
Authorization: Bearer <ROSETTA_API_KEY>

{
  "source_locale": "en",
  "target_locale": "crk",
  "method": "crk-coached-v1",
  "keys": {
    "greeting": "Hello, welcome to our app",
    "farewell": "Goodbye and thanks"
  }
}

Field	Type	Description
`source_locale`	string	BCP 47 source language code
`target_locale`	string	BCP 47 target language code
`method`	string	Plugin name o `"default"`
`keys`	object	Map ng key → source string na ita-translate

### Response Format

Your service must return a `translations` object. An optional `meta` object can include cost and diagnostic info:

```json
{
  "translations": {
    "greeting": "tânisi, pê-kîwêw ôta",
    "farewell": "ekosi mâka, kinanâskomitin"
  },
  "meta": {
    "model": "my-custom-pipeline/v1",
    "cost_usd": 0.0042,
    "method": "decompose-translate-validate"
  }
}

Field	Type	Required	Description
`translations`	object	✅	Map of key → translated string
`meta`	object	—	Optional metadata
`meta.cost_usd`	number	—	If present, displayed in rosetta's output
`errors`	object	—	For partial success (HTTP 207): map of key → `{ message }`

Minimal Express Server

import express from 'express';

const app = express();
app.use(express.json());

/**
 * rosetta API contract:
 *
 * Request:  { source_locale, target_locale, method, keys: { "key": "source" } }
 * Response: { translations: { "key": "translated" }, meta: { ... } }
 */
app.post('/translate', async (req, res) => {
  const { source_locale, target_locale, method, keys } = req.body;

  const translations = {};

  for (const [key, source] of Object.entries(keys)) {
    // --- Your pipeline goes here ---
    // Step 1: Morphological decomposition
    const morphemes = await decompose(source, source_locale);

    // Step 2: LLM translation with context
    const draft = await llmTranslate(morphemes, target_locale);

    // Step 3: FST validation
    const validated = await fstValidate(draft, target_locale);

    // Step 4: Post-processing (orthography normalization, etc.)
    translations[key] = await postProcess(validated);
  }

  res.json({
    translations,
    meta: {
      model: 'my-custom-pipeline/v1',
      method: 'decompose-translate-validate',
    },
  });
});

app.listen(3001, () => {
  console.log('Translation API running on http://localhost:3001');
});

Configuring i18n-rosetta

Point a translation pair at your running service in i18n-rosetta.config.json:

{
  "inputLocale": "en",
  "pairs": {
    "en:crk": {
      "method": "api",
      "endpoint": "http://localhost:3001/translate",
      "register": "Formal Plains Cree. Use SRO orthography."
    }
  }
}

Then run sync as usual:

npx i18n-rosetta sync

i18n-rosetta will POST your source strings to the endpoint and write the returned translations to crk.json.

Case Study: Plains Cree Pipeline

:::info Under Development The Plains Cree pipeline described below is under active development and is not yet running in production. Details here reflect the current design direction and may change as the project evolves. :::

The gds-mt-eval-harness project demonstrates this pattern. Its Plains Cree pipeline uses:

Morphological decomposition — Break polysynthetic Cree words into translatable morpheme chains
LLM translation — Context-enriched GPT-4o translation with coaching data (SRO orthography rules, register instructions)
FST validation — Finite-state transducer checks that outputs conform to Cree phonological rules
Confidence scoring — Each translation gets a confidence score based on FST pass rate and dictionary coverage

The entire pipeline runs as a single HTTP endpoint that i18n-rosetta calls via the api method.

Running Evaluations

After translating, you can evaluate output quality using the harness directly:

# Clone the harness
git clone https://github.com/gamedaysuits/gds-mt-eval-harness.git
cd gds-mt-eval-harness
pip install -e .

# Run the evaluation against your method's output
python eval/baseline_experiment.py --dataset data/edtekla-dev-v1.json --submit

This produces structured evaluation records with chrF++, BLEU, and exact match scores that can be used as regression baselines.

Authentication

If your API requires authentication, set the apiKey field or use an environment variable:

{
  "pairs": {
    "en:crk": {
      "method": "api",
      "endpoint": "https://my-mt-service.example.com/translate",
      "apiKey": "${CRK_API_KEY}"
    }
  }
}

Data Sovereignty & OCAP Principles

The api method is particularly important for Indigenous language communities. By self-hosting the translation pipeline, a community keeps full control over:

Proprietary coaching data — register instructions, orthography rules, and domain glossaries never leave community infrastructure.
Linguistic resources — curated dictionaries, FST grammars, and elder-verified translations remain under community ownership.
Access policies — the community decides who can call the endpoint and under what terms.

This aligns with OCAP® principles (Ownership, Control, Access, Possession), ensuring that sensitive language data is governed by the community rather than a third-party platform.

tip

Combine the api method with a private deployment (e.g., a community-hosted VM or on-prem server) for the strongest data-sovereignty posture. See Support a Low-Resource Language for a full walkthrough.

Cost Estimation

The api method returns null for cost estimation by default — your service controls pricing. If you want to provide cost transparency, have your API return a cost field in the metadata:

{
  "translations": { "...": "..." },
  "metadata": {
    "cost": {
      "estimatedCost": 0.0042,
      "currency": "USD",
      "source": "my-service-pricing"
    }
  }
}

Best Practices

Mag-return ng empty strings para sa mga failures — Huwag i-return ang source string bilang isang "translation." Mag-return ng "" at sasaluhin ito ng quality gate ng i18n-rosetta. I-i-skip ang key at ire-retry sa susunod na sync.
Isama ang mga confidence scores — Kung kaya ng inyong pipeline na mag-estimate ng quality, i-return ito sa metadata. Makakatulong po ito sa quality auditing.
Mag-implement ng health checks — Magdagdag ng GET /health endpoint para ma-verify ng i18n-rosetta ang connectivity bago mag-start ng isang malaking sync.
Mag-rate limit nang maayos — Kung may throughput limits ang inyong pipeline, mag-return ng 429 status codes. Magba-back off ang batch system ng i18n-rosetta.
I-log ang lahat — Pwedeng mag-fail silently ang mga multi-step pipelines. I-log ang input/output ng bawat step para sa debugging.

Licensing

Ang api method pattern ay fully open — walang licensing restrictions sa pag-wrap ng inyong sariling translation pipeline bilang isang HTTP service. Ang gds-mt-eval-harness ay available sa ilalim ng MIT license para sa mga reference implementations.

Tingnan Din

Translation Methods — overview ng bawat built-in method (openai, google, api, atbp.)
Plugin Specification — full schema para sa i18n-rosetta.config.json kasama ang mga api method fields
Support a Low-Resource Language — end-to-end guide para sa mga under-resourced languages, kasama ang OCAP principles
Architecture — kung paano gumagana ang sync loop, batching, at method dispatch ng i18n-rosetta
MT Evaluation — evaluation methodology, metrics, at ang leaderboard submission process
Method Leaderboard — live quality rankings across methods at language pairs

Bakit isang API Service?​

Architecture​

Pag-set Up ng Inyong Service​

Request Format​

Minimal Express Server​

Configuring i18n-rosetta​

Case Study: Plains Cree Pipeline​

Running Evaluations​

Authentication​

Data Sovereignty & OCAP Principles​

Cost Estimation​

Best Practices​

Licensing​

Tingnan Din​