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How champollion Works

champollion translates your app's locale files with one command. Here's what happens under the hood.

The Pipeline​

When you run npx champollion sync, champollion executes a six-stage pipeline:

Key design decisions:

  • Change detection via SHA-256 hashes. Champollion tracks every source value with a hash in .champollion.lock. When you update an English string, only that key gets re-translated. This is why sync is fast on repeat runs — it does minimal work.

  • Translation Memory caching. Before making any API call, champollion checks .champollion/tm.json for cached translations (keyed by source text + locale + method). On a typical re-sync after changing one key, 142 keys come from cache and 1 key hits the API.

  • Quality gate before write. Every translation passes five automated checks (empty, source echo, hallucination loop, length inflation, script compliance) before it touches your files. Failures are logged, never silently accepted.

  • Retry cascade on failure. If a batch fails (JSON parse error, API timeout), champollion retries with progressively smaller batches: full → half → individual. This isolates the problem key without blocking the rest.

Translation Methods​

Champollion supports four translation methods, each suitable for different scenarios:

MethodHow it worksBest for
llmStructured prompt to any OpenRouter modelWell-resourced languages
llm-coachedSame prompt + grammar rules, dictionary, and style notesLanguages where LLMs make predictable errors
google-translateGoogle Cloud Translation API batch requestHigh-resource languages with good GT support
apiHTTP POST to your own endpointCustom pipelines, community-controlled models

Methods are configured per language pair. You might use google-translate for French but llm-coached for Plains Cree — each pair gets the method that works best for it.

Coaching Data​

For llm-coached pairs, coaching data gives the LLM explicit linguistic knowledge: grammar rules, forced terminology, and style preferences. This is injected into every prompt as structured context.

coaching/crk.json
{
  "grammar_rules": ["Animate nouns take different plural forms than inanimate nouns"],
  "dictionary": {"welcome": "ᑕᓂᓯ", "settings": "ᐃᑕᐢᑌᐘᐃᓇ"},
  "style_notes": "Use Standard Roman Orthography (SRO) unless explicitly configured otherwise."
}

Coaching data is the primary mechanism for improving translation quality without fine-tuning a model. Change the rules → re-run sync → see if it helps. Iteration is instant.

Plugins​

Plugins are pre-packaged translation recipes for specific language pairs. They're JSON manifests — not code — that tell champollion which method to use, with what settings, and what quality has been benchmarked.

champollion plugin install ./crk-coached-v3/
champollion sync   # uses the installed plugin for en→crk

Plugins bridge the gap between research and production: a method that scores well in the MT Eval Arena can be packaged as a plugin and deployed here.

The Bigger Picture​

champollion is one half of a two-part ecosystem:

  • MT Eval Arena — where translation methods are developed and proven with reproducible benchmarking
  • champollion — where proven methods are deployed to translate real content

The Eval Harness Bridge connects the two. A method that proves itself in the Arena deploys here. Speaker feedback from production improves the next version.

Dive Deeper​