Examples¶

These examples use the real CLI and Python API entry points from this repository.

Translate Markdown with the CLI¶

translate -l "ko ja fr" -md

This translates Markdown files only and writes output under:

translations/ko/
translations/ja/
translations/fr/

Translate notebooks only¶

translate -l "zh-CN" -nb

Notebook links can later be normalized with:

migrate-links -l "zh-CN"

Translate images only¶

translate -l "pt-BR" -img

Image translation requires both an LLM provider and Azure AI Vision.

Preview without writing files¶

translate -l "de es" -md --dry-run

Dry runs are useful when checking token estimates, migration plans, or CI wiring.

Repair low-confidence translations¶

First evaluate translations:

evaluate -l "ko" -c 0.8

Then retranslate Markdown files below the threshold:

translate -l "ko" --fix -c 0.8 -md

Run from Python¶

from co_op_translator.api import run_translation

run_translation(
    language_codes="ko ja",
    root_dir="./course",
    markdown=True,
    yes=True,
)

Translate multiple roots from Python¶

from co_op_translator.api import run_translation

run_translation(
    language_codes="ko",
    markdown=True,
    root_dirs=[
        "./docs",
        "./labs",
    ],
)

Use explicit output groups¶

from co_op_translator.api import run_translation

run_translation(
    language_codes="ja",
    markdown=True,
    groups=[
        ("./course-a", "./localized/course-a"),
        ("./course-b", "./localized/course-b"),
    ],
)

Preserve glossary terms¶

from co_op_translator.api import run_translation

run_translation(
    language_codes="fr",
    markdown=True,
    glossaries=[
        "Co-op Translator",
        "Azure AI Foundry",
        "GitHub Actions",
    ],
)

Glossary terms are scoped to the API call and restored afterward.

CI-friendly translation command¶

translate -l "ko ja" -md -y -s

This auto-confirms prompts and saves DEBUG-level logs under logs/.