sepehr
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L0 quality detection layer to catch translation failures BEFORE they
reach users. Pure Python/TypeScript, zero new dependencies, no API calls.
Backend (Python — services/quality/):
- Script detection: 145 langs mapped to 23 scripts (Latin, Cyrillic,
Greek, Arabic, Hebrew, CJK, Hangul, Kana, Devanagari, Bengali, etc.)
- Language confusion detection (e.g. Arabic text for French target)
- Arabic-script variant discrimination (Persian/Urdu/Pashto/Kurdish
confusion — e.g. Persian text returned when Arabic was requested)
- Length sanity check (with numeric/short-source exemptions)
- Prompt leak detection (Translation: / Voici la traduction: / 翻译:)
- Repetition hallucination detection (token + character level)
- File text extraction for .docx/.xlsx/.pptx/.pdf (no translator
changes needed)
- Defensive pipeline that never raises (L0 must NEVER break a job)
Frontend (TypeScript — wordly.art---traduction-de-documents/src/utils/):
- Exact 1:1 mirror of the Python module
- Zero dependencies, works in browser AND Node.js
- Native Unicode regex (\\p{L}/u) and codePoint iteration
- 63 tests using Node's built-in test runner
Integration:
- Feature-flagged: QUALITY_L0_ENABLED=false (default)
- Observation only: logs structured events, never modifies files
- try/except wrapped: impossible to break a translation job
- Lazy imports: only loaded when flag is on
- Zero impact on existing tests / behavior
Tests:
- 111 Python tests covering all paths (config, script, length, leak,
pipeline, file_extractor) — 100% pass
- 63 TypeScript tests (Node --test) — 100% pass
- 174/174 total tests for the L0 layer
Bug fixes in script mapping:
- yi (Yiddish) -> hebrew (was incorrectly mapped to arabic)
- dv (Maldivian) -> thaana (was incorrectly mapped to arabic)
- ja (Japanese) -> hiragana_katakana (distinguishes from Chinese CJK)
Phase 1 (backend) + Phase 2 (frontend) of Track A complete.
Next: Track B1 (Word/Excel format preservation quick wins).
Closes Track A phase 1+2 of the dev plan.
36 lines
1.0 KiB
Python
36 lines
1.0 KiB
Python
"""
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Quality check layer for translations.
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Track A1 — L0 backend (observation only).
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Pure Python, no new dependencies, no network calls.
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Designed to be ADDITIVE: existing translation flow is untouched.
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Public API:
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QualityCheckResult — per-chunk result dataclass
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DocumentQualityResult — aggregated result dataclass
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evaluate_chunk(...) — score a single (source, translation) pair
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evaluate_document(...) — score a list of pairs and aggregate
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run_l0_check(...) — defensive wrapper used by the route
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extract_sample(...) — extract text from a finished file
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"""
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from .script_detector import (
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QualityCheckResult,
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DocumentQualityResult,
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evaluate_chunk,
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evaluate_document,
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detect_arabic_variant,
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)
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from .pipeline import run_l0_check
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from .file_extractor import extract_sample
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__all__ = [
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"QualityCheckResult",
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"DocumentQualityResult",
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"evaluate_chunk",
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"evaluate_document",
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"detect_arabic_variant",
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"run_l0_check",
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"extract_sample",
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]
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