Commit Graph

6 Commits

Author SHA1 Message Date
d92bbf0fa6 feat(qualite): retenter automatiquement les traductions livrees dans la mauvaise ecriture
Le defaut le plus sournois du pipeline etait silencieux : de l'arabe
livre pour une cible persane (meme ecriture, mauvaise langue) passe
inapercu et part chez le lecteur. Desormais chaque lot traduit est
verifie par le detecteur d'ecritures, et chaque segment fautif est
redemande une fois au moteur avec une consigne renforcee (nom de la
langue + lettres specifiques, ex. persan پ چ ژ گ). La seconde tentative
ne remplace la premiere que si elle passe le meme controle.

- services/quality/script_detector.py : extraction d'un controle
  script_issue() reutilisable ; detect_arabic_variant signale des
 ormais un long texte en ecriture arabe sans aucune lettre specifique
  de la langue cible (arabe pur livre pour du persan/ourdou/pachto)
- translators/segments.py : retry_wrong_script() + construction de la
  consigne renforcee, sans jamais faire echouer le travail
- Word, Excel, PDF : branchement apres la memoire de traduction et les
  validations humaines ; le texte inchange (chiffres, noms propres) ne
  declenche jamais de retentative
- 14 tests nouveaux (tests/test_translators/test_script_retry.py)
2026-09-01 20:52:23 +02:00
526c87348f feat(translation): quality pipeline overhaul + new features (audit 2026-08-29)
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Translation quality & format preservation:
- Word: merge adjacent same-format runs into one unit (sentence-level
  coherence like inline-tag handling); translate comments/balloons;
  dedupe textbox collection (was translated twice); RTL no longer
  overrides center/justify alignment; CJK/Arabic font hints (eastAsia/cs)
- PPTX: chart translations now actually reach the output file
  (ChartPart.blob is read-only — rewrite chart XML in the saved ZIP);
  CJK typeface hints (a:ea)
- Excel: sheet renames no longer break references — rewrite cell
  formulas (3D/quoted), defined names, data validations, cond. formats
- PDF: bold/italic honored (hebo/heit/hebi); table cells never merge;
  unchanged blocks left untouched (typography preserved, fixes duplicate
  hyperlinks); attempted/changed stats + route gate now cover PDF;
  CJK font paths; scanned PDFs via Mistral OCR (detection + admin settings)

Features:
- formality param (formal/informal) + automatic regional-variant prompts
- output_mode=bilingual docx (source above translation)
- per-user translation memory on Redis (falls back to LRU), context-hashed
- QA report + 0-100 confidence score in job status; L0 on by default
- OpenAI-compatible providers: whole chunk in ONE numbered-JSON request
  (~15x fewer calls) with per-item fallback; base prompt always present
  (custom prompt no longer replaces translation instructions)

Infra & marketing alignment:
- plan-based engine gating + vision gating (closes paid-engine leak);
  /providers/available filtered per plan; 107 languages exposed
- zh-CN/zh-TW validation fixed; libmagic disabled on Windows (native crash)
- admin: Mistral OCR settings + engine status dashboard; httpx<0.28 pin
  (TestClient breakage); Prometheus test fixture fixed
- marketing docs aligned with code (PDF+OCR, retention, engines, pricing)
- security: .env.ionos/.env.production/provider_settings.json removed

Tests: 1173 passed / 0 failed (6 network tests deselected: free Google
endpoint temporarily blocked from this machine)
2026-08-29 18:38:09 +02:00
c794eff823 feat(quality): A4 — L2 Pro premium judge (8 dims, gpt-4o, Pro-gated, opt-in)
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2026-07-14 16:56:04 +02:00
8d0fc818ef feat(metrics): C3 — Prometheus counters for L0/L1/format-loss/retry
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2026-07-14 16:53:16 +02:00
4d466699fd feat(quality): A3 — L1 LLM judge via API (5 chunks, 0.0003 USD/job)
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L1 quality layer — uses a cheap LLM via the OpenAI-compatible API to
validate translation quality. Designed to be the SECOND line of defense
after L0 (script detection, length, pattern).

Architecture:
  - sampler.py — picks 5 representative chunks per job (longest first,
    skips L0-failed indices, skips too-short or identical pairs)
  - llm_judge.py — OpenAI-compatible client, binary verdict per chunk
    (accurate / fluent / correct_language / no_leaks), JSON output,
    hard timeout, defensive (never raises), cost estimation built in
  - pipeline.py — defensive wrapper that integrates both, never breaks
    a translation job, always logs a structured event

Integration:
  - 5 feature flags in config.py (QUALITY_L1_ENABLED, _LOG_ONLY, etc.)
  - QUALITY_L1_LOG_ONLY=true by default: log-only mode, verdict NEVER
    blocks or retries a job
  - Reuses the chunks extracted by L0 (no double work)
  - Passes the set of L0-failed indices so L1 doesn't re-judge them
  - Wrapped in try/except so a misconfigured L1 NEVER breaks a job

Default config: deepseek-chat via DeepSeek API
  - Cost: ~0.0003 USD per job (5 chunks)
  - Speed: typically 1-2s per call, hard ceiling at 8s
  - Easy to swap: just set L1_JUDGE_BASE_URL and L1_JUDGE_MODEL

LLM judge is intentionally a SEPARATE model from the translator
(self-evaluation bias mitigation — Meta/Stanford papers 2024-2025).

Tests:
  test_sampler.py — 9 tests covering the sampling strategy
  test_llm_judge.py — 22 tests covering init, parsing, mocked API,
    cost estimation, env factory
  test_l1_pipeline.py — 6 tests covering the wrapper
  Total new: 37 tests, all pass
  Grand total quality+format: 264 tests passing (0 regression)

  All 36 new tests + 111 L0 tests + 117 existing translator tests = 264

Phase 1 (observation) for 2 weeks. Then QUALITY_L1_LOG_ONLY=false
to enable auto-retry via the fallback chain.
2026-07-14 16:39:47 +02:00
f403b2851d feat(quality): add L0 quality layer (Track A1 + A2 of dev plan)
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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.
2026-07-14 16:17:43 +02:00