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.
Word fixes:
W1 — Fix hyperlink double-collect: a run inside <w:hyperlink> was
previously collected twice (once via paragraph.runs, once via
the manual hyperlink iter). Now uses a dedup set of element
ids to collect each run exactly once.
NB: python-docx 1.x's paragraph.runs does NOT include runs
inside hyperlinks, so the iteration now does both:
paragraph.runs (direct children) + a manual iter of all
<w:r> in the tree (catches hyperlink runs).
W2 — Fix footnotes import: used document.part.package.part_related_by
which doesn't exist in python-docx 1.x, so footnotes were never
collected. Now uses document.part.related_parts to find the
footnotes part by content type, walks the XML directly with
lxml (avoids the 'r_lst' error from wrapping foreign elements
in python-docx's Paragraph class), and registers a post-save
callback to re-write the footnotes.xml part with translated
text (since python-docx doesn't manage that part on save).
Same fix applied to endnotes.
W4 — Chart matching by element path: was matching <a:t> and <c:v>
elements by string equality, so two charts with the same text
(e.g. two 'Revenue' series) would only have the first one
translated. Now stores the XPath-like element path at collect
time and navigates to the exact element at apply time. Falls
back to string matching for legacy entries without a path.
Excel fixes:
E2 — Translate cell comments: openpyxl Comment objects are now
collected and their text translated. The Comment object is
replaced in place after translation.
E3 — Translate cell hyperlink display labels: cell.hyperlink.display
(or .target if no display) is collected and translated. The
URL itself is never sent for translation, so it remains
intact. A run that already exists for the cell value is
not double-translated (the dedup check is automatic).
E4 — Chart matching by element path: same fix as W4 but for
Excel. Two charts in the same workbook with the same text
now each get their own translation.
Tests:
Added tests/test_translators/test_b1_format_fixes.py with 11 tests
covering all the fixes. All 11 pass. Existing translator tests
(38 word + 38 excel + 30 pptx = 106) still pass — 0 regressions.
Total tests for the quality+format layer: 228 passing
(111 L0 Python + 63 L0 TypeScript + 11 B1 + 43 other translator).
All fixes are surgical: existing translation flow is preserved.
The only new file path through the code is for footnotes/endnotes
which previously didn't work at all.
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.
Frontend:
- Fix Framer Motion / motion-dom build error by pinning framer-motion to
11.18.2 (compatible with React 19 and Next.js 16).
- Add cross-env and build:local script to bypass standalone symlink errors
on Windows without Developer Mode.
- Allow NEXT_OUTPUT=default to disable standalone output for local builds.
- Refactor i18n: split 14,177-line src/lib/i18n.tsx into per-locale,
per-namespace JSON files under src/lib/i18n/messages/.
- Load English synchronously; other locales loaded on demand via dynamic
imports (reduces initial bundle, improves maintainability).
- Remove unused next-intl message files src/messages/en.json and fr.json.
Backend:
- Remove insecure legacy /api/v1/download/{filename} and /api/v1/cleanup/{filename}
endpoints. The job-based /api/v1/download/{job_id} already enforces ownership.
- Deduplicate texts in TranslationService.translate_batch before sending them
to the provider, reducing API calls for repeated strings.
- Pin httpx to <0.28 to fix TestClient incompatibility with starlette 0.35.1.
- Add pytest-cov and ruff dev dependencies/config.
DevOps:
- Remove hardcoded Grafana password from docker-compose.yml and
docker-compose.monitoring.yml; use GRAFANA_PASSWORD env var.
- Change default TRANSLATION_SERVICE from ollama to google in
docker-compose.yml (Ollama is an optional profile).
- Add GRAFANA_PASSWORD to .env.example.
- Add .coverage and frontend/pnpm-workspace.yaml to .gitignore.
Tests:
- Update API versioning tests for removed legacy endpoints.
- Add tests/test_translation_service.py for deduplication behavior.
Verified:
- pnpm run build:local passes.
- uv run pytest tests/test_providers/* tests/test_translation_service.py
tests/test_story_3_5_api_versioning.py tests/test_download_endpoint.py
tests/test_translators/test_excel_translator.py: provider/translator tests
pass; one pre-existing French error-message test still fails (message is
returned in English, unrelated to this change).
Major changes across backend, frontend, infrastructure:
- Provider system with model selection (Google, DeepL, OpenAI, Ollama, Google Cloud)
- Admin panel: user management, pricing, settings
- Glossary system with CSV import/export
- Subscription and tier quota management
- Security hardening (rate limiting, API key auth, path traversal fixes)
- Docker compose for dev, prod, and IONOS deployment
- Alembic migrations for new tables
- Frontend: dashboard, pricing page, landing page, i18n (en/fr)
- Test suite and verification scripts
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
