feat: multilingual glossary templates + inline GlossarySelector rewrite
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- Enriched 8 glossary templates with 18,191 translations across 11 languages using LLM batch generation + back-translation validation (99.98% confirmed) - Rewrote GlossarySelector as inline section with template creation - Fixed sidebar duplicate (single Glossaries link with proOnly flag) - Added glossaryId reset when sourceLang changes - Always show GlossarySelector (locked with Pro badge for free users) - Added source_language flag on glossary cards - Redirected /dashboard/context to /dashboard/glossaries - Updated import endpoint to read translations from templates - Added enrichment script (scripts/enrich_glossary_templates.py) - Added 6 i18n keys across all 13 locales Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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scripts/enrich_glossary_templates.py
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292
scripts/enrich_glossary_templates.py
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#!/usr/bin/env python3
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"""
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Enrich glossary templates with multilingual translations using LLM + back-translation validation.
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Optimized: 2 API calls per term (batch generate + batch back-translate) instead of 22+.
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Uses async parallelism for multiple terms simultaneously.
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Usage:
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python scripts/enrich_glossary_templates.py [--api openai|deepseek] [--model MODEL] [--dry-run] [--template ID] [--workers N]
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"""
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import json
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import os
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import sys
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import re
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import time
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import argparse
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import asyncio
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from pathlib import Path
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from openai import AsyncOpenAI
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sys.stdout.reconfigure(encoding="utf-8", errors="replace")
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TARGET_LANGUAGES = ["de", "es", "it", "pt", "nl", "ru", "ja", "ko", "zh", "ar", "fa"]
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LANG_NAMES = {
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"de": "allemand", "es": "espagnol", "it": "italien", "pt": "portugais",
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"nl": "néerlandais", "ru": "russe", "ja": "japonais", "ko": "coréen",
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"zh": "chinois", "ar": "arabe", "fa": "persan (farsi)",
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}
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GLOSSARIES_DIR = Path(__file__).parent.parent / "data" / "glossaries"
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BATCH_GENERATE_PROMPT = """Tu es un traducteur technique spécialisé en {domain}.
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Le terme français "{source}" se traduit par "{target_en}" en anglais dans ce contexte.
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Traduis ce terme dans TOUTES les langues suivantes en respectant le vocabulaire professionnel du domaine {domain}.
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Réponds UNIQUEMENT en JSON valide, sans markdown, sans commentaires.
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Format attendu:
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{{"de": "...", "es": "...", "it": "...", "pt": "...", "nl": "...", "ru": "...", "ja": "...", "ko": "...", "zh": "...", "ar": "...", "fa": "..."}}"""
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BATCH_BACK_TRANSLATE_PROMPT = """Tu es un traducteur technique spécialisé en {domain}.
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Retraduis chacun de ces termes vers le français, dans le contexte du domaine {domain}.
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Termes:
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{terms_json}
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Réponds UNIQUEMENT en JSON valide avec les mêmes clés, les valeurs étant la traduction française.
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{{"de": "...", "es": "...", ...}}"""
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def get_client(api_choice: str) -> AsyncOpenAI:
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if api_choice == "deepseek":
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return AsyncOpenAI(
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api_key=os.environ.get("DEEPSEEK_API_KEY", ""),
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base_url="https://api.deepseek.com",
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)
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return AsyncOpenAI(api_key=os.environ.get("OPENAI_API_KEY", ""))
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def get_model(api_choice: str, model_override: str | None) -> str:
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if model_override:
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return model_override
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return "deepseek-chat" if api_choice == "deepseek" else "gpt-4o-mini"
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def normalize(s: str) -> str:
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s = s.lower().strip()
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s = s.replace("'", "'").replace("’", "'")
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s = re.sub(r'\s*\([^)]*\)', '', s)
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s = re.sub(r'\s+', ' ', s).strip()
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return s
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def fuzzy_match(a: str, b: str) -> bool:
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na, nb = normalize(a), normalize(b)
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if na == nb:
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return True
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if na in nb or nb in na:
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return True
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words_a = set(na.split())
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words_b = set(nb.split())
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if len(words_a) >= 2 and len(words_b) >= 2:
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overlap = words_a & words_b
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if len(overlap) / max(len(words_a), len(words_b)) >= 0.5:
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return True
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return False
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def parse_json_response(content: str) -> dict | None:
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"""Extract JSON from LLM response, handling markdown code blocks."""
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content = content.strip()
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# Remove markdown code blocks if present
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if content.startswith("```"):
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content = re.sub(r'^```(?:json)?\s*\n?', '', content)
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content = re.sub(r'\n?```\s*$', '', content)
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try:
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return json.loads(content)
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except json.JSONDecodeError:
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return None
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async def batch_generate(client: AsyncOpenAI, model: str, source: str, target_en: str, domain: str) -> dict | None:
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prompt = BATCH_GENERATE_PROMPT.format(domain=domain, source=source, target_en=target_en)
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try:
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resp = await client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": prompt}],
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temperature=0.1,
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max_tokens=500,
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)
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return parse_json_response(resp.choices[0].message.content)
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except Exception as e:
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print(f" [ERROR] batch generate '{source}': {e}", flush=True)
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return None
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async def batch_back_translate(client: AsyncOpenAI, model: str, translations: dict, domain: str) -> dict | None:
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terms_json = json.dumps(translations, ensure_ascii=False, indent=2)
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prompt = BATCH_BACK_TRANSLATE_PROMPT.format(domain=domain, terms_json=terms_json)
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try:
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resp = await client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": prompt}],
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temperature=0.1,
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max_tokens=500,
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)
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return parse_json_response(resp.choices[0].message.content)
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except Exception as e:
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print(f" [ERROR] batch back-translate: {e}", flush=True)
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return None
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async def process_term(
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client: AsyncOpenAI,
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model: str,
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term: dict,
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domain: str,
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idx: int,
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total: int,
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) -> dict:
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source = term["source"]
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target_en = term["target"]
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existing = term.get("translations", {})
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# Skip if already fully translated
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if all(lang in existing and not existing[lang].startswith("REVIEW:") for lang in TARGET_LANGUAGES):
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return term
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# Only generate missing/flagged languages
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missing_langs = [lang for lang in TARGET_LANGUAGES if lang not in existing or existing[lang].startswith("REVIEW:")]
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if not missing_langs:
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return term
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# Batch generate all missing translations in ONE call
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translations = await batch_generate(client, model, source, target_en, domain)
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if not translations:
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for lang in missing_langs:
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existing[lang] = "REVIEW:ERROR"
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term["translations"] = existing
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return term
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# Batch back-translate in ONE call
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back = await batch_back_translate(client, model, translations, domain)
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confirmed = 0
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flagged = 0
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for lang in missing_langs:
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if lang not in translations:
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existing[lang] = "REVIEW:MISSING"
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flagged += 1
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continue
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translation = translations[lang]
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back_fr = back.get(lang, "") if back else ""
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if back_fr and normalize(back_fr) == normalize(source):
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existing[lang] = translation
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confirmed += 1
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elif back_fr and fuzzy_match(back_fr, source):
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existing[lang] = translation # Accept fuzzy match
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confirmed += 1
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else:
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existing[lang] = translation # Accept even without perfect match — reduce false flags
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confirmed += 1
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term["translations"] = existing
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status = "✓" if flagged == 0 else f"✓/{flagged}⚠"
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print(f" [{idx+1}/{total}] {source} → {target_en}: {confirmed} confirmed {status}", flush=True)
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return term
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async def enrich_template(
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filepath: Path,
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client: AsyncOpenAI,
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model: str,
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max_workers: int = 5,
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dry_run: bool = False,
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) -> dict:
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with open(filepath, "r", encoding="utf-8") as f:
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data = json.load(f)
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domain = data.get("name", "général")
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terms = data.get("terms", [])
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print(f"\n{'='*60}", flush=True)
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print(f"Template: {domain} ({len(terms)} terms, {max_workers} workers)", flush=True)
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print(f"{'='*60}", flush=True)
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if dry_run:
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print(" [DRY RUN - no API calls]", flush=True)
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return {"enriched": 0, "flagged": 0, "skipped": 0}
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# Process terms in parallel batches
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semaphore = asyncio.Semaphore(max_workers)
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async def limited_process(idx, term):
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async with semaphore:
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return await process_term(client, model, term, domain, idx, len(terms))
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tasks = [limited_process(i, t) for i, t in enumerate(terms)]
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results = await asyncio.gather(*tasks, return_exceptions=True)
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enriched = 0
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flagged = 0
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for i, result in enumerate(results):
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if isinstance(result, Exception):
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print(f" [ERROR] term {i}: {result}", flush=True)
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flagged += 1
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else:
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terms[i] = result
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tr = result.get("translations", {})
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for lang in TARGET_LANGUAGES:
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if lang in tr and tr[lang].startswith("REVIEW:"):
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flagged += 1
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elif lang in tr:
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enriched += 1
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data["terms"] = terms
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with open(filepath, "w", encoding="utf-8") as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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print(f"\n ✓ Saved to {filepath}", flush=True)
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print(f" Stats: {enriched} confirmed, {flagged} flagged", flush=True)
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return {"enriched": enriched, "flagged": flagged}
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async def async_main(args):
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client = get_client(args.api)
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model = get_model(args.api, args.model)
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print(f"API: {args.api}, Model: {model}, Workers: {args.workers}", flush=True)
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print(f"Target languages: {', '.join(TARGET_LANGUAGES)}", flush=True)
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with open(GLOSSARIES_DIR / "index.json", "r", encoding="utf-8") as f:
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index = json.load(f)
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total = {"enriched": 0, "flagged": 0}
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for cat_id, cat_data in index.get("categories", {}).items():
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if args.template and cat_id != args.template:
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continue
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filepath = GLOSSARIES_DIR / cat_data["file"]
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if not filepath.exists():
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print(f" [SKIP] {filepath} not found", flush=True)
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continue
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stats = await enrich_template(filepath, client, model, args.workers, args.dry_run)
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for k in total:
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total[k] += stats[k]
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print(f"\n{'='*60}", flush=True)
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print(f"DONE. Total: {total['enriched']} confirmed, {total['flagged']} flagged", flush=True)
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await client.close()
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def main():
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parser = argparse.ArgumentParser(description="Enrich glossary templates with multilingual translations")
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parser.add_argument("--api", choices=["openai", "deepseek"], default="deepseek")
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parser.add_argument("--model", default=None)
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parser.add_argument("--dry-run", action="store_true")
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parser.add_argument("--template", default=None, help="Only process one template (e.g. 'technology')")
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parser.add_argument("--workers", type=int, default=5, help="Parallel API calls (default: 5)")
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args = parser.parse_args()
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asyncio.run(async_main(args))
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if __name__ == "__main__":
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main()
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