Localization Urdu

Localization Urdu Skill

Instructions

1. Translation pipeline

   • Extract sections by heading from source Markdown/MDX
   • Translate via OpenAI/ChatKit translation model
   • Keep glossary for robotics terms (JSON file)
   • Cache translations with checksum per section
   • Rerun only when source checksum changes

2. Storage

   • Option A: Markdown copies (.ur.mdx) alongside English
   • Option B: Neon table translations(section_id, lang, content)
   • Tag Qdrant payload with lang for bilingual RAG

3. Frontend

   • Add language toggle in Docusaurus layout
   • Load Urdu content when available, fallback to English
   • Ensure chat widget passes desired language to backend

4. Quality

   • Spot-check key technical terms
   • Allow glossary overrides via JSON config
   • Keep technical terms consistent across chapters

Examples

# Translation function
async def translate_section(content: str, glossary: dict) -> str:
    prompt = f"""Translate to Urdu. Keep technical terms from glossary.
    Glossary: {glossary}
    
    Content: {content}
    """
    # Call OpenAI
    return translated_content

// glossary.json
{
  "Physical AI": "فزیکل اے آئی",
  "Humanoid Robot": "ہیومنائیڈ روبوٹ",
  "ROS 2": "ROS 2",
  "NVIDIA Isaac": "NVIDIA Isaac"
}

Definition of Done

• Translation pipeline runs and caches results; reruns are incremental
• Urdu toggle renders translated sections for at least one sample chapter
• Backend can return Urdu-context answers when requested