- teach.py, questions.py: replace f-string SQL with parameterized CAST(:vec AS vector) queries
- auth.py: add reusable check_rate_limit() Redis helper
- teach.py: rate limit /chat to 30 req/10min per user
- tts.py: rate limit /speak to 60 req/hr per user
- teach.py: stronger system prompt — no clarifying questions, use markdown, answer directly
- TeachChat.jsx: render assistant messages with ReactMarkdown (already in package.json)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- FastAPI backend with JWT auth, roles (admin/moderator/user)
- PDF upload (up to 500MB) with streaming, PyMuPDF text extraction
- ChromaDB vectorization per page with metadata
- LiteLLM AI question extraction from PDF (not generation)
- Image extraction from PDF pages, graceful fallback
- Quiz modes: timed (countdown timer) + learning (answers shown inline)
- Page-by-page question navigation with dot navigator
- TTS endpoint using LiteLLM (Google Vertex / OpenAI voices)
- Admin dashboard: AI model management per task, user role management
- Moderator role: upload PDFs, create sections, generate quizzes
- Spaced repetition reminders via SMTP email (SM-2 intervals)
- APScheduler daily reminder jobs
- Celery + Redis for background PDF processing
- React frontend with all pages
- Docker Compose deployment (nginx + backend + celery + redis)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>