- teach.py: use _proxy_model() + pass api_key/api_base from settings (fixes LiteLLM provider error for openrouter/bedrock models)
- teach.py: accept model_id in ChatRequest so frontend can select model
- main.py: remove titan-embed-v2 from general seed, auto-delete legacy entry on startup
- main.py: kill stale idle-in-transaction DB connections at startup to prevent DDL lock hangs
- main.py: set lock_timeout=10s on DDL connection as fast-fail safety net
- TeachChat.jsx: fetch /teach/models, show selector dropdown in header when >1 model available
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Embedding:
- Embedding model now configurable via Admin UI (More tab) or LITELLM_EMBEDDING_MODEL env
- Calls LiteLLM proxy directly via httpx (bypasses LiteLLM library param validation)
- Passes dimensions=1024 to proxy; Redis setting overrides env var
- Default model: ge-gemini-embedding-001 (Gemini AI Studio, 1024-dim)
- Test button in admin UI to verify model works
- Fixed vector_service to use httpx + Redis model (was broken with non-prefixed model names)
Polly:
- Global enable/disable toggle in Admin → More settings (stored in Redis)
- /tts/voices filters out polly/* when disabled
- /tts/speak rejects polly requests when disabled
Job cancellation:
- POST /quizzes/job/{job_id}/cancel endpoint
- Cancel button on JobsPage for running jobs
- Celery task checks Redis status at each chunk boundary and exits cleanly
- Fixes DB lock on restart caused by cancelled jobs leaving open transactions
Admin UI:
- Settings tab renamed to "More" (heading: More Settings)
- Model row overflow fixed (minWidth: 0 + ellipsis on model_id)
- Embedding model search shows all proxy models (no auto-filter by "embed")
- Navbar correctly excludes cancelled/failed jobs from "extracting" count
README:
- Added Rebuild & Restart section with commands
- Updated embedding model reference
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>
