b876f13fac
- 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>
37 lines
851 B
Python
37 lines
851 B
Python
from datetime import datetime
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from pydantic import BaseModel
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class AIModelConfigCreate(BaseModel):
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model_config = {"protected_namespaces": ()}
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name: str
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model_id: str
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task: str # extraction, tts, general
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api_key: str | None = None
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is_active: bool = True
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is_default: bool = False
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class AIModelConfigResponse(BaseModel):
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model_config = {"protected_namespaces": ()}
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id: int
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name: str
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model_id: str
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task: str
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is_active: bool
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is_default: bool
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created_at: datetime
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class Config:
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from_attributes = True
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class AIModelConfigUpdate(BaseModel):
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model_config = {"protected_namespaces": ()}
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name: str | None = None
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model_id: str | None = None
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task: str | None = None
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api_key: str | None = None
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is_active: bool | None = None
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is_default: bool | None = None
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