dolgolyov.alexei
4cbce89129
Backend:
- Structured JSON logging (python-json-logger) with request ID correlation
- RequestIDMiddleware (server-generated UUID, no client trust)
- Global exception handlers: AppException, RequestValidationError, generic 500
— all return consistent {"error": {code, message, request_id}} format
- Async rate limiting with lock + stale key eviction on auth endpoints
- Health endpoint checks DB connectivity, returns version + status
- Custom exception classes (NotFoundException, ForbiddenException, etc.)
- OpenAPI docs with tag descriptions, conditional URL (disabled in production)
- LOG_LEVEL, DOCS_ENABLED, RATE_LIMIT_* settings added
Docker:
- Backend: multi-stage build (builder + runtime), non-root user, HEALTHCHECK
- Frontend: removed dead user, HEALTHCHECK directive
- docker-compose: restart policies, healthchecks, Redis service, named volumes
for uploads/PDFs, rate limit env vars forwarded
- Alembic migrations run only in Dockerfile CMD (removed from lifespan)
Nginx:
- server_tokens off
- CSP, Referrer-Policy, Permissions-Policy headers
- HSTS ready (commented, enable with TLS)
Config & Docs:
- .env.production.example with production-ready settings
- CLAUDE.md project conventions (structure, workflow, naming, how-to)
- .env.example updated with new variables
Review fixes applied:
- Rate limiter: async lock prevents race condition, stale key eviction
- Request ID: always server-generated (no log injection)
- Removed duplicate alembic migration from lifespan
- Removed dead app user from frontend Dockerfile
- Health check logs DB errors
- Rate limit env vars forwarded in docker-compose
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Personal AI Assistant
A client-server web application for managing personal health and life areas with AI-powered assistance. Upload documents, chat with AI specialists, receive proactive health reminders, and track critical information across multiple life domains.
Key Features
- AI Chat with Specialists — create chats using configurable skills (e.g., cardiologist, nutritionist). Each skill shapes the AI's behavior as a domain expert.
- Document Management — upload health records, lab results, prescriptions, and consultation notes. AI extracts and indexes content for intelligent retrieval.
- Proactive Notifications — AI analyzes your health profile and schedules reminders (checkups, medication reviews) via in-app, email, or Telegram.
- PDF Compilation — request AI-generated health summaries as downloadable PDF documents.
- Global Memory — AI maintains a shared memory of critical health information across all your chats.
- Multi-language — English and Russian support.
Tech Stack
| Layer | Technology |
|---|---|
| Backend | Python 3.12, FastAPI, SQLAlchemy 2.0, Alembic |
| Frontend | React 18, TypeScript, Vite, Shadcn/ui, Tailwind CSS |
| Database | PostgreSQL 16 |
| AI | Claude API (Anthropic) |
| Notifications | WebSocket, Email (SMTP), Telegram Bot |
| Deployment | Docker Compose |
Getting Started
Prerequisites: Docker and Docker Compose installed.
# Clone the repository
git clone https://git.dolgolyov-family.by/alexei.dolgolyov/personal-ai-assistant.git
cd personal-ai-assistant
# Copy environment config
cp .env.example .env
# Edit .env with your API keys and settings
# Start all services
docker compose up -d
# Create initial admin user
docker compose exec backend python scripts/seed_admin.py
The app will be available at http://localhost.
Project Structure
personal-ai-assistant/
├── backend/ # FastAPI application
├── frontend/ # React SPA
├── telegram-bot/ # Telegram notification bot
├── nginx/ # Reverse proxy config
├── plans/ # Phase subplans
├── docker-compose.yml
└── GeneralPlan.md # Full implementation plan
License
Private project. All rights reserved.