🧠 MindVault — AI-Powered Journal API

Production-ready FastAPI backend showcasing enterprise-level architecture, AI integration, and comprehensive DevOps practices.

MindVault is a sophisticated journaling API that demonstrates advanced backend engineering skills through AI-powered insights, comprehensive monitoring, production-ready architecture, and modern DevOps practices.

🎯 Engineering Highlights

🏗️ Architecture & Design

Modular FastAPI Architecture - Clean separation of concerns with routes, schemas, and services
Type-Safe Database Layer - SQLModel with Pydantic validation and automatic API documentation
AI Integration Pipeline - OpenAI GPT for intelligent journal reflections and analysis
Advanced Analytics Engine - Complex SQL queries for mood trends, streaks, and behavioral insights

🚀 DevOps & Production Features

Production Health Monitoring - Comprehensive endpoints with system metrics and database status
Multi-Stage Docker Builds - Optimized containers with security hardening and non-root users
Database Migration Management - Alembic for version-controlled schema evolution
Security-First Design - JWT authentication, rate limiting, input validation, and CORS protection

🛠️ Technical Stack

Layer	Technology	Purpose
Framework	FastAPI 0.116	High-performance async web framework
Database	PostgreSQL 15	Production database with connection pooling
ORM	SQLModel	Type-safe database operations
AI	OpenAI GPT	Intelligent content analysis and reflections
Auth	JWT + Passlib	Secure token-based authentication
Caching	slowapi	Redis-compatible rate limiting
Containerization	Docker + Compose	Multi-stage production builds
Migrations	Alembic	Database schema version control
Testing	Pytest	Comprehensive test coverage
Monitoring	Custom Health Checks	System and database monitoring

🚀 Quick Start

🐳 Docker Deployment (Recommended)

# Clone and navigate
git clone https://github.com/yourusername/mindvault-backend.git
cd mindvault-backend

# Start all services (PostgreSQL + API + pgAdmin)
docker-compose up --build -d

# Verify health status
curl http://localhost:8000/health/detailed

# Apply database migrations
docker-compose exec api alembic upgrade head

# Access interactive API docs
open http://localhost:8000/docs

💻 Local Development

# Set up environment
python3 -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your OpenAI API key and database settings

# Run migrations and start server
alembic upgrade head
uvicorn app.main:app --reload

📊 API Architecture

🔐 Authentication System

# Register new user
POST /auth/register
{
  "email": "[email protected]",
  "password": "secure_password"
}

# Login and receive JWT token
POST /auth/login
# Returns: {"access_token": "eyJ...", "token_type": "bearer"}

📝 Journal Management

# Create journal entry with AI reflection
POST /journals
Authorization: Bearer <token>
{
  "title": "Morning Reflection",
  "content": "Started the day with meditation and feel centered.",
  "mood": "peaceful"
}
# Automatically generates AI-powered reflection via OpenAI

📈 Advanced Analytics

# Get comprehensive statistics
GET /journals/stats
# Returns: total entries, word counts, most common moods, writing patterns

# Analyze mood trends over time
GET /journals/mood-trends
# Returns: daily mood distribution with temporal analysis

# Track writing streaks and habits
GET /journals/streak
# Returns: current streak, longest streak, consistency metrics

🔍 Powerful Search & Filtering

# Advanced filtering with pagination
GET /journals/filter?mood=happy&search=meditation&limit=10&offset=0
# Supports: mood filtering, full-text search, date ranges, pagination

💡 Advanced Features

🤖 AI-Powered Insights

Smart Reflections: OpenAI GPT analyzes journal entries and provides personalized insights
Mood Analysis: Intelligent sentiment detection and trend analysis
Content Enhancement: AI suggests themes and patterns in writing

📊 Analytics Dashboard Data

Writing Statistics: Word counts, entry frequency, time-based patterns
Mood Tracking: Emotional trends with statistical analysis
Habit Formation: Streak tracking and consistency metrics
Behavioral Insights: Weekly summaries and long-term trends

🔒 Production Security

JWT Authentication: Secure token-based user sessions
Rate Limiting: Protection against API abuse with slowapi
Input Validation: Comprehensive Pydantic schema validation
CORS Protection: Configurable cross-origin resource sharing
Container Security: Non-root users and minimal attack surface

🏥 Health Monitoring & Observability

System Health Endpoints

# Basic health check
curl http://localhost:8000/health
# {"status": "healthy", "timestamp": "2025-07-25T14:41:04Z", "service": "MindVault API"}

# Comprehensive system status
curl http://localhost:8000/health/detailed
# Returns: database status, memory usage, disk space, system info

Sample Detailed Health Response:

{
  "status": "healthy",
  "service": "MindVault API",
  "version": "1.0.0",
  "checks": {
    "database": {"status": "healthy", "type": "postgresql"},
    "memory": {"status": "healthy", "usage_percent": 28.2, "available_mb": 2814.54},
    "disk": {"status": "healthy", "usage_percent": 1.9, "free_gb": 208.14}
  },
  "environment": {
    "python_version": "3.11.13",
    "platform": "posix"
  }
}

Kubernetes-Ready Probes

# Readiness probe - is the app ready to serve traffic?
GET /health/ready

# Liveness probe - is the app still alive?
GET /health/live

🧪 Testing & Quality Assurance

# Run comprehensive test suite
pytest -v

# Generate coverage report
pytest --cov=app --cov-report=html

# Test specific modules
pytest tests/test_auth.py tests/test_journals.py -v

Test Coverage Areas:

✅ Authentication flows and JWT validation
✅ CRUD operations for journal entries
✅ AI integration and reflection generation
✅ Analytics and filtering endpoints
✅ Health monitoring systems
✅ Error handling and edge cases

🐳 Production Docker Architecture

Multi-Stage Build Optimization

# Build stage - includes compilation dependencies
FROM python:3.11-slim as builder
RUN apt-get update && apt-get install -y gcc python3-dev
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Production stage - minimal runtime image
FROM python:3.11-slim as production
# Copy only compiled packages, not build tools
# Non-root user for security
# Health check integration

Container Security Features

Non-root execution: App runs as dedicated user with minimal privileges
Minimal base image: Python slim for reduced attack surface
Health checks: Built-in container health monitoring
Resource optimization: Multi-stage builds for smaller production images

📁 Professional Project Structure

mindvault-backend/
├── app/
│   ├── routes/              # Modular API endpoints
│   │   ├── auth_routes.py   # Authentication endpoints
│   │   ├── journal_routes.py # Journal CRUD + analytics
│   │   ├── health_routes.py # System monitoring
│   │   └── ai_routes.py     # AI integration endpoints
│   ├── schemas/             # Pydantic models for validation
│   ├── ai/                  # OpenAI integration utilities
│   ├── models.py           # SQLModel database models
│   ├── auth.py             # JWT authentication logic
│   ├── database.py         # Database connection management
│   ├── config.py           # Environment configuration
│   └── main.py             # FastAPI application factory
├── tests/                  # Comprehensive test suite
├── alembic/                # Database migrations
├── docker-compose.yml      # Multi-service orchestration
├── Dockerfile             # Multi-stage production build
└── requirements.txt       # Dependency management

🔧 Configuration Management

Environment Variables

# Database Configuration
DATABASE_URL=postgresql://user:pass@localhost:5432/mindvault

# Authentication
SECRET_KEY=your-cryptographically-secure-key
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30

# AI Integration
OPENAI_API_KEY=your-openai-api-key

# Application Settings
DEBUG=False

Multi-Environment Support

Development: SQLite with debug logging
Testing: In-memory database with fixtures
Production: PostgreSQL with connection pooling
Docker: Container-optimized configuration

🚀 Production Deployment

Docker Compose Services

services:
  api:          # FastAPI backend with health checks
  db:           # PostgreSQL with persistent storage
  pgadmin:      # Database administration interface

Deployment Features

Health Monitoring: Comprehensive endpoint monitoring
Database Persistence: Volume-backed PostgreSQL storage
Service Discovery: Container networking with DNS resolution
Port Management: Configurable service exposure
Log Aggregation: Centralized container logging

💼 Enterprise-Ready Features

Scalability Considerations

Stateless Design: Horizontal scaling ready
Connection Pooling: Efficient database resource usage
Async Operations: Non-blocking I/O for high concurrency
Modular Architecture: Microservice decomposition ready

Monitoring & Observability

Health Endpoints: System status and metrics
Structured Logging: JSON-formatted application logs
Error Tracking: Comprehensive exception handling
Performance Metrics: Response time and resource monitoring

Security Best Practices

JWT Token Management: Secure authentication with expiration
Input Sanitization: SQL injection and XSS prevention
Rate Limiting: API abuse protection
Container Security: Non-root execution and minimal privileges

🔮 Architecture Evolution Path

Immediate Enhancements

Redis Caching: Session storage and performance optimization
Background Tasks: Celery integration for AI processing
API Versioning: Backward compatibility support
Request Logging: Comprehensive audit trails

Advanced Integrations

Kubernetes: Container orchestration and auto-scaling
Prometheus + Grafana: Metrics collection and visualization
ELK Stack: Centralized logging and analysis
CI/CD Pipeline: Automated testing and deployment

📚 Documentation & API Reference

Interactive API Docs: Available at /docs (Swagger UI)
Alternative Docs: Available at /redoc (ReDoc)
Health Monitoring: Real-time status at /health/detailed
OpenAPI Schema: Machine-readable API specification

🤝 Development Best Practices

This project demonstrates professional development standards:

✅ Type Safety: Full type hints with mypy compatibility
✅ Code Quality: Consistent formatting and linting
✅ Test Coverage: Comprehensive unit and integration tests
✅ Documentation: Self-documenting code with clear APIs
✅ Security: Production-ready authentication and validation
✅ Monitoring: Comprehensive health and performance tracking

🎯 Built for Portfolio Excellence

MindVault showcases:

Advanced Python Skills: FastAPI, async programming, type hints
Database Expertise: Complex queries, migrations, optimization
AI Integration: OpenAI API, intelligent content analysis
DevOps Proficiency: Docker, containerization, health monitoring
Production Readiness: Security, scalability, monitoring
Code Quality: Testing, documentation, professional structure

💡 This project demonstrates enterprise-level backend engineering with modern Python, comprehensive testing, AI integration, and production-ready DevOps practices.

Name		Name	Last commit message	Last commit date
Latest commit History 54 Commits
.github/workflows		.github/workflows
.vscode		.vscode
alembic		alembic
app		app
tests		tests
.bandit		.bandit
.dockerignore		.dockerignore
.env.template		.env.template
.gitignore		.gitignore
.gitignore.backup		.gitignore.backup
Dockerfile		Dockerfile
README.md		README.md
alembic.ini		alembic.ini
docker-compose.yml		docker-compose.yml
requirements.txt		requirements.txt
reset_db.sh		reset_db.sh

BackendCowboy/mindvault-backend

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