AI Project Management System

AI-powered project management system with Microsoft Teams integration, built with a Node.js/Express backend and Vue 3 frontend.

Features

• AI-Powered Project Planning — generate tasks, risks, timeline and Gantt chart via Google Gemini or OpenWebUI/Ollama; review and approve suggestions before they are applied
• Gantt Chart Generation — automatic SVG→PNG Gantt chart trimmed to actual task duration, exported and embedded in PDF reports
• PDF Export — full project analysis export (tasks, risks, timeline, Gantt chart) with Slovak diacritic support
• AI Chat — per-project AI assistant for follow-up questions
• Microsoft Teams Integration — SSO via Azure AD (MSAL), task sync to MS Planner, Teams channel creation
• Project & Task Management — create projects, assign tasks, track progress, manage team members and work teams
• Real-time Notifications — WebSocket-based in-app notifications
• Role-Based Access Control — Admin, Project Manager, Member roles with granular permissions
• Multi-language UI — English and Slovak (sk/en locale switching)
• System Administration — AI provider/model management, maintenance mode, system settings

Technology Stack

Backend

• Node.js v22+ with Express (ESM modules)
• PostgreSQL 15 via Sequelize ORM (Docker)
• JWT authentication + Microsoft MSAL SSO
• Google Gemini (@google/generative-ai) — primary AI provider
• OpenWebUI/Ollama — alternative local AI provider
• sharp — SVG to PNG conversion for Gantt charts
• ws — WebSocket server for real-time notifications
• Microsoft Graph API — Teams, Planner integration

Frontend

• Vue 3 with Composition API
• Pinia for state management
• PrimeVue component library
• Vue Router
• Axios
• Vite 5
• marked — markdown rendering for AI timeline output
• jsPDF — client-side PDF generation

Docker (Full Stack)

Run the entire application — PostgreSQL, backend, and frontend — with a single command.

Prerequisites

• Docker & Docker Compose
• A backend/.env file (copy from backend/.env.example and fill in your values)

Start everything

docker compose up --build -d

Service	Container	External Port
PostgreSQL 15	ai-project-management-db	9800
Backend (Node.js)	ai-project-management-backend	9801
Frontend (nginx)	ai-project-management-frontend	80

App is available at http://localhost. The frontend proxies /api/ and /socket.io/ to the backend automatically.

Useful commands

# View logs
docker compose logs -f

# Rebuild only the frontend (e.g. after UI changes)
docker compose up --build frontend -d

# Rebuild only the backend
docker compose up --build backend -d

# Stop all services
docker compose down

# Stop and remove volumes (wipes the database)
docker compose down -v

Environment & build args

Backend environment is read from backend/.env at runtime.
Frontend variables are baked in at build time via docker-compose.yml build args:

Build arg	Default in compose	Purpose
VITE_ENABLE_LOCAL_AUTH	"true"	Show email/password login form
VITE_API_BASE_URL	"http://localhost"	Base URL for WebSocket connection
VITE_API_URL	"http://localhost/api"	Base URL for REST API calls

To change them, edit the build.args section of the frontend service in docker-compose.yml and rebuild the frontend container.

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Getting Started (Local Development)

Prerequisites

• Node.js v18+
• Docker & Docker Compose (for PostgreSQL)
• Google Gemini API key or a running OpenWebUI/Ollama instance
• Microsoft Azure AD application (optional, for SSO + Teams integration)

1. Start the database

docker compose up -d

PostgreSQL runs on port 9800 (mapped to internal 5432).

2. Backend setup

cd backend
npm install
cp .env.example .env   # then fill in your values
node server.js

Backend runs on http://localhost:9801

Key .env variables:

Variable	Description
PORT	Backend port (default 9801)
DB_HOST / DB_PORT / DB_NAME / DB_USER / DB_PASSWORD	PostgreSQL connection
JWT_SECRET	JWT signing secret
AI_SERVICE_PROVIDER	gemini or openwebui
GEMINI_API_KEY	Google Gemini API key
OPENWEBUI_API_URL	OpenWebUI base URL
AZURE_AD_CLIENT_ID/SECRET/TENANT_ID	Azure AD for SSO
ENABLE_LOCAL_AUTH	true to allow email/password login

3. Frontend setup

cd frontend
npm install

Create frontend/.env:

VITE_API_BASE_URL=http://localhost:9801
VITE_API_URL=http://localhost:9801/api

npm run dev

Frontend runs on http://localhost:5173

Project Structure

backend/
├── config/           # DB, AI provider, MSAL configs
├── controllers/      # Route handlers
├── middleware/       # Auth, roles, maintenance, error handling
├── migrations/       # Sequelize DB migrations
├── models/           # Sequelize models (User, Project, Task, …)
├── prompts/          # AI prompt templates (en/ sk/)
├── routes/           # Express routers
├── services/         # Business logic
│   ├── aiServiceFactory.js         # Selects Gemini or OpenWebUI
│   ├── geminiProjectService.js     # Gemini AI logic
│   ├── openwebuiProjectService.js  # OpenWebUI AI logic
│   ├── ganttChartGenerator.js      # SVG→PNG Gantt generation
│   ├── promptLoaderService.js      # Locale-aware prompt loading
│   ├── teamsIntegrationService.js  # MS Teams/Planner
│   └── websocketService.js         # Real-time notifications
└── server.js

frontend/src/
├── components/       # Reusable UI components (ProjectForm, TaskCard, …)
├── composables/      # useProjectPdfExport, useNotifications, …
├── locales/          # en.json, sk.json
├── router/           # Vue Router
├── services/         # API client (api.js)
├── stores/           # Pinia stores (project, task, auth, …)
├── utils/            # Helpers
└── views/
    ├── projects/     # ProjectsView, ProjectDetailView, AIProjectSuggestionsView
    ├── admin/        # System settings, user management
    └── …

API Endpoints (summary)

Method	Path	Description
POST	/api/auth/login	Email/password login
GET	/api/auth/microsoft	Azure AD SSO
GET	/api/auth/me	Current user
GET/POST	/api/projects	List / create projects
GET/PUT/DELETE	/api/projects/:id	Project CRUD
POST	/api/projects/:id/ai-suggestions	Generate AI plan
POST	/api/projects/:id/apply-ai-suggestions	Apply approved suggestions
GET/POST	/api/tasks	List / create tasks
GET/PUT/DELETE	/api/tasks/:id	Task CRUD
POST	/api/ai/chat	AI chat message
GET	/api/ai/models	List available AI models
GET/PUT	/api/admin/settings	System settings
GET/POST	/api/work-teams	Work team management
GET	/api/notifications	User notifications

Teams Integration

• POST /api/teams/channel/:projectId - Create Teams channel
• POST /api/teams/message/:projectId - Send message to channel
• POST /api/teams/task - Create Teams task
• POST /api/teams/sync-task/:taskId - Sync task with Teams

Configuration

Microsoft Azure AD Setup

1. Register application in Azure Portal
2. Add redirect URI: http://localhost:5000/api/auth/callback
3. Generate client secret
4. Configure API permissions for Microsoft Graph

OpenAI Setup

1. Get API key from OpenAI platform
2. Add to environment variables
3. Configure model (gpt-4 recommended)

Development

Code Style

• Use clean, readable code with proper comments
• Follow ES6+ standards
• Use async/await for asynchronous operations
• Keep files modular and well-organized

Testing

# Backend tests
cd backend
npm test

# Frontend tests
cd frontend
npm test

Deployment

Backend Deployment

1. Build for production
2. Set environment variables
3. Deploy to hosting service (Heroku, AWS, Azure, etc.)

Frontend Deployment

npm run build

Deploy the dist folder to static hosting (Netlify, Vercel, etc.)

License

MIT License

Support

For issues and questions, please create an issue in the repository.