Architecture¶

CitizenAI is built with a modern, scalable architecture designed for government-scale citizen engagement. This document outlines the system design, components, and architectural decisions.

System Overview¶

CitizenAI follows a layered architecture pattern with clear separation of concerns:

graph TB
    subgraph "Presentation Layer"
        A[Web UI] 
        B[Mobile UI]
        C[API Gateway]
    end

    subgraph "Application Layer"
        D[Flask Application]
        E[Authentication Service]
        F[Chat Engine]
        G[Analytics Engine]
        H[Concern Manager]
    end

    subgraph "Integration Layer"
        I[IBM Watson API]
        J[External APIs]
        K[Message Queue]
    end

    subgraph "Data Layer"
        L[Session Store]
        M[File Storage]
        N[Analytics DB]
        O[Audit Logs]
    end

    A --> D
    B --> D
    C --> D

    D --> E
    D --> F
    D --> G
    D --> H

    F --> I
    G --> K
    H --> J

    E --> L
    F --> M
    G --> N
    H --> O

Technology Stack¶

Core Technologies¶

Component	Technology	Purpose
Web Framework	Flask 2.3+	HTTP server and routing
AI Engine	IBM Watson, IBM Granite	Natural language processing
Frontend	HTML5, CSS3, JavaScript	User interface
Visualization	Plotly.js	Interactive charts and graphs
Session Management	Flask-Session	User session handling
Authentication	Custom + OAuth	User authentication

Development Stack¶

# Core dependencies
CORE_STACK = {
    "runtime": "Python 3.11+",
    "web_framework": "Flask 2.3+",
    "ai_platform": "IBM Watson",
    "data_processing": "Pandas, NumPy",
    "visualization": "Plotly",
    "http_client": "Requests",
    "configuration": "python-dotenv"
}

# Development dependencies
DEV_STACK = {
    "testing": "pytest",
    "linting": "flake8, black",
    "documentation": "MkDocs Material",
    "type_checking": "mypy",
    "pre_commit": "pre-commit hooks"
}

Application Architecture¶

Modular Design¶

CitizenAI uses a modular architecture with clear boundaries:

src/
├── core/               # Core application logic
│   ├── __init__.py
│   ├── app.py         # Flask application factory
│   ├── config.py      # Configuration management
│   └── extensions.py  # Extension initialization
├── auth/              # Authentication module
│   ├── __init__.py
│   ├── models.py      # User models
│   ├── views.py       # Auth endpoints
│   └── utils.py       # Auth utilities
├── chat/              # Chat functionality
│   ├── __init__.py
│   ├── engine.py      # AI chat engine
│   ├── watson.py      # IBM Watson integration
│   └── handlers.py    # Message handlers
├── analytics/         # Analytics and reporting
│   ├── __init__.py
│   ├── processor.py   # Data processing
│   ├── visualizer.py  # Chart generation
│   └── exporters.py   # Data export
├── concerns/          # Concern management
│   ├── __init__.py
│   ├── models.py      # Concern models
│   ├── manager.py     # Business logic
│   └── notifications.py # Notification system
└── utils/             # Shared utilities
    ├── __init__.py
    ├── helpers.py     # Helper functions
    ├── validators.py  # Input validation
    └── decorators.py  # Custom decorators

Component Interaction¶

sequenceDiagram
    participant U as User
    participant W as Web UI
    participant A as Flask App
    participant C as Chat Engine
    participant I as IBM Watson
    participant D as Data Store

    U->>W: Send message
    W->>A: HTTP request
    A->>C: Process message
    C->>I: AI query
    I->>C: AI response
    C->>D: Store interaction
    C->>A: Return response
    A->>W: JSON response
    W->>U: Display answer

Data Architecture¶

Data Flow¶

flowchart TD
    A[User Input] --> B[Validation Layer]
    B --> C[Business Logic]
    C --> D[Data Processing]
    D --> E[Storage Layer]

    E --> F[Analytics Pipeline]
    F --> G[Aggregation Engine]
    G --> H[Visualization Layer]
    H --> I[Dashboard]

    C --> J[AI Processing]
    J --> K[Watson API]
    K --> L[Response Generation]
    L --> M[User Interface]

Storage Strategy¶

Session Storage¶

# File-based session storage
SESSION_CONFIG = {
    "type": "filesystem",
    "directory": "./flask_session",
    "threshold": 500,
    "mode": 0o600,  # Read/write for owner only
    "permanent": False,
    "use_signer": True
}

Analytics Data¶

# In-memory analytics with persistence
ANALYTICS_CONFIG = {
    "storage": "json_files",
    "directory": "./data/analytics",
    "retention_days": 90,
    "aggregation_levels": ["hourly", "daily", "weekly"],
    "real_time_buffer": 1000  # Keep last 1000 events in memory
}

File Storage¶

# Secure file handling
FILE_CONFIG = {
    "upload_folder": "./data/uploads",
    "max_file_size": 10 * 1024 * 1024,  # 10MB
    "allowed_extensions": [".jpg", ".png", ".pdf", ".txt"],
    "virus_scanning": True,
    "encryption": "AES-256"
}

Security Architecture¶

Security Layers¶

graph TB
    A[HTTPS/TLS] --> B[Web Application Firewall]
    B --> C[Rate Limiting]
    C --> D[Authentication]
    D --> E[Authorization]
    E --> F[Input Validation]
    F --> G[Output Encoding]
    G --> H[Audit Logging]

Security Implementation¶

Authentication Flow¶

class AuthenticationManager:
    """Handles user authentication and session management."""

    def authenticate(self, username: str, password: str) -> dict:
        """Authenticate user and create session."""
        # 1. Validate input
        if not self.validate_credentials(username, password):
            raise AuthenticationError("Invalid credentials")

        # 2. Check user exists and is active
        user = self.get_user(username)
        if not user or not user.is_active:
            raise AuthenticationError("User not found or inactive")

        # 3. Verify password
        if not self.verify_password(password, user.password_hash):
            self.log_failed_attempt(username)
            raise AuthenticationError("Invalid password")

        # 4. Create session
        session = self.create_session(user)
        self.log_successful_login(user)

        return {
            "user": user.to_dict(),
            "session": session.to_dict()
        }

Authorization System¶

class AuthorizationManager:
    """Role-based access control."""

    PERMISSIONS = {
        "viewer": ["read"],
        "staff": ["read", "write"],
        "manager": ["read", "write", "manage"],
        "admin": ["read", "write", "manage", "admin"]
    }

    def check_permission(self, user: User, resource: str, action: str) -> bool:
        """Check if user has permission for action on resource."""
        required_permission = f"{resource}:{action}"
        user_permissions = self.get_user_permissions(user)
        return required_permission in user_permissions

API Architecture¶

RESTful Design¶

CitizenAI follows REST principles with consistent patterns:

# URL patterns
API_PATTERNS = {
    "collections": "/api/v1/concerns",           # GET, POST
    "resources": "/api/v1/concerns/{id}",        # GET, PUT, DELETE
    "actions": "/api/v1/concerns/{id}/resolve",  # POST
    "filters": "/api/v1/concerns?status=open",   # GET with query params
}

# HTTP methods mapping
HTTP_METHODS = {
    "GET": "retrieve",
    "POST": "create", 
    "PUT": "update",
    "PATCH": "partial_update",
    "DELETE": "destroy"
}

API Versioning Strategy¶

# Version handling
class APIVersioning:
    """Handle API versioning and backwards compatibility."""

    SUPPORTED_VERSIONS = ["v1"]
    DEFAULT_VERSION = "v1"

    def get_version(self, request):
        """Extract API version from request."""
        # 1. Check URL path: /api/v1/endpoint
        if "/v" in request.path:
            return request.path.split("/")[2]

        # 2. Check header: X-API-Version: v1
        if "X-API-Version" in request.headers:
            return request.headers["X-API-Version"]

        # 3. Default version
        return self.DEFAULT_VERSION

AI Integration Architecture¶

Watson Integration¶

graph LR
    A[User Message] --> B[Preprocessing]
    B --> C[Intent Detection]
    C --> D[Watson Assistant]
    D --> E[Response Generation]
    E --> F[Post-processing]
    F --> G[User Response]

    subgraph Watson Services
        D --> H[Natural Language Understanding]
        D --> I[Natural Language Generation]
        D --> J[Discovery Service]
    end

AI Pipeline Implementation¶

class AIEngine:
    """Core AI processing engine."""

    def __init__(self, watson_config: dict):
        self.watson = WatsonAssistant(**watson_config)
        self.preprocessor = MessagePreprocessor()
        self.postprocessor = ResponsePostprocessor()

    async def process_message(self, message: str, context: dict) -> dict:
        """Process user message through AI pipeline."""

        # 1. Preprocess message
        processed_message = self.preprocessor.clean(message)

        # 2. Send to Watson
        watson_response = await self.watson.message(
            message=processed_message,
            context=context
        )

        # 3. Post-process response
        final_response = self.postprocessor.format(watson_response)

        # 4. Extract metadata
        metadata = {
            "confidence": watson_response.get("confidence", 0),
            "intent": watson_response.get("intent", "unknown"),
            "entities": watson_response.get("entities", []),
            "sentiment": self.analyze_sentiment(message)
        }

        return {
            "response": final_response,
            "metadata": metadata
        }

Performance Architecture¶

Caching Strategy¶

# Multi-level caching
CACHE_CONFIG = {
    "levels": {
        "memory": {
            "type": "in_memory",
            "size": "100MB",
            "ttl": 300  # 5 minutes
        },
        "redis": {
            "type": "redis",
            "host": "localhost",
            "ttl": 3600  # 1 hour
        },
        "file": {
            "type": "filesystem",
            "directory": "./cache",
            "ttl": 86400  # 24 hours
        }
    }
}

Asynchronous Processing¶

import asyncio
from concurrent.futures import ThreadPoolExecutor

class AsyncProcessor:
    """Handle asynchronous operations."""

    def __init__(self):
        self.executor = ThreadPoolExecutor(max_workers=10)

    async def process_chat_message(self, message: str) -> dict:
        """Process chat message asynchronously."""
        loop = asyncio.get_event_loop()

        # Run AI processing in thread pool
        result = await loop.run_in_executor(
            self.executor,
            self.ai_engine.process,
            message
        )

        return result

Scalability Considerations¶

Horizontal Scaling¶

graph TB
    A[Load Balancer] --> B[App Instance 1]
    A --> C[App Instance 2] 
    A --> D[App Instance N]

    B --> E[Shared Session Store]
    C --> E
    D --> E

    B --> F[Shared File Storage]
    C --> F
    D --> F

Database Scaling¶

# Database connection pooling
DATABASE_CONFIG = {
    "pool_size": 20,
    "max_overflow": 50,
    "pool_timeout": 30,
    "pool_recycle": 3600,
    "pool_pre_ping": True
}

# Read/write splitting for large deployments
class DatabaseManager:
    def __init__(self):
        self.write_db = create_engine(WRITE_DB_URL, **DATABASE_CONFIG)
        self.read_db = create_engine(READ_DB_URL, **DATABASE_CONFIG)

    def get_engine(self, operation: str):
        """Route database operations to appropriate instance."""
        if operation in ["INSERT", "UPDATE", "DELETE"]:
            return self.write_db
        return self.read_db

Monitoring and Observability¶

Health Monitoring¶

class HealthMonitor:
    """System health monitoring."""

    def check_system_health(self) -> dict:
        """Comprehensive system health check."""
        return {
            "status": "healthy",
            "timestamp": datetime.utcnow().isoformat(),
            "components": {
                "database": self.check_database(),
                "watson_api": self.check_watson(),
                "file_storage": self.check_storage(),
                "memory": self.check_memory(),
                "disk": self.check_disk()
            },
            "metrics": self.get_performance_metrics()
        }

Logging Architecture¶

# Structured logging configuration
LOGGING_CONFIG = {
    "version": 1,
    "disable_existing_loggers": False,
    "formatters": {
        "json": {
            "format": "%(asctime)s %(name)s %(levelname)s %(message)s",
            "class": "pythonjsonlogger.jsonlogger.JsonFormatter"
        }
    },
    "handlers": {
        "console": {
            "class": "logging.StreamHandler",
            "formatter": "json"
        },
        "file": {
            "class": "logging.handlers.RotatingFileHandler",
            "filename": "logs/citizenai.log",
            "maxBytes": 10485760,  # 10MB
            "backupCount": 5,
            "formatter": "json"
        }
    },
    "loggers": {
        "citizenai": {
            "handlers": ["console", "file"],
            "level": "INFO",
            "propagate": False
        }
    }
}

Deployment Architecture¶

Container Strategy¶

# Multi-stage Docker build
FROM python:3.11-slim as builder
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

FROM python:3.11-slim as runtime
WORKDIR /app
COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages
COPY . .
EXPOSE 5000
CMD ["gunicorn", "--bind", "0.0.0.0:5000", "app:app"]

Environment Configuration¶

# docker-compose.yml
version: '3.8'
services:
  citizenai:
    build: .
    ports:
      - "5000:5000"
    environment:
      - FLASK_ENV=production
      - WATSON_API_KEY=${WATSON_API_KEY}
    volumes:
      - ./data:/app/data
      - ./logs:/app/logs
    restart: unless-stopped

  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf
      - ./ssl:/etc/nginx/ssl
    depends_on:
      - citizenai

Future Architecture Considerations¶

Microservices Migration¶

graph TB
    A[API Gateway] --> B[Auth Service]
    A --> C[Chat Service]
    A --> D[Analytics Service]
    A --> E[Concern Service]

    B --> F[User DB]
    C --> G[Watson API]
    D --> H[Analytics DB]
    E --> I[Concern DB]

    subgraph Message Bus
        J[Event Stream]
    end

    B --> J
    C --> J
    D --> J
    E --> J

Cloud-Native Features¶

Service mesh for inter-service communication
Event-driven architecture with message queues
Auto-scaling based on demand
Circuit breakers for resilience
Distributed tracing for debugging

Best Practices¶

Code Organization¶

Architecture Best Practices

Separation of concerns: Each module has a single responsibility
Dependency injection: Use factories and dependency injection
Configuration management: Centralized configuration
Error handling: Consistent error handling patterns
Testing: Unit tests for each component
Documentation: Clear API documentation

Performance Optimization¶

Lazy loading: Load resources only when needed
Connection pooling: Reuse database connections
Response compression: Gzip compression for API responses
CDN usage: Static assets served via CDN
Database indexing: Proper database indexes

Next Steps¶

Testing - Learn about testing strategies and implementation
Deployment - Detailed deployment guide
Contributing - How to contribute to the codebase