AI System Architecture Design
AI System Architecture Design: Duory Language Learning Assistant
1. Overview
Duory integrates with Duolingo to provide AI-enhanced language learning features (translation, romanization, kana support) via a cross-platform mobile app. The architecture prioritizes low-latency AI processing, secure data synchronization, and scalable subscription management.
2. High-Level Architecture
Simplified Diagram Flow:
Mobile App (React Native) → API Gateway → Microservices → AI Models / Duolingo API
↓
Database & Cache ← Monitoring & Auth
3. Technology Stack & Versions
| Component | Technology & Version | Rationale |
|---|---|---|
| Mobile App | React Native 0.72 + TypeScript 5.1 | Cross-platform support, hot-reload for rapid iteration |
| Backend | Node.js 20 (Express 4.18), Python 3.11 (FastAPI) | Async I/O for AI tasks; Python for ML pipelines |
| AI Models | Hugging Face Transformers 4.33 (MarianMT, Whisper), spaCy 3.7 | Pre-trained translation/romanization; lightweight NLP |
| Database | PostgreSQL 15 (Relational), Redis 7.2 (Cache) | ACID compliance for user data; low-latency caching |
| Cloud & DevOps | AWS ECS/Fargate, Terraform 1.5, GitHub Actions | Serverless scaling; IaC for reproducibility |
| Security | Auth0/JWT, TLS 1.3, AWS KMS | OAuth2 for Duolingo; E2E encryption |
4. Core Components
a) Mobile Client (React Native):
- UI Layer: React Navigation 6.x, Redux Toolkit 1.9
- Offline Support: WatermelonDB 0.27 (local sync with Duolingo data)
- Subscription: RevenueCat 4.0 (unified iOS/Android billing)
b) Backend Microservices (Node.js/Python):
| Service | Function | Tech Stack |
|---|---|---|
| User Management | Auth, profile, subscriptions | Node.js + Auth0 |
| Duolingo Sync | OAuth2-based data ingestion | Python + Duolingo API v1 |
| AI Processing | Translation/romanization/kana generation | FastAPI + Hugging Face |
| Analytics | User behavior tracking (via Amazon Kinesis) | AWS Lambda + Athena |
c) AI Model Deployment:
- Translation: Fine-tuned MarianMT (e.g., EN→JA/ES/FR) on AWS SageMaker.
- Romanization/Kana: Rule-based spaCy pipelines + BERT for context-aware romanization (e.g., Japanese → Romaji).
- Optimization: Model quantization (via ONNX Runtime) for 200ms latency on mobile.
d) Data Flow:
- User submits phrase via app → API Gateway (AWS API Gateway) routes to AI Processing.
- AI Service checks Redis cache; if miss, calls Hugging Face model → returns translated/romanized output.
- Duolingo Sync Service pulls learning data every 2h (cron job) → stores in PostgreSQL.
5. Security & Compliance
- Data Encryption: AES-256 at rest (PostgreSQL), TLS 1.3 in transit.
- Duolingo Integration: OAuth2 scopes limited to
read:profileandread:progress. - GDPR/CCPA: Anonymized analytics; user data deletion API.
- Rate Limiting: Redis-based throttling (100 reqs/min per user).
6. Scalability & Performance
- AI Load Handling: Auto-scaling SageMaker endpoints (min 2, max 20 instances).
- Caching Strategy: Redis LRU cache for common phrases (TTL=24h).
- Database: PostgreSQL read-replicas for analytics; connection pooling via PgBouncer.
- Target Metrics:
- Translation latency: <500ms (p95)
- API throughput: 1,000 RPM per service
- Uptime: 99.95% (SLA)
7. Implementation Steps
Phase 1: MVP Setup (4 Weeks)
- Scaffold React Native app with Duolingo OAuth2 login.
- Deploy PostgreSQL + Redis on AWS RDS/ElastiCache.
- Containerize AI services (Docker) + deploy on ECS.
Phase 2: AI Integration (6 Weeks)
- Fine-tune MarianMT models for target languages (e.g., EN→JA).
- Implement rule-based romanization/kana pipelines in Python.
- Build caching middleware for AI outputs.
Phase 3: Monetization & Monitoring (2 Weeks)
- Integrate RevenueCat for subscription tiers (free trial → premium).
- Set up Prometheus/Grafana for latency/error tracking.
- Conduct load testing (Locust 2.15).
8. Future Extensions
- Personalized Review: GPT-4 for adaptive lesson recommendations.
- Offline AI: TensorFlow Lite models for on-device translation.
- Multimodal Input: Whisper ASR for speech-to-text practice.
Character Count: 3,182