Microservices Architecture Overview
To maintain high responsiveness and prevent heavy computational or external API workloads from degrading core backend performance, LearnWay decouples specialized subsystems into standalone microservices.Standalone Microservices Directory
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AI Microservice (
learnway-ai-service/):- Generative AI tutoring, dynamic onboarding skill assessments, and automated Capstone Project code evaluations.
- Injects course lesson slides and student history into Google Gemini prompt pipelines with Redis caching.
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Notification Microservice (
learnway-notification-service/):- Multi-channel delivery engine coordinating mobile push notifications (FCM), transactional emails (Resend), and in-app alert synchronization.
- Dedicated token lifecycle management and BullMQ background queue workers.
Key Benefits of Decomposition
- Independent Scalability: High-frequency LLM tutoring requests or mass push broadcast batches scale in dedicated container instances without consuming thread pool resources on the core REST/WebSocket server.
- Failure Isolation: Upstream rate limits or temporary third-party API latency (Gemini, FCM, Resend) never impact user login, lesson navigation, or PvP quiz battles.
- Optimized Resource Profiles: The AI service can be allocated memory and compute tuned for prompt streaming and payload transformations, while the core backend remains lightweight.