Feat/part2 LMS Integration and Nudge Engine#10
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- Setup FastAPI app, routers, and health checks - Add Pydantic domain schemas and env configuration - Build LMS mock adapter and nudge engine skeleton - Add Pytest suite with 86% coverage and CI workflow - Add Dockerfile and docker-compose
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LMS Integration & Nudging Engine for TAP Multilingual Voice Agent
Summary
This PR introduces LMS integration and a rule-based nudging engine to enhance learner engagement. The system now fetches learner context from TAP LMS and generates personalized nudge plans to determine when and how to engage students effectively.
✨ Key Changes
Added FrappeLMSAdapter using httpx
Integrated TAP LMS REST APIs:
get_learner_profile
get_learner_activity
log_engagement
Implemented rule-based decision logic for:
Call Reason & Nudge Type (Inactivity, Pending Assignments, Milestones)
Next Task Recommendation
Urgency Levels (Low / Medium / High)
Personalized Messaging (Student + Parent)
GET /api/v1/nudge/{learner_id}/context
→ Fetch raw learner activity from LMS
GET /api/v1/nudge/{learner_id}/plan
→ Generate structured nudge plan for orchestration
🧪 Testing & Quality
Added/updated tests in tests/test_nudge_engine.py
✅ 45 tests passing
📊 Test coverage: 82.28%
🧹 Linting & formatting via Ruff
⚙️ How to Test
Run server
uvicorn app.main:app --reload
Open API docs
http://localhost:8000/docs
Run tests
pytest tests/ -v
Use sample learner IDs:
learner_001
learner_002
learner_003
✅ Checklist
Modular architecture followed
No hardcoded secrets
Unit tests added
Linting & formatting applied
💡 Notes for Reviewers
Focus areas:
FrappeLMSAdapter API interactions
Rule evaluation logic in NudgeEngine
API response structure for /plan endpoint