John Patrick TeruelandCopilot
aa4dd497e2
- Complete dual-layer architecture for fraud detection - Phase 1: Docker Compose infrastructure (Kafka, Zookeeper, Redis) - Phase 2: Mock ML models (Layer 1 fast, Layer 2 deep) - Phase 3: FastAPI transaction ingestion with JWT auth - Phase 4: Kafka consumer pipeline with feature extraction - Phase 5: Test transaction producer with demo scenarios - Phase 6: Clean code structure and comprehensive documentation - Phase 7: End-to-end validation and demo readiness Demonstrates key system design patterns: - Microservices architecture with decoupled components - Asynchronous event processing via Kafka - Tiered decision-making (fast path vs deep analysis) - Resilience patterns (circuit breaker for graceful degradation) - Behavioral profiling with Redis caching Ready for academic demonstration with clear pipeline observability. Co-authored-by: Copilot <[email protected]>
26 lines
478 B
Bash
26 lines
478 B
Bash
# PayGuard Configuration Template
|
|
|
|
# API Configuration
|
|
API_HOST=0.0.0.0
|
|
API_PORT=8000
|
|
|
|
# JWT Configuration
|
|
SECRET_KEY=payguard-demo-secret-key-change-in-production
|
|
ALGORITHM=HS256
|
|
|
|
# Kafka Configuration
|
|
KAFKA_BROKER=localhost:9092
|
|
KAFKA_TOPIC=transactions
|
|
KAFKA_GROUP=fraud-detection-consumer-group
|
|
|
|
# Redis Configuration
|
|
REDIS_HOST=localhost
|
|
REDIS_PORT=6379
|
|
|
|
# Logging
|
|
LOG_LEVEL=INFO
|
|
|
|
# Circuit Breaker Configuration
|
|
CIRCUIT_BREAKER_FAIL_MAX=3
|
|
CIRCUIT_BREAKER_RESET_TIMEOUT=30
|