SIENTIAPDE-1350: Integrate cleanup workflow and update default task queue names.

model_manager/worker/worker.py

@@ -2,9 +2,11 @@
 
 This module provides the main worker implementation for the Sientia DataOps Model Manager system.
 It orchestrates Temporal workers, manages task queues, and handles the lifecycle of
-model training workflows.
+model training and cleanup workflows.
 
-The worker supports the train_model-queue task queue for ML model training workflows.
+The worker supports two task queues:
+- train_model-queue: For ML model training workflows
+- cleanup-queue: For file cleanup workflows
 
 Key Features:
 - Automatic scaling with PollerBehaviorAutoscaling
@@ -12,14 +14,18 @@ Key Features:
 - Comprehensive error handling and logging
 - Graceful shutdown with cleanup
 - ML model training pipeline orchestration
+- Automated cleanup schedule management
 
 Environment Variables:
 - TEMPORAL_HOST: Temporal server address (default: localhost:7233)
-- TEMPORAL_NAMESPACE: Temporal namespace (default: model_manager)
+- TEMPORAL_NAMESPACE: Temporal namespace (default: model-manager)
+- TEMPORAL_USE_TLS: Enable TLS for Temporal connection (default: false)
+- TRAIN_TASK_QUEUE: Task queue for training workflows (default: train_model-queue)
+- CLEANUP_TASK_QUEUE: Task queue for cleanup workflows (default: cleanup-queue)
 - POD_ID: Kubernetes pod identifier for metrics
 - HTTP_METRICS_PORT: Prometheus metrics server port (default: 9090)
 - HTTP_SDK_METRICS_PORT: Temporal SDK metrics port (default: 9091)
-- PROJECT_NAME: Project name for notifications (default: model_manager)
+- PROJECT_NAME: Project name for notifications (default: model-manager)
 """
 
 from temporalio import client, workflow