SIENTIAPDE-1182

Remove Docker configuration files and refactor project structure

- Deleted docker-compose.yml and Dockerfile as part of the project restructuring.
- Updated README.md to reflect changes in project setup and configuration.
- Introduced a new __init__.py file in the laborious package to provide an overview of the system.
- Enhanced documentation across various modules, including metrics, activities, and workflows, to improve clarity and usability.
- Added comprehensive docstrings and comments to key classes and methods for better maintainability.

laborious/metrics.py

@@ -1,25 +1,55 @@
+"""
+Laborious Metrics Module
+
+This module defines all Prometheus metrics used by the Sientia DataOps Laborious system
+for monitoring and observability. The metrics provide insights into system performance,
+prediction quality, and operational health.
+
+The metrics are designed to be scraped by Prometheus and can be visualized in
+Grafana or other monitoring dashboards to provide real-time visibility into
+the system's operation.
+
+Key Metric Categories:
+- Application Health: Overall system status and availability
+- Prediction Operations: Count and performance of prediction operations
+- Data Quality: Confidence levels and validation results
+- Export Operations: Database and OPC export performance
+- Response Times: Performance monitoring for various operations
+
+Metric Labels:
+- pod_id: Kubernetes pod identifier for multi-instance deployments
+- model_name: Name of the ML model being used
+- pipeline_name: Name of the prediction pipeline
+- opc_server_id: Identifier for OPC server operations
+"""
+
 from prometheus_client import Gauge, Counter, Histogram
 
+# Application health metric
 APP_UP = Gauge(
     "app_up",
     "Indicates if the application is running (1) or shutting down (0)",
     ["pod_id"],
 )
 
+# Core labels used across multiple metrics
 CORE_LABELS = ["pod_id", "model_name", "pipeline_name"]
 
+# Prediction operation metrics
 PREDICTIONS_WRITTEN_COUNT = Counter(
     "laborious_predictions_written_count",
     "Number of predictions written to the database table predictions",
     CORE_LABELS,
 )
 
+# Prediction quality metrics
 PREDICTION_CONFIDENCE_MONITOR = Gauge(
     "laborious_prediction_confidence_monitor",
     "Current confidence of each prediction",
     CORE_LABELS,
 )
 
+# Performance monitoring metrics
 PREDICTION_RESPONSE_TIME_MONITOR = Histogram(
     "laborious_prediction_response_time_monitor",
     "Current response time of each prediction",
@@ -27,6 +57,7 @@ PREDICTION_RESPONSE_TIME_MONITOR = Histogram(
     buckets=[0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]
 )
 
+# OPC export metrics
 PREDICTION_OPC_WRITING_COUNT = Counter(
     "laborious_prediction_opc_writing_count",
     "Number of predictions written to the OPC server",