SIENTIAPDE-1084

Remove all module docstrings and the versioning information from the Laborious package, activities, utils, and workflows. This cleanup enhances code readability and reduces unnecessary comments in the codebase.

laborious/__init__.py

@@ -1,82 +0,0 @@
-"""
-Sientia DataOps Laborious Package
-
-A high-performance, scalable machine learning prediction system built on Temporal.io
-for industrial data processing and ML model inference. The Laborious system provides
-enterprise-grade ML model management, batch prediction processing, and real-time
-data export capabilities.
-
-Package Overview:
-    The Laborious package implements a comprehensive ML workflow orchestration
-    system that integrates with MLFlow for model management, PostgreSQL for data
-    storage, and OPC servers for real-time industrial data export.
-
-Key Components:
-    - activities: Temporal activity implementations for ML operations
-    - workflows: Temporal workflow definitions for prediction orchestration
-    - worker: Main worker implementation for workflow execution
-    - utils: Utility functions and configuration management
-    - metrics: Prometheus metrics for monitoring and observability
-
-Main Features:
-    - Batch prediction processing using MLFlow models
-    - Data quality validation and filtering
-    - Real-time data export to OPC servers
-    - PostgreSQL data persistence
-    - Comprehensive monitoring and metrics
-    - Automatic retry policies and error handling
-
-Architecture:
-    The system uses Temporal.io for workflow orchestration with clear separation
-    of concerns between data loading, ML operations, quality validation, and
-    data export. It supports multiple OPC servers and implements configurable
-    data quality gates throughout the prediction pipeline.
-
-Example Usage:
-    >>> from laborious.worker.worker import main
-    >>> import asyncio
-    >>> 
-    >>> # Start the Laborious worker
-    >>> asyncio.run(main())
-    
-    >>> # Or use specific components
-    >>> from laborious.activities.activities import Activities
-    >>> from laborious.workflows.predictions_batch import PredictionsBatch
-
-Dependencies:
-    - temporalio: Temporal workflow orchestration
-    - psycopg2-binary: PostgreSQL database adapter
-    - sqlalchemy: Database ORM and connection management
-    - asyncua: OPC UA client implementation
-    - redis: Caching and session management
-    - prometheus-client: Metrics collection and export
-
-Environment Configuration:
-    The system is configured through environment variables for database
-    connections, MLFlow servers, OPC servers, and other external services.
-    See the README.md for complete configuration documentation.
-
-License:
-    This project is licensed under the terms specified in the LICENSE file.
-
-For more information, see the project README.md and documentation.
-"""
-
-__version__ = "0.4.4"
-__author__ = "Sientia DataOps Team"
-__description__ = "ML prediction system built on Temporal.io for industrial data processing"
-__keywords__ = ["machine-learning", "temporal", "mlflow", "opc", "postgresql", "industrial"]
-__url__ = "https://github.com/Aignosi/sientia-dataops-laborious"
-
-# Import key components for easy access
-from . import metrics
-from . import activities
-from . import workflows
-from . import worker
-
-__all__ = [
-    "metrics",
-    "activities", 
-    "workflows",
-    "worker"
-]