"""
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"
]