SIENTIAPDE-1081

Enhance documentation across multiple modules with detailed parameter descriptions and usage examples

README.md

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+# Sientia DataOps Laborious
+
+The Sientia DataOps Laborious is a Temporal-based workflow application that handles batch predictions and data processing for industrial data. It integrates with MLFlow for model management, PostgreSQL for data storage, and OPC for real-time data output. The module is designed to process data in a reliable and scalable manner using Temporal.io's workflow orchestration capabilities. It's get data from Scouter sinks, process it, make predictions using MLFlow models and generates metrics for the predictions.
+
+## Key Features
+
+- Batch predictions using MLFlow models
+- Data transformation and preprocessing
+- Workflow orchestration using Temporal.io
+- Integration with PostgreSQL for data storage
+- OPC integration for real-time data output
+- Comprehensive error handling and notifications
+- Configurable data filters and quality gates
+- Scalable deployment architecture
+
+## Workflows
+
+### Predictions Batch
+The main workflow that orchestrates batch predictions. Steps:
+
+- prepare_activity: Prepares the activity with schedule and model information
+- load_custom_query: Loads data using a custom query
+- prediction_process: Executes the prediction process using the Prediction Process sub-workflow
+
+#### Workflow inputs:
+
+- `schedule_name`: The schedule name of the activity
+- `model_name`: The model name of the activity
+- `model_id`: The model id of the activity
+- `query`: The custom query to load data
+- `schema`: The schema of the data
+- `table_name`: The name of the table to process
+- `input_filters`: The filters to be applied during prediction
+- `mlflow_transform_filters`: The filters to be applied during prediction
+- `mlflow_predict_filters`: The filters to be applied during prediction
+- `model_retention`: The model retention period in minutes
+- `path_priority`: The path priority
+
+
+### Prediction Process
+Sub-workflow that handles individual prediction processing:
+
+- get_last_timestamp: Gets the last timestamp of the data
+- input_gate: Filters input data based on configured rules
+- repeat_last_prediction: Repeats the last prediction if the data is empty
+- request_transform: Makes predictions using MLFlow models
+- mlflow_response_gate: Handles prediction or transform responses and filters
+- mlflow_content_gate: Filters transform responses based on configured rules
+- request_predict: Makes predictions using MLFlow models
+- format_and_export_prediction: Formats and exports predictions using the
+    Format and Export Prediction sub-workflow
+
+### Format and Export Prediction
+Sub-workflow that handles prediction formatting and export:
+
+- format_prediction: Formats prediction data if path flag is None
+- format_default_prediction: Formats default prediction data if path flag is not None
+- export_to_postgres: Exports formatted predictions to PostgreSQL
+- write_to_opc: Writes predictions to OPC server
+
+## Environment variables
+
+- `POSTGRES_HOST`
+- `POSTGRES_PORT`
+- `POSTGRES_USER`
+- `POSTGRES_PASSWORD`
+- `POSTGRES_DBNAME`
+- `POSTGRES_MIN_CONNECTIONS`
+- `POSTGRES_MAX_CONNECTIONS`
+
+- `MLFLOW_HOST`
+- `MLFLOW_PORT`
+- `MLFLOW_USERNAME`
+- `MLFLOW_PASSWORD`
+
+- `OPC_CONFIG` - json string containing the opc configuration for multiple opc servers
+For single opc server use:
+- `OPC_URL`
+- `OPC_NAME`
+- `OPC_SERVER_URI`
+- `OPC_CERT_PATH`
+- `OPC_PRIVATE_KEY_PATH`
+- `OPC_SERVER_CERT_PATH`
+- `OPC_RECONNECTION_INTERVAL`
+
+- `TEMPORAL_HOST`
+- `TEMPORAL_NAMESPACE`
+
+## Application deployment
+
+The application can be deployed using the following command:
+
+```bash
+helm upgrade --install sientia-dataops-laborious sientia/sientia-module -n sientia --create-namespace -f ./values.yaml
+```