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SIENTIAPDE-1478

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Enhance README and Codebase with PI Web API Integration

- Updated README.md to include details about PI Web API integration, including configuration and export capabilities.
- Modified Activities class to incorporate PI Web API export operations and error handling.
- Added new API class for handling PI Web API interactions, including writing prediction and confidence data.
- Updated prediction workflows to support PI Web API output configuration.
- Enhanced worker and sub-workflows to include PI Web API in task queues and export processes.
- Improved documentation and error handling for PI Web API connections and configurations.
This commit is contained in:
vitor-aignosi
2026-01-09 09:30:26 -03:00
parent e8b7105e9b
commit 7cfa34a963
7 changed files with 132 additions and 29 deletions
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3
laborious/workflows/predictions_batch.py
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@@ -61,7 +61,10 @@ class PredictionsBatch:
- model_retention (int, optional): Model retention period in minutes
- path_priority (list[str]): Decision path priority configuration
- opc_output_config (dict, optional): OPC server export configuration
- pi_web_api_output_config (dict, optional): PI Web API export configuration
- datetime_columns (list[str], optional): Columns to treat as datetime
- save_transform (bool, optional): Whether to save transformed data (default: True)
- prediction_store_policy (str, optional): Data retention policy (default: 'lts:1')
Returns:
None: The workflow completes successfully when the child workflow finishes

12
laborious/workflows/sub_workflows/format_and_export_prediction.py
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@@ -26,6 +26,7 @@ class FormatAndExportPrediction:
Export Destinations:
- PostgreSQL Database: Persistent storage with timestamp conversion
- PI Web API: Real-time industrial system integration for prediction and confidence values
- OPC Servers: Real-time industrial system integration
- Prometheus Metrics: Performance monitoring and operational visibility
"""
@@ -38,9 +39,10 @@ class FormatAndExportPrediction:
This method orchestrates the complete data export process by:
1. Determining the appropriate formatting strategy based on path_flag
2. Formatting prediction data according to quality and requirements
3. Exporting data to OPC servers for real-time industrial access
4. Persisting data to PostgreSQL database with comprehensive metadata
5. Recording performance metrics for operational monitoring
3. Exporting data to PI Web API for real-time industrial access (if configured)
4. Exporting data to OPC servers for real-time industrial access (if configured)
5. Persisting data to PostgreSQL database with comprehensive metadata
6. Recording performance metrics for operational monitoring
The method implements flexible formatting strategies:
- Normal predictions: Full data formatting with confidence scores
@@ -61,8 +63,10 @@ class FormatAndExportPrediction:
- model_name (str): Name of the ML model
- schema (str): Database schema for data storage
- table_name (str): Target table for data persistence
- opc_output_config (dict[str, Any]): OPC server export configuration
Optional keys:
- opc_output_config (dict[str, Any]): OPC server export configuration
- pi_web_api_output_config (dict[str, Any]): PI Web API export configuration
Contains endpoint, prediction_tags, and confidence_tags mappings
- transformed_data (dict[str, Any]): Transformed data to export separately
Only processed when path_flag is None
- transform_table_name (str): Target table for transformed data export

19
laborious/workflows/sub_workflows/prediction_process.py
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@@ -66,7 +66,10 @@ class PredictionProcess:
- mlflow_predict_filters (dict): MLFlow prediction filters
- model_retention (int): Model retention period in minutes
- path_priority (list[str]): Decision path priority configuration
- opc_output_config (dict): OPC server export configuration
- opc_output_config (dict, optional): OPC server export configuration
- pi_web_api_output_config (dict, optional): PI Web API export configuration
- save_transform (bool, optional): Whether to save transformed data (default: True)
- prediction_store_policy (str, optional): Data retention policy (default: 'lts:1')
Returns:
None: The workflow completes successfully when export workflow finishes
@@ -235,7 +238,17 @@ class PredictionProcess:
Args:
data: Input data for processing
path_flag: Path decision from filter (STOP, CONTINUE, REPEAT)
input_data: Complete workflow input configuration
input_data: Complete workflow input configuration including:
- metadata (dict): Workflow execution metadata
- schema (str): Database schema
- table_name (str): Target table for predictions
- transform_table_name (str): Target table for transformed data
- model_id (str): ML model identifier
- model_name (str): ML model name
- model_config (dict, optional): Model configuration
- opc_output_config (dict, optional): OPC server export configuration
- pi_web_api_output_config (dict, optional): PI Web API export configuration
- prediction_store_policy (str, optional): Data retention policy
confidence: Confidence level from filter validation
last_timestamp: Last processed timestamp
comment: Additional information about the filter result
@@ -245,7 +258,7 @@ class PredictionProcess:
Path Handling:
- STOP: Terminates workflow execution
- CONTINUE: Proceeds with normal processing
- CONTINUE: Delegates to FormatAndExportPrediction workflow with current data
- REPEAT: Repeats last prediction if available
"""
metadata = input_data['metadata']