User Tools

Site Tools


ai_pre_execution_validator

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision
Previous revision
ai_pre_execution_validator [2025/05/29 14:28] – [Example 1: Integrated Configuration and Environment Validation] eagleeyenebulaai_pre_execution_validator [2025/05/29 14:35] (current) – [Advanced Examples] eagleeyenebula
Line 140: Line 140:
  
 Below are advanced usage patterns and examples for extending the utility: Below are advanced usage patterns and examples for extending the utility:
- 
---- 
- 
 ==== Example 1: Integrated Configuration and Environment Validation ==== ==== Example 1: Integrated Configuration and Environment Validation ====
 Perform both validations in a single workflow: Perform both validations in a single workflow:
Line 170: Line 167:
  
 ==== Example 2: Adding Custom Config Validation Logic ==== ==== Example 2: Adding Custom Config Validation Logic ====
-Extend `validate_configto include additional checks: +**Extend **validate_config** to include additional checks:** 
-```python+<code> 
 +python
 class CustomPreExecutionValidator(PreExecutionValidator): class CustomPreExecutionValidator(PreExecutionValidator):
     @staticmethod     @staticmethod
Line 186: Line 184:
 if CustomPreExecutionValidator.validate_config(config): if CustomPreExecutionValidator.validate_config(config):
     print("Custom validation passed.")     print("Custom validation passed.")
-``` +</code>
- +
----+
  
 ==== Example 3: Dynamic Dependency Injection ==== ==== Example 3: Dynamic Dependency Injection ====
-Ensure additional library dependencies: +**Ensure additional library dependencies:** 
-```python+<code> 
 +python
 def check_custom_environment(): def check_custom_environment():
     try:     try:
Line 204: Line 201:
 if PreExecutionValidator.check_environment() and check_custom_environment(): if PreExecutionValidator.check_environment() and check_custom_environment():
     print("All dependencies are installed.")     print("All dependencies are installed.")
-``` +</code>
- +
----+
  
 ==== Example 4: CI/CD Integration ==== ==== Example 4: CI/CD Integration ====
-Integrate validator in continuous integration pipelines: +**Integrate validator in continuous integration pipelines:** 
-```python+<code> 
 +python
 config_path = "pipeline_config.json" config_path = "pipeline_config.json"
  
Line 228: Line 224:
  
 validate_in_ci() validate_in_ci()
-``` +</code>
- +
----+
  
 ===== Best Practices ===== ===== Best Practices =====
  
 1. **Define Required Fields Carefully**: 1. **Define Required Fields Carefully**:
-   Ensure that the `required_fieldslist in `validate_config` comprehensively covers all pipeline needs.+     Ensure that the **required_fields** list in `validate_config` comprehensively covers all pipeline needs.
  
 2. **Log Validation Results**: 2. **Log Validation Results**:
-   Leverage logging to track all validation checks during execution for transparency.+     Leverage logging to track all validation checks during execution for transparency.
  
 3. **Modular Validation**: 3. **Modular Validation**:
-   Use separate validation methods for configuration and environment readiness to ensure modularity.+     Use separate validation methods for configuration and environment readiness to ensure modularity.
  
 4. **Use in CI/CD Workflows**: 4. **Use in CI/CD Workflows**:
-   Incorporate the validator into continuous integration/deployment pipelines for pre-execution checks.+     Incorporate the validator into continuous integration/deployment pipelines for pre-execution checks.
  
 5. **Customizable Extensions**: 5. **Customizable Extensions**:
-   Extend the validator for different projects by defining custom checks in child classes. +     Extend the validator for different projects by defining custom checks in child classes.
- +
---- +
 ===== Extensibility ===== ===== Extensibility =====
  
-### Adding New Validation Rules: +**Adding New Validation Rules:** 
-#### Example: Validating File Accessibility +**Example: Validating File Accessibility** 
-Check if files mentioned in the configuration exist: +**Check if files mentioned in the configuration exist:** 
-```python+<code> 
 +python
 @staticmethod @staticmethod
 def validate_file_paths(config): def validate_file_paths(config):
Line 268: Line 260:
         return False         return False
     return True     return True
-```+</code>
  
-### Integrating with Orchestrators: +**Integrating with Orchestrators:** 
-Combine the validator with pipeline orchestrators: +  Combine the validator with pipeline orchestrators: 
-```python+<code> 
 +python
 from ai_pipeline_orchestrator import AIOrchestrator from ai_pipeline_orchestrator import AIOrchestrator
  
Line 279: Line 272:
    PreExecutionValidator.check_environment():    PreExecutionValidator.check_environment():
     orchestrator.execute_pipeline()     orchestrator.execute_pipeline()
-``` +</code>
- +
----+
  
 ===== Conclusion ===== ===== Conclusion =====
  
-The **AI Pre-Execution Validator** is an essential component for ensuring pipeline readiness by validating configurations and environment setups before execution. Its extensible and modular architecture allows it to adapt to varied project needsoffering reliability and stability to AI workflowsWhether used in standalone scripts or part of larger orchestratorthis tool safeguards against unforeseen errors and dependency issues.+The **AI Pre-Execution Validator** is a critical tool for maintaining robust and error-free AI workflows. By performing thorough checks on configurations, system environments, and resource availability, it ensures that every component is properly aligned before execution beginsThis early validation step acts as a protective layer, preventing execution failures and reducing debugging time by catching potential issues before they surface in runtime. 
 + 
 +Its modular and extensible architecture allows seamless integration into a wide range of projects from lightweight scripts to complexmulti-stage orchestration systemsDevelopers can easily customize the validator to meet domain-specific criteria or organizational standards, making it versatile asset for production environments. As AI systems grow in complexitytools like the Pre-Execution Validator become indispensable for maintaining operational integrity and delivering consistent, reliable results. 
 + 
 + 
 + 
 + 
 + 
 + 
 + 
ai_pre_execution_validator.1748528931.txt.gz · Last modified: 2025/05/29 14:28 by eagleeyenebula