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ai_pre_execution_validator

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ai_pre_execution_validator [2025/05/29 14:24] – [Class Overview] eagleeyenebulaai_pre_execution_validator [2025/05/29 14:35] (current) – [Advanced Examples] eagleeyenebula
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 **Overview of Methods** **Overview of Methods**
  
-**Method 1: "validate_config(config)"+**Method 1:** "validate_config(config)"
  
 **Signature**: **Signature**:
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 ===== Workflow ===== ===== Workflow =====
  
-### Step-by-Step Workflow for Using `PreExecutionValidator`:+**Step-by-Step Workflow for Using "PreExecutionValidator":**
  
 1. **Import the Class**: 1. **Import the Class**:
-   Import the `PreExecutionValidator` in your script: +     * Import the **PreExecutionValidator** in your script: 
-   ```python+   <code> 
 +   python
    from ai_pre_execution_validator import PreExecutionValidator    from ai_pre_execution_validator import PreExecutionValidator
-   ```+   </code>
  
 2. **Load Configuration**: 2. **Load Configuration**:
-   Specify your pipeline configuration in dictionary format: +     * Specify your pipeline configuration in dictionary format: 
-   ```python+   <code> 
 +   python
    config = {    config = {
        "data_source": "database",        "data_source": "database",
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        "deployment_path": "/path/to/deployment",        "deployment_path": "/path/to/deployment",
    }    }
-   ```+   </code>
  
 3. **Validate Configuration**: 3. **Validate Configuration**:
-   Validate the configuration using `validate_config`: +     * Validate the configuration using "validate_config": 
-   ```python+   <code>    
 +   python
    is_valid = PreExecutionValidator.validate_config(config)    is_valid = PreExecutionValidator.validate_config(config)
    if not is_valid:    if not is_valid:
        print("Configuration validation failed. Check logs for details.")        print("Configuration validation failed. Check logs for details.")
-   ```+   </code>
  
 4. **Check Environment Readiness**: 4. **Check Environment Readiness**:
-   Verify dependency readiness using `check_environment`: +     * Verify dependency readiness using "check_environment": 
-   ```python+   <code> 
 +   python
    is_ready = PreExecutionValidator.check_environment()    is_ready = PreExecutionValidator.check_environment()
    if not is_ready:    if not is_ready:
        print("Environment validation failed. Ensure all dependencies are installed.")        print("Environment validation failed. Ensure all dependencies are installed.")
-   ```+   </code>
  
 5. **Integrate with Flow**: 5. **Integrate with Flow**:
-   Leverage the validator before executing your pipelines: +     * Leverage the validator before executing your pipelines: 
-   ```python+   <code> 
 +   python
    if is_valid and is_ready:    if is_valid and is_ready:
        print("All validations passed. You can proceed.")        print("All validations passed. You can proceed.")
    else:    else:
        print("Fix the issues before proceeding.")        print("Fix the issues before proceeding.")
-   ``` +   </code>
- +
----+
  
 ===== Advanced Examples ===== ===== Advanced Examples =====
  
 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:
-```python+<code> 
 +python
 config = { config = {
     "data_source": "database",     "data_source": "database",
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 run_pipeline() run_pipeline()
-``` +</code>
- +
----+
  
 ==== Example 2: Adding Custom Config Validation Logic ==== ==== Example 2: Adding Custom Config Validation Logic ====
-Extend `validate_config` to include additional checks: +**Extend **validate_config** to include additional checks:** 
-```python+<code> 
 +python
 class CustomPreExecutionValidator(PreExecutionValidator): class CustomPreExecutionValidator(PreExecutionValidator):
     @staticmethod     @staticmethod
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 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:
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 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"
  
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 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_fields` list 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):
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         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
  
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    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 needs, offering reliability and stability to AI workflows. Whether used in standalone scripts or part of a larger orchestrator, this 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 begins. This 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 complex, multi-stage orchestration systems. Developers can easily customize the validator to meet domain-specific criteria or organizational standards, making it a versatile asset for production environments. As AI systems grow in complexity, tools like the Pre-Execution Validator become indispensable for maintaining operational integrity and delivering consistent, reliable results. 
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ai_pre_execution_validator.1748528649.txt.gz · Last modified: 2025/05/29 14:24 by eagleeyenebula