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ai_error_tracker [2025/04/25 23:40] – external edit 127.0.0.1ai_error_tracker [2025/05/26 23:33] (current) – [Best Practices] eagleeyenebula
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 ====== AI Error Tracker ====== ====== AI Error Tracker ======
-* **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:+**[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:
 The **AI Error Tracker System** is a robust framework for logging, monitoring, and analyzing application errors. It integrates with a SQLite database to keep a structured historical record of errors, including timestamps, messages, severity levels, and specific contexts. This system is designed for long-term error tracking and supports dynamic querying for error analysis. The **AI Error Tracker System** is a robust framework for logging, monitoring, and analyzing application errors. It integrates with a SQLite database to keep a structured historical record of errors, including timestamps, messages, severity levels, and specific contexts. This system is designed for long-term error tracking and supports dynamic querying for error analysis.
 +
 +{{youtube>_DtDT5qiDxk?large}}
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 +-------------------------------------------------------------
  
 The **ErrorTracker** class is at the heart of the system, streamlining error tracking and providing tools for developers to monitor and debug issues effectively. The **ErrorTracker** class is at the heart of the system, streamlining error tracking and providing tools for developers to monitor and debug issues effectively.
- 
---- 
  
 ===== Purpose ===== ===== Purpose =====
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   * **Long-Term Error Persistence**: Ensuring errors are retained for future analysis.   * **Long-Term Error Persistence**: Ensuring errors are retained for future analysis.
   * **Diagnostic Support**: Enabling developers to isolate and address recurring or critical issues.   * **Diagnostic Support**: Enabling developers to isolate and address recurring or critical issues.
- 
---- 
- 
 ===== Key Features ===== ===== Key Features =====
  
 1. **SQLite-Based Database for Error Tracking**: 1. **SQLite-Based Database for Error Tracking**:
-   - Uses an embedded SQLite database (`errors.db`) to log and store all errors encountered.+   * Uses an embedded SQLite database (**errors.db**) to log and store all errors encountered.
      
 2. **Structured Error Logs**: 2. **Structured Error Logs**:
-   - Logs errors with metadata, such as: +   * Logs errors with metadata, such as: 
-     - Timestamp of occurrence +     * Timestamp of occurrence 
-     - Error message details +     * Error message details 
-     - Context (e.g., subsystem where the error occurred) +     * Context (e.g., subsystem where the error occurred) 
-     - Severity levels (`LOW`, `MEDIUM`, `HIGH`, `CRITICAL`).+     * Severity levels (**LOW**, **MEDIUM**, **HIGH**, **CRITICAL**).
  
 3. **Dynamic Querying and Retrieval**: 3. **Dynamic Querying and Retrieval**:
-   - Supports retrieving error logs based on filtering criteria, such as severity level.+   * Supports retrieving error logs based on filtering criteria, such as severity level.
  
 4. **Ease of Integration**: 4. **Ease of Integration**:
-   - Easily integrates into existing codebases with minimal configuration.+   * Easily integrates into existing codebases with minimal configuration.
  
 5. **Extensibility**: 5. **Extensibility**:
-   - The database schema and logging mechanisms are designed to be extendable for larger applications.+   * The database schema and logging mechanisms are designed to be extendable for larger applications.
  
 6. **Error Logging Alert System**: 6. **Error Logging Alert System**:
-   - Error logging automatically records issues and can be extended to generate alerts in critical cases. +   * Error logging automatically records issues and can be extended to generate alerts in critical cases.
- +
---- +
 ===== Architecture ===== ===== Architecture =====
  
 The **ErrorTracker** class is responsible for: The **ErrorTracker** class is responsible for:
-  - Initializing the SQLite database schema. +  * Initializing the SQLite database schema. 
-  - Recording structured logs into the database. +  * Recording structured logs into the database. 
-  - Querying errors dynamically based on severity or other criteria. +  * Querying errors dynamically based on severity or other criteria.
- +
-### Class Overview+
  
-```python+**Class Overview** 
 +<code> 
 +python
 import sqlite3 import sqlite3
 import logging import logging
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             logging.error(f"Failed to fetch errors: {e}")             logging.error(f"Failed to fetch errors: {e}")
             return []             return []
-``` +</code>
- +
---- +
 ===== Usage Examples ===== ===== Usage Examples =====
  
 Here are advanced usage examples for leveraging the Error Tracker in real-world applications, including basic error logging, filtering, and extending the system. Here are advanced usage examples for leveraging the Error Tracker in real-world applications, including basic error logging, filtering, and extending the system.
  
---- 
  
-### Example 1: Logging and Viewing Errors+**Example 1: Logging and Viewing Errors**
  
 Log a simple error into the system and view the resulting logs. Log a simple error into the system and view the resulting logs.
  
-```python+<code> 
 +python
 from ai_error_tracker import ErrorTracker from ai_error_tracker import ErrorTracker
- +</code> 
-# Initialize the Error Tracker+**Initialize the Error Tracker** 
 +<code>
 error_tracker = ErrorTracker() error_tracker = ErrorTracker()
- +</code> 
-# Log an error+**Log an error** 
 +<code>
 error_tracker.log_error( error_tracker.log_error(
     error_message="Failed to connect to the database.",     error_message="Failed to connect to the database.",
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     severity="CRITICAL"     severity="CRITICAL"
 ) )
- +</code> 
-# Fetch and print all errors+**Fetch and print all errors** 
 +<code>
 errors = error_tracker.fetch_errors() errors = error_tracker.fetch_errors()
 for error in errors: for error in errors:
     print(error)     print(error)
-```+</code>
  
 **Logs & Output Example:** **Logs & Output Example:**
 +<code>
 (1, '2023-11-05 12:30:21', 'Failed to connect to the database.', 'Database Connection', 'CRITICAL') (1, '2023-11-05 12:30:21', 'Failed to connect to the database.', 'Database Connection', 'CRITICAL')
 +</code>
  
 +**Example 2: Filtering Errors by Severity**
  
----+ * Retrieve errors based on their severity level to prioritize debugging efforts.
  
-### Example 2: Filtering Errors by Severity +<code> 
- +python 
-Retrieve errors based on their severity level to prioritize debugging efforts. +</code> 
- +**Log a few example errors** 
-```python +<code>
-# Log a few example errors+
 error_tracker.log_error("Warning: Deprecated API used.", "API Module", "MEDIUM") error_tracker.log_error("Warning: Deprecated API used.", "API Module", "MEDIUM")
 error_tracker.log_error("File not found during upload.", "File Upload", "LOW") error_tracker.log_error("File not found during upload.", "File Upload", "LOW")
- +</code> 
-# Fetch errors with severity MEDIUM+**Fetch errors with severity MEDIUM** 
 +<code>
 medium_severity_errors = error_tracker.fetch_errors(severity="MEDIUM") medium_severity_errors = error_tracker.fetch_errors(severity="MEDIUM")
- 
 print("MEDIUM Severity Errors:") print("MEDIUM Severity Errors:")
 for error in medium_severity_errors: for error in medium_severity_errors:
     print(error)     print(error)
-```+</code>
  
 **Logs & Output Example:** **Logs & Output Example:**
 +<code>
 MEDIUM Severity Errors: (2, '2023-11-05 12:35:11', 'Warning: Deprecated API used.', 'API Module', 'MEDIUM') MEDIUM Severity Errors: (2, '2023-11-05 12:35:11', 'Warning: Deprecated API used.', 'API Module', 'MEDIUM')
 +</code>
  
  
- +**Example 3: Integrating into AI Pipelines** 
---- +<code>
- +
-### Example 3: Integrating into AI Pipelines +
 The system integrates seamlessly into AI workflows to log pipeline errors dynamically. The system integrates seamlessly into AI workflows to log pipeline errors dynamically.
- +</code> 
-```python +<code> 
-# Simulate a pipeline with error handling+python 
 +</code> 
 +**Simulate a pipeline with error handling** 
 +<code>
 def execute_pipeline(): def execute_pipeline():
     # Simulating a failure     # Simulating a failure
     raise RuntimeError("Invalid data format in pipeline.")     raise RuntimeError("Invalid data format in pipeline.")
- +</code> 
-# Capture pipeline errors and log them+**Capture pipeline errors and log them** 
 +<code>
 try: try:
     execute_pipeline()     execute_pipeline()
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         severity="CRITICAL"         severity="CRITICAL"
     )     )
-``` +</code>
 **Logs & Output Example (Database):** **Logs & Output Example (Database):**
 +<code>
 (3, '2023-11-05 12:40:55', 'Invalid data format in pipeline.', 'Pipeline Execution', 'CRITICAL') (3, '2023-11-05 12:40:55', 'Invalid data format in pipeline.', 'Pipeline Execution', 'CRITICAL')
 +</code>
  
 +**Example 4: Adding Custom Error Handling Attributes**
 + * You can extend the system to include additional fields for improved context.
  
- +<code> 
---- +python
- +
-### Example 4: Adding Custom Error Handling Attributes +
- +
-You can extend the system to include additional fields for improved context. +
- +
-```python+
 class AdvancedErrorTracker(ErrorTracker): class AdvancedErrorTracker(ErrorTracker):
     def log_advanced_error(self, error_message, context=None, severity="LOW", stack_trace=None):     def log_advanced_error(self, error_message, context=None, severity="LOW", stack_trace=None):
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         except Exception as e:         except Exception as e:
             logging.error(f"Failed to log advanced error: {e}")             logging.error(f"Failed to log advanced error: {e}")
- +</code> 
-# Example usage of extended tracker+**Example usage of extended tracker** 
 +<code>
 advanced_tracker = AdvancedErrorTracker() advanced_tracker = AdvancedErrorTracker()
 try: try:
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         stack_trace=traceback.format_exc()         stack_trace=traceback.format_exc()
     )     )
-``` +</code>
- +
----+
  
 ===== Best Practices ===== ===== Best Practices =====
  
 1. **Persist Logs Securely**: 1. **Persist Logs Securely**:
-   - Regularly back up the SQLite database or use a centralized logging system in production.+   * Regularly back up the SQLite database or use a centralized logging system in production.
  
 2. **Use Severity Levels Appropriately**: 2. **Use Severity Levels Appropriately**:
-   - Assign severity levels (`LOW`, `MEDIUM`, `HIGH`, `CRITICAL`) to prioritize debugging.+   * Assign severity levels (**LOW**, **MEDIUM**, **HIGH**, **CRITICAL**) to prioritize debugging.
  
 3. **Extend for Specialized Use Cases**: 3. **Extend for Specialized Use Cases**:
-   - Include additional metadata fields, such as stack traces, user IDs, or application states.+   * Include additional metadata fields, such as stack traces, user IDs, or application states.
  
 4. **Monitor and Analyze Trends**: 4. **Monitor and Analyze Trends**:
-   - Periodically analyze logged errors to uncover common problem areas or recurring bugs.+   * Periodically analyze logged errors to uncover common problem areas or recurring bugs.
  
 5. **Integrate with Monitoring Tools**: 5. **Integrate with Monitoring Tools**:
-   - Pair with external alerting tools, such as email notifications for critical errors. +   * Pair with external alerting tools, such as email notifications for critical errors.
- +
---- +
 ===== Conclusion ===== ===== Conclusion =====
  
 The **AI Error Tracker System** provides a structured, scalable, and easy-to-use framework for logging and analyzing application errors. Its extensible design and rich feature set make it a valuable tool for modern software workflows, supporting everything from debugging to long-term trend analysis. With features like severity-based filtering and dynamic querying, the ErrorTracker simplifies error management while offering flexibility for complex use cases. The **AI Error Tracker System** provides a structured, scalable, and easy-to-use framework for logging and analyzing application errors. Its extensible design and rich feature set make it a valuable tool for modern software workflows, supporting everything from debugging to long-term trend analysis. With features like severity-based filtering and dynamic querying, the ErrorTracker simplifies error management while offering flexibility for complex use cases.
  
ai_error_tracker.1745624445.txt.gz · Last modified: 2025/04/25 23:40 by 127.0.0.1