Differences

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--- ai_error_tracker [2025/05/26 23:22] – [Purpose] eagleeyenebula
+++ ai_error_tracker [2025/05/26 23:33] (current) – [Best Practices] eagleeyenebula
@@ Line 23: / Line 23: @@
 . **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.
 . **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**).
 . **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.
 . **Ease of Integration**:
-   - Easily integrates into existing codebases with minimal configuration.
+   * Easily integrates into existing codebases with minimal configuration.
 . **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.
 . **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 =====
 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 logging
@@ Line 125: / Line 122: @@
             logging.error(f"Failed to fetch errors: {e}")
             return []
-```
+</code>
----
 ===== 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.
----
-### Example 1: Logging and Viewing Errors
+**Example 1: Logging and Viewing Errors**
 Log a simple error into the system and view the resulting logs.
-```python
+<code>
+python
 from ai_error_tracker import ErrorTracker
+</code>
-# Initialize the Error Tracker
+**Initialize the Error Tracker**
+<code>
 error_tracker = ErrorTracker()
+</code>
-# Log an error
+**Log an error**
+<code>
 error_tracker.log_error(
     error_message="Failed to connect to the database.",
@@ Line 151: / Line 147: @@
     severity="CRITICAL"
 )
+</code>
-# Fetch and print all errors
+**Fetch and print all errors**
+<code>
 errors = error_tracker.fetch_errors()
 for error in errors:
     print(error)
-```
+</code>
 **Logs & Output Example:**
+<code>
 (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("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")
 print("MEDIUM Severity Errors:")
 for error in medium_severity_errors:
     print(error)
-```
+</code>
 **Logs & Output Example:**
+<code>
 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.
+</code>
-```python
+<code>
-# Simulate a pipeline with error handling
+python
+</code>
+**Simulate a pipeline with error handling**
+<code>
 def execute_pipeline():
     # Simulating a failure
     raise RuntimeError("Invalid data format in pipeline.")
+</code>
-# Capture pipeline errors and log them
+**Capture pipeline errors and log them**
+<code>
 try:
     execute_pipeline()
@@ Line 207: / Line 209: @@
         severity="CRITICAL"
     )
-```
+</code>
 **Logs & Output Example (Database):**
+<code>
 (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):
     def log_advanced_error(self, error_message, context=None, severity="LOW", stack_trace=None):
@@ Line 238: / Line 237: @@
         except Exception as 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()
 try:
@@ Line 251: / Line 251: @@
         stack_trace=traceback.format_exc()
     )
-```
+</code>
----
 ===== Best Practices =====
 . **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.
 . **Use Severity Levels Appropriately**:
-   - Assign severity levels (`LOW`, `MEDIUM`, `HIGH`, `CRITICAL`) to prioritize debugging.
+   * Assign severity levels (**LOW**, **MEDIUM**, **HIGH**, **CRITICAL**) to prioritize debugging.
 . **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.
 . **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.
 . **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 =====
 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.