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ai_feedback_collector [2025/05/27 02:05] – [Key Features] eagleeyenebulaai_feedback_collector [2025/05/27 02:16] (current) – [AI Feedback Collector] eagleeyenebula
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 ====== AI Feedback Collector ====== ====== AI Feedback Collector ======
 **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**: **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:
-The **AI Feedback Collector System** enables the systematic collection of model predictions, user feedback, and related metrics for long-term analysis and refinement of AI systems. By leveraging a structured SQLite database, this system ensures persistence, traceability, and detailed insights for model monitoring, debugging, and optimization over time.+The **AI Feedback Collector System** enables the systematic collection of model predictions, user feedback, and related metrics for long-term analysis and refinement of AI systems. By leveraging a structured **SQLite database**, this system ensures persistence, traceability, and detailed insights for model monitoring, debugging, and optimization over time.
  
 {{youtube>NKMCvl89fPg?large}} {{youtube>NKMCvl89fPg?large}}
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 ==== Class Overview ==== ==== Class Overview ====
  
-```python+<code> 
 +python
 import sqlite3 import sqlite3
 import logging import logging
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         except Exception as e:         except Exception as e:
             logging.error(f"Failed to log feedback: {e}")             logging.error(f"Failed to log feedback: {e}")
-```+</code>
  
-- **Inputs**: +**Inputs**: 
-  - `input_data`: Features provided to the model for prediction. +  * **input_data**: Features provided to the model for prediction. 
-  - `prediction`: Model-generated output based on `input_data`. +  * **prediction**: Model-generated output based on **input_data**. 
-  - `actual_value`: Ground truth value to evaluate the prediction. +  * **actual_value**: Ground truth value to evaluate the prediction. 
-  - `model_version`: The version identifier of the AI model. +  * **model_version**: The version identifier of the AI model. 
-  - `latency`: Total time taken for model prediction (in seconds).+  * **latency**: Total time taken for model prediction (**in seconds**).
  
-- **Database Schema**: +* **Database Schema**: 
-  - Table: `feedback` +  * Table: **feedback** 
-    - `id`: Primary key (integer) +    * **id**: Primary key (**integer**) 
-    - `timestamp`: Timestamp of feedback logging (text) +    * **timestamp**: Timestamp of feedback logging (**text**) 
-    - `input_data`: Serialized representation of features (text) +    * **input_data**: Serialized representation of features (**text**) 
-    - `prediction`: Serialized model prediction (text) +    * **prediction**: Serialized model prediction (**text**) 
-    - `actual_value`: Serialized ground truth values (text) +    * **actual_value**: Serialized ground truth values (**text**) 
-    - `model_version`: Model version identifier (text) +    * **model_version**: Model version identifier (**text**) 
-    - `latency`: Latency of the prediction (real) +    * **latency**: Latency of the prediction (**real**)
- +
----+
  
 ===== Usage Examples ===== ===== Usage Examples =====
  
 Here are some advanced usage examples to demonstrate the system's capabilities. Here are some advanced usage examples to demonstrate the system's capabilities.
- 
---- 
- 
 ==== Example 1: Basic Feedback Logging ==== ==== Example 1: Basic Feedback Logging ====
  
 In this example, we log model predictions along with their input data, actual values, and latency. In this example, we log model predictions along with their input data, actual values, and latency.
  
-```python+<code> 
 +python
 from ai_feedback_collector import FeedbackCollector from ai_feedback_collector import FeedbackCollector
 import time import time
- +</code> 
-# Simulated objects+**Simulated objects** 
 +<code>
 model = ...  # Trained model model = ...  # Trained model
 test_data = ...  # Test data as input test_data = ...  # Test data as input
 true_labels = ...  # Actual ground truth true_labels = ...  # Actual ground truth
 model_version = "v1.0" model_version = "v1.0"
- +</code> 
-# Initialize FeedbackCollector+**Initialize FeedbackCollector** 
 +<code>
 feedback_collector = FeedbackCollector() feedback_collector = FeedbackCollector()
- +</code> 
-# Log predictions and related feedback+**Log predictions and related feedback** 
 +<code>
 start_time = time.time() start_time = time.time()
 predictions = model.predict(test_data) predictions = model.predict(test_data)
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     latency=end_time - start_time     latency=end_time - start_time
 ) )
-``` +</code>
 **Explanation**: **Explanation**:
-- **Input Data**: Represents the data sent to the model (e.g., features for prediction). +   * **Input Data**: Represents the data sent to the model (e.g., **features for prediction**). 
-- **Prediction**: Stores model outputs for the input data. +   * **Prediction**: Stores model outputs for the input data. 
-- **Latency Calculation**: Measures time between prediction start and completion. +   * **Latency Calculation**: Measures time between prediction start and completion.
- +
---- +
 ==== Example 2: Error Analysis and Retrieval from Database ==== ==== Example 2: Error Analysis and Retrieval from Database ====
  
 Use the stored feedback to retrieve and analyze cases where predictions failed or were incorrect. Use the stored feedback to retrieve and analyze cases where predictions failed or were incorrect.
  
-```python+<code> 
 +python
 import sqlite3 import sqlite3
- +</code> 
-# Retrieve incorrect predictions from the database+**Retrieve incorrect predictions from the database** 
 +<code>
 def fetch_errors(db_path="feedback.db"): def fetch_errors(db_path="feedback.db"):
     with sqlite3.connect(db_path) as conn:     with sqlite3.connect(db_path) as conn:
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         cursor.execute(query)         cursor.execute(query)
         return cursor.fetchall()         return cursor.fetchall()
- +</code> 
-# Fetch and print incorrect feedback logs+**Fetch and print incorrect feedback logs** 
 +<code>
 errors = fetch_errors() errors = fetch_errors()
 for error in errors: for error in errors:
     print(f"Error Entry: {error}")     print(f"Error Entry: {error}")
-``` +</code>
 **Explanation**: **Explanation**:
-- Identifies cases where the `prediction` field does not match the `actual_value` field. +   * Identifies cases where the **prediction** field does not match the **actual_value** field. 
-- Enables targeted debugging for incorrect predictions. +   * Enables targeted debugging for incorrect predictions.
- +
---- +
 ==== Example 3: Adding Advanced Metrics ==== ==== Example 3: Adding Advanced Metrics ====
  
 The system can be extended by adding fields like **confidence scores**, **user feedback**, or **error ratings**. The system can be extended by adding fields like **confidence scores**, **user feedback**, or **error ratings**.
  
-```python+<code> 
 +python
 class ExtendedFeedbackCollector(FeedbackCollector): class ExtendedFeedbackCollector(FeedbackCollector):
     def _initialize_database(self):     def _initialize_database(self):
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         except Exception as e:         except Exception as e:
             logging.error(f"Failed to log extended feedback: {e}")             logging.error(f"Failed to log extended feedback: {e}")
-``` +</code>
- +
---- +
 ==== Example 4: Real-Time API Integration ==== ==== Example 4: Real-Time API Integration ====
  
 Integrate feedback logging with an API to log user interactions in real time. Integrate feedback logging with an API to log user interactions in real time.
  
-```python+<code> 
 +python
 from flask import Flask, request, jsonify from flask import Flask, request, jsonify
  
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     )     )
     return jsonify({"message": "Feedback logged successfully"})     return jsonify({"message": "Feedback logged successfully"})
-```+</code>
  
 **Explanation**: **Explanation**:
-- Extends the system into a web-based architecture, enabling client-side applications to log feedback instantly. +    * Extends the system into a web-based architecture, enabling client-side applications to log feedback instantly.
- +
---- +
 ===== Use Cases ===== ===== Use Cases =====
  
 1. **Model Tracking Across Versions**: 1. **Model Tracking Across Versions**:
-   - Identify how performance improves or degrades with version changes.+   * Identify how performance improves or degrades with version changes.
        
 2. **Bias and Drift Monitoring**: 2. **Bias and Drift Monitoring**:
-   - Periodically analyze feedback logs for data or concept drift.+   * Periodically analyze feedback logs for data or concept drift.
  
 3. **Auditing and Compliance**: 3. **Auditing and Compliance**:
-   - Maintain detailed logs for compliance in regulated sectors (e.g., finance, healthcare).+   * Maintain detailed logs for compliance in regulated sectors (e.g., **finance, healthcare**).
  
 4. **Real-Time Error Detection**: 4. **Real-Time Error Detection**:
-   - Detect incorrect predictions while the system is running in production.+   * Detect incorrect predictions while the system is running in production.
  
 5. **Data Pipeline Debugging**: 5. **Data Pipeline Debugging**:
-   - Trace stale or malformed inputs impacting predictions. +   * Trace stale or malformed inputs impacting predictions.
- +
---- +
 ===== Best Practices ===== ===== Best Practices =====
  
 1. **Secure Logging**: 1. **Secure Logging**:
-   - Protect sensitive data attributes in `input_data` and `actual_value` fields during database storage.+   * Protect sensitive data attributes in **input_data** and **actual_value** fields during database storage.
  
 2. **Schema Evolution**: 2. **Schema Evolution**:
-   - Plan for gradual additions to schema by modularizing extensions (e.g., adding metrics or tags as new columns).+   * Plan for gradual additions to schema by modularizing extensions (e.g., adding metrics or tags as new columns).
  
 3. **Performance Optimization**: 3. **Performance Optimization**:
-   - Index frequently queried fields like `model_version` or `timestamp` in the database.+   * Index frequently queried fields like **model_version** or **timestamp** in the database.
  
 4. **Visualization and Aggregation**: 4. **Visualization and Aggregation**:
-   - Use tools like **SQL dashboards** or Python **pandas** for deeper analytics of logged feedback records.+   * Use tools like **SQL dashboards** or Python **pandas** for deeper analytics of logged feedback records.
  
 5. **Regular Maintenance**: 5. **Regular Maintenance**:
-   - Schedule database cleanup jobs to archive older records or large tables. +   * Schedule database cleanup jobs to archive older records or large tables.
- +
---- +
 ===== Conclusion ===== ===== Conclusion =====
  
 The **AI Feedback Collector System** is a powerful tool for maintaining traceable, detailed records of model predictions and performance metrics. By supporting structured feedback logging, model version comparisons, and extensible schemas, it enables robust AI lifecycle management. Whether for debugging, compliance, or optimization, this system ensures practical and scalable feedback collection for modern AI applications. The **AI Feedback Collector System** is a powerful tool for maintaining traceable, detailed records of model predictions and performance metrics. By supporting structured feedback logging, model version comparisons, and extensible schemas, it enables robust AI lifecycle management. Whether for debugging, compliance, or optimization, this system ensures practical and scalable feedback collection for modern AI applications.
ai_feedback_collector.1748311520.txt.gz · Last modified: 2025/05/27 02:05 by eagleeyenebula