📅 2025-07-09 — Session: Refactored and Enhanced Data Processing Pipeline
🕒 14:50–16:10
🏷️ Labels: Python, Data Processing, Script Optimization, QA, Pipeline
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary objective of this session was to refactor and enhance the data processing pipeline scripts to improve flexibility, maintainability, and robustness.
Key Activities
- Fixed Hardcoded Output Directory: Updated the
09_run_promptflow.py
script to generalize the output directory lookup, enabling dynamic glob pattern generation based on flow names. - Parameterization of Legacy Paths: Transitioned Python scripts to use parameterized paths instead of hardcoded values, enhancing directory management.
- Modular Update for Pipeline Orchestration: Updated the
main()
function for better modularization and avoidance of hardcoded references. - Revised Directory Naming Strategy: Implemented a structured directory naming strategy for better project organization.
- Directory Structure Setup for Selenium Scraping: Defined new
Path
variables for consistent output directory organization for Selenium scraping and PromptFlow model scoring. - QA Guide and Report: Developed a QA guide for the JobAI pipeline datasets and evaluated the
00_csv_raw
dataset for quality assurance. - Justification for JSONL and CSV Outputs: Provided strategic reasoning for maintaining both JSONL and CSV outputs.
- Log Quality Assurance and Row ID Renaming: Proposed improvements for log data quality and field naming.
- Filtering Quality Evaluation: Assessed the filtering logic in the
label_scored
file to ensure efficient data processing. - Script Optimization: Suggested improvements for script robustness, including query identifier addition and column normalization.
- Hash Computation Consistency: Ensured consistent hash computation across pipeline stages.
- Structured Reflection and Enhancements: Analyzed the semantic enhancement script within the job intelligence workflow.
- Upgraded
main()
Function: Enhanced themain()
function with features like structured logging and configurable debug support. - Export Script Upgrade: Improved the
02b_export_results_to_jsonl.py
script for better logging and directory handling.
Achievements
- Improved the flexibility and maintainability of the data processing pipeline.
- Enhanced the robustness and organization of scripts and directory structures.
- Established a comprehensive QA framework for data processing.
Pending Tasks
- Further improvements to downstream processes as suggested in the modular update.
- Implementation of proposed log data quality improvements.
Conclusion
The session successfully refactored and enhanced multiple components of the data processing pipeline, setting a foundation for future improvements.