📅 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 the main() 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.