đ 2025-04-24 â Session: Enhanced Automation with Modular Flow Designs
đ 18:20â19:40
đˇď¸ Labels: Automation, Promptflow, DAG, README, Python
đ Project: Dev
â Priority: MEDIUM
Session Goal
The session aimed to enhance automation processes by designing and implementing modular flow systems using various scripting and orchestration tools.
Key Activities
- Crafted JSONL Entries: Developed JSONL entries for defining high-leverage meta-flows, focusing on automation and orchestration.
- Standardized Input Schema: Synchronized naming and structure across data processing flows, including defining a unified input schema and updating DAG configurations.
- Designed Dynamic Folder Analysis: Created a refined approach for folder-based flow design, enhancing modularity and reusability.
- YAML DAG for README Generation: Implemented a YAML-based DAG for generating README files using Azure MLâs prompt flow.
- Script Development: Developed Python scripts for reading folder files and generating README.md files, ensuring robust file handling and clean formatting.
- Output References Fix: Addressed issues with output references in PromptFlow, ensuring explicit declaration of output keys.
- README Audit and Update: Conducted a thorough audit and update of README documentation to align with actual flow designs.
- Self-Healing Packaging System: Conceptualized a self-healing system for automating the detection and repair of flow inconsistencies.
Achievements
- Successfully implemented modular and dynamic flow designs that enhance automation efficiency.
- Improved documentation accuracy and usability for future users.
Pending Tasks
- Further testing and refinement of the self-healing packaging system to ensure robustness.
- Continued development of modular flow fixer pipeline using DAG architecture.