Enhanced DAG and PromptFlow Automation

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📅 2025-04-24 — Session: Enhanced DAG and PromptFlow Automation

🕒 20:25–22:35
🏷️ Labels: DAG, Promptflow, Python, LLM, Automation
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The goal of this session was to enhance the automation capabilities of Directed Acyclic Graphs (DAGs) and PromptFlow by implementing, debugging, and optimizing various scripts and templates.

Key Activities

  • Extended the DAG by implementing Steps 3-5, which included detecting inconsistencies, rendering fix instructions, and generating fixes using an LLM.
  • Implemented the final node of the DAG to write generated fixes with optional backup functionality.
  • Developed Python scripts to read flow folder files and extract variables for consistency checks.
  • Debugged and fixed errors in DAG node references and PromptFlow configurations.
  • Updated the extract_variables function to improve variable extraction and remove unused inputs.
  • Enhanced inconsistency detection in Python tools and modified the LLM wrapper for structured JSON output.
  • Implemented OpenAI Function Calling in the llm_wrapper.py script for better error handling and structured outputs.
  • Created a schema blueprint for LLM function calls and a checklist for debugging and flow stabilization.

Achievements

  • Successfully implemented and debugged various components of the DAG and PromptFlow.
  • Improved the consistency and reliability of variable management and error handling.
  • Enhanced the modularity and precision of templates and scripts used in PromptFlow.

Pending Tasks

  • Further testing and validation of the implemented changes to ensure robustness.
  • Continuous monitoring and optimization of the DAG and PromptFlow processes.

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