Enhanced DAG and PromptFlow Automation

2 min read

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

🕒 20:10–22:36
🏷️ Labels: DAG, Promptflow, Python, LLM, Automation
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance the Directed Acyclic Graph (DAG) and PromptFlow automation processes by implementing and refining various components, including variable extraction, inconsistency detection, and LLM integration.

Key Activities

  • Defined a modular DAG for flow_fixer to read and process files in Azure ML.
  • Extended the DAG with steps for detecting inconsistencies and generating fixes using an LLM.
  • Implemented the final DAG node write_fixes to manage file outputs and backups.
  • Developed Python scripts for reading and extracting variables from YAML and Jinja2 files.
  • Updated functions to improve variable extraction and inconsistency detection.
  • Modified the LLM wrapper to ensure structured JSON output.
  • Implemented OpenAI Function Calling in llm_wrapper.py for enhanced automation.
  • Provided a schema for LLM function calls and a checklist for debugging and flow stabilization.

Achievements

  • Successfully implemented and refined multiple components of the DAG and PromptFlow setup.
  • Enhanced error handling and output structuring in the LLM wrapper.
  • Improved the consistency and traceability of variable management and DAG workflows.

Pending Tasks

  • Further optimization of Python scripts for directory traversal and JSONL output.
  • Continued refinement of Jinja2 templates and flow DAGs for modular prompting.

Graph View

Backlinks