Integrated YAML-driven flow runner with FastAPI

2 min read

📅 2025-04-16 — Session: Integrated YAML-driven flow runner with FastAPI

🕒 22:05–23:30
🏷️ Labels: Fastapi, Promptflow, Integration, Debugging, Configuration
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to integrate a YAML-driven flow runner with a FastAPI backend, enhance PromptFlow’s tracing capabilities, and address various configuration and debugging issues.

Key Activities

  • Backend Integration: Implemented a YAML-driven flow runner with FastAPI, including backend endpoints and frontend components.
  • PromptFlow Tracing: Integrated PromptFlow’s tracing capabilities, focusing on modular design and efficient flow execution monitoring.
  • AI Architecture Assessment: Conducted a strategic assessment of AI orchestration architecture, identifying strengths and weaknesses.
  • API Key Handling: Resolved issues with OpenAI API key setup in Python, especially for multiprocessing contexts.
  • Environment Troubleshooting: Addressed dotenv issues in Uvicorn applications and set up a .env file for LLM Flow Engine configuration.
  • API Testing: Created a cheat sheet for FastAPI testing using various tools and techniques.
  • System Evaluation: Assessed the AI flow system and provided actionable recommendations for improvement.

Achievements

  • Successfully integrated YAML-driven flow runner with FastAPI.
  • Enhanced observability and debugging through PromptFlow’s tracing and UI capabilities.
  • Developed comprehensive guides for API key handling and environment troubleshooting.

Pending Tasks

  • Further explore the differences between a custom flow engine and the PromptFlow SDK for potential integration or migration.
  • Leverage design patterns from PromptFlow to enhance workflow efficiency while maintaining customization.

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