📅 2025-04-14 — Session: Refactoring AI Service Architecture and Modular Orchestration
🕒 13:55–16:06
🏷️ Labels: Refactoring, Ai Architecture, Modularity, YAML, Python, Debugging
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to refactor the AI service architecture and design a modular AI orchestration system.
Key Activities
- Refactoring AI Service Architecture: Focused on decoupling components to enhance reusability and testability. Emphasized separating prompt semantics, LLM communication, and execution logic.
- Designing Modular AI Orchestration: Planned a modular AI orchestration system using a Prompt Flow OS, detailing core abstractions and integration strategies.
- YAML-based AI Prompt Engine: Created a YAML-based AI prompt engine compatible with Vertex AI-style pipelines.
- Codebase Runner Analysis: Analyzed different script runners in a codebase and recommended future actions.
- Debugging and Fixes: Addressed various Python import errors and KeyErrors in pipelines, providing solutions for robust execution.
Achievements
- Established a structured approach for refactoring AI service architecture.
- Designed a vision for a modular AI orchestration system.
- Implemented a YAML-based AI prompt engine for better compatibility.
- Provided actionable recommendations for codebase runner improvements.
Pending Tasks
- Further development and testing of the modular AI orchestration system.
- Implementing logging, caching, and UI improvements for the prompt-chain execution.
Tags
refactoring, [[AI]] architecture, modularity, YAML, [[Python]], debugging