Refactored AI Processing for Modular Architecture

📅 2025-02-22 — Session: Refactored AI Processing for Modular Architecture

🕒 21:15–22:25
🏷️ Labels: Ai Processing, Modular Design, Refactoring, Python, Data Structure
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary goal of this session was to rethink and refactor the class structure within the AI processing system to enhance modularity, scalability, and clarity in responsibilities.

Key Activities

• Proposed a new class structure focusing on separation of concerns and scalability.
• Refactored AICaller and AIProcessor classes to improve workflow management and implement prompt wrappers as decorators.
• Enhanced the modularity of the AI processing architecture by moving AI functions out of the AIProcessor class.
• Implemented dynamic function lookup in AIProcessor to avoid hardcoding and improve maintainability.
• Optimized data structure and processing logic for AI text retrieval using a dictionary structure for efficient lookups.
• Transformed retrieved_texts data structure to ensure data integrity and efficient processing.

Achievements

• Successfully restructured the AI processing classes to support modular design.
• Improved separation of concerns and workflow management in AI applications.
• Enhanced flexibility and maintainability of the AI processing system.

Pending Tasks

• Further enhancements to the modular AI processing architecture are needed, focusing on additional class responsibilities and scalability improvements.