Refactored AI and Text Processing Architecture

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📅 2025-02-22 — Session: Refactored AI and Text Processing Architecture

🕒 21:00–23:55
🏷️ Labels: AI, Refactoring, Modularity, Class Design, Data Processing
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The goal of this session was to refactor the AI and text processing architecture to improve modularity, scalability, and clarity in class responsibilities.

Key Activities

  • Refactored AIProcessor and TextProcessor classes to separate concerns and define distinct roles.
  • Proposed restructuring of classes for modular AI processing, emphasizing separation of concerns and scalability.
  • Refactored AICaller and AIProcessor classes to enhance separation of concerns and implement prompt wrappers as decorators.
  • Enhanced modularity by moving AI functions out of the AIProcessor class.
  • Implemented dynamic function lookup in AIProcessor to enhance modularity and maintainability.
  • Optimized data structure and processing logic for AI text retrieval using dictionaries for efficient lookups.
  • Transformed retrieved_texts from a list of dictionaries to a dictionary format for data integrity and efficient lookups.
  • Developed a reusable semantic search function using FAISS for flexible querying and retrieval.
  • Enhanced multi-chunk AI processing by passing full chunk metadata to AI wrapper functions.
  • Updated functions to handle full chunk objects and associated metadata.

Achievements

  • Improved the modularity and scalability of the AI processing architecture.
  • Enhanced the separation of concerns and clarity in class responsibilities.
  • Developed reusable and efficient semantic search functions.
  • Optimized data structures for better processing logic and efficiency.

Pending Tasks

  • Further enhancements to the modular AI processing system for improved flexibility and scalability.
  • Continue debugging and refining the chunks_info input for AI function execution.

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