Refactored AI and Research Functions

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📅 2025-02-07 — Session: Refactored AI and Research Functions

🕒 18:50–19:30
🏷️ Labels: Refactoring, Ai Integration, Economics, Json Schemas, Dynamic Functions
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to refactor existing code for better modularity and maintainability, enhance AI query flexibility, and develop structured prompts and schemas for economic research.

Key Activities

  • Refactoring: Unified ChunkProcessor and ChunkEnricher into a ChunkHandler class, improving modularity and maintainability.
  • Dynamic Function Calls: Implemented strategies for scalable AI interactions using dynamic function management.
  • Prompt Development: Created tailored prompts for economic research functions, focusing on summarizing papers and generating questions.
  • Schema Design: Developed JSON schemas for question generation and case study retrieval, enhancing academic research capabilities.

Achievements

  • Successfully refactored classes into a single handler, improving code structure.
  • Enhanced AI architecture with dynamic function calls for better scalability.
  • Developed comprehensive prompts and schemas to support structured economic research.

Pending Tasks

  • Further testing and validation of the refactored ChunkHandler class.
  • Integration of new prompts and schemas into existing workflows.

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