Refactored AI and Research Functions

• Day: 2025-02-07
• Time: 18:50 to 19:30
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Refactoring, Ai Integration, Economics, Json Schemas, Dynamic Functions

Description

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.

Evidence

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• event_ids: []