Modularization and Migration of RAG Codebase

• Day: 2025-02-11
• Time: 03:00 to 07:30
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Modularization, Codebase, RAG, Python, Automation, AI

Description

Session Goal:

The session aimed to modularize and migrate the RAG (Retrieval-Augmented Generation) codebase to a new structured v2/ directory, enhancing scalability and maintainability.

Key Activities:

• Debugging: Addressed InvalidUpdateError in Career Growth Mapping Flow, focusing on state updates and error handling.
• Implementation: Developed Network Activation & Outreach Flow and Freelance & Side Hustle Flow, leveraging automation and AI.
• Planning: Defined architectural overview for a modular workflow system and AI-powered career management systems.
• Execution: Extracted function and class definitions from Python codebase and Jupyter Notebooks using command-line tools and scripts.
• Refactoring: Analyzed codebase fragmentation and proposed refactoring strategies for a clean, modular RAG pipeline.
• File Management: Initialized Python files for the v2/ directory and outlined a modular organization plan.
• Migration: Completed migration of remaining components in the RAG codebase, ensuring all functions and classes are correctly placed.

Achievements:

• Successfully modularized the RAG codebase, organizing it into logical modules.
• Implemented robust error handling and state management in various flows.
• Enhanced the scalability and maintainability of the codebase through refactoring and modular design.

Pending Tasks:

• Final cleanup tasks and verification of the modularized codebase to ensure all components are functioning as expected.

Evidence

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• event_ids: []