📅 2025-02-20 — Session: Refactored Embedding and Retrieval Pipelines
🕒 19:40–20:15
🏷️ Labels: Refactoring, Embedding, CLI, Modular, Scalability
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to plan and execute refactoring of the embedding and retrieval pipelines to enhance modularity and scalability.
Key Activities
- Developed a refactoring plan to separate embedder and retriever components in the RAG pipeline.
- Outlined the architecture for a modular embedding orchestrator for AI pipelines.
- Designed a unified embedding service for flexible document embedding, storage in FAISS and Parquet, and future API integration.
- Refined the CLI parser to improve structure, validation, and error handling.
- Implemented CLI input validation for Python scripts, including handling in Jupyter Notebooks.
Achievements
- A comprehensive refactoring plan was created, detailing design decisions and proposed file structures.
- A framework for embedding management in AI pipelines was proposed.
- A modular and flexible embedding service framework was designed.
- CLI parser was enhanced with better validation and error handling.
Pending Tasks
- Implement the proposed modular embedding orchestrator.
- Integrate the refined CLI parser into existing workflows.