📅 2025-02-20 — Session: Refactored RAG pipeline for modular embedding
🕒 19:40–20:15
🏷️ Labels: Refactoring, Embedding, Rag Pipeline, Modular Design, CLI
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to refactor the retrieval-augmented generation (RAG) pipeline to separate and modularize the embedder and retriever components, ensuring scalability and integration.
Key Activities
- Developed a refactoring plan to separate the embedder and retriever components of the RAG pipeline, detailing design decisions and the proposed file structure.
- Outlined the architecture for a modular embedding orchestrator to handle dynamic data embedding strategies using Langchain.
- Designed a unified embedding service capable of handling multiple embedding scripts, with storage solutions in FAISS and Parquet, and future API integration.
- Refined a command-line interface (CLI) parser to improve structure and validation, including error handling and default modes.
- Provided solutions for managing CLI arguments in Jupyter Notebooks using
argparse.
Achievements
- Completed the design of a flexible, modular embedder and retriever system for the RAG pipeline.
- Implemented improvements in CLI parsing and validation for enhanced automation and error handling.
Pending Tasks
- Implement the proposed modular embedding orchestrator and integrate it with existing systems.
- Develop API endpoints for the unified embedding service to facilitate external access and integration.