Refactored and Optimized Data Processing Pipelines

📅 2025-11-21 — Session: Refactored and Optimized Data Processing Pipelines

🕒 21:00–23:00
🏷️ Labels: Refactoring, Data Processing, Pipeline, Embedding, Cache Management
📂 Project: Dev

Session Goal: The session aimed to refactor and optimize various components of the data processing pipelines, focusing on canonicalization, embedding, and cache management.

Key Activities:

• Designed and implemented a canonicalizer module for the data processing pipeline, integrating it with existing components and providing unit tests.
• Developed a detailed refactoring plan for the TEI pipeline, identifying areas for improvement and providing a prioritized checklist.
• Outlined a refactoring strategy for the services/papers module, focusing on separation of concerns and clean architecture.
• Implemented a disk fast-path in the file system layer for managing papers, including patches for helper functions.
• Refactored the app/services/papers.py file to streamline code and improve maintainability by delegating operations to helper modules.
• Provided a complete replacement for the pipeline/embedding/engine.py file, standardizing the embedding API.
• Designed and implemented CLI scripts for the data processing pipeline, focusing on TEI parsing, embedding, and Chroma integration.
• Developed an orchestration script for FastAPI data ingestion, including environment setup and health checks.

Achievements:

• Completed the refactoring of the papers service layer, enhancing code quality and maintainability.
• Successfully integrated a disk fast-path for paper management, improving performance.
• Standardized the embedding API, facilitating easier integration with existing components.

Pending Tasks:

• Further testing and validation of the refactored components to ensure stability and performance.
• Continue unifying ingestion flows for enhanced predictability and idempotency in the pipeline.