Researchers apply Unix philosophy to ML pipelines with modular, independently swappable stages to isolate performance issues in retrieval systems.

Open-source prototype treats each ML pipeline stage (PII redaction, chunking, deduplication, embeddings, evaluation) as a separate plugin with typed contracts, similar to Unix pipes

Solves debugging challenges by allowing single-stage swaps while keeping others constant, enabling direct precision/recall comparisons through re-evaluation

Uses double-underscore delimiters to mark stage boundaries in feature definitions, making it easy to modify individual components without affecting downstream processes

Currently in prototype phase; developers seek community feedback on design assumptions before moving toward production use

Available on GitHub at mloda-ai/rag_integration for review and contribution