Show HN: RAG-LCC – config-driven RAG framework for fast experimentation

Category: library

Tags: rag, retrieval-augmented-generation, experimental-framework

Score: 6.0/10 (Innovation: 6, Technical: 6, Documentation: 7, Utility: 5)

RAG-LCC is an experimental RAG framework that emphasizes constraint-aware retrieval and context assembly, treating classification, chunking, and filtering as first-class architectural tools. It's interesting because it shifts focus from scaling context windows to improving coherence and correctness on modest hardware, with multi-mode retrieval and inspectable pipelines.

Target audience: ai-ml researchers, backend devs

Repository: https://github.com/HarinezumIgel/RAG-LCC · Python · MIT · 8 stars

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