Show HN: RAG-LCC – config-driven RAG framework for fast experimentation (github.com)

The recently announced RAG-LCC (Retrieval-Augmented Generation with Local Corpus & Classification) framework introduces a novel architecture aimed at enhancing experimentation in AI/ML by prioritizing constraints and correctness over mere scalability. Instead of solely increasing context size, RAG-LCC treats chunking, classification, and retrieval strategies as integral tools for managing context. It is particularly significant for researchers and engineers dealing with complex, large, or ambiguous documents, as the framework emphasizes semantic clarity and coherence. By analyzing and filtering documents before they are processed by large language models (LLMs), RAG-LCC reduces the chances of ambiguities and contradictions that can mislead LLM responses. Key technical details include a customizable retrieval system that supports multiple strategies like BM25, vector search, and graph-based retrieval, all designed to optimize for context integrity rather than mere relevance. With six distinct retrieval modes and a focus on conflict avoidance, this framework can effortlessly operate on constrained hardware, making it accessible for a wider array of users, from researchers to practitioners. RAG-LCC not only allows for in-depth experimentation with advanced retrieval techniques but also aims to foster understanding of the complexities involved in context assembly, making it a valuable tool for learning and research in the AI/ML community.