Entropy- RAG

Entropy-RAG is a Retrieval-Augmented Generation (RAG) framework that introduces entropy-balanced information retrieval. It is designed to maintain semantic diversity and contextual stability across retrieved documents, improving the consistency and interpretability of language model reasoning.

This implementation draws inspiration from physics-informed optimization and nonlinear diffusion principles used in the RDT Kernel and Topological Adam algorithms. It integrates these ideas into a retrieval system that measures and balances entropy across topics, allowing for adaptive, self-regularizing search behavior.

Key features include:

Entropy-regulated topic selection for stable document retrieval

Adaptive coupling constant (Ω) for dynamic information balance

Integration with sentence-transformer embeddings for high-quality vectorization

Support for benchmarking and evaluation of retrieval diversity, coherence, and entropy metrics

Example scripts for synthetic and language-model-based evaluation

Entropy-RAG can be used as a standalone retrieval library or as a plug-in component for larger RAG, LLM, or differentiable physics systems. It is intended for developers and researchers exploring adaptive retrieval methods, interpretability, and physics-inspired machine learning architectures.