RAG_Techniques and rago
The first is an educational resource demonstrating RAG implementation patterns, while the second is an optimization tool that would experimentally tune those patterns—making them complements that work together in a RAG development workflow.
About RAG_Techniques
NirDiamant/RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
Covers advanced RAG patterns including agentic retrieval loops, hybrid search strategies (dense-sparse retrieval fusion), query optimization techniques, and multi-document reasoning—beyond basic retrieval pipelines. Implementations target popular frameworks like LangChain and LlamaIndex with code-first Jupyter notebooks, focusing on practical enhancements for production-grade systems.
About rago
liebherr-aerospace/rago
RAGO (Retrieval Augmented Generation Optimizer) is a toolkit that automatically discovers the best configuration for your RAG system through smart experimentation
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