RAG_Techniques and RAGHub
These are ecosystem siblings—one serves as a comprehensive educational resource documenting RAG implementation techniques and best practices, while the other functions as a curated registry or aggregator of the broader RAG framework ecosystem itself.
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 RAGHub
Andrew-Jang/RAGHub
A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and resources. Contribute and explore the evolving RAG ecosystem.
Organizes RAG tools across specialized categories—frameworks, evaluation/optimization systems, data preparation, and engines—with live activity tracking to distinguish actively maintained projects from outdated ones. Curated by the r/RAG community, it catalogs both established frameworks (LangChain, LlamaIndex, Haystack) and emerging tools like Korvus (database-native RAG) and Swiftide (Rust-based streaming), helping developers navigate rapid ecosystem fragmentation. Includes evaluation frameworks, model leaderboards, and resources to address the full RAG development lifecycle beyond basic framework selection.
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