FlexRAG and RAG_Techniques
FlexRAG is a production-ready RAG implementation framework, while RAG_Techniques is an educational repository of RAG methodologies and approaches—making them complementary resources where practitioners learn techniques from the latter and implement them using the former.
About FlexRAG
ictnlp/FlexRAG
FlexRAG: A RAG Framework for Information Retrieval and Generation.
Supports text, multimodal, and web-accessible RAG scenarios through a modular pipeline architecture with integrated retrieval metrics and reranking components. Built on vectorized indexing (Faiss, LanceDB) with pre-trained retrievers available on HuggingFace Hub, enabling end-to-end workflows from corpus preparation through system evaluation and benchmarking.
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.
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