RAG_Techniques and HiRAG
These are **complements**: NirDiamant/RAG_Techniques provides a broad survey of RAG implementation patterns and methodologies, while HiRAG represents a specific advanced technique (hierarchical knowledge retrieval) that could be studied as one instantiation or integrated with the techniques in the survey repository.
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.
This project helps developers and AI practitioners enhance the accuracy and contextual richness of their RAG (Retrieval-Augmented Generation) systems. It provides advanced techniques for improving how AI models retrieve information and generate responses. Users input their existing RAG system components and learn how to apply cutting-edge methods to get more relevant and comprehensive AI-generated outputs.
About HiRAG
hhy-huang/HiRAG
[EMNLP'25 findings] This is the official repo for the paper, HiRAG: Retrieval-Augmented Generation with Hierarchical Knowledge.
This project helps you get more accurate and comprehensive answers from large language models (LLMs) when querying your specific documents or knowledge base. You provide your textual content, and it processes it to enable a system that understands the hierarchical relationships within your information. The result is a more insightful and detailed response to your questions. This is for data scientists, researchers, or anyone building advanced question-answering systems over their proprietary data.
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