Awesome-GraphRAG and Awesome-RAG
These are complementary resources that address different architectural approaches to RAG: one focuses specifically on graph-based retrieval methods while the other covers RAG development broadly, so users would consult both depending on whether they need general RAG techniques or specifically graph-enhanced retrieval strategies.
About Awesome-GraphRAG
DEEP-PolyU/Awesome-GraphRAG
Awesome-GraphRAG: A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation.
This project compiles a comprehensive list of research and open-source tools related to Graph-based Retrieval-Augmented Generation (GraphRAG). It helps researchers, PhD students, and AI practitioners explore advanced methods for building more accurate and context-aware customized Large Language Models (LLMs). The project categorizes and explains various techniques for organizing knowledge, retrieving information, and integrating it with LLMs, moving beyond traditional text-chunking approaches.
About Awesome-RAG
liunian-Jay/Awesome-RAG
💡 Awesome RAG: A resource of Retrieval-Augmented Generation (RAG) for LLMs, focusing on the development of technology.
This resource provides a curated list of the latest research papers, frameworks, and datasets specifically focused on Retrieval-Augmented Generation (RAG) for large language models (LLMs). It helps researchers and developers stay current with cutting-edge advancements in making LLMs more accurate and knowledgeable. You'll find links to academic papers, code repositories, and relevant evaluation datasets for RAG system development.
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