graphrag and gfm-rag
About graphrag
microsoft/graphrag
A modular graph-based Retrieval-Augmented Generation (RAG) system
This system helps you make sense of large amounts of unstructured text data, like research papers or internal documents. It processes your text to identify key entities and relationships, outputting a structured knowledge graph that your AI can then use to answer complex questions or find insights more effectively. This is designed for researchers, analysts, or anyone who needs to extract precise information and reasoning from extensive narrative data using large language models.
About gfm-rag
RManLuo/gfm-rag
[NeurIPS'25, ICLR'26] Graph Foundation Model for Retrieval Augmented Generation
This project helps domain experts and researchers get more accurate answers from large language models (LLMs) by giving them relevant information from a collection of documents. It takes your documents and questions, builds a "knowledge graph" to understand relationships, and then uses that graph to find the most relevant document snippets for the LLM to use. Anyone who needs to extract precise answers from vast amounts of text will find this useful.
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