This article introduces Graph RAG, a method for enhancing Language Model (LLM) applications by incorporating knowledge graphs. It explains the limitations of traditional text embedding-based retrieval and how Graph RAG addresses them by providing a global understanding of the knowledge base through community detection and report generation.
This article explores how to implement a retriever over a knowledge graph containing structured information to power RAG (Retrieval-Augmented Generation) applications.
This guide explains how to build and use knowledge graphs with R2R. It covers setup, basic example, construction, navigation, querying, visualization, and advanced examples.
Learn about the LLM Knowledge Graph Builder, an online tool that uses machine learning models to transform unstructured data into a knowledge graph. This tool is integrated with a Retrieval-Augmented Generation (RAG) chatbot and is part of Neo4j's GraphRAG Ecosystem Tools.
This article discusses GNN-RAG, a new AI method that combines the language understanding abilities of LLMs with the reasoning abilities of GNNs for Retrieval-Augmented Generation (RAG) style. This approach improves KGQA performance by utilizing GNNs for retrieval and RAG for reasoning.
