Demo Experience | Comparing Graph RAG with Vector Retrieval and Natural Language Generation Retrieval

Background Introduction

Graph RAG (Graph-based Retrieval Augmentation) is a concept first introduced by NebulaGraph in the industry. It leverages knowledge graphs combined with Large Language Models (LLMs) to provide search engines with more comprehensive contextual information. This technology helps users obtain more intelligent and precise search results at a lower cost. Currently, the technology has also shown promising results when integrated with vector databases.

This Demo allows you to experience the differences between Graph RAG, Vector RAG, Text2Cypher, and other retrieval enhancement technologies. You can intuitively feel how graph technology complements and optimizes traditional techniques such as embedding and vector search, enabling users to obtain search results that better meet their expectations at a lower cost and higher efficiency.

Demo Experience

The emergence of Graph RAG technology has brought a new perspective to the processing and retrieval of massive information. By integrating knowledge graphs and graph storage into the Large Language Model (LLM) technology stack, Graph RAG takes context learning to a new level.

Currently, users can easily set up Graph RAG with just 3 lines of code based on NebulaGraph database, or even integrate more complex RAG logic, such as Graph+Vector RAG.

Feel free to learn more about this technology through our blog, and if you are interested in graph database technology, you can click on Contact Us to get a free trial opportunity from Yueshu Graph Database and effortlessly build your customized knowledge graph application!

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