LlamaIndex has released a notebook implementation of Microsoft’s GraphRAG, a sophisticated Retrieval-Augmented Generation (RAG) system that uses knowledge graphs to enhance LLMs.
This integration aims to improve the accuracy and relevance of information retrieved by LLMs, making them more effective in generating coherent and contextually accurate responses.
By integrating with LlamaIndex, GraphRAG can be used to enhance AI models in various fields, including healthcare, finance, and legal, where precise information retrieval is crucial.
Integration with NebulaGraph
The implementation leverages NebulaGraph, a popular graph database, to build and query the knowledge graph.
This integration simplifies the process of setting up and using GraphRAG, making it accessible for various applications.
The notebook implementation includes step-by-step instructions for setting up a NebulaGraph cluster, creating a knowledge graph index, and building a GraphRAG query engine. This makes it easier for developers to get started with GraphRAG without needing deep expertise in graph databases or machine learning.
GraphRAG, Outperforms Traditional RAG
Jerry Liu, co-founder and CEO of LlamaIndex in a recent interview said that basic RAG systems can have primitive interfaces and poor quality understanding and planning, lack function calling or tool use and are stateless (with no memory).
“RAG was really just the beginning,” Liu said. Many core concepts of naive RAG are “kind of dumb” and make “very suboptimal decisions.”
In recent evaluations, GraphRAG demonstrated its ability to answer “global questions” that address the entire dataset, a task where naive RAG approaches often fail.
By considering all input texts, GraphRAG’s community summaries provide more comprehensive and diverse answers.
This method also uses a map-reduce approach, grouping community reports up to the LLM context window size, mapping the question across each group to create community answers, and reducing these into a final global answer.
