GRAPHRAG AND ROLE OF GRAPH DATABASES IN ADVANCING AI

Abstract

This article explores the synergy between GraphRAG and graph databases in advancing AI capabilities. Graph RAG, a novel approach to query-focused summarization that combines knowledge graph generation, retrieval-augmented generation (RAG), and query-focused summarization (QFS) to support human sensemaking over large text corpora. While traditional RAG methods excel at answering specific questions, they struggle with global queries about entire datasets. Graph RAG addresses this limitation by using an LLM to build a graph-based text index in two stages: first deriving an entity knowledge graph from source documents, then pre-generating community summaries for groups of related entities. Given a query, each community summary generates a partial response, which is then synthesized into a final answer. Evaluations on datasets in the 1 million token range show that Graph RAG substantially improves the comprehensiveness and diversity of answers compared to naïve RAG baselines, while also demonstrating favorable performance against global text summarization approaches. The hierarchical nature of the graph index allows for efficient querying at different levels of granularity. This approach offers a scalable solution for global sensemaking tasks over large private document collections, with potential applications in scientific discovery, intelligence analysis, and other domains requiring complex information synthesis.