From Local to Global: A Graph RAG Approach to Query-Focused Summarization

The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over private and/or previously unseen document collections. However, RAG fails on global questions directed at an entire text corpus, such as “What are the main themes in the dataset?”, since this is inherently a query-focused summarization (QFS) task, rather than an explicit retrieval task. Prior QFS methods, meanwhile, fail to scale to the quantities of text indexed by typical RAG systems. To combine the strengths of these contrasting methods, we propose a Graph RAG approach to question answering over private text corpora that scales with both the generality of user questions and the quantity of source text to be indexed. Our approach uses an LLM to build a graph-based text index in two stages: first to derive an entity knowledge graph from the source documents, then to pre-generate community summaries for all groups of closely-related entities. Given a question, each community summary is used to generate a partial response, before all partial responses are again summarized in a final response to the user.

we are already witnessing attempts to automate human-like sensemaking in complex domains like scientific discovery (Microsoft, 2023) and intelligence analysis

Retrieval-augmented generation (RAG, Lewis et al., 2020) is an established approach to answering user questions over entire datasets, but it is designed for situations where these answers are contained locally within regions of text whose retrieval provides sufficient grounding for the generation task. Instead, a more appropriate task framing is query-focused summarization (QFS, Dang, 2006), and in particular, query-focused abstractive summarization that generates natural language summaries and not just concatenated excerpts

In recent years, however, such distinctions between summarization tasks that are abstractive versus extractive, generic versus query-focused, and single-document versus multi-document, have become less relevant.

In this paper, we present a Graph RAG approach based on global summarization of an LLM-derived knowledge graph (Figure 1). In contrast with related work that exploits the structured retrieval and traversal affordances of graph indexes (subsection 4.2), we focus on a previously unexplored quality of graphs in this context: their inherent modularity (Newman, 2006) and the ability of community detection algorithms to partition graphs into modular communities of closely-related nodes (e.g., Louvain, Blondel et al., 2008; Leiden, Traag et al., 2019). LLM-generated summaries of these community descriptions provide complete coverage of the underlying graph index and the input documents it represents. Query-focused summarization of an entire corpus is then made possible using a map-reduce approach: first using each community summary to answer the query independently and in parallel, then summarizing all relevant partial answers into a final global answer.

we used an LLM to generate a diverse set of activity-centered sensemaking questions from short descriptions of two representative real-world datasets, containing podcast transcripts and news articles respectively.

extracting the broad class of “named entities” like people, places, and organizations is generally applicable, domains with specialized knowledge (e.g., science, medicine, law) will benefit from few-shot examples specialized to those domains. We also support a secondary extraction prompt for any additional covariates we would like to associate with the extracted node instances. Our default covariate prompt aims to extract claims linked to detected entities, including the subject, object, type, description, source text span, and start and end dates.

The index created in the previous step can be modelled as an homogeneous undirected weighted graph in which entity nodes are connected by relationship edges, with edge weights representing the normalized counts of detected relationship instances. Given such a graph, a variety of community detection algorithms may be used to partition the graph into communities of nodes with stronger connections to one another than to the other nodes in the graph

The next step is to create report-like summaries of each community in the Leiden hierarchy, using a method designed to scale to very large datasets. These summaries are independently useful in their own right as a way to understand the global structure and semantics of the dataset, and may themselves be used to make sense of a corpus in the absence of a question. For example, a user may scan through community summaries at one level looking for general themes of interest, then follow links to the reports at the lower level that provide more details for each of the subtopics.