{"id":1027041,"date":"2024-04-23T11:20:10","date_gmt":"2024-04-23T18:20:10","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&#038;p=1027041"},"modified":"2025-10-15T08:58:42","modified_gmt":"2025-10-15T15:58:42","slug":"graphrag","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/graphrag\/","title":{"rendered":"Project GraphRAG"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background  has-background- card-background--full-bleed\">\n\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"2633\" height=\"1836\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/04\/graphrag-banner.png\" class=\"attachment-full size-full\" alt=\"Image of an entity network with color-coded communities\" style=\"\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/04\/graphrag-banner.png 2633w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/04\/graphrag-banner-300x209.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/04\/graphrag-banner-1024x714.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/04\/graphrag-banner-768x536.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/04\/graphrag-banner-1536x1071.png 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/04\/graphrag-banner-2048x1428.png 2048w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2024\/04\/graphrag-banner-240x167.png 240w\" sizes=\"auto, (max-width: 2633px) 100vw, 2633px\" \/>\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 \">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 class=\"wp-block-heading\" id=\"project-graphrag\">Project GraphRAG<\/h1>\n\n\n\n<p>LLM-Derived Knowledge Graphs<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<div class=\"wp-block-media-text has-video  has-vertical-margin-small  has-vertical-padding-none  is-stacked-on-mobile\" style=\"grid-template-columns:40% auto\"><figure class=\"wp-block-media-text__media video-wrapper\"><iframe class=\"media-text__video\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/jCjyaQL-7mA?enablejsapi=1&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen><\/iframe><\/figure><div class=\"wp-block-media-text__content\">\n<h2 class=\"wp-block-heading\" id=\"graphrag\">GraphRAG<\/h2>\n\n\n\n<p>GraphRAG (Graphs + Retrieval Augmented Generation) is a technique for richly understanding text datasets by combining text extraction, network analysis, and LLM prompting and summarization into a single end-to-end system.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-fill\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/from-local-to-global-a-graph-rag-approach-to-query-focused-summarization\/\">Read the pre-print<\/a><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n\n\n<h4 class=\"wp-block-heading\" id=\"microsoft-discovery\">Microsoft Discovery<\/h4>\n\n\n\n<p>GraphRAG and LazyGraphRAG technology is now available through <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/transforming-rd-with-agentic-ai-introducing-microsoft-discovery\/#:~:text=Get%20started%20today%20by%20using%20Azure%20HPC%20and%20Azure%20AI%20Foundry%20infrastructure.&text=Microsoft%20Discovery%20is%20built%20on,make%20any%20adjustments%20as%20needed.\">Microsoft Discovery<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, an agentic platform for scientific research built in Azure.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"open-source\">Open Source<\/h4>\n\n\n\n<p>The GraphRAG library <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/graphrag-new-tool-for-complex-data-discovery-now-on-github\/\">is available on GitHub<\/a>. The GraphRAG solution accelerator repository, an API experience hosted on Azure, has been archived but can still be referenced below. BenchmarkQED, a set of tools for evaluating RAG based systems, is also <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/github.com\/microsoft\/benchmark-qed\">available on GitHub<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.<\/p>\n\n\n\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/github.com\/microsoft\/graphrag\">GraphRAG Library<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n\n\n\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/github.com\/microsoft\/benchmark-qed\">BenchmarkQED<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"claim-extraction-and-hallucination-detection\">Claim Extraction and Hallucination Detection<\/h4>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/claimify-extracting-high-quality-claims-from-language-model-outputs\/\">Claimify Video<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/video\/veritrail-detect-hallucination-and-trace-provenance-in-ai-workflows\/\">Veritrail Video<\/a><\/p>\n\n\n\n<p>Archived: <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/github.com\/Azure-Samples\/graphrag-accelerator\">GraphRAG Solution Accelerator<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> <\/p>\n\n\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>LLM-Derived Knowledge Graphs GraphRAG (Graphs + Retrieval Augmented Generation) is a technique for richly understanding text datasets by combining text extraction, network analysis, and LLM prompting and summarization into a single end-to-end system. GraphRAG and LazyGraphRAG technology is now available through Microsoft Discovery (opens in new tab), an agentic platform for scientific research built in [&hellip;]<\/p>\n","protected":false},"featured_media":1027164,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556,13555],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-1027041","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-search-information-retrieval","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[1029069],"related-downloads":[],"related-videos":[1138726],"related-groups":[734455],"related-events":[],"related-opportunities":[],"related-posts":[1005408,1039806,1051977,1052793,1079073,1098888,1101987,1106013,1134179,1145803],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Jonathan 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