{"id":1079073,"date":"2024-09-09T09:15:55","date_gmt":"2024-09-09T16:15:55","guid":{"rendered":""},"modified":"2024-11-05T06:40:58","modified_gmt":"2024-11-05T14:40:58","slug":"graphrag-auto-tuning-provides-rapid-adaptation-to-new-domains","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/graphrag-auto-tuning-provides-rapid-adaptation-to-new-domains\/","title":{"rendered":"GraphRAG auto-tuning provides rapid adaptation to new domains"},"content":{"rendered":"\n
\"GraphRAG<\/figure>\n\n\n\n

GraphRAG uses large language models (LLMs) to create a comprehensive knowledge graph that details entities and their relationships from any collection of text documents. This graph enables GraphRAG to leverage the semantic structure of the data and generate responses to complex queries that require a broad understanding of the entire text. In previous blog posts,\u00a0<\/a>we introduced GraphRAG<\/a> and demonstrated how it could be applied to news articles<\/a>. In this blog post, we show that it can also be tuned to any domain to enhance the quality of the results.<\/p>\n\n\n\n

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