The approach taken in Microsoft researchers' article on GraphRAG introduces a novel pipeline that condenses and transforms information from diverse documents into natural language text.
Starting with input documents, the use of LLM to extract structured information about entities and their relationships enables the creation of a knowledge graph.
The advantage of representing data as a knowledge graph lies in its ability to quickly combine information about entities from multiple sources, yielding more coherent insights.
The process showcases the innovative summarization of a condensed graph structure into natural language, allowing for effective communication of complex information across documents.
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