フランスでGraphRAGを利用している企業

GraphRAG is a structured, hierarchical approach to Retrieval Augmented Generation (RAG), as opposed to naive semantic-search approaches using plain text snippets. The GraphRAG process involves extracting a knowledge graph out of raw text, building a community hierarchy, generating summaries for these communities, and then leveraging these structures when performing RAG-based tasks. It aims to improve LLM outputs using real-world information, particularly for reasoning about private datasets that the LLM has not been trained on. GraphRAG shows substantial improvements in answering complex questions by utilizing knowledge graphs and community summaries.