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GraphRAG (Graph-based Retrieval-Augmented Generation) is a framework that combines knowledge graphs and retrieval-augmented generation technology, effectively handling cross-modal scenarios and supporting various complex data types and application scenarios. Below, we will introduce the main cross-modal scenarios supported by GraphRAG.
GraphRAG can build a relational graph between text and images, using graph retrieval technology to quickly find the most relevant text or image nodes for a given query. For example, feature vectors of images and embedding vectors of text can be stored in the same graph structure, establishing connections by calculating similarity. This scenario is suitable for automatically answering questions based on images.
GraphRAG can process video content by extracting key frame features from the video and embedding them into a knowledge graph, achieving cross-modal retrieval and analysis between video and text. For example, users can retrieve relevant segments in a video by inputting a text description.
In e-commerce or social media platforms, GraphRAG can combine user behavior data with multi-modal features of products/content (such as images, text descriptions, etc.) to build user-product graphs or user-content graphs, thus providing personalized recommendations. For example, by analyzing users’ historical behavior and preferences, combined with product images and text descriptions, it can recommend the most relevant products to users.
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GraphRAG can support the retrieval and analysis of multi-language documents. By constructing a multi-language document graph, embedding documents in different languages into the same knowledge graph, users can query in one language, and the system can retrieve and return relevant documents while supporting cross-language translation and summary generation.
In academic research, GraphRAG can be used to analyze literature, identify research trends, and discover new research directions. By constructing a graph structure containing nodes such as authors, papers, and research fields, the system can help researchers quickly locate relevant literature and potential collaborators.
In legal scenarios, GraphRAG can be used for case analysis and legal consulting. By constructing a graph structure containing nodes such as case records and judicial opinions, the system can help lawyers and researchers quickly find relevant legal cases and citations.
GraphRAG can be used for automated content creation, such as news summaries and story generation. By constructing a graph structure containing nodes such as news events, characters, and locations, the system can generate customized news summaries or stories based on user queries.
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In the medical field, GraphRAG can help integrate and analyze medical records, medical research, and treatment guidelines, providing doctors with diagnostic support and personalized treatment recommendations. For example, by constructing a graph structure containing nodes such as patient records, symptoms, and treatment plans, the system can quickly provide relevant medical information.
GraphRAG, through its powerful graph structure design and multi-modal data processing capabilities, can effectively support the aforementioned cross-modal scenarios. It can not only handle complex multi-modal data but also provide richer contextual information through knowledge graphs, thus enhancing the quality of information retrieval and generation.
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