KAG
A generation framework based on knowledge enhancement for building professional knowledge services
Product Details
KAG (Knowledge Augmented Generation) is a professional domain knowledge service framework that aims to bidirectionally enhance large language models and knowledge graphs through the advantages of knowledge graphs and vector retrieval, and solve the problems of RAG (Retrieval Augmentation Generation) technology such as the large gap between vector similarity and knowledge reasoning correlation, and its insensitivity to knowledge logic. KAG's performance on multi-hop question answering tasks is significantly better than methods such as NaiveRAG and HippoRAG. For example, the F1 score on hotpotQA increased by 19.6% and on 2wiki by 33.5%. KAG has been successfully used in two professional knowledge question and answer tasks of Ant Group, including government question and answer and health question and answer. Compared with the RAG method, the professionalism has been significantly improved.
Main Features
How to Use
Target Users
KAG's target audience is developers and enterprise users, especially those teams that need to build knowledge services in professional fields. It is suitable for users who need to process a large amount of professional knowledge, conduct complex reasoning and question and answer systems. KAG helps users transform domain knowledge into computable logical forms by providing a complete set of frameworks and tools, thereby improving the professionalism and accuracy of question and answer systems.
Examples
Government Q&A: Use the government knowledge graph constructed by KAG to achieve accurate answers to professional questions such as policies and regulations.
Health Q&A: Integrate medical knowledge through KAG and provide professional health consulting services.
Risk mining: In the financial field, use expert rules defined by KAG to identify and analyze potential risky applications and developers.
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