KET-RAG
KET-RAG is a retrieval-enhanced generation framework combined with knowledge graphs for efficient document indexing and answer generation.
Product Details
KET-RAG (Knowledge-Enhanced Text Retrieval Augmented Generation) is a powerful retrieval-enhanced generation framework that combines knowledge graph technology. It achieves efficient knowledge retrieval and generation through multi-granularity indexing frameworks such as knowledge graph skeleton and text-keyword bipartite graph. This framework significantly improves retrieval and generation quality while reducing indexing costs, and is suitable for large-scale RAG application scenarios. KET-RAG is developed based on Python, supports flexible configuration and expansion, and is suitable for developers and researchers who need efficient knowledge retrieval and generation.
Main Features
How to Use
Target Users
This product is suitable for developers, researchers, and enterprise application developers who need efficient knowledge retrieval and generation. It can help users quickly retrieve relevant information in large-scale documents and generate high-quality answers. It is suitable for scenarios such as question and answer systems, intelligent customer service, and knowledge management.
Examples
In question answering systems, KET-RAG can quickly retrieve the knowledge base and generate accurate answers.
Used in intelligent customer service scenarios, KET-RAG can retrieve relevant knowledge and generate responses based on user questions.
In knowledge management systems, KET-RAG can help users quickly locate and generate knowledge fragments.
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