Method, device and electronic equipment for all-weather RAG intelligent agent automatic newspaper design

Through the all-weather RAG intelligent automatic newspaper design method, a multi-level tree structure and personalized vector database are used, combined with a large model, the problem of the inability to achieve the integration of accuracy, real-time and private knowledge in the existing technology is solved, and efficient, personalized and real-time newspaper design generation is achieved.

CN119476247BActive Publication Date: 2025-03-25ZHEJIANG FANGZHENG PRINTING CO LTD
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Patent Information

Application Number
CN202510066713.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-01-16
Publication Date
2025-03-25
Estimated Expiration
2045-01-16

AI Technical Summary

Technical Problem

The prior art cannot realize automatic intelligent newspaper design around the clock, and the big model has problems of accuracy, real-time and private knowledge integration in information acquisition and design generation.

Method used

The all-weather RAG agent automatic newspaper design method is adopted to obtain the newspaper category and layout information selected by the user, and a multi-level tree structure (RAPTOR tree) is constructed, combined with the personalized vector database in the private domain container and the basic LLM model of the public domain, to realize the Agent design of vectorization, clustering and thinking chains, and automatically design and generate newspapers.

Benefits of technology

It realizes all-weather automatic newspaper design, improves the accuracy and personalization of the design, solves the problem of private knowledge integration, and enhances real-time and user interaction experience.

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Abstract

The present application belongs to the field of data processing technology, and provides a method, device and electronic device for automatic newspaper design by RAG intelligent agents around the clock. The preset newspaper design generation model in the method is obtained after training based on a personalized vector database configured in a private domain container as a training set and a basic LLM large model and a basic text map model based on a public domain. The personalized vector database at least stores a communication design reading template that conforms to the content of the user's newspaper. After obtaining the user's selected newspaper category and input layout information, the article semantics obtained from the layout information can be understood, a multi-level tree structure can be constructed with a RAPTOR tree, the layout information can be input into the multi-level tree structure, and the Agent design and summary of the vectorization, clustering, and thinking chain matching the output layout information can be used to automatically design the newspaper generation layout, thereby realizing automatic newspaper design around the clock.
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