Using Rag Technology to Design an Intelligent Information System for Support Exploratory Search

This paper presents the integration of a large language model (LLM) into an intelligent information and analytical system for exploratory search support within specific texts. An overview of the capabilities of modern large language models is provided. Special attention is paid to the Retrieval Augmented Generation (RAG) technology used for efficient formation and control of search contexts. The design and development of the system's business logic functions and user interface elements are detailed. Experimental results applying the developed system to texts with specific characteristics are presented. Conclusions are drawn regarding the system's applicability for automating ontology population. Conclusions are drawn regarding the system's applicability for automating the process of populating ontologies, as well as filling other knowledge repositories built using automated monitoring of open big data and intelligent data processing.