AUTO-ASSIGNMENT OF NEW ENTITIES IN A HIERARCHICAL STRUCTURE WITH AUTOMATIC HIERARCHY EXTENSIONS USING GENERATIVE ARTIFICIAL INTELLIGENCE

Abstract

The present disclosure relates to an automatic assignment of new entities in an existing hierarchical structure by leveraging retrieval-augmented generation (RAG) that improves the performance of a generative artificial intelligence (AI) model by generating a suggested hierarchical structure. For an automatic assignment, a user input may be received that may include a hierarchical structure and a query for adding one or more new entities to the existing hierarchical structure. For each new embedding vector associated with the query for adding a new entity, a retrieval-augmented generation may generate a context from the hierarchical structure based on a relevance to the query, and an additional context from a knowledge base. The retrieval-augmented generation may input a human-readable prompt, combining query with the context, additional context and a system prompt, to the generative AI model such as a large language model for generating the suggested hierarchical structure.

CPC Classifications