Application of a Hybrid Approach in the Synthesis of a Knowledge Extraction Module of an Intelligent Assistant for a Microcontroller Technical Specialist

A Retrieval-Augmented Generation (RAG) approach is widely used as a key element for intelligent assistants. However, the knowledge extraction stage from technical text corpora is fraught with difficulties due to the presence of highly specialized terminology, tables, and abbreviations. The goal of this study is to develop methodological support for knowledge extraction for an intelligent assistant for a technical specialist in the field of microcontroller-based device design. This study systematically compares and analyzes the computational performance of knowledge extraction methods and their various combinations. The results showed that the hybrid version of the baseline methods (hybrid_v2_dense) provides the best R@1 (45.2%), MRR@5 (49.8%) and nDCG@5 (52.0%) values, while the R@5 level remains comparable to BM25. Among the extended configurations of the hybrid_v2 family, the best R@5 value (57.7%) is achieved by the hybrid_v2_dense_splade method, while the best values of R@1 (48.9%), MRR@5 (52.1%), and nDCG@5 (53.7%) are achieved by the hybrid_v2_dense_unicoil method. Based on the obtained results, an expert decision tree was formed for selecting the knowledge extraction module configuration considering hardware limitations. These results provide experimental evidence of the effectiveness of the developed methodological support for knowledge extraction for an intelligent assistant of a technical specialist.