Retrieval-Augmented Generation (RAG) for bibliographic analysis of complex texts as the case study of “Semiramis” by Alexey S. Khomyakov
TL;DR
The author examines application of the retrieval-augmented generation (RAG) technology to bibliographic processing of large unstructured philosophical texts as exemplified by Alexey S. Khomyakov’s “Semiramis” to make the practical contribution to developing digital hermeneutics and demonstrates AI potential for the humanities research.
Abstract
The author examines application of the retrieval-augmented generation (RAG) technology to bibliographic processing of large unstructured philosophical texts as exemplified by Alexey S. Khomyakov’s “Semiramis”. She describes the principles of RAG system operation based on text semantic analysis and vector indexing that enables to extract relevant fragments promptly. The author also focuses on the challenges of processing “Semiramis”, hard-to-analyze threevolume uncompleted work without distinct topical structure. The paper comprises RAG and vector indexing description, as well as the findings of system testing on complex issues that demand intellectual analysis of the historiosophic content. The cited examples of the system responses demonstrate its ability to extract and classify arguments and ideas scattered within the text. The author concludes on the prospectivity of the method not only for Khomyakov’s legacy studies but also for other large-scale sources (diaries, memoirs, archival documents, etc.), hard to describe with traditional bibliographic methods. The paper makes the practical contribution to developing digital hermeneutics and demonstrates AI potential for the humanities research.