Comparative analysis of RAG methods for building Russian-speaking intelligent services

The paper discusses one of the currently most popular approaches to building various types of intelligent assistants and query-response systems based on large language models (LLMs), based on in-context learning or retrieval augmented generation (RAG). The recent proliferation of publications on this topic is primarily English-oriented and utilizes leading-quality models such as GPT-4o and their developments. At the same time, evaluations of RAG context search methods for Russian language tasks are practically absent, which makes it an urgent task to conduct research aimed at adapting and evaluating these methods for the Russian language.