ChatGPT, or more broadly, the underlying large language models (LLMs) it represents, primarily contributes to vector search by generating high-quality semantic embeddings. These embeddings transform unstructured data like text queries or documents into dense vector representations, capturing their contextual meaning, which is crucial for efficient similarity matching. In high-load systems, this involves using LLMs to vectorize vast amounts of data offline or on-demand for real-time queries, before specialized vector databases handle the actual search and indexing. While ChatGPT doesn't perform the vector search itself, it can also enhance the process by refining user queries or reranking initial search results based on deeper semantic understanding. This enables systems to move beyond keyword matching towards richer, intent-driven information retrieval, significantly improving relevance and user experience in complex applications. Therefore, its role is foundational in preparing and interpreting data for vector search, rather than executing the search algorithms in real-time within high-load environments. More details: https://new.zebra-tv.ru/bitrix/rk.php?id=48835&goto=https://abcname.com.ua