Enhancing Cloud-Based Large Language Model Processing with Elasticsearch
  and Transformer Models
Chunhe Ni Jiang Wu Hongbo Wang Wenran Lu Chenwei Zhang
Large Language Models (LLMs) are a class of generative AI models built using
the Transformer network, capable of leveraging vast datasets to identify,
summarize, translate, predict, and generate language. LLMs promise to
revolutionize society, yet training these foundational models poses immense
challenges. Semantic vector search within large language models is a potent
technique that can significantly enhance search result accuracy and relevance.
Unlike traditional keyword-based search methods, semantic search utilizes the
meaning and context of words to grasp the intent behind queries and deliver
more precise outcomes. Elasticsearch emerges as one of the most popular tools
for implementing semantic search an exceptionally scalable and robust search
engine designed for indexing and searching extensive datasets. In this article,
we delve into the fundamentals of semantic search and explore how to harness
Elasticsearch and Transformer models to bolster large language model processing
paradigms. We gain a comprehensive understanding of semantic search principles
and acquire practical skills for implementing semantic search in real-world
model application scenarios.