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.

翻译：大语言模型是一类基于Transformer网络构建的生成式人工智能模型，能够利用海量数据集实现语言识别、摘要、翻译、预测与生成功能。大语言模型有望彻底改变社会格局，但训练这些基础模型仍面临巨大挑战。大语言模型中的语义向量搜索是一种强大技术，可显著提升搜索结果准确性与相关性。与传统的基于关键词的搜索方法不同，语义搜索利用词语的含义和上下文来理解查询意图，并提供更精确的结果。Elasticsearch作为实现语义搜索最流行的工具之一，是专为索引与搜索大规模数据集设计的极具可扩展性与鲁棒性的搜索引擎。本文深入探讨语义搜索的基本原理，并研究如何利用Elasticsearch与Transformer模型强化大语言模型处理范式。通过本文，读者将全面理解语义搜索原理，并掌握在实际模型应用场景中实现语义搜索的实用技能。