How Embeddings Power Retrieval-Augmented Generation (RAG) Systems
English summary
This Medium tutorial by Cletus Jay Ajibade provides a beginner-friendly guide to how Retrieval-Augmented Generation (RAG) systems leverage embeddings, vector databases, and large language models to search private company data. It explains the workflow of converting data into vector embeddings, performing similarity search, and using LLMs to generate context-aware answers. The piece is an introductory overview aimed at demystifying enterprise AI search architecture.
Chinese summary
这篇由 Cletus Jay Ajibade 发布于 Medium 的教程面向初学者，介绍了检索增强生成（RAG）系统如何利用嵌入、向量数据库和大语言模型来搜索企业私有数据。文章解释了将数据转换为向量嵌入、进行相似性搜索并使用大模型生成上下文感知答案的流程，旨在帮助读者理解企业 AI 搜索的架构。
Key points
Explains how text data is transformed into numerical embeddings for semantic search.
解释文本数据如何转换为数值嵌入以进行语义搜索。
Describes the role of vector databases in storing and retrieving relevant information.
描述向量数据库在存储和检索相关信息中的作用。
Illustrates the integration with large language models to generate responses from retrieved context.
展示与大语言模型集成，根据检索到的上下文生成回应。
Targets beginners who want to understand RAG for private enterprise data applications.
面向希望了解企业私有数据 RAG 应用的初学者。