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所在平台: Udemy |
课程主页: https://www.udemy.com/course/spring-ai-text-to-sql/
课程评论：没有评论
课程名称：文本转SQL春季AI实施与RAG 概述：当前，构建AI应用程序非常流行。对于Java开发者而言，使用Spring AI是构建AI应用程序的最佳选择。为了学习如何利用Spring AI构建AI应用程序，我们需要一个具体的例子。文本转SQL是一个典型的应用，通过AI提升生产力。借助文本转SQL，非技术人员可以使用自然语言描述数据库查询需求，这些查询会被发送给大型语言模型（LLM）。LLM能够生成SQL语句来响应用户查询，并执行SQL语句，将查询结果返回给用户。文本转SQL是AI应用程序的良好示例。 在本课程中，我们将使用Spring AI创建一个文本转SQL应用程序。学习完本课程后，您将掌握以下内容： - 如何使用ChatClient发送请求到LLM并接收响应。 - 如何提取数据库元数据并将其包含在发送至LLM的提示中。 - 如何使用Spring AI顾问拦截ChatClient请求以处理请求和响应。 - 如何使用嵌入模型和向量存储实现数据库元数据的语义搜索。 - 如何使用LLM生成数据库表和SQL语句的摘要。 - 如何让LLM自动重新选择表。 - 如何允许用户通过消息历史手动重新选择表。 - 如何使用函数执行和验证SQL语句。 - 如何将元数据索引器和文本转SQL应用作为无服务器函数部署到AWS Lambda。 - 如何在数据库中存储表元数据。 本课程涵盖了Spring AI的所有主要方面，包括ChatClient、顾问、嵌入模型、向量存储、聊天记忆和函数调用。您在本课程中学到的内容将帮助您使用Spring AI构建其他AI应用程序。代码已更新至Spring AI 1.0.0版本。您还可以获得一本免费的PDF书籍。 源代码：本课程提供文本转SQL应用的完整源代码，源代码可以在第五节的资源中下载。您也可以将您的GitHub账户邮箱发送给我，以访问私人GitHub仓库。
Building AI applications is very popular these days. For Java developers, the best choice for building AI applications is using Spring AI. To learn how to use Spring AI to build AI applications, we need to have a concrete example. Text to SQL, is a typical usage of using AI to improve productivity. By using text to SQL, non-technical people use natural language to describe database query requirements. These queries are sent to LLM. LLM can generate SQL statements to answer user queries. LLM can also use tools to execute SQL statements, and return the query results to the user. Text to SQL is a good example of AI applications.In this course, we will use Spring AI to create a text to SQL application. After learning this course, you will know:How to use ChatClient to send requests to LLM and receive responses.How to extract database metadata and include them in the prompt sent to LLM.How to use Spring AI advisors to intercept ChatClient requests to process requests and responses.How to use embedding model and vector store to implement semantic search of database metadata.How to use LLM to generate summary of database tables and SQL statements.How to use LLM to re-select tables automatically.How to allow user to manually re-select tables using message history.How to execute and validate SQL statements using functions.How to deployment metadata indexer and Text-to-SQL application as serverless functions on AWS Lambda.How to store table metadata in a database.This course covers all major aspects of Spring AI, including ChatClient, advisors, embedding models, vector stores, chat memory and function calling.What you have learned in this course can help you build other AI applications using Spring AI.The code has been updated to Spring AI 1.0.0 release version.You can also get a free copy of the PDF book.Source code: This course provides full source code of the text to SQL application. The source code can be downloaded from resource of 5th lecture. You can also send me your GitHub account email to access the private GitHub repository.
