Text To Sql - Spring Ai Implementation With Rag

[0dayddl]

Text To Sql - Spring Ai Implementation With Rag
Published 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.33 GB | Duration: 1h 59m​

Build a Text to SQL application using Spring AI

What you'll learn

Learn how to use Spring AI 1.0 to build AI applications

Text to SQL implementation using LLM

Database metadata searching using vector store

Function calling in Spring AI to execute SQL statements

Requirements

Basic knowledge of Java

Basic knowledge of LLM

Description

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.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.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 access the private GitHub repository.

Overview

Section 1: Introduction

Lecture 1 Course introduction

Section 2: Spring AI Basic

Lecture 2 Spring AI Introduction

Section 3: Basic Text to SQL

Lecture 3 Basic Text to SQL

Lecture 4 Basic text to SQL

Lecture 5 Database metadata extraction

Lecture 6

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[0dayddl]

Text To Sql - Spring Ai Implementation With Rag
Published 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.33 GB | Duration: 1h 59m​

Build a Text to SQL application using Spring AI

What you'll learn

Learn how to use Spring AI 1.0 to build AI applications

Text to SQL implementation using LLM

Database metadata searching using vector store

Function calling in Spring AI to execute SQL statements

Requirements

Basic knowledge of Java

Basic knowledge of LLM

Description

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.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.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 access the private GitHub repository.

Overview

Section 1: Introduction

Lecture 1 Course introduction

Section 2: Spring AI Basic

Lecture 2 Spring AI Introduction

Section 3: Basic Text to SQL

Lecture 3 Basic Text to SQL

Lecture 4 Basic text to SQL

Lecture 5 Database metadata extraction

Lecture 6

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RapidGator

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AlfaFile

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TurboBit

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