Text To Sql - Spring Ai Implementation With Rag

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 [code] Database metadata

Lecture 7 Low cardinality values

Section 4: Database metadata search using RAG

Lecture 8 Embedding model and vector store

Lecture 9 [code] Chroma

Lecture 10 Database metadata index

Lecture 11 [code] Database metadata index

Lecture 12 Generate table summary using LLM

Lecture 13 [code] Generate table summary

Lecture 14 Include SQL sample queries

Lecture 15 [code] Include SQL sample queries

Lecture 16 Generate SQL statement summary using LLM

Lecture 17 Reduce LLM prompt content size

Section 5: Table re-selection

Lecture 18 Table re-selection using LLM

Lecture 19 [code] Re-select tables

Lecture 20 Text to SQL using table re-selection

Lecture 21 Manual table re-selection using chat memory

Lecture 22 [code] Re-select tables using chat memory

Section 6: Functions to execute and validate SQL statements

Lecture 23 Use function to execute SQL statements

Lecture 24 [code] Execute SQL statements

Lecture 25 Use function to validate SQL statements

Lecture 26 [code] Validate SQL statements

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