Building a Spring Boot Reference Documentation Assistant with Spring AI & GPT-4

Introduction

Welcome back, friends! Dan Vega here, and today we’re diving into an exciting project that combines the power of AI with Java and Spring Boot. Over the past few videos, we've been laying the foundation for building intelligent applications using Spring AI, exploring fundamental concepts, effective prompt crafting, response handling, and techniques like Retrieval-Augmented Generation (RAG).

In this article, we will bring everything together to create a useful tool for accessing Spring Boot reference documentation. We'll build a Spring Boot reference documentation assistant that enables us to ask questions and receive updated responses directly from the framework's documentation, utilizing a vector database for context.

Project Overview

We all know that the training data for most language models (LLMs) has a cut-off point, which often leaves us unable to access the most up-to-date information. This is especially true for the Spring Boot reference documentation, which is continuously being updated. The goal of our project is to create an assistant that captures the latest documentation updates by indexing them into a vector database, enabling efficient similarity searches.

We'll be summoning Spring AI and utilizing PGVector for our vector storage. Ultimately, by presenting the right context from our documentation, we can query the LLM and get accurate responses.

Project Setup

We will use the following technology stack:

Java Version: 21
Spring Boot Version: 3.2.5
Docker: To spin up a PG Vector database
Spring AI: For AI interactions
Spring Shell: To build a command-line application

You can create your project using Spring Initializr at start.spring.io;


Place your `spring-boot-reference.pdf` file in a `docs` directory within your resources.

### Implementing the Assistant

Next, we create the core functionality to populate our vector store with the documentation content. This task will involve reading the PDF file, splitting it into manageable chunks, and inserting those chunks into the PG Vector database for retrieval.

The key component, `ReferenceDocsLoader`, checks if the database has been populated already. If not, it processes the PDF and feeds the data into the vector store.

```java
@PostConstruct
public void init() (
    int count = jdbcClient.queryForObject("SELECT COUNT(*) FROM documents", Integer.class);
    if (count == 0) {
        // Load PDF and populate vector store
    )
}

Command Implementation

With our database populated, we can now build a command-line interface using Spring Shell. This allows us to send questions to our assistant.

@ShellComponent
public class SpringAssistantCommand (

    @ShellMethod("Ask a question")
    public String question(@ShellOption(defaultValue = "What is Spring Boot?") String message) {
        // Process question and return answer
    )
}

Building a Native Image

Finally, we can compile our application into a native executable using GraalVM. This results in significant performance improvements and allows easy distribution.

./mvnw -Pnative native:compile

Running the Application

After completing the build, you will have a native executable that can be run with the following command:

./target/ai-docs

This instantly puts you into the command-line interface, ready to start asking questions about Spring Boot.

Keywords

Spring AI
GPT-4
Java 21
Spring Boot
PG Vector
Docker
Retrieval-Augmented Generation (RAG)
Spring Shell
Native Image

FAQ

Q1: What is the purpose of this project?
A1: The goal is to build an assistant that lets users ask questions about the Spring Boot reference documentation, returning up-to-date responses based on the latest documentation.

Q2: What technology stack is used in this project?
A2: The project utilizes Java 21, Spring Boot 3.2.5, PGVector for the database, Docker for containerization, and Spring AI for language model interactions.

Q3: How does the PDF loading process work?
A3: The application reads the Spring Boot reference documentation PDF, splits it into smaller chunks, and stores these chunks in a PG Vector database for efficient querying.

Q4: Can I run this application as a command-line utility?
A4: Yes, the application is built using Spring Shell, enabling it to be run as a command-line utility where users can type their questions directly.

Q5: How do I deploy this application locally?
A5: You can compile the application into a native executable using GraalVM, which can then be run on your local machine without needing a Java Runtime Environment.