The Complete Spring Boot Guide

Spring Boot
Mastery

Build production-ready Java applications with the most powerful framework. From REST APIs to microservices, master every concept with real examples.

Auto-configuration
Production Ready
Data Access
Ecosystem
Why Spring Boot?

Everything you need
to build modern applications

Spring Boot makes it easy to create production-grade Spring-based applications that you can "just run" with minimal configuration.

Click any card to explore the concept, see examples, and test your knowledge.

Quick Start

Get started in minutes

Follow these simple steps to create and run your first Spring Boot application.

create-project.sh
01# Using Spring Boot CLI
02spring init --dependencies=web,data-jpa,h2 myapp
03
04# Or using Maven
05mvn archetype:generate \
06 -DgroupId=com.example \
07 -DartifactId=myapp \
08 -DarchetypeArtifactId=maven-archetype-quickstart
Deep Dive

Core Concepts

Master the fundamental building blocks of Spring Boot applications

Inversion of Control (IoC)

The Foundation of Spring

IoC is a design principle where the control of object creation and lifecycle is transferred from the application code to the Spring container. Instead of your code creating dependencies, Spring injects them automatically.

Key Points

Container manages object lifecycle
Loose coupling between components
Easier testing with mock objects
Configuration-based dependency management
Example
01// Traditional approach (tight coupling)
02public class OrderService {
03 private PaymentService paymentService = new PaymentService();
04}
05
06// IoC approach (loose coupling)
07@Service
08public class OrderService {
09 private final PaymentService paymentService;
10
11 @Autowired
12 public OrderService(PaymentService paymentService) {
13 this.paymentService = paymentService;
14 }
15}
Artificial Intelligence

Spring AI

Bring the power of Generative AI to your Spring applications. Build intelligent features with a portable, unified API across all major AI providers.

Supported AI Providers

OpenAI

GPT-4, GPT-3.5, DALL-E, Whisper

Anthropic

Claude 3 Opus, Sonnet, Haiku

Google Vertex AI

Gemini Pro, PaLM 2

Amazon Bedrock

Titan, Claude, Llama 2

Azure OpenAI

GPT-4, GPT-3.5, DALL-E

Ollama

Llama 3, Mistral, Phi-3

Mistral AI

Mistral Large, Medium, Small

Hugging Face

Any HF model

Key Capabilities

Chat Models

Integrate with OpenAI, Azure OpenAI, Anthropic, Google Vertex AI, Amazon Bedrock, Ollama, and more for conversational AI.

Image Generation

Generate images using DALL-E, Stability AI, and other image models with a unified API.

Embedding Models

Transform text into vector embeddings for semantic search, RAG, and similarity matching.

Vector Stores

Native integrations with Pinecone, Milvus, Chroma, PGVector, Redis, and Weaviate.

Function Calling

Let AI models call your Java methods and Spring beans with automatic serialization.

Guardrails & Safety

Built-in content moderation, output validation, and safety controls for production use.

Quick Start Configuration

Maven Dependencies

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-pgvector-store-spring-boot-starter</artifactId>
</dependency>

Application Properties

# OpenAI Configuration
spring.ai.openai.api-key=${OPENAI_API_KEY}
spring.ai.openai.chat.options.model=gpt-4
spring.ai.openai.chat.options.temperature=0.7

# Vector Store (PGVector)
spring.ai.vectorstore.pgvector.index-type=HNSW
spring.ai.vectorstore.pgvector.distance-type=COSINE_DISTANCE
spring.ai.vectorstore.pgvector.dimensions=1536

# Embedding Model
spring.ai.openai.embedding.options.model=text-embedding-3-small

Complete Code Examples

Basic Chat Client

Simple chat interaction with any supported AI model

@RestController
@RequestMapping("/api/chat")
public class ChatController {

    private final ChatClient chatClient;

    public ChatController(ChatClient.Builder builder) {
        this.chatClient = builder
            .defaultSystem("You are a helpful assistant.")
            .build();
    }

    @PostMapping
    public String chat(@RequestBody String message) {
        return chatClient.prompt()
            .user(message)
            .call()
            .content();
    }

    // Streaming response for real-time chat
    @GetMapping(value = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<String> streamChat(@RequestParam String message) {
        return chatClient.prompt()
            .user(message)
            .stream()
            .content();
    }
}

Supported Vector Stores

PGVectorPineconeMilvusChromaRedisWeaviateQdrantNeo4jElasticsearchMongoDB Atlas

Easily switch between vector stores without changing your application code

Spring AI Architecture

Your Application

Controllers, Services, Business Logic

Spring AI

Unified API, Advisors, Memory

ChatClient
VectorStore
ImageModel
Embedding

AI Providers

OpenAI, Anthropic, Google, AWS...

OpenAI
Claude
Gemini
Bedrock

Best Practices

Use Environment Variables

Never hardcode API keys. Use Spring profiles and environment variables for secure configuration.

Implement Rate Limiting

Add rate limiting to your AI endpoints to prevent abuse and manage costs effectively.

Cache Embeddings

Cache document embeddings to reduce API calls and improve response times for repeated queries.

Use Async Operations

Leverage reactive streams and async processing for better scalability with AI operations.

Practical Examples

Learn by building

Complete, production-ready code examples you can use as a starting point for your projects.

REST API with CRUD Operations

Complete REST API example with all HTTP methods, validation, and error handling

001package com.example.demo.model;
002
003import jakarta.persistence.*;
004import jakarta.validation.constraints.*;
005import java.math.BigDecimal;
006import java.time.LocalDateTime;
007
008@Entity
009@Table(name = "products")
010public class Product {
011
012 @Id
013 @GeneratedValue(strategy = GenerationType.IDENTITY)
014 private Long id;
015
016 @NotBlank(message = "Name is required")
017 @Size(min = 2, max = 100)
018 private String name;
019
020 @Size(max = 500)
021 private String description;
022
023 @NotNull(message = "Price is required")
024 @DecimalMin(value = "0.0", inclusive = false)
025 private BigDecimal price;
026
027 @Min(0)
028 private Integer quantity = 0;
029
030 @Column(name = "created_at")
031 private LocalDateTime createdAt;
032
033 @Column(name = "updated_at")
034 private LocalDateTime updatedAt;
035
036 @PrePersist
037 protected void onCreate() {
038 createdAt = LocalDateTime.now();
039 updatedAt = LocalDateTime.now();
040 }
041
042 @PreUpdate
043 protected void onUpdate() {
044 updatedAt = LocalDateTime.now();
045 }
046
047 // Getters and Setters...
048}
Best Practices

Layered Architecture

Spring Boot applications follow a layered architecture pattern that promotes separation of concerns, testability, and maintainability.

Application Layers

Presentation Layer

Layer 1

Handles HTTP requests, input validation, and response formatting

@RestController@ControllerRequest/Response DTOsValidation

Service Layer

Layer 2

Contains business logic, orchestrates operations, manages transactions

@ServiceBusiness Logic@TransactionalEvent Publishing

Repository Layer

Layer 3

Handles data persistence, database queries, and entity management

@RepositoryJpaRepositoryCustom QueriesEntity Mapping

Database Layer

Layer 4

Actual data storage, indexing, and data integrity

PostgreSQLMySQLMongoDBRedis Cache

Recommended Project Structure

Why This Structure?

  • Separation of concerns - Each package has a clear responsibility
  • Easy navigation - Find code quickly by type or domain
  • Testability - Mock dependencies at layer boundaries
  • Scalability - Add features without restructuring