Spring Boot Ai

🚀 Spring AI Project Practical Guide: Building an Intelligent Chat Server from Scratch

This guide will teach you step by step how to build an intelligent chat server using the Spring AI framework. By combining generative artificial intelligence (GenAI) and large language models (LLMs), we can provide users with an intelligent conversational experience. This article will detail the project's architectural design, implementation details, and deployment process.

🚀 Quick Start

Project Overview

Features

• Natural Language Processing (NLP): Parses user input and understands intent.
• Chat Generation: Generates intelligent responses using LLMs.
• Context Memory: Provides a coherent conversational experience through conversation history.
• Scalable Plugin Architecture: Supports multiple AI models and data sources.

Core Components

1. MCP Server (Message Control Plane)
   • Responsible for message routing, processing, and context management.
   • Provides REST API interfaces to integrate various AI services.
2. Chat Server
   • Handles user input, calls MCP for parsing and generating responses.
   • Provides Swagger UI for API testing and documentation viewing.

Project Architecture

Technology Selection

• Spring Boot: Quickly builds the backend service framework.
• Spring AI: Integrates GenAI functional modules.
• Ollama: A locally run large language model.
• Docker: Containerized deployment to ensure environmental consistency.

File Structure

chat-server/
├── src/main/kotlin/
│   └── com.example.chatserver/
│       ├── ChatServerApplication.kt
│       ├── controller/ (Controllers)
│       ├── service/ (Business Logic)
│       └── repository/ (Data Access Layer)
├── src/test/kotlin/
│   └── com.example.chatserver.test/
│       └── ChatServerApplicationTest.kt
└── resources/
    ├── application.yml
    └── Dockerfile

Detailed Implementation

MCP Server Configuration

Add the following configuration to application.yml to specify the address and port of the MCP server:

mcp:
  server:
    host: localhost
    port: 8081

Chat Service Implementation

Write the main application class ChatServerApplication.kt to integrate Spring AI components:

@SpringBootApplication
class ChatServerApplication

fun main(args: Array<String>) {
    runApplication<ChatServerApplication>(*args)
}

Custom Chat Logic

Handle user requests in the controller and generate responses through MCP:

@RestController
@RequestMapping("/chat")
class ChatController(
    private val chatService: ChatService,
    private val mcpClient: McpClient
) {

    @PostMapping
    fun chat(@RequestParam question: String): String {
        return chatService.generateResponse(question)
    }
}

Testing and Deployment

Unit Testing

Write integration test cases to verify the chat functionality:

@SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT)
class ChatServerApplicationTest {

    @Autowired
    private lateinit var restTemplate: TestRestTemplate

    @Test
    fun testBasicChat() {
        val response = restTemplate.getForObject(
            "http://localhost:{port}/chat",
            String::class.java,
            "question=你好"
        )
        assertThat(response).contains("您好！有什么可以帮助您的吗？")
    }
}

Docker Deployment

Build the image and start the service:

docker-compose up -d

Using Other AI Models

Spring AI supports multiple AI models, such as Bedrock and Claude. Simply add the corresponding dependencies and configure them in application.yml.

Documentation and Resources

• Spring AI Official Documentation
• Docker Documentation
• Project Sample Code: GitHub Repository

By following the above steps, you can quickly build a fully functional intelligent chat server and expand and optimize it according to your actual needs.