1. 引言
随着大语言模型(LLM)技术的不断发展,将AI能力集成到企业应用中变得越来越重要。Spring AI是Spring生态系统的最新成员,旨在简化AI服务与Spring应用的集成过程。
本文将详细介绍如何利用Spring AI构建一个简单的问答系统,帮助开发者快速入门AI应用开发。
2. 环境准备
2.1 项目依赖
首先,创建一个Spring Boot项目,并添加必要的依赖
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>org.example</groupId>
<artifactId>spring-ai-demo</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<maven.compiler.source>21</maven.compiler.source>
<maven.compiler.target>21</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<repositories>
<repository>
<id>spring-milestones</id>
<name>Spring Milestones</name>
<url>https://repo.spring.io/milestone</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
<repository>
<id>spring-snapshots</id>
<name>Spring Snapshots</name>
<url>https://repo.spring.io/snapshot</url>
<releases>
<enabled>false</enabled>
</releases>
</repository>
<repository>
<name>Central Portal Snapshots</name>
<id>central-portal-snapshots</id>
<url>https://central.sonatype.com/repository/maven-snapshots/</url>
<releases>
<enabled>false</enabled>
</releases>
<snapshots>
<enabled>true</enabled>
</snapshots>
</repository>
</repositories>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
<version>3.4.2</version>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-core</artifactId>
<version>1.0.0-M6</version>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
<version>1.0.0-M6</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.30</version>
</dependency>
<dependency>
<groupId>cn.hutool</groupId>
<artifactId>hutool-all</artifactId>
<version>5.8.25</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.8.1</version>
<configuration>
<source>21</source>
<target>21</target>
<encoding>utf-8</encoding>
</configuration>
</plugin>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
<version>3.2.0</version>
<executions>
<execution>
<goals>
<goal>repackage</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
2.2 配置API密钥
在application.yml中配置API密钥
server:
port: 5555
spring:
ai:
openai:
api-key: sk-xxxxx # 需要替换为上图所示的硅基流动API密钥
base-url: https://api.siliconflow.cn/
embedding:
options:
model: BAAI/bge-m3
chat:
options:
model: deepseek-ai/DeepSeek-V3
注意:为了安全起见,建议通过环境变量注入API密钥,而不是直接硬编码在配置文件中。
3. 核心代码实现
3.1 主应用类
创建Spring Boot应用的入口类:
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.document.Document;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.openai.OpenAiChatModel;
import org.springframework.ai.vectorstore.SimpleVectorStore;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import java.util.List;
@SpringBootApplication
public class QaApplication {
public static void main(String[] args) {
SpringApplication.run(QaApplication.class, args);
}
@Bean
public ChatClient chatClient(OpenAiChatModel model){
return ChatClient
.builder(model)
.build();
}
@Bean
public VectorStore vectorStore(EmbeddingModel embeddingModel) {
VectorStore vectorStore = SimpleVectorStore.builder(embeddingModel).build();
// 构建测试数据
List<Document> documents =
List.of(new Document("Hello Spring AI"),
new Document("Hello Spring Boot"));
// 添加到向量数据库
vectorStore.add(documents);
return vectorStore;
}
}
3.2 请求模型
创建一个简单的模型类来封装问题请求:
import lombok.Data;
@Data
public class QuestionRequest {
private String question;
private String sessionId;
}
3.3 问答服务
实现问答核心服务:
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.chat.prompt.PromptTemplate;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import java.util.HashMap;
import java.util.Map;
@Service
public class QaService {
private final ChatClient chatClient;
private final PromptTemplate promptTemplate;
@Autowired
public QaService(ChatClient chatClient) {
this.chatClient = chatClient;
// 创建一个提示模板,指导AI如何回答问题
this.promptTemplate = new PromptTemplate("""
你是一个智能问答助手,请简洁、准确地回答用户的问题。
如果你不知道答案,请直接说不知道,不要编造信息。
用户问题: {question}
回答:
""");
}
public String getAnswer(String question) {
// 准备模板参数
Map<String, Object> parameters = new HashMap<>();
parameters.put("question", question);
// 创建提示
Prompt prompt = promptTemplate.create(parameters);
// 调用AI获取回答
return chatClient.prompt(prompt).call().content();
}
}
3.4 REST控制器
创建REST API接口,处理问题请求:
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestBody;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;
import java.util.HashMap;
import java.util.Map;
@RestController
@RequestMapping("/api/qa")
public class QaController {
private final QaService qaService;
private final ConversationService conversationService;
private final KnowledgeBaseQaService knowledgeBaseQaService;
@Autowired
public QaController(QaService qaService,
ConversationService conversationService,
KnowledgeBaseQaService knowledgeBaseQaService
) {
this.qaService = qaService;
this.conversationService = conversationService;
this.knowledgeBaseQaService = knowledgeBaseQaService;
}
@PostMapping("/ask")
public Map<String, String> askQuestion(@RequestBody QuestionRequest request) {
String answer = qaService.getAnswer(request.getQuestion());
Map<String, String> response = new HashMap<>();
response.put("question", request.getQuestion());
response.put("answer", answer);
return response;
}
@PostMapping("/ask-session")
public Map<String, String> askSession(@RequestBody QuestionRequest request) {
String answer = conversationService.chat(request.getSessionId(),request.getQuestion());
Map<String, String> response = new HashMap<>();
response.put("question", request.getQuestion());
response.put("answer", answer);
return response;
}
@PostMapping("/ask-knowledge")
public Map<String, String> askKnowledge(@RequestBody QuestionRequest request) {
String answer = knowledgeBaseQaService.getAnswerWithKnowledgeBase(request.getQuestion());
Map<String, String> response = new HashMap<>();
response.put("question", request.getQuestion());
response.put("answer", answer);
return response;
}
}
3.5 简单HTML前端
在src/main/resources/static目录下创建一个简单的HTML页面(qa.html):
<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>AI问答系统</title>
<style>
body {
font-family: Arial, sans-serif;
max-width: 800px;
margin: 0 auto;
padding: 20px;
}
.container {
border: 1px solid #ddd;
border-radius: 5px;
padding: 20px;
margin-top: 20px;
}
.question-form {
margin-bottom: 20px;
}
#question {
width: 100%;
padding: 10px;
margin-bottom: 10px;
border: 1px solid #ddd;
border-radius: 4px;
}
button {
padding: 10px 15px;
background-color: #4CAF50;
color: white;
border: none;
border-radius: 4px;
cursor: pointer;
}
button:hover {
background-color: #45a049;
}
.answer {
margin-top: 20px;
padding: 15px;
background-color: #f9f9f9;
border-radius: 4px;
white-space: pre-wrap;
}
.loading {
color: #888;
font-style: italic;
display: none;
}
</style>
</head>
<body>
<h1>AI问答系统</h1>
<div class="container">
<div class="question-form">
<h2>请输入您的问题</h2>
<textarea id="question" rows="4" placeholder="例如:什么是Spring AI?"></textarea>
<button id="ask-button">提问</button>
<p class="loading" id="loading">AI正在思考中,请稍候...</p>
</div>
<div class="answer" id="answer-container" style="display:none;">
<h2>回答</h2>
<div id="answer-text"></div>
</div>
</div>
<script>
document.getElementById('ask-button').addEventListener('click', async function() {
const question = document.getElementById('question').value.trim();
if (!question) {
alert('请输入问题');
return;
}
// 显示加载状态
document.getElementById('loading').style.display = 'block';
document.getElementById('answer-container').style.display = 'none';
try {
// 普通模式 /api/qa/ask
// 会话模式 /api/qa/ask-session
// 知识库模式 /api/qa/ask-knowledge
const response = await fetch('/api/qa/ask', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({ question: question, sessionId: '12345' })
});
if (!response.ok) {
throw new Error('服务器错误');
}
const data = await response.json();
// 显示回答
document.getElementById('answer-text').textContent = data.answer;
document.getElementById('answer-container').style.display = 'block';
} catch (error) {
console.error('Error:', error);
document.getElementById('answer-text').textContent = '发生错误: ' + error.message;
document.getElementById('answer-container').style.display = 'block';
} finally {
// 隐藏加载状态
document.getElementById('loading').style.display = 'none';
}
});
</script>
</body>
</html>
4. 运行与测试
完成上述代码后,运行Spring Boot应用:
mvn spring-boot:run
或者使用IDE直接运行QaApplication类。
启动后,访问http://localhost:5555/qa.html,即可使用问答系统。在文本框中输入问题,点击"提问"按钮后,系统会将问题发送给AI,并展示回答结果。
5. 功能扩展
这个基础的问答系统可以通过以下方式进行扩展
5.1 添加对话历史
改进服务,支持多轮对话
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.messages.AssistantMessage;
import org.springframework.ai.chat.messages.Message;
import org.springframework.ai.chat.messages.SystemMessage;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
@Service
public class ConversationService {
private final ChatClient chatClient;
// TODO 此处仅为简单模拟,实际应为数据库或其他存储方式
private final Map<String, List<Message>> conversations = new ConcurrentHashMap<>();
@Autowired
public ConversationService(ChatClient chatClient) {
this.chatClient = chatClient;
}
public String chat(String sessionId, String userMessage) {
// 获取或创建会话历史
List<Message> messages = conversations.computeIfAbsent(sessionId, k -> new ArrayList<>());
// 添加用户消息
messages.add(new UserMessage(userMessage));
// 创建带有历史上下文的提示
Prompt prompt = new Prompt(messages);
// 调用AI
String response = chatClient.prompt(prompt).call().content();
// 保存AI回复
messages.add(new AssistantMessage(response));
// 管理会话长度,避免超出Token限制
if (messages.size() > 10) {
messages = messages.subList(messages.size() - 10, messages.size());
conversations.put(sessionId, messages);
}
return response;
}
public void clearConversation(String sessionId) {
conversations.remove(sessionId);
}
}
5.2 添加知识库集成
使用向量存储和检索增强生成(RAG)
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.chat.prompt.PromptTemplate;
import org.springframework.ai.document.Document;
import org.springframework.ai.embedding.Embedding;
import org.springframework.ai.vectorstore.SearchRequest;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
@Service
public class KnowledgeBaseQaService {
private final ChatClient chatClient;
private final VectorStore vectorStore;
@Autowired
public KnowledgeBaseQaService(
ChatClient chatClient,
VectorStore vectorStore) {
this.chatClient = chatClient;
this.vectorStore = vectorStore;
}
public String getAnswerWithKnowledgeBase(String question) {
// 在知识库中搜索相关文档
List<Document> relevantDocs = vectorStore.similaritySearch(question);
// 构建上下文
StringBuilder context = new StringBuilder();
for (Document doc : relevantDocs) {
context.append(doc.getText()).append("\n\n");
}
// 创建提示模板
PromptTemplate promptTemplate = new PromptTemplate("""
你是一个智能问答助手。请根据以下提供的信息回答用户问题。
如果无法从提供的信息中找到答案,请基于你的知识谨慎回答,并明确指出这是你的一般性了解。
参考信息:
{context}
用户问题: {question}
回答:
""");
// 准备参数
Map<String, Object> parameters = new HashMap<>();
parameters.put("context", context.toString());
parameters.put("question", question);
// 创建提示并调用AI
Prompt prompt = promptTemplate.create(parameters);
return chatClient.prompt(prompt).call().content();
}
}
6. 总结
本文详细介绍了如何使用Spring AI创建一个简单的问答系统。通过Spring AI提供的抽象层,我们能够轻松地集成大语言模型,无需深入了解底层API细节。这种方式可以让开发者专注于业务逻辑,同时保持了Spring生态系统的一致性。