Spring AI实现一个简单的问答系统

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。

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