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Spring Ai Alibaba开发指南

news 2025/9/30 11:02:03

参考资料：

参考视频

视频对应的资料，包括MD文件

SpringBoot搭建教程

参考demo及学习笔记

申请阿里云百炼-APIKey：

打开阿里云百炼，并登录：https://www.aliyun.com/product/bailian

点击免费体验，进入API申请页面

点击密钥管理，申请新的密钥

记录下申请的密钥

SpringAIAlibaba框架搭建：

说明

1. JDK及SpringBoot版本要求

搭建的时候记得选用JDK17+，不用系统安装，用IDEA下载的也可以

SpringBoot版本要求3.2.x或者3.3.x

搭建流程

依赖和配置

结合自己申请的阿里云的Key添加如下配置

server.port=8899
spring.application.name=SpringAIAlibabaDemo
spring.ai.dashscope.api-key=sk-7f931f0af******

添加依赖和版本管理如下：

<properties><maven.compiler.source>17</maven.compiler.source><maven.compiler.target>17</maven.compiler.target><spring-ai.version>1.0.0-M5</spring-ai.version></properties><dependencies><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-test</artifactId><scope>test</scope></dependency><dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-starter</artifactId><version>1.0.0-M5.1</version></dependency></dependencies><repositories><repository><id>spring-milestones</id><name>Spring Milestones</name><url>https://repo.spring.io/milestone</url><snapshots><enabled>false</enabled></snapshots></repository></repositories>

编写基本的问答


import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatOptions;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
import org.springframework.ai.chat.memory.InMemoryChatMemory;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;@RestController
public class DeepSeekController {private static final String DEFAULT_PROMPT = "你是一个博学的智能聊天助手，请根据用户提问回答！";private final ChatClient dashScopeChatClient;public DeepSeekController(ChatClient.Builder chatClientBuilder) {this.dashScopeChatClient = chatClientBuilder.defaultSystem(DEFAULT_PROMPT)// 实现 Chat Memory 的 Advisor// 在使用 Chat Memory 时，需要指定对话 ID，以便 Spring AI 处理上下文。.defaultAdvisors(new MessageChatMemoryAdvisor(new InMemoryChatMemory()))// 实现 Logger 的 Advisor.defaultAdvisors(new SimpleLoggerAdvisor())// 设置 ChatClient 中 ChatModel 的 Options 参数.defaultOptions(DashScopeChatOptions.builder().withTopP(0.7).build()).build();}@GetMapping("/simple/chat")public String simpleChat(String query) {return dashScopeChatClient.prompt(query).call().content();}
}

postman进行测试

SpringAi 整合Rag系统

基于阿里云百炼的模型进行问答和Rag向量模型

首先添加Rag向量的相关配置


import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.document.Document;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.vectorstore.SimpleVectorStore;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;import java.util.List;@Configuration
public class RagConfig {@BeanChatClient chatClient(ChatClient.Builder builder) {return builder.defaultSystem("你将作为一名Java开发语言的专家，对于用户的使用需求作出解答").build();}@BeanVectorStore vectorStore(EmbeddingModel embeddingModel) {SimpleVectorStore simpleVectorStore = SimpleVectorStore.builder(embeddingModel).build();// 生成一个说明的文档List<Document> documents = List.of(new Document("产品说明:名称：Java开发语言\n" +"产品描述：Java是一种面向对象开发语言。\n" +"特性：\n" +"1. 封装\n" +"2. 继承\n" +"3. 多态\n"));simpleVectorStore.add(documents);return simpleVectorStore;}}

然后进行访问


import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.QuestionAnswerAdvisor;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;@RestController
public class RagController {@Autowiredprivate ChatClient dashScopeChatClient;@Autowiredprivate VectorStore vectorStore;@GetMapping(value = "/chat", produces = "text/plain; charset=UTF-8")public String generation(String userInput) {// 发起聊天请求并处理响应return dashScopeChatClient.prompt().user(userInput).advisors(new QuestionAnswerAdvisor(vectorStore)).call().content();}
}

测试

Spring AI的其他功能

图像模型

调用阿里云百炼的画图模型


import com.alibaba.cloud.ai.dashscope.api.DashScopeImageApi;
import com.alibaba.cloud.ai.dashscope.image.DashScopeImageModel;
import com.alibaba.cloud.ai.dashscope.image.DashScopeImageOptions;
import jakarta.servlet.http.HttpServletResponse;
import org.springframework.ai.image.ImagePrompt;
import org.springframework.ai.image.ImageResponse;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.http.MediaType;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;import java.io.InputStream;
import java.net.URI;
import java.net.URL;@RestController
public class ImageModelController {@Autowiredprivate DashScopeImageModel imageModel;@GetMapping("getImage")public void getImageabli(@RequestParam(value = "msg", defaultValue = "生成一直小猫")String msg, HttpServletResponse res) {ImageResponse response = imageModel.call(new ImagePrompt(msg,DashScopeImageOptions.builder().withModel(DashScopeImageApi.DEFAULT_IMAGE_MODEL).withN(1)//要生成的图像数。必须介于 1 和 10 之间。.withHeight(1024)//生成的图像的高宽度。.withWidth(1024).build()));//获取生成图像地址String imageUrl = response.getResult().getOutput().getUrl();try {//使用输出流在浏览器输出URL url = URI.create(imageUrl).toURL();InputStream in = url.openStream();res.setHeader("Content-Type", MediaType.IMAGE_PNG_VALUE);res.getOutputStream().write(in.readAllBytes());res.getOutputStream().flush();} catch (Exception e) {e.printStackTrace();}}
}

需要用浏览器进行测试

音频模型

根据文字生成音频


import com.alibaba.cloud.ai.dashscope.audio.DashScopeSpeechSynthesisModel;
import com.alibaba.cloud.ai.dashscope.audio.DashScopeSpeechSynthesisOptions;
import com.alibaba.cloud.ai.dashscope.audio.synthesis.SpeechSynthesisPrompt;
import com.alibaba.cloud.ai.dashscope.audio.synthesis.SpeechSynthesisResponse;
import jakarta.servlet.http.HttpServletResponse;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.nio.ByteBuffer;@RestController
public class TextToSpeechController {@Autowiredprivate DashScopeSpeechSynthesisModel speechSynthesisModel;private static final String FILE_PATH = "src/main/resources/tts";@GetMapping("/tts")public void tts(@RequestParam(value = "voice", defaultValue = "床前明月光， 疑是地上霜。 举头望明月， 低头思故乡。")String voice) throws IOException {// 使用构建器模式创建 DashScopeSpeechSynthesisOptions 实例并设置参数DashScopeSpeechSynthesisOptions options = DashScopeSpeechSynthesisOptions.builder().withSpeed(1.0)        // 设置语速.withPitch(0.9)         // 设置音调.withVolume(60)         // 设置音量.build();SpeechSynthesisResponse response = speechSynthesisModel.call(new SpeechSynthesisPrompt(voice,options));File file = new File(FILE_PATH + "/output.mp3");try (FileOutputStream fos = new FileOutputStream(file)) {ByteBuffer byteBuffer = response.getResult().getOutput().getAudio();fos.write(byteBuffer.array());} catch (IOException e) {throw new IOException(e.getMessage());}}}