【开发AI】Spring AI Alibaba：集成AI应用的Java项目实战

在当今AI技术快速发展的时代，Java开发者也希望能够轻松地在自己的应用中集成先进的AI能力。Spring AI Alibaba作为一个专为Java开发者设计的AI应用框架，让这一目标变得简单而直接。本篇文章将通过一个完整的实战项目，带领你体验如何使用Spring AI Alibaba快速构建具有AI能力的Java应用。

一、技术背景与框架概述

1.1 Spring AI Alibaba简介

Spring AI Alibaba是基于Spring Boot和Spring AI构建的AI应用框架，专为Java开发者量身打造。它提供了统一的API抽象层，简化了AI模型的集成和调用过程。官方地址：Spring AI Alibaba 官网、Spring AI Alibaba 仓库

1.2 核心特性

Spring AI Alibaba框架具有以下核心特性：

• 统一抽象层：屏蔽不同AI服务提供商的接口差异
• 阿里云百炼大模型集成：便捷接入通义千问等阿里系AI服务
• 工具调用机制：支持Function Calling，让AI能够调用实际代码功能
• 可观测性支持：与ARMS等监控工具无缝集成
• RAG知识库解决方案：集成向量数据库，支持检索增强生成

1.3 与其他方案的比较

下表比较了Spring AI Alibaba与其他几种AI集成方案：

特性	Spring AI Alibaba	原生Spring Boot集成	LangChain4j	Spring Cloud Alibaba AI
集成难度	简单（开箱即用）	复杂（需自行处理）	中等	中等
配置项	最少	最多	中等	中等
百炼大模型支持	完全支持	需手动实现	部分支持	完全支持
工具调用	原生支持	需额外开发	需额外开发	支持
可观测性	内置支持	需自行集成	有限支持	内置支持

对于Java开发者而言，Spring AI Alibaba提供了最平缓的学习曲线和最便捷的开发体验。

二、环境准备与项目配置

2.1 开发环境要求

在开始之前，请确保你的开发环境满足以下要求：

• JDK 17+
• Maven 3.6+
• IDE（推荐IntelliJ IDEA或各类基于VsCode的IDE）
• 阿里云账号（用于获取API密钥）
• 阿里云百炼大模型访问权限

2.2 创建项目基础结构

首先，创建一个基本的Spring Boot项目。你可以使用Spring Initializr（https://start.spring.io/）创建一个包含以下基础依赖的项目：

• Spring Web
• Spring Boot DevTools
• Lombok（可选，简化代码）

三、项目配置与依赖设置

3.1 Maven依赖配置

在项目的pom.xml文件中添加必要的依赖和仓库配置：

<?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>com.example</groupId><artifactId>spring-ai-alibaba-demo</artifactId><version>1.0.0</version><packaging>jar</packaging><name>spring-ai-alibaba-demo</name><properties><java.version>17</java.version><spring-boot.version>3.5.3</spring-boot.version><spring-ai-alibaba.version>1.0.0</spring-ai-alibaba.version></properties><dependencies><!-- Spring Boot Web依赖 --><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId><version>${spring-boot.version}</version></dependency><!-- Spring AI Alibaba核心依赖 --><dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-starter</artifactId><version>${spring-ai-alibaba.version}</version></dependency><!-- Spring AI Alibaba - 百炼大模型集成 --><dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-starter-dashscope</artifactId><version>${spring-ai-alibaba.version}</version></dependency><!-- Lombok（可选，简化代码） --><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><version>1.18.28</version><scope>provided</scope></dependency></dependencies><repositories><!-- 阿里云Maven仓库 --><repository><id>alimaven</id><name>Alibaba Maven Repository</name><url>https://maven.aliyun.com/repository/public</url></repository></repositories><build><plugins><plugin><groupId>org.springframework.boot</groupId><artifactId>spring-boot-maven-plugin</artifactId><version>${spring-boot.version}</version></plugin></plugins></build>
</project>

3.2 API密钥配置

访问阿里云百炼大模型控制台，创建一个API密钥。获取到AccessKey ID和AccessKey Secret后，在项目的application.yml文件中进行配置：

spring:ai:alibaba:dashscope:api-key: your-api-key-here

四、Spring AI Alibaba核心概念

在深入编码之前，我们需要了解Spring AI Alibaba的几个核心概念：

4.1 ChatClient接口

ChatClient是Spring AI Alibaba与AI模型交互的核心接口。它提供了两种主要方法：

• StreamType stream(StreamRequest request)：以流的形式获取AI模型的响应
• StringType say(SayRequest request)：获取AI模型的完整响应

4.2 模型适配器

模型适配器负责将Spring AI Alibaba的API转换为具体AI服务的调用。对于阿里云百炼大模型，我们主要使用DashscopeChatClient。

4.3 工具调用（Function Calling）

工具调用是让AI模型调用实际代码功能的机制。Spring AI Alibaba通过@Tool注解支持这一功能，可以实现：

• 让AI智能体执行数据库操作
• 调用外部API
• 处理文件和数据
• 与其他服务交互

五、基于Java开发的AI多轮对话应用实战（支持DeepSeek、SiliconFlow、Gemini等多种模型切换）

5.1 项目介绍

1.功能特性

基于 Spring Boot 和 Vue 3 的 AI 聊天应用：

• 🤖 支持多种 AI 模型（DeepSeek、SiliconFlow、Gemini）
• 💬 实时聊天界面
• 📱 响应式设计
• 🎨 现代化 UI 设计
• 📝 Markdown 渲染支持
• 🔄 流式响应支持

2.技术栈

1.环境要求

• Java 17+
• Node.js 18+
• Maven 3.6+

2.后端

• Spring Boot 3.2.0
• Spring AI 1.0.0-M4
• DeepSeek API
• SiliconFlow API
• WebFlux (响应式编程)

3.前端

• Vue 3 + TypeScript
• Vite
• Vue Markdown Renderer
• Element Plus Icons

5.2 pom和yaml文件

1.完整版pom文件如下：

<?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>com.alibaba.cloud.ai</groupId><artifactId>my-deepseek-chat</artifactId><version>1.0.0</version><packaging>jar</packaging><name>My DeepSeek Chat</name><description>AI多轮对话应用，支持多种模型切换</description><properties><maven.compiler.source>17</maven.compiler.source><maven.compiler.target>17</maven.compiler.target><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><spring-boot.version>3.4.0</spring-boot.version><spring-ai.version>1.0.0</spring-ai.version><spring-ai-alibaba.version>1.0.0.3</spring-ai-alibaba.version><!-- Node and npm --><node.version>v20.12.2</node.version><npm.version>10.9.2</npm.version><npm.build.skip>true</npm.build.skip></properties><dependencies><!-- Spring Boot Starter --><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-webflux</artifactId></dependency><!-- Spring AI Models --><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-starter-model-deepseek</artifactId></dependency><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-starter-model-openai</artifactId></dependency><!-- 启用Vertex AI Gemini和阿里云DashScope支持 --><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-starter-model-vertex-ai-gemini</artifactId></dependency><dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-starter-dashscope</artifactId><version>${spring-ai-alibaba.version}</version></dependency><!-- JSON处理 --><dependency><groupId>com.fasterxml.jackson.core</groupId><artifactId>jackson-databind</artifactId></dependency><!--Lombok插件--><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><version>1.18.30</version><optional>true</optional></dependency><!--knife4j依赖库 - 适配Spring Boot 3.x--><dependency><groupId>com.github.xiaoymin</groupId><artifactId>knife4j-openapi3-jakarta-spring-boot-starter</artifactId><version>4.4.0</version></dependency></dependencies><dependencyManagement><dependencies><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-dependencies</artifactId><version>${spring-boot.version}</version><type>pom</type><scope>import</scope></dependency><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-bom</artifactId><version>${spring-ai.version}</version><type>pom</type><scope>import</scope></dependency><dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-bom</artifactId><version>${spring-ai-alibaba.version}</version><type>pom</type><scope>import</scope></dependency></dependencies></dependencyManagement><build><finalName>my-deepseek-chat</finalName><plugins><plugin><groupId>org.springframework.boot</groupId><artifactId>spring-boot-maven-plugin</artifactId><version>${spring-boot.version}</version><executions><execution><goals><goal>repackage</goal></goals></execution></executions></plugin><!-- Frontend Maven Plugin --><plugin><groupId>com.github.eirslett</groupId><artifactId>frontend-maven-plugin</artifactId><version>1.11.0</version><executions><execution><id>install node and npm</id><goals><goal>install-node-and-npm</goal></goals><configuration><skip>${npm.build.skip}</skip><nodeVersion>${node.version}</nodeVersion><npmVersion>${npm.version}</npmVersion><workingDirectory>ui</workingDirectory></configuration></execution><execution><id>npm install</id><goals><goal>npm</goal></goals><configuration><skip>${npm.build.skip}</skip><arguments>install</arguments><workingDirectory>ui</workingDirectory></configuration></execution><execution><id>npm build</id><goals><goal>npm</goal></goals><phase>generate-resources</phase><configuration><skip>${npm.build.skip}</skip><arguments>run build</arguments><workingDirectory>ui</workingDirectory></configuration></execution></executions></plugin><!-- Copy frontend resources --><plugin><artifactId>maven-resources-plugin</artifactId><version>3.2.0</version><executions><execution><id>copy-react-build</id><phase>process-resources</phase><goals><goal>copy-resources</goal></goals><configuration><skip>${npm.build.skip}</skip><outputDirectory>${project.build.outputDirectory}/static</outputDirectory><resources><resource><directory>ui/dist</directory><filtering>false</filtering></resource></resources></configuration></execution></executions></plugin><!-- Maven Compiler Plugin --><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-compiler-plugin</artifactId><version>3.11.0</version><configuration><source>17</source><target>17</target><encoding>UTF-8</encoding></configuration></plugin></plugins></build><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>aliyunmaven</id><name>aliyun</name><url>https://maven.aliyun.com/repository/public</url></repository></repositories></project>

2.完整版yml文件如下（需替换api-key）：

server:port: "10517"spring:application:name: my-deepseek-chatai:# DeepSeek配置deepseek:api-key: sk-base-url: "https://api.deepseek.com"chat:options:model: deepseek-chattemperature: 0.7max-tokens: 2048# OpenAI配置 (用于SiliconFlow)openai:api-key: sk-base-url: "https://api.siliconflow.cn/v1"chat:options:# 硅基流动常用模型名称model: "deepseek-ai/DeepSeek-V2.5"temperature: 0.7max-tokens: 2048# 硅基流动特定配置user: "spring-ai-siliconflow"completions-path: "/chat/completions"# OpenRouter配置openrouter:api-key: sk-base-url: "https://openrouter.ai/api/v1"chat:options:model: "meta-llama/llama-3.1-8b-instruct:free"temperature: 0.7max-tokens: 2048completions-path: "/chat/completions"# 阿里云百炼配置aliyunbailian:api-key: sk-base-url: "https://dashscope.aliyuncs.com/api/v1"chat:options:model: "qwen-turbo"temperature: 0.7max-tokens: 2048completions-path: "/services/aigc/text-generation/generation"# Vertex AI Gemini配置vertex:ai:gemini:project-id: "801245353276"location: "us-central1"api-key: xxxxxbase-url: "https://generativelanguage.googleapis.com/v1beta"chat:options:model: "gemini-1.5-flash"temperature: 0.7max-output-tokens: 2048# 注意：如需使用其他模型，请取消注释并配置相应的API key# DashScope配置 (阿里云通义千问)dashscope:api-key: sk-chat:options:model: qwen-plustemperature: 0.7max-tokens: 2048# Vertex AI Gemini配置# vertex:#   ai:#     gemini:#       project-id: your-project-id#       location: us-central1#       api-key: your-vertex-ai-api-key#       chat:#         options:#           model: gemini-1.5-flash#           temperature: 0.7#           max-output-tokens: 2048# 日志配置
logging:level:"[com.alibaba.cloud.ai]": DEBUG"[org.springframework.ai]": DEBUGroot: INFOpattern:console: "%d{yyyy-MM-dd HH:mm:ss} [%thread] %-5level %logger{36} - %msg%n"

5.3 核心代码如下：

1.基础类（请求体、响应体、消息对象）

package com.alibaba.cloud.ai.chat.model;import io.swagger.v3.oas.annotations.media.Schema;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;import java.util.List;
@Data
@Builder
@AllArgsConstructor
@NoArgsConstructor
@Schema(description = "聊天请求参数")
public class ChatRequest {@Schema(description = "用户消息内容", required = true, example = "你好，请介绍一下自己")private String message;@Schema(description = "使用的AI模型名称", example = "siliconflow", defaultValue = "siliconflow")@Builder.Defaultprivate String model = "siliconflow";@Schema(description = "历史对话记录，用于保持上下文")private List<ChatMessage> history;@Schema(description = "是否使用流式响应", example = "false")@Builder.Defaultprivate boolean stream = false;}

package com.alibaba.cloud.ai.chat.model;import com.fasterxml.jackson.annotation.JsonFormat;
import io.swagger.v3.oas.annotations.media.Schema;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;import java.time.LocalDateTime;@Schema(description = "聊天消息")
@Data
@Builder
@AllArgsConstructor
@NoArgsConstructor
public class ChatMessage {@Schema(description = "消息唯一标识")private String id;@Schema(description = "消息角色", example = "user", allowableValues = {"user", "assistant", "system"})private String role;@Schema(description = "消息内容", required = true, example = "你好，请介绍一下自己")private String content;@JsonFormat(pattern = "yyyy-MM-dd HH:mm:ss")@Schema(description = "消息时间戳", example = "2023-12-21 10:30:00")private LocalDateTime timestamp;@Schema(description = "使用的AI模型名称", example = "siliconflow")private String model;}

package com.alibaba.cloud.ai.chat.model;import io.swagger.v3.oas.annotations.media.Schema;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;@Data
@Builder
@AllArgsConstructor
@Schema(description = "聊天响应结果")
public class ChatResponse {@Schema(description = "AI回复的消息内容", example = "你好！我是AI助手，很高兴为您服务。")private String message;@Schema(description = "使用的AI模型名称", example = "siliconflow")private String model;@Schema(description = "请求是否成功", example = "true")private boolean success;@Schema(description = "错误信息（仅在success为false时有值）")private String error;@Schema(description = "响应时间戳", example = "1703123456789")private long timestamp;public ChatResponse() {this.success = true;this.timestamp = System.currentTimeMillis();}public ChatResponse(String message, String model) {this();this.message = message;this.model = model;}public static ChatResponse success(String message, String model) {return new ChatResponse(message, model);}public static ChatResponse error(String error) {ChatResponse response = new ChatResponse();response.setSuccess(false);response.setError(error);return response;}}

2.核心服务代码

package com.alibaba.cloud.ai.chat.service;import com.alibaba.cloud.ai.chat.model.ChatMessage;
import com.alibaba.cloud.ai.chat.model.ChatRequest;
import com.alibaba.cloud.ai.chat.model.ChatResponse;
import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatModel;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
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.UserMessage;
import org.springframework.ai.deepseek.DeepSeekChatModel;
import org.springframework.ai.openai.OpenAiChatModel;
import org.springframework.ai.vertexai.gemini.VertexAiGeminiChatModel;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.stereotype.Service;
import reactor.core.publisher.Flux;import java.util.ArrayList;
import java.util.List;@Service
public class ChatService {private static final Logger logger = LoggerFactory.getLogger(ChatService.class);@Autowired(required = false)private DeepSeekChatModel deepSeekChatModel;@Autowired(required = false)private OpenAiChatModel openAiChatModel;@Autowired(required = false)private VertexAiGeminiChatModel vertexAiGeminiChatModel;@Autowired(required = false)private DashScopeChatModel dashScopeChatModel;public ChatResponse chat(ChatRequest request) {logger.info("收到聊天请求，模型: 【{}】, 消息: {}", request.getModel(), request.getMessage());try {ChatClient chatClient = getChatClient(request.getModel());if (chatClient == null) {return ChatResponse.error("不支持的模型: " + request.getModel());}// 构建消息列表List<Message> messages = buildMessages(request);// 调用AI模型String response = chatClient.prompt().messages(messages).call().content();logger.info("AI响应: {}", response);return ChatResponse.success(response, request.getModel());} catch (Exception e) {logger.error("聊天请求处理失败", e);return ChatResponse.error("处理请求时发生错误: " + e.getMessage());}}public Flux<String> chatStream(ChatRequest request) {logger.info("收到流式聊天请求，模型: 【{}】, 消息: {}", request.getModel(), request.getMessage());try {ChatClient chatClient = getChatClient(request.getModel());if (chatClient == null) {return Flux.error(new RuntimeException("不支持的模型: " + request.getModel()));}// 构建消息列表List<Message> messages = buildMessages(request);// 用于收集完整内容StringBuilder contentBuilder = new StringBuilder();// 调用AI模型流式接口return chatClient.prompt().messages(messages).stream().content().doOnNext(content -> {
//                        logger.info("ChatService接收到流式内容片段: [{}]", content);contentBuilder.append(content);}).doOnComplete(() -> {String completeContent = contentBuilder.toString();logger.info("ChatService流式内容传输完成，完整内容: [{}]", completeContent);}).doOnError(error -> logger.error("ChatService流式传输错误: ", error));} catch (Exception e) {logger.error("流式聊天请求处理失败", e);return Flux.error(e);}}private ChatClient getChatClient(String model) {logger.debug("获取ChatClient，模型: 【{}】", model);switch (model.toLowerCase()) {case "deepseek":if (deepSeekChatModel != null) {logger.debug("使用【DeepSeek】模型");return ChatClient.create(deepSeekChatModel);} else {logger.warn("DeepSeek模型未配置或不可用");return null;}case "siliconflow":case "openai":case "openrouter":if (openAiChatModel != null) {logger.debug("使用OpenAI兼容模型 【{}】", model);return ChatClient.create(openAiChatModel);} else {logger.warn("OpenAI模型未配置或不可用");return null;}case "aliyunbailian":if (dashScopeChatModel != null) {logger.debug("使用DashScope模型 【阿里云百炼】");return ChatClient.create(dashScopeChatModel);} else {logger.warn("阿里云百炼模型未配置或不可用");return null;}case "gemini":if (vertexAiGeminiChatModel != null) {logger.debug("使用【Vertex AI Gemini】模型");return ChatClient.create(vertexAiGeminiChatModel);} else {logger.warn("Gemini模型未配置或不可用");return null;}default:logger.warn("不支持的模型类型: 【{}】", model);return null;}}private List<Message> buildMessages(ChatRequest request) {List<Message> messages = new ArrayList<>();// 添加历史消息if (request.getHistory() != null && !request.getHistory().isEmpty()) {for (ChatMessage historyMsg : request.getHistory()) {if ("user".equals(historyMsg.getRole())) {messages.add(new UserMessage(historyMsg.getContent()));} else if ("assistant".equals(historyMsg.getRole())) {messages.add(new AssistantMessage(historyMsg.getContent()));}}}// 添加当前用户消息messages.add(new UserMessage(request.getMessage()));return messages;}public List<String> getAvailableModels() {List<String> models = new ArrayList<>();if (deepSeekChatModel != null) {models.add("deepseek");}if (openAiChatModel != null) {models.add("siliconflow");// 暂时使用同一个OpenAI模型支持多个服务models.add("openrouter");}if (dashScopeChatModel != null) {models.add("aliyunbailian");}if (vertexAiGeminiChatModel != null) {models.add("gemini");}return models;}
}

3.接口控制器

package com.alibaba.cloud.ai.chat.controller;import com.alibaba.cloud.ai.chat.model.ChatRequest;
import com.alibaba.cloud.ai.chat.model.ChatResponse;
import com.alibaba.cloud.ai.chat.service.ChatService;
import com.alibaba.cloud.ai.chat.util.ModelTestUtil;
import com.alibaba.cloud.ai.chat.util.SiliconFlowValidator;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.node.JsonNodeFactory;
import com.fasterxml.jackson.databind.node.ObjectNode;
import io.swagger.v3.oas.annotations.Operation;
import io.swagger.v3.oas.annotations.Parameter;
import io.swagger.v3.oas.annotations.responses.ApiResponse;
import io.swagger.v3.oas.annotations.responses.ApiResponses;
import io.swagger.v3.oas.annotations.tags.Tag;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;
import org.springframework.http.MediaType;
import reactor.core.publisher.Flux;
import reactor.core.publisher.Mono;import java.util.List;
import java.util.Map;@RestController
@RequestMapping("/api/chat")
@CrossOrigin(origins = "*")
@Tag(name = "聊天管理", description = "提供AI聊天相关的接口服务")
public class ChatController {private static final Logger logger = LoggerFactory.getLogger(ChatController.class);// 重用ObjectMapper实例private static final ObjectMapper objectMapper = new ObjectMapper();@Autowiredprivate ChatService chatService;@Autowiredprivate ModelTestUtil modelTestUtil;@Autowiredprivate SiliconFlowValidator siliconFlowValidator;@PostMapping("/message")@Operation(summary = "普通聊天", description = "发送消息给AI并获取回复，支持历史对话上下文")@ApiResponses({@ApiResponse(responseCode = "200", description = "聊天成功"),@ApiResponse(responseCode = "400", description = "请求参数错误"),@ApiResponse(responseCode = "500", description = "服务器内部错误")})public ChatResponse chat(@Parameter(description = "聊天请求参数", required = true) @RequestBody ChatRequest request) {logger.info("收到聊天请求: {}", request.getMessage());return chatService.chat(request);}@PostMapping(value = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)@Operation(summary = "流式聊天", description = "发送消息给AI并以流式方式获取回复，适用于长文本生成场景")@ApiResponses({@ApiResponse(responseCode = "200", description = "流式响应成功"),@ApiResponse(responseCode = "400", description = "请求参数错误"),@ApiResponse(responseCode = "500", description = "服务器内部错误")})public Flux<String> chatStream(@Parameter(description = "聊天请求参数", required = true) @RequestBody ChatRequest request) {logger.info("收到流式聊天请求: {}", request.getMessage());return chatService.chatStream(request).map(content -> {// 使用类级别的ObjectMapper实例ObjectNode jsonNode = JsonNodeFactory.instance.objectNode();jsonNode.put("content", content);try {String jsonData = objectMapper.writeValueAsString(jsonNode);logger.debug("发送SSE数据: {}", jsonData);return jsonData;} catch (JsonProcessingException e) {logger.error("JSON序列化失败", e);return "{\"content:\"\"}";}}).concatWith(Mono.just("[DONE]")).onErrorResume(e -> {logger.error("处理流式请求时发生错误", e);ObjectNode errorNode = JsonNodeFactory.instance.objectNode();errorNode.put("error", "处理请求时发生错误");try {return Mono.just(objectMapper.writeValueAsString(errorNode));} catch (JsonProcessingException ex) {return Mono.just("{\"error\":\"系统错误\"}");}});}@GetMapping("/models")@Operation(summary = "获取可用模型", description = "获取当前系统支持的所有AI模型列表")@ApiResponses({@ApiResponse(responseCode = "200", description = "获取成功"),@ApiResponse(responseCode = "500", description = "服务器内部错误")})public Map<String, Object> getModels() {List<String> models = chatService.getAvailableModels();return Map.of("success", true,"models", models,"count", models.size());}@GetMapping("/health")@Operation(summary = "健康检查", description = "检查聊天服务的运行状态")@ApiResponses({@ApiResponse(responseCode = "200", description = "服务正常"),@ApiResponse(responseCode = "503", description = "服务不可用")})public Map<String, Object> health() {return Map.of("status", "ok","timestamp", System.currentTimeMillis(),"service", "my-deepseek-chat");}@GetMapping("/test-models")@Operation(summary = "测试模型连接", description = "测试所有配置的AI模型的连接状态")@ApiResponses({@ApiResponse(responseCode = "200", description = "测试完成"),@ApiResponse(responseCode = "500", description = "测试失败")})public Map<String, Object> testModels() {try {Map<String, String> testResults = modelTestUtil.testAllModels();return Map.of("success", true,"timestamp", System.currentTimeMillis(),"results", testResults);} catch (Exception e) {logger.error("模型测试失败", e);return Map.of("success", false,"error", e.getMessage(),"timestamp", System.currentTimeMillis());}}@GetMapping("/validate-siliconflow")@Operation(summary = "验证硅基流动配置", description = "详细验证硅基流动API的配置和连接状态")@ApiResponses({@ApiResponse(responseCode = "200", description = "验证完成"),@ApiResponse(responseCode = "500", description = "验证失败")})public Map<String, Object> validateSiliconFlow() {try {Map<String, Object> validationResults = siliconFlowValidator.validateSiliconFlow();return Map.of("success", true,"timestamp", System.currentTimeMillis(),"validation", validationResults);} catch (Exception e) {logger.error("硅基流动验证失败", e);return Map.of("success", false,"error", e.getMessage(),"timestamp", System.currentTimeMillis());}}
}

5.4 效果演示

1.启动后端服务

2.启动前端服务

3.首页及配置展示

4.普通聊天和流式聊天（带有记忆功能）

5.5 完整版代码下载（即拿即用）

https://download.csdn.net/download/weixin_44330367/91962077

六、实现工具调用功能

工具调用是让AI模型调用实际代码功能的关键机制。Spring AI Alibaba通过@Tool注解简化了这一过程。

6.1 创建工具服务

package com.example.springaialibabademo.service;import com.alibaba.cloud.ai.annotation.Tool;
import com.alibaba.cloud.ai.annotation.Tools;
import com.alibaba.cloud.ai.tool.ToolCallback;
import com.alibaba.cloud.ai.tool.ToolContext;
import com.alibaba.cloud.ai.tool.annotation.ToolMethod;
import org.springframework.stereotype.Service;@Service
@Tools({@Tool(name = "calculator", description = "计算器工具，可以进行基本的数学运算")
})
public class ToolService {@ToolMethod(name = "add",description = "两个数字相加",parameters = """{"type": "object", "properties": {"a": {"type": "number", "description": "第一个加数"},"b": {"type": "number", "description": "第二个加数"}}}""")public double add(double a, double b) {return a + b;}@ToolMethod(name = "subtract",description = "两个数字相减",parameters = """{"type": "object", "properties": {"a": {"type": "number", "description": "被减数"},"b": {"type": "number", "description": "减数"}}}""")public double subtract(double a, double b) {return a - b;}@ToolMethod(name = "multiply",description = "两个数字相乘",parameters = """{"type": "object", "properties": {"a": {"type": "number", "description": "第一个乘数"},"b": {"type": "number", "description": "第二个乘数"}}}""")public double multiply(double a, double b) {return a * b;}@ToolMethod(name = "divide",description = "两个数字相除",parameters = """{"type": "object", "properties": {"a": {"type": "number", "description": "被除数"},"b": {"type": "number", "description": "除数"}}}""")public double divide(double a, double b) {if (b == 0) {throw new IllegalArgumentException("除数不能为0");}return a / b;}@ToolMethod(name = "calculateExpression",description = "计算数学表达式，支持+、-、*、/和括号",parameters = """{"type": "object", "properties": {"expression": {"type": "string", "description": "要计算的数学表达式"}}}""")public double calculateExpression(String expression) {// 这里使用简单的JavaScript引擎来计算表达式// 注意：在生产环境中应使用更安全的表达式计算库try {return (double) new javax.script.ScriptEngineManager().getEngineByName("javascript").eval(expression);} catch (Exception e) {throw new IllegalArgumentException("无效的表达式: " + expression);}}
}

6.2 工具调用控制器

package com.example.springaialibabademo.controller;import com.alibaba.cloud.ai.ChatClient;
import com.alibaba.cloud.ai.annotation.OpenAIApiKey;
import com.alibaba.cloud.ai.annotation.Tool;
import com.alibaba.cloud.ai.callback.ToolCallback;
import com.alibaba.cloud.ai.request.MessageRequest;
import com.alibaba.cloud.ai.request.SayRequest;
import com.alibaba.cloud.ai.response.StringType;
import org.springframework.web.bind.annotation.*;import javax.annotation.Resource;
import java.util.ArrayList;
import java.util.List;@RestController
@RequestMapping("/api/ai/tools")
public class ToolAIController {@Resourceprivate ChatClient chatClient;@PostMapping("/calculate")public StringType calculate(@RequestBody CalculatorRequest request) {// 构建提示，让AI使用工具进行计算List<MessageRequest> messages = new ArrayList<>();messages.add(MessageRequest.builder().role("user").content("请计算以下表达式：" + request.getExpression()).build());// 调用AI模型，让它决定是否需要使用工具return chatClient.say(SayRequest.builder().model("qwen-max").messages(messages).build());}// 请求参数类record CalculatorRequest(String expression) {}
}

6.3 工具调用演示页面

<!DOCTYPE html>
<html xmlns:th="http://www.thymeleaf.org">
<head><title>AI工具调用演示</title><style>body { font-family: Arial, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; }#result-area { border: 1px solid #ccc; margin: 10px 0; padding: 15px; height: 150px; overflow-y: auto; }#expression-input { width: 70%; padding: 10px; }#calculate-btn { padding: 10px 20px; }.error { color: red; }</style>
</head>
<body><h1>Spring AI Alibaba 工具调用演示</h1><div><input type="text" id="expression-input" placeholder="输入数学表达式，如 &quot;3 + 5 * 2&quot;"/><button id="calculate-btn">计算</button></div><div id="result-area"></div><div id="error-message" class="error"></div><script>document.getElementById('calculate-btn').addEventListener('click', function() {const input = document.getElementById('expression-input');const resultArea = document.getElementById('result-area');const errorMessage = document.getElementById('error-message');const expression = input.value.trim();if (!expression) return;// 清空错误信息errorMessage.textContent = '';// 清空结果区域resultArea.innerHTML = '计算中...';// 清空输入框input.value = '';fetch('/api/ai/tools/calculate', {method: 'POST',headers: {'Content-Type': 'application/json'},body: JSON.stringify({ expression: expression })}).then(response => response.json()).then(data => {resultArea.innerHTML = `<strong>表达式:</strong> ${expression}<br/><strong>结果:</strong> ${data.value}`}).catch(error => {resultArea.textContent = '';errorMessage.textContent = '计算失败，请检查表达式是否正确。';console.error('AI工具调用错误:', error);});});</script>
</body>
</html>

七、实现RAG知识库功能

检索增强生成(RAG)是结合向量数据库和AI模型，实现知识密集型任务的重要技术。Spring AI Alibaba简化了这一过程。

7.1 依赖配置

首先，在pom.xml中添加向量数据库支持：

<dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-starter-vector</artifactId><version>${spring-ai-alibaba.version}</version>
</dependency>
<dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-starter-vector-qdrant</artifactId><version>${spring-ai-alibaba.version}</version>
</dependency>

7.2 配置向量数据库

在application.yml中添加向量数据库配置：

spring:ai:alibaba:vector:qdrant:url: http://localhost:6333api-key: admin

7.3 文档处理与向量化服务

package com.example.springaialibabademo.service;import com.alibaba.cloud.ai.vector.Document;
import com.alibaba.cloud.ai.vector.VectorStore;
import com.alibaba.cloud.ai.vector.qdrant.QdrantVectorStore;
import org.springframework.stereotype.Service;
import org.springframework.web.multipart.MultipartFile;import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.List;@Service
public class KnowledgeService {private final VectorStore vectorStore;public KnowledgeService(QdrantVectorStore vectorStore) {this.vectorStore = vectorStore;}/*** 上传文档到知识库*/public void uploadDocument(MultipartFile file, String fileName) throws IOException {try (BufferedReader reader = new BufferedReader(new InputStreamReader(file.getInputStream()))) {StringBuilder contentBuilder = new StringBuilder();String line;while ((line = reader.readLine()) != null) {contentBuilder.append(line).append("\n");}// 创建文档Document document = Document.builder().id(fileName).text(contentBuilder.toString()).build();// 向量化并存储vectorStore.putDocument(document);}}/*** 从知识库中检索相关信息*/public List<Document> retrieve相关信息(String query, int topK) {// 创建查询文档Document queryDocument = Document.builder().text(query).build();// 执行检索return vectorStore.similaritySearch(queryDocument, topK);}
}

7.4 RAG控制器

package com.example.springaialibabademo.controller;import com.alibaba.cloud.ai.ChatClient;
import com.alibaba.cloud.ai.request.MessageRequest;
import com.alibaba.cloud.ai.request.SayRequest;
import com.alibaba.cloud.ai.response.StringType;
import com.example.springaialibabademo.service.KnowledgeService;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.*;
import org.springframework.web.multipart.MultipartFile;import javax.annotation.Resource;
import java.util.List;
import java.util.stream.Collectors;@RestController
@RequestMapping("/api/ai/knowledge")
public class KnowledgeController {@Resourceprivate ChatClient chatClient;@Resourceprivate KnowledgeService knowledgeService;@PostMapping("/upload")public ResponseEntity<String> uploadDocument(@RequestParam("file") MultipartFile file,@RequestParam("fileName") String fileName) {try {knowledgeService.uploadDocument(file, fileName);return ResponseEntity.ok("文档上传成功");} catch (Exception e) {return ResponseEntity.badRequest().body("上传失败: " + e.getMessage());}}@PostMapping("/query")public ResponseEntity<?> queryKnowledge(@RequestBody KnowledgeQueryRequest request) {try {// 从知识库检索相关内容List<Document> relevantDocuments = knowledgeService.retrieve相关信息(request.getQuery(), 3);// 如果有相关文档，将其内容整合到提示中String enrichedQuery = request.getQuery();if (!relevantDocuments.isEmpty()) {String context = relevantDocuments.stream().map(Document::getText).collect(Collectors.joining(" "));enrichedQuery = """请基于以下知识回答问题。如果问题无法从提供的知识中回答，请明确指出。知识:%s问题:%s""".formatted(context, request.getQuery());}// 调用AI模型StringType response = chatClient.say(SayRequest.builder().model("qwen-max").messages(MessageRequest.builder().role("user").content(enrichedQuery).build()).build());return ResponseEntity.ok(response);} catch (Exception e) {return ResponseEntity.badRequest().body("查询失败: " + e.getMessage());}}// 请求参数类record KnowledgeQueryRequest(String query) {}
}

7.5 RAG功能演示页面

<!DOCTYPE html>
<html xmlns:th="http://www.thymeleaf.org">
<head><title>RAG知识库演示</title><style>body { font-family: Arial, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; }.tab { margin-bottom: 20px; display: flex; border: 1px solid #ccc; border-radius: 4px; overflow: hidden; }.tab-button { padding: 10px 20px; background-color: #f0f0f0; cursor: pointer; border: none; outline: none; }.tab-button.active { background-color: white; font-weight: bold; }.tab-content { display: none; padding: 15px; border: 1px solid #ccc; border-top: none; }.upload-area { display: flex; flex-direction: column; gap: 10px; }#knowledge-result { border: 1px solid #ccc; margin: 10px 0; padding: 15px; height: 250px; overflow-y: auto; }.error { color: red; }</style>
</head>
<body><h1>Spring AI Alibaba RAG知识库演示</h1><div class="tab"><button class="tab-button active" id="upload-tab">上传文档</button><button class="tab-button" id="query-tab">知识查询</button></div><div class="tab-content active" id="upload-tab-content"><div class="upload-area"><input type="file" id="file-input"/><input type="text" id="file-name" placeholder="请输入文件名"/><button id="upload-btn">上传文档</button></div><div id="upload-status"></div></div><div class="tab-content" id="query-tab-content"><input type="text" id="knowledge-query" placeholder="输入你的问题..."/><button id="query-btn">查询知识</button><div id="knowledge-result"></div></div><div id="error-message" class="error"></div><script>// 标签切换逻辑document.getElementById('upload-tab').addEventListener('click', function() {document.getElementById('upload-tab').classList.add('active');document.getElementById('query-tab').classList.remove('active');document.getElementById('upload-tab-content').classList.add('active');document.getElementById('query-tab-content').classList.remove('active');});document.getElementById('query-tab').addEventListener('click', function() {document.getElementById('query-tab').classList.add('active');document.getElementById('upload-tab').classList.remove('active');document.getElementById('query-tab-content').classList.add('active');document.getElementById('upload-tab-content').classList.remove('active');});// 上传文档功能document.getElementById('upload-btn').addEventListener('click', function() {const fileInput = document.getElementById('file-input');const fileName = document.getElementById('file-name').value;const uploadStatus = document.getElementById('upload-status');const errorMessage = document.getElementById('error-message');if (!fileInput.files.length) {errorMessage.textContent = '请选择一个文件';return;}if (!fileName) {errorMessage.textContent = '请输入文件名';return;}const file = fileInput.files[0];const formData = new FormData();formData.append('file', file);formData.append('fileName', fileName);uploadStatus.textContent = '上传中...';errorMessage.textContent = '';fetch('/api/ai/knowledge/upload', {method: 'POST',body: formData}).then(response => {if (response.ok) {return response.text();} else {return response.json().then(data => ({ error: data.message }));}}).then(data => {if (data.error) {throw new Error(data.error);}uploadStatus.textContent = data;}).catch(error => {uploadStatus.textContent = '';errorMessage.textContent = '上传失败: ' + error.message;});});// 知识查询功能document.getElementById('query-btn').addEventListener('click', function() {const queryInput = document.getElementById('knowledge-query');const resultArea = document.getElementById('knowledge-result');const errorMessage = document.getElementById('error-message');const query = queryInput.value.trim();if (!query) return;// 清空错误信息errorMessage.textContent = '';// 清空结果区域resultArea.innerHTML = '查询中...';// 清空输入框queryInput.value = '';fetch('/api/ai/knowledge/query', {method: 'POST',headers: {'Content-Type': 'application/json'},body: JSON.stringify({ query: query })}).then(response => response.json()).then(data => {resultArea.innerHTML = `<strong>回答:</strong><br/>${data.value}`;}).catch(error => {resultArea.textContent = '';errorMessage.textContent = '查询失败，请重试。';console.error('知识查询错误:', error);});});</script>
</body>
</html>

八、Spring AI Alibaba的高级特性

8.1 多智能体(Multi-Agent)支持

Spring AI Alibaba提供了强大的多智能体支持，可以构建复杂的AI协作系统：

@Service
public class MultiAgentService {@Resourceprivate ChatClient chatClient;public StringType collaborate(String... prompts) {List<MessageRequest> messages = new ArrayList<>();// 创建多个智能体for (int i = 0; i < prompts.length; i++) {messages.add(MessageRequest.builder().role("assistant").name("Agent " + (i + 1)).content(prompts[i]).build());}// 用户提示messages.add(MessageRequest.builder().role("user").content("请各位智能体协作解决上述问题").build());return chatClient.say(SayRequest.builder().model("qwen-max").messages(messages).build());}
}

8.2 与Spring生态的深度集成

Spring AI Alibaba设计为与Spring生态无缝集成：

@RestController
@RequestMapping("/api/transactions")
public class TransactionController {@Resourceprivate TransactionService transactionService;@Resourceprivate ChatClient chatClient;@PostMapping("/analyze")public ResponseEntity<?> analyzeTransactions(@RequestBody List<Transaction> transactions) {try {// 业务逻辑处理TransactionAnalysis analysis = transactionService.analyze(transactions);// 准备上下文信息String context = """分析了%d笔交易，发现以下异常情况：%s""" .formatted(transactions.size(),analysis.getIssues().isEmpty() ? "无异常" : analysis.getIssues().toString());// 使用AI提供自然语言解释StringType explanation = chatClient.say(SayRequest.builder().model("qwen-max").messages(MessageRequest.builder().role("system").content("你是一个交易分析师，需要用通俗易懂的中文解释交易分析结果").build(),MessageRequest.builder().role("user").content("请根据以下交易分析结果，用自然语言提供详细解释：" + context).build()).build(););// 将解释合并到结果中analysis.setExplanation(explanation.getValue());return ResponseEntity.ok(analysis);} catch (Exception e) {return ResponseEntity.status(500).body(Map.of("error", e.getMessage()));}}
}

九、性能优化与最佳实践

9.1 性能优化策略

优化点	建议方案	适用场景
模型调用成本优化	实现响应缓存机制，避免重复调用	高频相同查询的场景
上下文长度优化	使用上下文压缩技术，减少token使用	长文本处理场景
工具调用优化	实现智能体预过滤，减少不必要的工具调用	复杂多步骤任务
向量检索优化	调整向量相似度阈值，减少无关文档检索	大规模知识库
异步处理	对非实时请求使用消息队列异步处理	批量处理场景

9.2 代码优化示例

@Service
public class OptimizedAIService {@Resourceprivate ChatClient chatClient;@Resourceprivate CacheManager cacheManager;private final ConcurrentMap<String, String> responseCache = new ConcurrentHashMap<>();/*** 带缓存的AI调用*/public StringType generateWithCache(String prompt, int ttlSeconds) {// 生成缓存键String cacheKey = "ai-response:" + prompt;// 先检查缓存String cachedResponse = responseCache.get(cacheKey);if (cachedResponse != null) {return StringType.builder().value(cachedResponse).build();}// 调用AI模型StringType response = chatClient.say(SayRequest.builder().model("qwen-max").messages(MessageRequest.builder().role("user").content(prompt).build()).build());// 缓存结果responseCache.put(cacheKey, response.getValue());// 设置ttl后清除缓存cacheManager.getCache("default").put(cacheKey, response.getValue(), ttlSeconds, TimeUnit.SECONDS);return response;}/*** 上下文压缩*/public StringType generateWithContextCompression(String context, String query) {// 实现简单的上下文压缩逻辑String compressedContext = compressContext(context, 500); // 限制token数量return chatClient.say(SayRequest.builder().model("qwen-max").messages(MessageRequest.builder().role("system").content("你是一个上下文压缩专家，擅长从长文本中提取关键信息").build(),MessageRequest.builder().role("user").content("基于以下压缩上下文回答问题：\n上下文：" + compressedContext + "\n问题：" + query).build()).build());}private String compressContext(String context, int maxTokens) {// 简单的压缩逻辑，实际应用中应使用更复杂的算法或token计数器if (context.length() <= maxTokens) {return context;}// 保留开始和结束的部分，中间用省略号连接return context.substring(0, maxTokens/2) + " [...] " + context.substring(context.length() - maxTokens/2);}
}

9.3 异常处理与重试机制

@Service
public class ResilientAIService {@Resourceprivate ChatClient chatClient;@Resourceprivate RetryTemplate retryTemplate;@Resourceprivate CircuitBreaker circuitBreaker;public StringType generateWithRetryAndCircuit(String prompt) {// 使用断路器保护return circuitBreaker.run(() -> {try {// 使用重试模板return retryTemplate.execute(context -> generateAIResponse(prompt),context -> {if (context.getRetryCount() >= 3) {return null; // 达到最大重试次数，返回null}// 可以记录重试信息return true; // 继续重试});} catch (Exception e) {throw new AIInvocationException("AI调用失败", e);}});}private StringType generateAIResponse(String prompt) {return chatClient.say(SayRequest.builder().model("qwen-max").messages(MessageRequest.builder().role("user").content(prompt).build()).build());}
}

十、应用场景与案例分析

10.1 客户智能服务体系

一家电子商务公司希望通过Spring AI Alibaba构建智能客服系统，实现：

1. 自动回答客户关于订单、产品、物流的问题
2. 根据客户历史购买行为提供个性化推荐
3. 处理退款和投诉，必要时自动升级给人工客服

解决方案：

• 使用Spring AI Alibaba集成通义千问大模型
• 通过工具调用接入订单系统、产品数据库
• 实现多智能体协作，包括"客服专员"、“产品专家”、“订单处理专家”
• 与现有CRM系统集成，记录所有AI客服交互

技术架构：

10.2 金融交易分析系统

一家金融服务公司希望利用Spring AI Alibaba构建交易监控和分析系统：

1. 自动检测异常交易模式
2. 生成合规报告
3. 在发现可疑活动时报警
4. 提供自然语言解释

解决方案：

• 使用Spring AI Alibaba处理交易数据
• 通过RAG技术接入内部合规政策文档
• 实现多智能体协作，包括"交易分析智能体"、“合规检查智能体”、“风险评估智能体”
• 与警报系统和报告生成系统集成

技术架构：

十一、总结与展望

11.1 Spring AI Alibaba的优势总结

通过本实战项目的探索，我们可以看到Spring AI Alibaba具有以下显著优势：

1. 降低集成门槛：通过统一的API抽象，Java开发者无需深入了解AI模型细节即可快速集成AI能力
2. 与Spring生态无缝集成：可以轻松与现有的Spring Boot应用结合，享受Spring框架的所有特性
3. 丰富的AI功能支持：从基础的对话交互到复杂的工具调用、多智能体协作，一应俱全
4. 阿里云生态优势：与阿里云百炼大模型的深度集成，享受云原生AI服务
5. 可观测性内置：支持与ARMS等监控工具集成，便于生产环境运维

11.2 未来发展方向

随着AI技术的不断进步，Spring AI Alibaba也将持续演进：

1. 更丰富的模型支持：预计会支持更多第三方AI模型和服务
2. 更强大的多模态能力：不仅处理文本，还将支持图像、音频、视频等多模态数据
3. 更深入的工具调用：提供更安全、更高效的函数调用机制
4. 更完善的Agent框架：支持更复杂的智能体协作和决策机制
5. 企业级特性增强：包括更强大的安全控制、审计能力、企业级集成能力

结语

Spring AI Alibaba为Java开发者打开了一扇通往AI应用开发的大门。无论你是想要在现有应用中添加智能特性，还是从零构建AI原生应用，Spring AI Alibaba都提供了理想的开发框架。通过与阿里云百炼大模型的深度集成，Java开发者现在可以轻松构建具有竞争力的AI应用，再次证明了Java在现代应用开发中的强大生命力。

希望本实战项目能帮助你快速掌握Spring AI Alibaba的使用方法，开启你的Java AI应用开发之旅！