Spring AI与智能代理模式的深度解析
本文深入探讨Spring AI框架中的智能代理模式实现,结合最新的技术趋势和实践案例,为Java开发者提供全面的AI Agent开发指南。
📋 目录
🚀 引言
在人工智能技术快速发展的今天,AI Agent(智能代理)成为了连接传统应用与AI能力的重要桥梁。Spring AI作为Spring生态系统中的新成员,为Java开发者提供了构建智能代理系统的完整解决方案。
根据Anthropic的最新研究报告《构建高效代理》,现代AI代理系统应当强调简单性 和可组合性,而非复杂的框架设计。Spring AI正是基于这一理念,提供了简洁而强大的API来实现智能代理模式。
🧠 智能代理模式概述
什么是智能代理模式
智能代理模式(Agentic Patterns)是一种设计模式,它允许系统在运行时动态地调整行为和决策流程。在AI应用中,这种模式主要体现为两种形态:
- 工作流(Workflow):通过预定义的代码路径协调LLM和工具的系统
- 智能体(Agent):LLM能够动态指导自身流程和工具使用的系统
智能代理的核心特征
java
public interface IntelligentAgent {
/**
* 代理的核心能力
*/
// 感知环境
void perceive(Environment environment);
// 决策制定
Decision makeDecision(Context context);
// 执行动作
ActionResult executeAction(Action action);
// 学习适应
void learn(Feedback feedback);
}
🏗️ Spring AI框架架构
核心组件架构图
┌─────────────────────────────────────────────────────────────┐
│ Spring AI Framework │
├─────────────────┬─────────────────┬─────────────────────────┤
│ Chat Client │ Embedding │ Vector Store │
│ │ Client │ │
├─────────────────┼─────────────────┼─────────────────────────┤
│ Function │ Image │ ETL Pipeline │
│ Calling │ Generation │ │
├─────────────────┼─────────────────┼─────────────────────────┤
│ Prompt │ Audio │ Evaluation │
│ Templates │ Processing │ │
└─────────────────┴─────────────────┴─────────────────────────┘
依赖配置
xml
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
<version>1.0.0-M4</version>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-tika-document-reader</artifactId>
<version>1.0.0-M4</version>
</dependency>
⚙️ 智能代理的核心实现
1. 基础代理接口设计
java
@Component
public interface AIAgent {
/**
* 处理用户输入并生成响应
*/
AgentResponse processRequest(AgentRequest request);
/**
* 获取代理能力描述
*/
AgentCapabilities getCapabilities();
/**
* 代理配置更新
*/
void updateConfiguration(AgentConfiguration config);
}
2. Spring AI集成实现
java
@Service
@Slf4j
public class SpringAIAgent implements AIAgent {
private final ChatClient chatClient;
private final VectorStore vectorStore;
private final DocumentReader documentReader;
public SpringAIAgent(ChatClient.Builder chatClientBuilder,
VectorStore vectorStore) {
this.chatClient = chatClientBuilder.build();
this.vectorStore = vectorStore;
this.documentReader = new TikaDocumentReader();
}
@Override
public AgentResponse processRequest(AgentRequest request) {
try {
// 1. 上下文理解与增强
String enhancedPrompt = enhancePromptWithContext(request);
// 2. 向量检索相关信息
List<Document> relevantDocs = retrieveRelevantDocuments(request.getQuery());
// 3. 构建最终提示
String finalPrompt = buildFinalPrompt(enhancedPrompt, relevantDocs);
// 4. 调用AI模型生成响应
ChatResponse response = chatClient.call(
new Prompt(finalPrompt,
OpenAiChatOptions.builder()
.withModel("gpt-4")
.withTemperature(0.7f)
.withMaxTokens(1000)
.build())
);
// 5. 处理和包装响应
return processResponse(response, request);
} catch (Exception e) {
log.error("代理处理请求失败", e);
return AgentResponse.error("处理请求时发生错误: " + e.getMessage());
}
}
private String enhancePromptWithContext(AgentRequest request) {
return String.format("""
作为一个专业的AI助手,请基于以下上下文回答问题:
用户问题:%s
时间戳:%s
用户偏好:%s
请提供准确、有用的回答。
""",
request.getQuery(),
Instant.now(),
request.getUserPreferences()
);
}
private List<Document> retrieveRelevantDocuments(String query) {
// 使用向量检索查找相关文档
List<Document> documents = vectorStore.similaritySearch(
SearchRequest.query(query).withTopK(5)
);
return documents;
}
private String buildFinalPrompt(String basePrompt, List<Document> docs) {
StringBuilder promptBuilder = new StringBuilder(basePrompt);
if (!docs.isEmpty()) {
promptBuilder.append("\n\n参考信息:\n");
docs.forEach(doc ->
promptBuilder.append("- ").append(doc.getContent()).append("\n")
);
}
return promptBuilder.toString();
}
private AgentResponse processResponse(ChatResponse response, AgentRequest request) {
String content = response.getResult().getOutput().getContent();
return AgentResponse.builder()
.content(content)
.requestId(request.getRequestId())
.timestamp(Instant.now())
.confidence(calculateConfidence(response))
.metadata(extractMetadata(response))
.build();
}
private double calculateConfidence(ChatResponse response) {
// 基于响应特征计算置信度
return 0.85; // 简化实现
}
private Map<String, Object> extractMetadata(ChatResponse response) {
Map<String, Object> metadata = new HashMap<>();
metadata.put("model", response.getMetadata().getModel());
metadata.put("usage", response.getMetadata().getUsage());
return metadata;
}
}
3. 函数调用能力增强
java
@Component
public class AgentFunctionRegistry {
@Bean
@Description("获取当前天气信息")
public Function<WeatherRequest, WeatherResponse> getCurrentWeather() {
return request -> {
// 调用天气API获取信息
return WeatherService.getWeather(request.getLocation());
};
}
@Bean
@Description("搜索产品信息")
public Function<ProductSearchRequest, List<Product>> searchProducts() {
return request -> {
// 执行产品搜索
return ProductService.search(request.getKeywords(), request.getCategory());
};
}
@Bean
@Description("计算数学表达式")
public Function<MathRequest, MathResult> calculateMath() {
return request -> {
// 执行数学计算
return MathCalculator.evaluate(request.getExpression());
};
}
}
💼 实战案例:构建智能对话代理
场景描述
我们来构建一个智能客服代理,它能够:
- 理解用户问题并提供准确回答
- 查询订单状态和物流信息
- 处理退换货请求
- 推荐相关产品
1. 代理配置类
java
@Configuration
@EnableConfigurationProperties(AgentProperties.class)
public class CustomerServiceAgentConfig {
@Bean
public CustomerServiceAgent customerServiceAgent(
ChatClient.Builder chatClientBuilder,
VectorStore vectorStore,
OrderService orderService,
ProductService productService) {
return new CustomerServiceAgent(
chatClientBuilder.build(),
vectorStore,
orderService,
productService
);
}
@Bean
public VectorStore vectorStore() {
return new SimpleVectorStore(new InMemoryEmbeddingStore());
}
}
2. 客服代理实现
java
@Service
@Slf4j
public class CustomerServiceAgent extends SpringAIAgent {
private final OrderService orderService;
private final ProductService productService;
private final IntentClassifier intentClassifier;
public CustomerServiceAgent(ChatClient chatClient,
VectorStore vectorStore,
OrderService orderService,
ProductService productService) {
super(chatClient, vectorStore);
this.orderService = orderService;
this.productService = productService;
this.intentClassifier = new IntentClassifier();
}
@Override
public AgentResponse processRequest(AgentRequest request) {
// 1. 意图识别
Intent intent = intentClassifier.classify(request.getQuery());
// 2. 根据意图路由到不同处理逻辑
return switch (intent.getType()) {
case ORDER_INQUIRY -> handleOrderInquiry(request, intent);
case PRODUCT_SEARCH -> handleProductSearch(request, intent);
case RETURN_REQUEST -> handleReturnRequest(request, intent);
case GENERAL_QUESTION -> handleGeneralQuestion(request, intent);
default -> super.processRequest(request);
};
}
private AgentResponse handleOrderInquiry(AgentRequest request, Intent intent) {
String orderId = intent.getExtractedEntities().get("orderId");
if (orderId != null) {
OrderInfo order = orderService.getOrderById(orderId);
if (order != null) {
String response = formatOrderResponse(order);
return AgentResponse.success(response);
}
}
return AgentResponse.error("未找到订单信息,请提供正确的订单号");
}
private AgentResponse handleProductSearch(AgentRequest request, Intent intent) {
String keywords = intent.getExtractedEntities().get("keywords");
List<Product> products = productService.searchProducts(keywords);
String response = formatProductSearchResponse(products);
return AgentResponse.success(response);
}
private String formatOrderResponse(OrderInfo order) {
return String.format("""
📦 订单信息查询结果:
订单号:%s
状态:%s
下单时间:%s
预计送达:%s
物流信息:%s
如需更多帮助,请随时联系我们!
""",
order.getOrderId(),
order.getStatus(),
order.getCreateTime(),
order.getEstimatedDelivery(),
order.getShippingInfo()
);
}
private String formatProductSearchResponse(List<Product> products) {
if (products.isEmpty()) {
return "抱歉,没有找到匹配的产品。请尝试其他关键词搜索。";
}
StringBuilder response = new StringBuilder("🛍️ 为您找到以下产品:\n\n");
products.stream().limit(5).forEach(product ->
response.append(String.format("• %s - ¥%.2f\n %s\n\n",
product.getName(),
product.getPrice(),
product.getDescription()))
);
return response.toString();
}
}
3. 意图分类器
java
@Component
public class IntentClassifier {
private final ChatClient chatClient;
private final Map<String, Pattern> intentPatterns;
public IntentClassifier(ChatClient.Builder chatClientBuilder) {
this.chatClient = chatClientBuilder.build();
this.intentPatterns = initializePatterns();
}
public Intent classify(String userInput) {
// 1. 基于规则的快速分类
Intent ruleBasedIntent = classifyWithRules(userInput);
if (ruleBasedIntent.getConfidence() > 0.8) {
return ruleBasedIntent;
}
// 2. 使用AI模型进行分类
return classifyWithAI(userInput);
}
private Intent classifyWithRules(String input) {
for (Map.Entry<String, Pattern> entry : intentPatterns.entrySet()) {
Matcher matcher = entry.getValue().matcher(input.toLowerCase());
if (matcher.find()) {
return Intent.builder()
.type(IntentType.valueOf(entry.getKey()))
.confidence(0.9)
.extractedEntities(extractEntities(input, matcher))
.build();
}
}
return Intent.builder()
.type(IntentType.UNKNOWN)
.confidence(0.0)
.build();
}
private Intent classifyWithAI(String input) {
String prompt = String.format("""
请分析以下用户输入的意图,并返回JSON格式的结果:
用户输入:"%s"
可能的意图类型:
- ORDER_INQUIRY: 查询订单
- PRODUCT_SEARCH: 搜索产品
- RETURN_REQUEST: 退换货
- GENERAL_QUESTION: 一般问题
返回格式:
{
"intent": "意图类型",
"confidence": 置信度(0-1),
"entities": {
"key": "value"
}
}
""", input);
ChatResponse response = chatClient.call(new Prompt(prompt));
return parseIntentFromResponse(response.getResult().getOutput().getContent());
}
private Map<String, Pattern> initializePatterns() {
Map<String, Pattern> patterns = new HashMap<>();
patterns.put("ORDER_INQUIRY", Pattern.compile("(订单|物流|快递|发货).*?(查询|状态|进度)"));
patterns.put("PRODUCT_SEARCH", Pattern.compile("(搜索|查找|推荐).*?(产品|商品|物品)"));
patterns.put("RETURN_REQUEST", Pattern.compile("(退货|换货|退款|申请退)"));
return patterns;
}
private Map<String, String> extractEntities(String input, Matcher matcher) {
Map<String, String> entities = new HashMap<>();
// 提取订单号
Pattern orderPattern = Pattern.compile("\\b\\d{10,}\\b");
Matcher orderMatcher = orderPattern.matcher(input);
if (orderMatcher.find()) {
entities.put("orderId", orderMatcher.group());
}
return entities;
}
private Intent parseIntentFromResponse(String response) {
// 解析AI返回的JSON结果
// 这里简化实现,实际应使用JSON解析库
return Intent.builder()
.type(IntentType.GENERAL_QUESTION)
.confidence(0.7)
.build();
}
}
⚡ 性能优化与最佳实践
1. 响应缓存策略
java
@Component
public class AgentResponseCache {
private final RedisTemplate<String, AgentResponse> redisTemplate;
private final Duration cacheTTL = Duration.ofMinutes(30);
public Optional<AgentResponse> getCachedResponse(String query) {
String cacheKey = generateCacheKey(query);
AgentResponse cached = redisTemplate.opsForValue().get(cacheKey);
return Optional.ofNullable(cached);
}
public void cacheResponse(String query, AgentResponse response) {
String cacheKey = generateCacheKey(query);
redisTemplate.opsForValue().set(cacheKey, response, cacheTTL);
}
private String generateCacheKey(String query) {
return "agent:response:" + DigestUtils.md5Hex(query.toLowerCase().trim());
}
}
2. 异步处理优化
java
@Service
public class AsynchronousAgentProcessor {
private final TaskExecutor taskExecutor;
private final AgentResponseCache responseCache;
@Async
public CompletableFuture<AgentResponse> processRequestAsync(AgentRequest request) {
return CompletableFuture.supplyAsync(() -> {
// 检查缓存
Optional<AgentResponse> cached = responseCache.getCachedResponse(request.getQuery());
if (cached.isPresent()) {
return cached.get();
}
// 处理请求
AgentResponse response = processRequest(request);
// 缓存结果
responseCache.cacheResponse(request.getQuery(), response);
return response;
}, taskExecutor);
}
}
3. 监控与指标
java
@Component
public class AgentMetrics {
private final MeterRegistry meterRegistry;
private final Counter requestCounter;
private final Timer responseTimer;
private final Gauge activeRequests;
public AgentMetrics(MeterRegistry meterRegistry) {
this.meterRegistry = meterRegistry;
this.requestCounter = Counter.builder("agent.requests.total")
.description("代理请求总数")
.register(meterRegistry);
this.responseTimer = Timer.builder("agent.response.time")
.description("代理响应时间")
.register(meterRegistry);
this.activeRequests = Gauge.builder("agent.requests.active")
.description("活跃请求数")
.register(meterRegistry, this, AgentMetrics::getActiveRequestCount);
}
public void recordRequest(AgentRequest request, Duration duration, boolean success) {
requestCounter.increment(
Tags.of(
Tag.of("intent", request.getIntent().toString()),
Tag.of("success", String.valueOf(success))
)
);
responseTimer.record(duration);
}
private double getActiveRequestCount() {
// 返回当前活跃请求数
return 0; // 简化实现
}
}
📊 总结
Spring AI为Java开发者提供了构建智能代理系统的强大工具集。通过本文的深入探讨,我们了解了:
🎯 核心要点
- 架构设计:Spring AI采用模块化设计,支持多种AI能力的组合使用
- 智能代理模式:强调简单性和可组合性,避免过度复杂的框架设计
- 实践应用:通过客服代理案例展示了完整的实现流程
- 性能优化:缓存、异步处理和监控是生产环境的关键要素
🚀 发展趋势
- 多模态能力:未来的AI代理将支持文本、图像、音频等多种模态
- 自主学习:代理系统将具备更强的自适应和学习能力
- 边缘计算:轻量级代理将在边缘设备上运行
- 安全性增强:AI安全和隐私保护将成为重要考量
💡 实践建议
- 从简单开始:优先实现核心功能,逐步增加复杂性
- 重视测试:AI系统的不确定性要求更全面的测试策略
- 监控为先:建立完善的监控和告警机制
- 持续优化:基于用户反馈不断改进代理能力
Spring AI正在快速发展,为Java生态系统带来了强大的AI能力。通过合理的架构设计和最佳实践,我们能够构建出高效、可靠的智能代理系统,为用户提供更好的体验。
📚 参考资料
作者简介:一名正在实习的Java开发工程师,热爱技术分享,专注于性能优化和系统架构设计。
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