1. 引言概述
1.1 什么是 Spring AI Alibaba Graph
Spring AI Alibaba Graph 是阿里云团队基于 Spring AI 生态开发的一个强大的工作流编排框架，专门用于构建复杂的 AI 应用。它采用声明式编程模型，通过图结构来定义和管理 AI 工作流，让开发者能够像搭积木一样构建智能应用。
1.2 核心价值与优势
声明式编程模型：开发者只需要描述"做什么"，而不需要关心"怎么做"，大大降低了复杂 AI 应用的开发门槛。
状态驱动执行：基于状态机的执行模型，确保数据在各个节点间的安全传递和状态的一致性管理。
异步优先设计：原生支持异步执行和流式处理，能够高效处理大规模并发请求。
Spring 生态集成：深度集成 Spring 框架，支持依赖注入、AOP、监控等企业级特性。
1.3 应用场景
- 智能客服系统：问题分类 → 知识检索 → 答案生成 → 质量评估
- 内容创作平台：需求分析 → 内容生成 → 质量检查 → 发布审核
- 数据分析流水线：数据收集 → 清洗处理 → 模型推理 → 结果可视化
- 智能决策系统：信息收集 → 风险评估 → 策略制定 → 执行监控
2. 核心架构与设计理念
2.1 整体架构设计
Spring AI Alibaba Graph 采用分层架构设计，从下到上包括：
┌─────────────────────────────────────────┐
│ 应用层 (Application) │
│ ┌─────────────┐ ┌─────────────────┐ │
│ │ Controller │ │ Service │ │
│ └─────────────┘ └─────────────────┘ │
├─────────────────────────────────────────┤
│ 编排层 (Orchestration) │
│ ┌─────────────┐ ┌─────────────────┐ │
│ │ StateGraph │ │ CompiledGraph │ │
│ └─────────────┘ └─────────────────┘ │
├─────────────────────────────────────────┤
│ 执行层 (Execution) │
│ ┌─────────────┐ ┌─────────────────┐ │
│ │ Node │ │ Edge │ │
│ └─────────────┘ └─────────────────┘ │
├─────────────────────────────────────────┤
│ 基础层 (Infrastructure) │
│ ┌─────────────┐ ┌─────────────────┐ │
│ │ Checkpoint │ │ Serializer │ │
│ └─────────────┘ └─────────────────┘ │
└─────────────────────────────────────────┘
2.2 核心设计理念
2.2.1 声明式编程范式
传统的命令式编程需要开发者详细描述每一步的执行逻辑，而 Graph 采用声明式编程范式：
// 声明式：描述工作流结构
StateGraph graph = new StateGraph(OverAllState.class)
.addNode("classify", questionClassifierNode)
.addNode("retrieve", knowledgeRetrievalNode)
.addNode("generate", answerGenerationNode)
.addEdge("classify", "retrieve")
.addEdge("retrieve", "generate")
.addEdge("generate", StateGraph.END);
2.2.2 状态驱动执行模型
所有的数据流转都通过 OverAllState 进行管理，确保状态的一致性和可追溯性：
public class OverAllState {
private Map<String, Object> data = new HashMap<>();
private List<String> nodeHistory = new ArrayList<>();
private String currentNode;
// 状态合并策略
public OverAllState merge(Map<String, Object> updates) {
// 实现状态合并逻辑
}
}
2.2.3 异步优先架构
框架原生支持异步执行，通过 AsyncNodeGenerator 实现非阻塞的流式处理：
public class AsyncNodeGenerator implements AsyncGenerator<NodeOutput> {
@Override
public CompletableFuture<Optional<NodeOutput>> next() {
// 异步执行节点逻辑
return CompletableFuture.supplyAsync(() -> {
// 节点执行逻辑
});
}
}
2.3 数据流转架构
Graph 的数据流转遵循 “构建 → 编译 → 执行” 的三阶段模式：
2.3.1 构建阶段 (Build Phase)
在这个阶段，开发者通过 StateGraph API 定义工作流的结构：
StateGraph graph = new StateGraph(OverAllState.class)
.addNode("nodeA", nodeActionA)
.addNode("nodeB", nodeActionB)
.addConditionalEdges("nodeA", this::routingLogic,
Map.of("condition1", "nodeB", "condition2", StateGraph.END));
2.3.2 编译阶段 (Compile Phase)
StateGraph 被编译成 CompiledGraph，进行优化和验证：
CompiledGraph compiledGraph = graph.compile(
CompileConfig.builder()
.checkpointSaver(new MemorySaver())
.interruptBefore("nodeB")
.build()
);
2.3.3 执行阶段 (Execute Phase)
通过 AsyncNodeGenerator 执行工作流，支持流式处理和检查点恢复：
AsyncGenerator<NodeOutput> generator = compiledGraph.stream(
OverAllState.builder().put("input", "user question").build(),
RunnableConfig.builder().threadId("thread-1").build()
);
3. 核心概念深度解析
3.1 StateGraph：工作流的设计蓝图
StateGraph 是整个框架的核心，它定义了工作流的结构和执行逻辑。
3.1.1 基本结构
public class StateGraph {
public static final String START = "__start__";
public static final String END = "__end__";
public static final String ERROR = "__error__";
private final Set<Node> nodes = new HashSet<>();
private final Set<Edge> edges = new HashSet<>();
private final KeyStrategyFactory keyStrategyFactory;
private final StateSerializer stateSerializer;
}
3.1.2 节点管理
StateGraph 支持多种类型的节点添加：
// 添加普通节点
public StateGraph addNode(String nodeId, AsyncNodeAction nodeAction)
// 添加带配置的节点
public StateGraph addNode(String nodeId, AsyncNodeActionWithConfig nodeAction)
// 添加子图节点
public StateGraph addNode(String nodeId, CompiledGraph subGraph)
// 添加命令节点
public StateGraph addNode(String nodeId, AsyncCommandAction commandAction)
3.1.3 边的类型与路由
简单边：直接连接两个节点
graph.addEdge("nodeA", "nodeB");
条件边：根据条件动态路由
graph.addConditionalEdges("nodeA", this::routingFunction,
Map.of("path1", "nodeB", "path2", "nodeC"));
动态边：运行时确定目标节点
graph.addConditionalEdges("nodeA", (state) -> {
if (state.value("score").map(s -> (Double)s > 0.8).orElse(false)) {
return "highQualityPath";
}
return "normalPath";
});
3.2 OverAllState：数据中枢与状态管理
OverAllState 是工作流中所有数据的载体，负责状态的存储、传递和合并。
3.2.1 状态结构设计
public class OverAllState {
private Map<String, Object> data;
private List<String> nodeHistory;
private String currentNode;
private HumanFeedback humanFeedback;
private boolean isResume;
// 状态访问方法
public Optional<Object> value(String key) {
return Optional.ofNullable(data.get(key));
}
// 状态更新方法
public Map<String, Object> updateState(Map<String, Object> updates) {
data.putAll(updates);
return updates;
}
}
3.2.2 状态合并策略
框架提供了灵活的状态合并机制：
public class OverAllStateBuilder {
private KeyStrategyFactory keyStrategyFactory;
public OverAllState merge(OverAllState current, Map<String, Object> updates) {
Map<String, Object> mergedData = new HashMap<>(current.data());
for (Map.Entry<String, Object> entry : updates.entrySet()) {
String key = entry.getKey();
Object newValue = entry.getValue();
KeyStrategy strategy = keyStrategyFactory.getStrategy(key);
Object mergedValue = strategy.merge(mergedData.get(key), newValue);
mergedData.put(key, mergedValue);
}
return new OverAllState(mergedData, current.nodeHistory(), current.currentNode());
}
}
3.2.3 状态序列化与持久化
支持多种序列化策略：
public interface StateSerializer {
byte[] serialize(OverAllState state) throws Exception;
OverAllState deserialize(byte[] data, Class<? extends OverAllState> clazz) throws Exception;
}
// Jackson 序列化实现
public static class JacksonSerializer implements StateSerializer {
private final ObjectMapper objectMapper = new ObjectMapper();
@Override
public byte[] serialize(OverAllState state) throws Exception {
return objectMapper.writeValueAsBytes(state);
}
}
3.3 Node：功能模块与业务逻辑
节点是工作流中的基本执行单元，每个节点封装特定的业务逻辑。
3.3.1 节点接口设计
@FunctionalInterface
public interface NodeAction {
Map<String, Object> apply(OverAllState state) throws Exception;
}
@FunctionalInterface
public interface AsyncNodeAction {
CompletableFuture<Map<String, Object>> apply(OverAllState state) throws Exception;
}
3.3.2 节点生命周期
节点的执行遵循标准的生命周期：
- 初始化：从状态中提取所需参数
- 执行：执行核心业务逻辑
- 输出：生成状态更新
- 清理：释放资源
public class CustomNode implements NodeAction {
@Override
public Map<String, Object> apply(OverAllState state) throws Exception {
// 1. 初始化：提取输入参数
String input = (String) state.value("input").orElseThrow();
// 2. 执行：业务逻辑处理
String result = processInput(input);
// 3. 输出：返回状态更新
return Map.of("output", result, "processed", true);
}
private String processInput(String input) {
// 具体业务逻辑
return "processed: " + input;
}
}
3.4 Edge：路由器与流程控制
边定义了节点之间的连接关系和数据流转路径。
3.4.1 边的类型系统
public abstract class Edge {
protected final String sourceNodeId;
protected final String targetNodeId;
public abstract boolean shouldExecute(OverAllState state);
public abstract String getTargetNode(OverAllState state);
}
// 简单边：无条件连接
public class SimpleEdge extends Edge {
@Override
public boolean shouldExecute(OverAllState state) {
return true;
}
}
// 条件边：基于条件的路由
public class ConditionalEdge extends Edge {
private final Function<OverAllState, Boolean> condition;
@Override
public boolean shouldExecute(OverAllState state) {
return condition.apply(state);
}
}
3.4.2 动态路由机制
支持运行时动态确定路由路径：
public class DynamicEdge extends Edge {
private final Function<OverAllState, String> routingFunction;
private final Map<String, String> pathMapping;
@Override
public String getTargetNode(OverAllState state) {
String routingKey = routingFunction.apply(state);
return pathMapping.getOrDefault(routingKey, StateGraph.END);
}
}
4. 预定义组件与工具箱
4.1 LlmNode：大语言模型节点
LlmNode 是框架中最重要的预定义节点之一，封装了与大语言模型的交互逻辑。
4.1.1 核心功能特性
public class LlmNode implements NodeAction {
public static final String LLM_RESPONSE_KEY = "llm_response";
private String systemPrompt;
private String userPrompt;
private Map<String, Object> params;
private List<Message> messages;
private List<Advisor> advisors;
private List<ToolCallback> toolCallbacks;
private ChatClient chatClient;
private Boolean stream;
}
4.1.2 流式处理支持
@Override
public Map<String, Object> apply(OverAllState state) throws Exception {
initNodeWithState(state);
if (Boolean.TRUE.equals(stream)) {
Flux<ChatResponse> chatResponseFlux = stream();
var generator = StreamingChatGenerator.builder()
.startingNode("llmNode")
.startingState(state)
.mapResult(response -> Map.of(
StringUtils.hasLength(this.outputKey) ? this.outputKey : "messages",
Objects.requireNonNull(response.getResult().getOutput())
))
.build(chatResponseFlux);
return Map.of(outputKey, generator);
} else {
ChatResponse response = call();
return Map.of("messages", response.getResult().getOutput());
}
}
4.1.3 模板渲染机制
支持动态模板渲染：
private String renderPromptTemplate(String prompt, Map<String, Object> params) {
PromptTemplate promptTemplate = new PromptTemplate(prompt);
return promptTemplate.render(params);
}
private void initNodeWithState(OverAllState state) {
// 从状态中获取动态参数
if (StringUtils.hasLength(userPromptKey)) {
this.userPrompt = (String) state.value(userPromptKey).orElse(this.userPrompt);
}
// 渲染模板
if (StringUtils.hasLength(userPrompt) && !params.isEmpty()) {
this.userPrompt = renderPromptTemplate(userPrompt, params);
}
}
4.2 ToolNode：工具调用节点
ToolNode 负责处理大语言模型的工具调用请求。
4.2.1 工具执行机制
public class ToolNode implements NodeAction {
private List<ToolCallback> toolCallbacks;
private ToolCallbackResolver toolCallbackResolver;
@Override
public Map<String, Object> apply(OverAllState state) throws Exception {
AssistantMessage assistantMessage = getAssistantMessage(state);
ToolResponseMessage toolResponseMessage = executeFunction(assistantMessage, state);
return Map.of("messages", toolResponseMessage);
}
private ToolResponseMessage executeFunction(AssistantMessage assistantMessage, OverAllState state) {
List<ToolResponseMessage.ToolResponse> toolResponses = new ArrayList<>();
for (AssistantMessage.ToolCall toolCall : assistantMessage.getToolCalls()) {
String toolName = toolCall.name();
String toolArgs = toolCall.arguments();
ToolCallback toolCallback = resolve(toolName);
String toolResult = toolCallback.call(toolArgs, new ToolContext(Map.of("state", state)));
toolResponses.add(new ToolResponseMessage.ToolResponse(
toolCall.id(), toolName, toolResult
));
}
return new ToolResponseMessage(toolResponses, Map.of());
}
}
4.3 HumanNode：人机交互节点
HumanNode 实现了人工干预和反馈机制。
4.3.1 中断策略
public class HumanNode implements NodeAction {
private String interruptStrategy; // "always" or "conditioned"
private Function<OverAllState, Boolean> interruptCondition;
private Function<OverAllState, Map<String, Object>> stateUpdateFunc;
@Override
public Map<String, Object> apply(OverAllState state) throws GraphRunnerException {
boolean shouldInterrupt = interruptStrategy.equals("always") ||
(interruptStrategy.equals("conditioned") && interruptCondition.apply(state));
if (shouldInterrupt) {
interrupt(state);
return processHumanFeedback(state);
}
return Map.of();
}
}
4.4 代码执行节点
框架提供了强大的代码执行能力，支持多种编程语言。
4.4.1 CodeExecutorNodeAction
public class CodeExecutorNodeAction implements NodeAction {
private final CodeExecutor codeExecutor;
private final String codeLanguage;
private final String code;
private final CodeExecutionConfig codeExecutionConfig;
@Override
public Map<String, Object> apply(OverAllState state) throws Exception {
List<Object> inputs = extractInputsFromState(state);
Map<String, Object> result = executeWorkflowCodeTemplate(
CodeLanguage.fromValue(codeLanguage), code, inputs
);
return Map.of(outputKey, result);
}
}
4.4.2 多语言支持
private static final Map<CodeLanguage, TemplateTransformer> CODE_TEMPLATE_TRANSFORMERS = Map.of(
CodeLanguage.PYTHON3, new Python3TemplateTransformer(),
CodeLanguage.JAVASCRIPT, new NodeJsTemplateTransformer(),
CodeLanguage.JAVA, new JavaTemplateTransformer()
);
5. 高级特性与扩展能力
5.1 检查点机制与状态恢复
检查点机制是 Graph 框架的重要特性，支持工作流的暂停、恢复和容错。
5.1.1 检查点保存器接口
public interface BaseCheckpointSaver {
record Tag(String threadId, Collection<Checkpoint> checkpoints) {}
Collection<Checkpoint> list(RunnableConfig config);
Optional<Checkpoint> get(RunnableConfig config);
RunnableConfig put(RunnableConfig config, Checkpoint checkpoint) throws Exception;
boolean clear(RunnableConfig config);
}
5.1.2 内存检查点保存器
public class VersionedMemorySaver implements BaseCheckpointSaver, HasVersions {
private final Map<String, TreeMap<Integer, Tag>> _checkpointsHistoryByThread = new HashMap<>();
private final ReentrantLock _lock = new ReentrantLock();
@Override
public RunnableConfig put(RunnableConfig config, Checkpoint checkpoint) throws Exception {
_lock.lock();
try {
String threadId = config.threadId();
TreeMap<Integer, Tag> history = _checkpointsHistoryByThread
.computeIfAbsent(threadId, k -> new TreeMap<>());
int version = history.isEmpty() ? 1 : history.lastKey() + 1;
history.put(version, new Tag(threadId, List.of(checkpoint)));
return config.withCheckpointId(String.valueOf(version));
} finally {
_lock.unlock();
}
}
}
5.1.3 Redis 检查点保存器
public class RedisSaver implements BaseCheckpointSaver {
private final RedisTemplate<String, Object> redisTemplate;
private final StateSerializer serializer;
@Override
public RunnableConfig put(RunnableConfig config, Checkpoint checkpoint) throws Exception {
String key = buildKey(config.threadId(), checkpoint.id());
byte[] serializedCheckpoint = serializer.serialize(checkpoint.state());
redisTemplate.opsForValue().set(key, serializedCheckpoint);
return config;
}
}
5.2 并行执行与分支合并
框架支持复杂的并行执行模式。
5.2.1 并行分支定义
StateGraph graph = new StateGraph(OverAllState.class)
.addNode("input", inputNode)
.addNode("branch1", branch1Node)
.addNode("branch2", branch2Node)
.addNode("merge", mergeNode)
.addEdge("input", "branch1")
.addEdge("input", "branch2")
.addEdge("branch1", "merge")
.addEdge("branch2", "merge");
5.2.2 状态合并策略
public class MergeNode implements NodeAction {
@Override
public Map<String, Object> apply(OverAllState state) throws Exception {
// 等待所有分支完成
List<Object> branch1Results = (List<Object>) state.value("branch1_results").orElse(List.of());
List<Object> branch2Results = (List<Object>) state.value("branch2_results").orElse(List.of());
// 合并结果
List<Object> mergedResults = new ArrayList<>();
mergedResults.addAll(branch1Results);
mergedResults.addAll(branch2Results);
return Map.of("merged_results", mergedResults);
}
}
5.3 子图与模块化设计
支持将复杂工作流拆分为可复用的子图模块。
5.3.1 子图定义
// 定义数据处理子图
StateGraph dataProcessingSubGraph = new StateGraph(OverAllState.class)
.addNode("validate", dataValidationNode)
.addNode("transform", dataTransformNode)
.addNode("enrich", dataEnrichmentNode)
.addEdge("validate", "transform")
.addEdge("transform", "enrich");
CompiledGraph compiledSubGraph = dataProcessingSubGraph.compile();
// 在主图中使用子图
StateGraph mainGraph = new StateGraph(OverAllState.class)
.addNode("input", inputNode)
.addNode("process", compiledSubGraph) // 子图作为节点
.addNode("output", outputNode)
.addEdge("input", "process")
.addEdge("process", "output");
5.4 流式处理与实时响应
5.4.1 StreamingChatGenerator
public class StreamingChatGenerator {
public static Builder builder() {
return new Builder();
}
public static class Builder {
private String startingNode;
private OverAllState startingState;
private Function<ChatResponse, Map<String, Object>> mapResult;
public AsyncGenerator<Map<String, Object>> build(Flux<ChatResponse> chatResponseFlux) {
return new AsyncGenerator<Map<String, Object>>() {
private final Iterator<ChatResponse> iterator = chatResponseFlux.toIterable().iterator();
@Override
public CompletableFuture<Optional<Map<String, Object>>> next() {
return CompletableFuture.supplyAsync(() -> {
if (iterator.hasNext()) {
ChatResponse response = iterator.next();
return Optional.of(mapResult.apply(response));
}
return Optional.empty();
});
}
};
}
}
}
6. 源码实现原理剖析
6.1 CompiledGraph 执行引擎
CompiledGraph 是工作流的执行引擎，负责将声明式的图结构转换为可执行的状态机。
6.1.1 编译过程
public class StateGraph {
public CompiledGraph compile(CompileConfig config) {
// 1. 验证图结构
validateGraph();
// 2. 构建节点映射
Map<String, AsyncNodeActionWithConfig> compiledNodes = compileNodes();
// 3. 构建边映射
Map<String, List<EdgeValue>> compiledEdges = compileEdges();
// 4. 创建编译后的图
return new CompiledGraph(this, compiledNodes, compiledEdges, config);
}
private void validateGraph() {
// 检查图的连通性
// 检查是否存在循环依赖
// 验证节点和边的有效性
}
}
6.1.2 AsyncNodeGenerator 状态机
public class AsyncNodeGenerator implements AsyncGenerator<NodeOutput> {
private int iterations = 0;
private final RunnableConfig config;
private OverAllState state;
private String currentNodeId;
@Override
public CompletableFuture<Optional<NodeOutput>> next() {
return CompletableFuture.supplyAsync(() -> {
try {
// 1. 检查迭代次数限制
if (iterations >= maxIterations) {
return Optional.empty();
}
// 2. 获取当前节点
String nodeId = getCurrentNodeId();
if (nodeId == null || StateGraph.END.equals(nodeId)) {
return Optional.empty();
}
// 3. 执行节点
AsyncNodeActionWithConfig nodeAction = nodes.get(nodeId);
Map<String, Object> result = evaluateAction(nodeAction, state);
// 4. 更新状态
state = updateState(state, result);
// 5. 确定下一个节点
String nextNodeId = nextNodeId(nodeId, state);
// 6. 添加检查点
addCheckpoint(state, nextNodeId);
// 7. 构建输出
NodeOutput output = new NodeOutput(nodeId, state, result);
iterations++;
currentNodeId = nextNodeId;
return Optional.of(output);
} catch (Exception e) {
handleError(e);
return Optional.empty();
}
});
}
}
6.2 状态管理机制
6.2.1 状态序列化
public interface StateSerializer {
byte[] serialize(OverAllState state) throws Exception;
OverAllState deserialize(byte[] data, Class<? extends OverAllState> clazz) throws Exception;
}
public class JacksonSerializer implements StateSerializer {
private final ObjectMapper objectMapper;
public JacksonSerializer() {
this.objectMapper = new ObjectMapper();
this.objectMapper.registerModule(new JavaTimeModule());
this.objectMapper.disable(SerializationFeature.WRITE_DATES_AS_TIMESTAMPS);
}
@Override
public byte[] serialize(OverAllState state) throws Exception {
return objectMapper.writeValueAsBytes(state);
}
@Override
public OverAllState deserialize(byte[] data, Class<? extends OverAllState> clazz) throws Exception {
return objectMapper.readValue(data, clazz);
}
}
6.2.2 状态合并策略
public class KeyStrategyFactory {
private final Map<String, KeyStrategy> strategies = new HashMap<>();
public KeyStrategy getStrategy(String key) {
return strategies.getOrDefault(key, DefaultKeyStrategy.INSTANCE);
}
public void registerStrategy(String key, KeyStrategy strategy) {
strategies.put(key, strategy);
}
}
public interface KeyStrategy {
Object merge(Object currentValue, Object newValue);
}
public class AppendKeyStrategy implements KeyStrategy {
@Override
public Object merge(Object currentValue, Object newValue) {
if (currentValue instanceof List && newValue instanceof List) {
List<Object> merged = new ArrayList<>((List<?>) currentValue);
merged.addAll((List<?>) newValue);
return merged;
}
return newValue;
}
}
6.3 异步执行机制
6.3.1 AsyncGenerator 接口
public interface AsyncGenerator<T> {
CompletableFuture<Optional<T>> next();
default <R> AsyncGenerator<R> map(Function<T, R> mapper) {
return () -> this.next().thenApply(opt -> opt.map(mapper));
}
default <R> AsyncGenerator<R> flatMap(Function<T, AsyncGenerator<R>> mapper) {
return new FlatMapAsyncGenerator<>(this, mapper);
}
default AsyncGenerator<T> filter(Predicate<T> predicate) {
return () -> this.next().thenCompose(opt -> {
if (opt.isPresent() && predicate.test(opt.get())) {
return CompletableFuture.completedFuture(opt);
}
return this.next();
});
}
}
6.3.2 响应式流集成
public class FlowGenerator {
public static <T> Flow.Publisher<T> fromAsyncGenerator(AsyncGenerator<T> generator) {
return new GeneratorPublisher<>(generator);
}
public static <T> AsyncGenerator<T> fromPublisher(Flow.Publisher<T> publisher) {
return new PublisherAsyncGenerator<>(publisher);
}
}
public class GeneratorPublisher<T> implements Flow.Publisher<T> {
private final AsyncGenerator<T> generator;
@Override
public void subscribe(Flow.Subscriber<? super T> subscriber) {
subscriber.onSubscribe(new GeneratorSubscription<>(generator, subscriber));
}
}
7. 实战应用案例分析
7.1 智能客服系统
7.1.1 系统架构设计
@Configuration
public class CustomerServiceGraphConfig {
@Bean
public CompiledGraph customerServiceGraph(
ChatClient chatClient,
KnowledgeBaseService knowledgeBaseService,
QualityAssessmentService qualityService) {
// 问题分类节点
LlmNode questionClassifier = LlmNode.builder()
.chatClient(chatClient)
.systemPromptTemplate("""
你是一个专业的客服问题分类器。请将用户问题分类为以下类型之一：
- TECHNICAL: 技术问题
- BILLING: 账单问题
- GENERAL: 一般咨询
- COMPLAINT: 投诉建议
只返回分类结果，格式：{"category": "分类名称"}
""")
.userPromptTemplateKey("user_question")
.outputKey("question_category")
.build();
// 知识检索节点
KnowledgeRetrievalNode knowledgeRetrieval = KnowledgeRetrievalNode.builder()
.knowledgeBaseService(knowledgeBaseService)
.questionKey("user_question")
.categoryKey("question_category")
.outputKey("relevant_knowledge")
.build();
// 答案生成节点
LlmNode answerGenerator = LlmNode.builder()
.chatClient(chatClient)
.systemPromptTemplate("""
基于以下知识库内容，为用户提供准确、友好的回答：
知识库内容：{relevant_knowledge}
要求：
1. 回答要准确、完整
2. 语气要友好、专业
3. 如果知识库中没有相关信息，请诚实告知
""")
.userPromptTemplateKey("user_question")
.paramsKey("template_params")
.outputKey("generated_answer")
.build();
// 质量评估节点
QualityAssessmentNode qualityAssessment = QualityAssessmentNode.builder()
.qualityService(qualityService)
.questionKey("user_question")
.answerKey("generated_answer")
.knowledgeKey("relevant_knowledge")
.outputKey("quality_score")
.build();
// 人工审核节点
HumanNode humanReview = HumanNode.builder()
.interruptStrategy("conditioned")
.interruptCondition(state -> {
Double score = (Double) state.value("quality_score").orElse(1.0);
return score < 0.7; // 质量分数低于0.7需要人工审核
})
.build();
// 构建工作流图
StateGraph graph = new StateGraph(CustomerServiceState.class)
.addNode("classify", questionClassifier)
.addNode("retrieve", knowledgeRetrieval)
.addNode("generate", answerGenerator)
.addNode("assess", qualityAssessment)
.addNode("review", humanReview)
.addNode("finalize", this::finalizeAnswer)
// 定义流程路径
.addEdge(StateGraph.START, "classify")
.addEdge("classify", "retrieve")
.addEdge("retrieve", "generate")
.addEdge("generate", "assess")
// 条件分支：根据质量分数决定是否需要人工审核
.addConditionalEdges("assess", this::shouldReview, Map.of(
"review", "review",
"finalize", "finalize"
))
.addEdge("review", "finalize")
.addEdge("finalize", StateGraph.END);
return graph.compile(CompileConfig.builder()
.checkpointSaver(new RedisSaver(redisTemplate))
.interruptBefore("review")
.build());
}
private String shouldReview(OverAllState state) {
Double score = (Double) state.value("quality_score").orElse(1.0);
return score < 0.7 ? "review" : "finalize";
}
private Map<String, Object> finalizeAnswer(OverAllState state) {
String answer = (String) state.value("generated_answer").orElse("");
Double score = (Double) state.value("quality_score").orElse(0.0);
// 记录服务日志
logCustomerService(state);
return Map.of(
"final_answer", answer,
"confidence_score", score,
"timestamp", Instant.now()
);
}
}
7.1.2 状态定义
public class CustomerServiceState extends OverAllState {
public Optional<String> getUserQuestion() {
return value("user_question").map(String.class::cast);
}
public Optional<String> getQuestionCategory() {
return value("question_category").map(String.class::cast);
}
public Optional<List<String>> getRelevantKnowledge() {
return value("relevant_knowledge").map(list -> (List<String>) list);
}
public Optional<String> getGeneratedAnswer() {
return value("generated_answer").map(String.class::cast);
}
public Optional<Double> getQualityScore() {
return value("quality_score").map(Double.class::cast);
}
public static CustomerServiceStateBuilder builder() {
return new CustomerServiceStateBuilder();
}
public static class CustomerServiceStateBuilder extends OverAllStateBuilder {
public CustomerServiceStateBuilder userQuestion(String question) {
return (CustomerServiceStateBuilder) put("user_question", question);
}
public CustomerServiceStateBuilder sessionId(String sessionId) {
return (CustomerServiceStateBuilder) put("session_id", sessionId);
}
public CustomerServiceStateBuilder userId(String userId) {
return (CustomerServiceStateBuilder) put("user_id", userId);
}
}
}
7.2 内容创作平台
7.2.1 多模态内容生成
@Configuration
public class ContentCreationGraphConfig {
@Bean
public CompiledGraph contentCreationGraph(
ChatClient chatClient,
ImageGenerationService imageService,
ContentModerationService moderationService) {
// 需求分析节点
LlmNode requirementAnalysis = LlmNode.builder()
.chatClient(chatClient)
.systemPromptTemplate("""
分析用户的内容创作需求，提取关键信息：
1. 内容类型（文章、视频脚本、社交媒体帖子等）
2. 目标受众
3. 内容主题和关键词
4. 风格要求
5. 长度要求
返回JSON格式的分析结果。
""")
.userPromptTemplateKey("user_requirement")
.outputKey("requirement_analysis")
.build();
// 内容大纲生成
LlmNode outlineGeneration = LlmNode.builder()
.chatClient(chatClient)
.systemPromptTemplate("""
基于需求分析结果，生成详细的内容大纲：
需求分析：{requirement_analysis}
大纲应包括：
1. 标题建议
2. 章节结构
3. 关键要点
4. 预估字数
""")
.paramsKey("template_params")
.outputKey("content_outline")
.build();
// 并行内容生成
StateGraph parallelGeneration = new StateGraph(OverAllState.class)
.addNode("text_generation", createTextGenerationNode(chatClient))
.addNode("image_generation", createImageGenerationNode(imageService))
.addNode("seo_optimization", createSEOOptimizationNode(chatClient))
.addEdge(StateGraph.START, "text_generation")
.addEdge(StateGraph.START, "image_generation")
.addEdge(StateGraph.START, "seo_optimization")
.addEdge("text_generation", StateGraph.END)
.addEdge("image_generation", StateGraph.END)
.addEdge("seo_optimization", StateGraph.END);
CompiledGraph parallelGenerationCompiled = parallelGeneration.compile();
// 内容整合节点
ContentIntegrationNode contentIntegration = ContentIntegrationNode.builder()
.textKey("generated_text")
.imagesKey("generated_images")
.seoKey("seo_suggestions")
.outputKey("integrated_content")
.build();
// 内容审核节点
ContentModerationNode contentModeration = ContentModerationNode.builder()
.moderationService(moderationService)
.contentKey("integrated_content")
.outputKey("moderation_result")
.build();
// 构建主工作流
StateGraph mainGraph = new StateGraph(ContentCreationState.class)
.addNode("analyze", requirementAnalysis)
.addNode("outline", outlineGeneration)
.addNode("generate", parallelGenerationCompiled)
.addNode("integrate", contentIntegration)
.addNode("moderate", contentModeration)
.addNode("publish", this::publishContent)
.addEdge(StateGraph.START, "analyze")
.addEdge("analyze", "outline")
.addEdge("outline", "generate")
.addEdge("generate", "integrate")
.addEdge("integrate", "moderate")
// 条件发布：通过审核才能发布
.addConditionalEdges("moderate", this::shouldPublish, Map.of(
"publish", "publish",
"reject", StateGraph.END
))
.addEdge("publish", StateGraph.END);
return mainGraph.compile(CompileConfig.builder()
.checkpointSaver(new VersionedMemorySaver())
.build());
}
}
7.3 数据分析流水线
7.3.1 实时数据处理
@Configuration
public class DataAnalysisGraphConfig {
@Bean
public CompiledGraph dataAnalysisGraph(
DataSourceService dataSourceService,
MLModelService mlModelService,
VisualizationService visualizationService) {
// 数据收集节点
DataCollectionNode dataCollection = DataCollectionNode.builder()
.dataSourceService(dataSourceService)
.sourceConfigKey("data_sources")
.outputKey("raw_data")
.build();
// 数据清洗节点
CodeExecutorNodeAction dataCleaningNode = CodeExecutorNodeAction.builder()
.codeExecutor(new DockerCodeExecutor())
.codeLanguage("python3")
.code("""
import pandas as pd
import numpy as np
def clean_data(raw_data):
df = pd.DataFrame(raw_data)
# 处理缺失值
df = df.dropna()
# 处理异常值
Q1 = df.quantile(0.25)
Q3 = df.quantile(0.75)
IQR = Q3 - Q1
df = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]
# 数据标准化
numeric_columns = df.select_dtypes(include=[np.number]).columns
df[numeric_columns] = (df[numeric_columns] - df[numeric_columns].mean()) / df[numeric_columns].std()
return df.to_dict('records')
result = clean_data(inputs[0])
""")
.params(Map.of("raw_data", "raw_data"))
.outputKey("cleaned_data")
.build();
// 特征工程节点
FeatureEngineeringNode featureEngineering = FeatureEngineeringNode.builder()
.inputDataKey("cleaned_data")
.featureConfigKey("feature_config")
.outputKey("features")
.build();
// 模型推理节点
MLInferenceNode mlInference = MLInferenceNode.builder()
.mlModelService(mlModelService)
.featuresKey("features")
.modelConfigKey("model_config")
.outputKey("predictions")
.build();
// 结果可视化节点
VisualizationNode visualization = VisualizationNode.builder()
.visualizationService(visualizationService)
.dataKey("predictions")
.chartConfigKey("chart_config")
.outputKey("charts")
.build();
// 报告生成节点
ReportGenerationNode reportGeneration = ReportGenerationNode.builder()
.predictionsKey("predictions")
.chartsKey("charts")
.templateKey("report_template")
.outputKey("final_report")
.build();
StateGraph graph = new StateGraph(DataAnalysisState.class)
.addNode("collect", dataCollection)
.addNode("clean", dataCleaningNode)
.addNode("engineer", featureEngineering)
.addNode("infer", mlInference)
.addNode("visualize", visualization)
.addNode("report", reportGeneration)
.addEdge(StateGraph.START, "collect")
.addEdge("collect", "clean")
.addEdge("clean", "engineer")
.addEdge("engineer", "infer")
.addEdge("infer", "visualize")
.addEdge("visualize", "report")
.addEdge("report", StateGraph.END);
return graph.compile(CompileConfig.builder()
.checkpointSaver(new FileSystemSaver("/data/checkpoints"))
.maxIterations(100)
.build());
}
}
8. 性能优化与最佳实践
8.1 性能优化策略
8.1.1 异步执行优化
// 配置线程池
@Configuration
public class GraphExecutorConfig {
@Bean
@Primary
public Executor graphExecutor() {
ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
executor.setCorePoolSize(10);
executor.setMaxPoolSize(50);
executor.setQueueCapacity(200);
executor.setThreadNamePrefix("graph-exec-");
executor.setRejectedExecutionHandler(new ThreadPoolExecutor.CallerRunsPolicy());
executor.initialize();
return executor;
}
}
// 异步节点实现
public class OptimizedAsyncNode implements AsyncNodeAction {
private final Executor executor;
@Override
public CompletableFuture<Map<String, Object>> apply(OverAllState state) {
return CompletableFuture.supplyAsync(() -> {
// 节点逻辑
return processData(state);
}, executor);
}
}
8.1.2 状态序列化优化
// 使用高效的序列化器
public class ProtobufStateSerializer implements StateSerializer {
@Override
public byte[] serialize(OverAllState state) throws Exception {
StateProto.State proto = convertToProto(state);
return proto.toByteArray();
}
@Override
public OverAllState deserialize(byte[] data, Class<? extends OverAllState> clazz) throws Exception {
StateProto.State proto = StateProto.State.parseFrom(data);
return convertFromProto(proto, clazz);
}
}
// 状态压缩
public class CompressedStateSerializer implements StateSerializer {
private final StateSerializer delegate;
@Override
public byte[] serialize(OverAllState state) throws Exception {
byte[] data = delegate.serialize(state);
return compress(data);
}
private byte[] compress(byte[] data) throws IOException {
ByteArrayOutputStream baos = new ByteArrayOutputStream();
try (GZIPOutputStream gzos = new GZIPOutputStream(baos)) {
gzos.write(data);
}
return baos.toByteArray();
}
}
8.1.3 检查点优化
// 批量检查点保存
public class BatchCheckpointSaver implements BaseCheckpointSaver {
private final BaseCheckpointSaver delegate;
private final Queue<CheckpointBatch> batchQueue = new ConcurrentLinkedQueue<>();
private final ScheduledExecutorService scheduler = Executors.newScheduledThreadPool(1);
public BatchCheckpointSaver(BaseCheckpointSaver delegate) {
this.delegate = delegate;
// 每秒批量保存一次
scheduler.scheduleAtFixedRate(this::flushBatch, 1, 1, TimeUnit.SECONDS);
}
@Override
public RunnableConfig put(RunnableConfig config, Checkpoint checkpoint) throws Exception {
batchQueue.offer(new CheckpointBatch(config, checkpoint));
return config;
}
private void flushBatch() {
List<CheckpointBatch> batch = new ArrayList<>();
CheckpointBatch item;
while ((item = batchQueue.poll()) != null) {
batch.add(item);
}
if (!batch.isEmpty()) {
// 批量保存
saveBatch(batch);
}
}
}
8.2 监控与观测
8.2.1 指标收集
@Component
public class GraphMetricsCollector implements GraphLifecycleListener {
private final MeterRegistry meterRegistry;
private final Timer.Sample currentExecution;
@Override
public void onGraphStart(String graphName, OverAllState initialState) {
meterRegistry.counter("graph.executions.started", "graph", graphName).increment();
currentExecution = Timer.start(meterRegistry);
}
@Override
public void onNodeStart(String nodeId, OverAllState state) {
meterRegistry.counter("graph.nodes.executed", "node", nodeId).increment();
}
@Override
public void onNodeComplete(String nodeId, OverAllState state, Map<String, Object> result) {
Timer.Sample.stop(meterRegistry.timer("graph.node.duration", "node", nodeId));
}
@Override
public void onGraphComplete(String graphName, OverAllState finalState) {
currentExecution.stop(meterRegistry.timer("graph.execution.duration", "graph", graphName));
meterRegistry.counter("graph.executions.completed", "graph", graphName).increment();
}
@Override
public void onError(String location, Exception error) {
meterRegistry.counter("graph.errors", "location", location, "error", error.getClass().getSimpleName()).increment();
}
}
8.2.2 分布式追踪
@Component
public class GraphTracingListener implements GraphLifecycleListener {
private final Tracer tracer;
private final Map<String, Span> activeSpans = new ConcurrentHashMap<>();
@Override
public void onGraphStart(String graphName, OverAllState initialState) {
Span span = tracer.nextSpan()
.name("graph.execution")
.tag("graph.name", graphName)
.tag("thread.id", Thread.currentThread().getName())
.start();
activeSpans.put(graphName, span);
}
@Override
public void onNodeStart(String nodeId, OverAllState state) {
Span parentSpan = activeSpans.get(getCurrentGraphName());
Span nodeSpan = tracer.nextSpan(parentSpan.context())
.name("node.execution")
.tag("node.id", nodeId)
.start();
activeSpans.put(nodeId, nodeSpan);
}
@Override
public void onNodeComplete(String nodeId, OverAllState state, Map<String, Object> result) {
Span nodeSpan = activeSpans.remove(nodeId);
if (nodeSpan != null) {
nodeSpan.tag("node.result.size", String.valueOf(result.size()));
nodeSpan.end();
}
}
@Override
public void onGraphComplete(String graphName, OverAllState finalState) {
Span graphSpan = activeSpans.remove(graphName);
if (graphSpan != null) {
graphSpan.tag("graph.final.state.size", String.valueOf(finalState.data().size()));
graphSpan.end();
}
}
}
### 8.3 最佳实践指南
#### 8.3.1 图设计原则
**单一职责原则**：每个节点应该只负责一个明确的功能
```java
// 好的设计：职责明确
public class EmailValidationNode implements NodeAction {
@Override
public Map<String, Object> apply(OverAllState state) {
String email = (String) state.value("email").orElseThrow();
boolean isValid = EmailValidator.isValid(email);
return Map.of("email_valid", isValid);
}
}
// 避免：职责混乱
public class UserProcessingNode implements NodeAction {
@Override
public Map<String, Object> apply(OverAllState state) {
// 验证邮箱
// 发送通知
// 更新数据库
// 生成报告
// ... 太多职责
}
}
状态最小化原则：只在状态中保存必要的数据
// 好的设计：精简状态
public class OptimizedState extends OverAllState {
// 只保存必要的业务数据
public Optional<String> getUserId() {
return value("user_id").map(String.class::cast);
}
public Optional<String> getCurrentStep() {
return value("current_step").map(String.class::cast);
}
}
// 避免：冗余状态
public class BloatedState extends OverAllState {
// 包含大量临时数据、中间结果、调试信息等
}
8.3.2 错误处理策略
优雅降级：当某个节点失败时，提供备选方案
public class ResilientLlmNode implements NodeAction {
private final List<ChatClient> chatClients; // 多个模型备选
private final FallbackService fallbackService;
@Override
public Map<String, Object> apply(OverAllState state) throws Exception {
Exception lastException = null;
// 尝试多个模型
for (ChatClient client : chatClients) {
try {
String response = client.prompt()
.user((String) state.value("prompt").orElseThrow())
.call()
.content();
return Map.of("response", response, "model", client.getClass().getSimpleName());
} catch (Exception e) {
lastException = e;
continue;
}
}
// 所有模型都失败，使用备选方案
String fallbackResponse = fallbackService.generateFallbackResponse(state);
return Map.of("response", fallbackResponse, "fallback", true);
}
}
重试机制：对于临时性错误，实现智能重试
@Component
public class RetryableNodeWrapper implements NodeAction {
private final NodeAction delegate;
private final RetryTemplate retryTemplate;
public RetryableNodeWrapper(NodeAction delegate) {
this.delegate = delegate;
this.retryTemplate = RetryTemplate.builder()
.maxAttempts(3)
.exponentialBackoff(1000, 2, 10000)
.retryOn(TransientException.class)
.build();
}
@Override
public Map<String, Object> apply(OverAllState state) throws Exception {
return retryTemplate.execute(context -> {
try {
return delegate.apply(state);
} catch (Exception e) {
if (isRetryable(e)) {
throw new TransientException(e);
}
throw e;
}
});
}
}
8.3.3 测试策略
单元测试：测试单个节点的功能
@ExtendWith(MockitoExtension.class)
class LlmNodeTest {
@Mock
private ChatClient chatClient;
@Mock
private ChatClient.ChatClientRequestSpec requestSpec;
@Mock
private ChatClient.CallResponseSpec responseSpec;
@Test
void shouldGenerateResponseSuccessfully() {
// Given
LlmNode node = LlmNode.builder()
.chatClient(chatClient)
.systemPromptTemplate("You are a helpful assistant")
.userPromptTemplateKey("user_input")
.build();
OverAllState state = OverAllState.builder()
.put("user_input", "Hello, how are you?")
.build();
when(chatClient.prompt()).thenReturn(requestSpec);
when(requestSpec.system(anyString())).thenReturn(requestSpec);
when(requestSpec.user(anyString())).thenReturn(requestSpec);
when(requestSpec.call()).thenReturn(responseSpec);
when(responseSpec.content()).thenReturn("I'm doing well, thank you!");
// When
Map<String, Object> result = node.apply(state);
// Then
assertThat(result).containsEntry("messages", "I'm doing well, thank you!");
}
}
集成测试：测试完整的工作流
@SpringBootTest
@TestPropertySource(properties = {
"spring.ai.dashscope.api-key=test-key"
})
class GraphIntegrationTest {
@Autowired
private CompiledGraph testGraph;
@Test
void shouldExecuteCompleteWorkflow() {
// Given
OverAllState initialState = OverAllState.builder()
.put("user_question", "What is Spring AI?")
.build();
RunnableConfig config = RunnableConfig.builder()
.threadId("test-thread")
.build();
// When
List<NodeOutput> outputs = new ArrayList<>();
AsyncGenerator<NodeOutput> generator = testGraph.stream(initialState, config);
CompletableFuture<Optional<NodeOutput>> future;
while ((future = generator.next()).join().isPresent()) {
outputs.add(future.join().get());
}
// Then
assertThat(outputs).isNotEmpty();
NodeOutput finalOutput = outputs.get(outputs.size() - 1);
assertThat(finalOutput.state().value("final_answer")).isPresent();
}
}
9. 生态集成与扩展
9.1 Spring 生态集成
9.1.1 Spring Boot 自动配置
@Configuration
@ConditionalOnClass(StateGraph.class)
@EnableConfigurationProperties(GraphProperties.class)
public class GraphAutoConfiguration {
@Bean
@ConditionalOnMissingBean
public StateSerializer stateSerializer() {
return new JacksonSerializer();
}
@Bean
@ConditionalOnMissingBean
public BaseCheckpointSaver checkpointSaver(GraphProperties properties) {
if (properties.getCheckpoint().getType() == CheckpointType.REDIS) {
return new RedisSaver(redisTemplate, stateSerializer());
}
return new MemorySaver();
}
@Bean
@ConditionalOnProperty(name = "spring.ai.graph.metrics.enabled", havingValue = "true")
public GraphMetricsCollector graphMetricsCollector(MeterRegistry meterRegistry) {
return new GraphMetricsCollector(meterRegistry);
}
}
@ConfigurationProperties(prefix = "spring.ai.graph")
@Data
public class GraphProperties {
private Checkpoint checkpoint = new Checkpoint();
private Execution execution = new Execution();
private Metrics metrics = new Metrics();
@Data
public static class Checkpoint {
private CheckpointType type = CheckpointType.MEMORY;
private String redisKeyPrefix = "graph:checkpoint:";
private Duration ttl = Duration.ofHours(24);
}
@Data
public static class Execution {
private int maxIterations = 100;
private Duration timeout = Duration.ofMinutes(30);
private int corePoolSize = 10;
private int maxPoolSize = 50;
}
}
9.1.2 Spring Security 集成
@Component
public class SecurityAwareGraphExecutor {
private final CompiledGraph graph;
@PreAuthorize("hasRole('USER')")
public CompletableFuture<OverAllState> executeGraph(
OverAllState initialState,
Authentication authentication) {
// 在状态中注入用户信息
OverAllState secureState = initialState.toBuilder()
.put("user_id", authentication.getName())
.put("user_roles", authentication.getAuthorities())
.build();
RunnableConfig config = RunnableConfig.builder()
.threadId("user-" + authentication.getName())
.build();
return executeGraphAsync(secureState, config);
}
}
9.2 云原生集成
9.2.1 Kubernetes 部署
apiVersion: apps/v1
kind: Deployment
metadata:
name: graph-application
spec:
replicas: 3
selector:
matchLabels:
app: graph-application
template:
metadata:
labels:
app: graph-application
spec:
containers:
- name: app
image: graph-application:latest
env:
- name: SPRING_PROFILES_ACTIVE
value: "kubernetes"
- name: SPRING_AI_DASHSCOPE_API_KEY
valueFrom:
secretKeyRef:
name: ai-secrets
key: dashscope-api-key
resources:
requests:
memory: "512Mi"
cpu: "500m"
limits:
memory: "1Gi"
cpu: "1000m"
livenessProbe:
httpGet:
path: /actuator/health
port: 8080
initialDelaySeconds: 30
periodSeconds: 10
readinessProbe:
httpGet:
path: /actuator/health/readiness
port: 8080
initialDelaySeconds: 5
periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
name: graph-service
spec:
selector:
app: graph-application
ports:
- port: 80
targetPort: 8080
type: LoadBalancer
9.2.2 配置管理
# application-kubernetes.yml
spring:
ai:
graph:
checkpoint:
type: redis
redis-key-prefix: "k8s:graph:checkpoint:"
execution:
max-iterations: 200
timeout: PT45M
metrics:
enabled: true
redis:
host: redis-service
port: 6379
password: ${REDIS_PASSWORD}
datasource:
url: jdbc:postgresql://postgres-service:5432/graphdb
username: ${DB_USERNAME}
password: ${DB_PASSWORD}
management:
endpoints:
web:
exposure:
include: health,metrics,prometheus
endpoint:
health:
show-details: always
metrics:
export:
prometheus:
enabled: true
9.3 第三方服务集成
9.3.1 消息队列集成
@Component
public class MessageQueueGraphTrigger {
private final CompiledGraph graph;
private final RabbitTemplate rabbitTemplate;
@RabbitListener(queues = "graph.execution.requests")
public void handleGraphExecutionRequest(GraphExecutionRequest request) {
try {
OverAllState initialState = OverAllState.builder()
.putAll(request.getInitialData())
.build();
RunnableConfig config = RunnableConfig.builder()
.threadId(request.getThreadId())
.build();
AsyncGenerator<NodeOutput> generator = graph.stream(initialState, config);
// 异步处理结果
processGraphOutputAsync(generator, request.getCallbackQueue());
} catch (Exception e) {
sendErrorResponse(request.getCallbackQueue(), e);
}
}
private void processGraphOutputAsync(AsyncGenerator<NodeOutput> generator, String callbackQueue) {
CompletableFuture.runAsync(() -> {
try {
CompletableFuture<Optional<NodeOutput>> future;
while ((future = generator.next()).join().isPresent()) {
NodeOutput output = future.join().get();
// 发送中间结果
GraphExecutionUpdate update = new GraphExecutionUpdate(
output.nodeId(),
output.state().data(),
false
);
rabbitTemplate.convertAndSend(callbackQueue, update);
}
// 发送最终结果
GraphExecutionUpdate finalUpdate = new GraphExecutionUpdate(
null,
generator.getCurrentState().data(),
true
);
rabbitTemplate.convertAndSend(callbackQueue, finalUpdate);
} catch (Exception e) {
sendErrorResponse(callbackQueue, e);
}
});
}
}
9.3.2 外部 API 集成
@Component
public class ExternalApiNode implements NodeAction {
private final WebClient webClient;
private final CircuitBreaker circuitBreaker;
public ExternalApiNode(WebClient.Builder webClientBuilder) {
this.webClient = webClientBuilder
.codecs(configurer -> configurer.defaultCodecs().maxInMemorySize(10 * 1024 * 1024))
.build();
this.circuitBreaker = CircuitBreaker.ofDefaults("external-api");
}
@Override
public Map<String, Object> apply(OverAllState state) throws Exception {
String apiUrl = (String) state.value("api_url").orElseThrow();
Map<String, Object> requestData = (Map<String, Object>) state.value("request_data").orElse(Map.of());
Supplier<Map<String, Object>> apiCall = () -> {
try {
String response = webClient.post()
.uri(apiUrl)
.bodyValue(requestData)
.retrieve()
.bodyToMono(String.class)
.timeout(Duration.ofSeconds(30))
.block();
return Map.of("api_response", response, "success", true);
} catch (Exception e) {
return Map.of("error", e.getMessage(), "success", false);
}
};
return circuitBreaker.executeSupplier(apiCall);
}
}
10. 总结与展望
10.1 核心价值总结
Spring AI Alibaba Graph 作为一个强大的工作流编排框架，为构建复杂 AI 应用提供了以下核心价值：
1. 降低开发复杂度
- 声明式编程模型让开发者专注于业务逻辑而非底层实现
- 预定义组件减少了重复开发工作
- 图形化的工作流设计直观易懂
2. 提升系统可靠性
- 检查点机制确保工作流的容错能力
- 状态驱动的执行模型保证数据一致性
- 完善的错误处理和重试机制
3. 增强系统扩展性
- 模块化的节点设计支持功能复用
- 子图机制实现复杂工作流的分层管理
- 插件化的架构支持自定义扩展
4. 优化性能表现
- 异步优先的设计提升并发处理能力
- 流式处理支持实时响应
- 智能的状态管理减少内存占用
10.2 技术创新点
1. 状态机与图结构的完美结合
Graph 框架将有限状态机的严谨性与图结构的灵活性相结合，创造了一种新的工作流编排范式。这种设计既保证了执行的确定性，又提供了足够的灵活性来处理复杂的业务场景。
2. 异步流式处理架构
基于 AsyncGenerator 的异步执行模型，不仅提升了系统的并发处理能力，还为实时流式处理提供了原生支持。这在处理大语言模型的流式输出时特别有价值。
3. 智能状态合并机制
通过 KeyStrategy 接口提供的可插拔状态合并策略，框架能够智能地处理不同类型数据的合并逻辑，这在并行分支合并时尤为重要。
10.3 应用前景展望
1. AI Agent 生态建设
随着大语言模型能力的不断提升，基于 Graph 框架构建的 AI Agent 将会更加智能和自主。框架提供的工具调用、人机交互等能力为构建复杂 Agent 系统奠定了基础。
2. 多模态应用开发
框架的模块化设计天然适合多模态应用的开发。通过组合文本、图像、音频等不同模态的处理节点，可以构建出功能强大的多模态 AI 应用。
3. 企业级 AI 平台
框架与 Spring 生态的深度集成，使其非常适合作为企业级 AI 平台的核心引擎。结合微服务架构、云原生技术，可以构建出高可用、高扩展的 AI 服务平台。
10.4 发展建议
1. 生态建设
- 建立更丰富的预定义节点库
- 提供更多第三方服务的集成组件
- 开发可视化的工作流设计器
2. 性能优化
- 进一步优化状态序列化性能
- 提供更智能的资源调度策略
- 支持分布式执行能力
3. 开发体验
- 提供更完善的调试工具
- 增强错误诊断能力
- 完善文档和示例
10.5 结语
Spring AI Alibaba Graph 代表了 AI 应用开发的一个重要方向。它不仅解决了当前 AI 应用开发中的诸多痛点，更为未来更复杂、更智能的 AI 系统奠定了坚实的基础。
通过声明式的编程模型、强大的状态管理能力、灵活的扩展机制，Graph 框架让构建复杂 AI 应用变得像搭积木一样简单。这不仅降低了 AI 应用的开发门槛，也为 AI 技术的普及和应用创新提供了强有力的支撑。
随着 AI 技术的不断发展和应用场景的不断扩展，相信 Spring AI Alibaba Graph 将会在更多领域发挥重要作用，推动 AI 应用开发进入一个新的时代。
参考资料