Spring AI Alibaba Graph使用案例人類反饋

1、Spring AI Alibaba Graph 是社區核心實現之一，也是整個框架在設計理念上區別于 Spring AI 只做底層原子抽象的地方，Spring AI Alibaba 期望幫助開發者更容易的構建智能體應用。基于 Graph 開發者可以構建工作流、多智能體應用。Spring AI Alibaba Graph 在設計理念上借鑒 Langgraph，因此在一定程度上可以理解為是 Java 版的 Langgraph 實現，社區在此基礎上增加了大量預置 Node、簡化了 State 定義過程等，讓開發者更容易編寫對等低代碼平臺的工作流、多智能體等。

2、框架核心概念包括：StateGraph（狀態圖，用于定義節點和邊）、Node（節點，封裝具體操作或模型調用）、Edge（邊，表示節點間的跳轉關系）以及?OverAllState（全局狀態，貫穿流程共享數據）。這些設計使開發者能夠方便地管理工作流中的狀態和邏輯流轉。

3、人類反饋復原案例?

在實際業務場景中，經常會遇到人類介入的場景，人類的不同操作將影響工作流不同的走向

以下實現一個簡單案例：包含三個節點，擴展節點、人類節點、翻譯節點

  • 擴展節點：AI 模型流式對問題進行擴展輸出
  • 人類節點：通過對用戶的反饋，決定是直接結束，還是接著執行翻譯節點
  • 翻譯節點：將問題翻譯為其他語種或者英文

實戰代碼可見：spring-ai-alibaba-examples?下的 graph 目錄，本章代碼為其 human-node 模塊

4、pom文件

這里使用 1.0.0.3-SNAPSHOT。在定義 StateGraph 方面和 1.0.0.2 有些變動

<?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><parent><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-graph-example</artifactId><version>${revision}</version><relativePath>../pom.xml</relativePath></parent><groupId>com.alibaba.cloud.ai.graph</groupId><artifactId>human-node</artifactId><properties><spring-ai-alibaba.version>1.0.0.3-SNAPSHOT</spring-ai-alibaba.version></properties><dependencies><dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-starter-dashscope</artifactId></dependency><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-autoconfigure-model-chat-client</artifactId></dependency><dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-graph-core</artifactId><version>${spring-ai-alibaba.version}</version></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency></dependencies></project>

?

5、

OverAllState的?keyStrategies 中存儲的字段

  • query：用戶的問題
  • expandernumber：擴展的數量
  • expandercontent：擴展的內容
  • feedback：人類反饋的內容
  • humannextnode：人類反饋后的下一個節點
  • translatelanguage：翻譯的目標語言，默認為英文
  • translatecontent：翻譯的內容

定義 ExpanderNode，邊的連接為：

START -> expander -> humanfeedback
humanfeedback -> translate
humanfeedback -> END
translate -> END

代碼如下

package com.alibaba.cloud.ai.graph.config;import com.alibaba.cloud.ai.graph.GraphRepresentation;
import com.alibaba.cloud.ai.graph.KeyStrategy;
import com.alibaba.cloud.ai.graph.KeyStrategyFactory;
import com.alibaba.cloud.ai.graph.StateGraph;
import com.alibaba.cloud.ai.graph.action.AsyncEdgeAction;
import com.alibaba.cloud.ai.graph.dispatcher.HumanFeedbackDispatcher;
import com.alibaba.cloud.ai.graph.exception.GraphStateException;
import com.alibaba.cloud.ai.graph.node.ExpanderNode;
import com.alibaba.cloud.ai.graph.node.HumanFeedbackNode;
import com.alibaba.cloud.ai.graph.node.TranslateNode;
import com.alibaba.cloud.ai.graph.state.strategy.ReplaceStrategy;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;import java.util.HashMap;
import java.util.Map;import static com.alibaba.cloud.ai.graph.action.AsyncNodeAction.node_async;/*** @author yingzi* @since 2025/6/13*/
@Configuration
public class GraphHumanConfiguration {private static final Logger logger = LoggerFactory.getLogger(GraphHumanConfiguration.class);@Beanpublic StateGraph humanGraph(ChatClient.Builder chatClientBuilder) throws GraphStateException {KeyStrategyFactory keyStrategyFactory = () -> {HashMap<String, KeyStrategy> keyStrategyHashMap = new HashMap<>();// 用戶輸入keyStrategyHashMap.put("query", new ReplaceStrategy());keyStrategyHashMap.put("thread_id", new ReplaceStrategy());keyStrategyHashMap.put("expander_number", new ReplaceStrategy());keyStrategyHashMap.put("expander_content", new ReplaceStrategy());// 人類反饋keyStrategyHashMap.put("feed_back", new ReplaceStrategy());keyStrategyHashMap.put("human_next_node", new ReplaceStrategy());// 是否需要翻譯keyStrategyHashMap.put("translate_language", new ReplaceStrategy());keyStrategyHashMap.put("translate_content", new ReplaceStrategy());return keyStrategyHashMap;};StateGraph stateGraph = new StateGraph(keyStrategyFactory).addNode("expander", node_async(new ExpanderNode(chatClientBuilder))).addNode("translate", node_async(new TranslateNode(chatClientBuilder))).addNode("human_feedback", node_async(new HumanFeedbackNode()))
//                 START -> expander.addEdge(StateGraph.START, "expander")
//                 expander -> humanfeedback.addEdge("expander", "human_feedback")
//                 人類節點的下一個邊是條件邊，由 HumanFeedbackDispatcher 控制下一步跳轉到哪一個節點
//                 humanfeedback -> translate
//                 humanfeedback -> END.addConditionalEdges("human_feedback", AsyncEdgeAction.edge_async((new HumanFeedbackDispatcher())), Map.of("translate", "translate", StateGraph.END, StateGraph.END))
//                translate -> END.addEdge("translate", StateGraph.END);// 添加 PlantUML 打印GraphRepresentation representation = stateGraph.getGraph(GraphRepresentation.Type.PLANTUML,"human flow");logger.info("\n=== expander UML Flow ===");logger.info(representation.content());logger.info("==================================\n");return stateGraph;}
}

6、node代碼如下

ExpanderNode
package com.alibaba.cloud.ai.graph.node;import com.alibaba.cloud.ai.graph.NodeOutput;
import com.alibaba.cloud.ai.graph.OverAllState;
import com.alibaba.cloud.ai.graph.action.NodeAction;
import com.alibaba.cloud.ai.graph.async.AsyncGenerator;
import com.alibaba.cloud.ai.graph.streaming.StreamingChatGenerator;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.chat.prompt.PromptTemplate;
import reactor.core.publisher.Flux;import java.util.Arrays;
import java.util.List;
import java.util.Map;/*** @author yingzi* @since 2025/6/13*/public class ExpanderNode implements NodeAction {private static final Logger logger = LoggerFactory.getLogger(ExpanderNode.class);private static final PromptTemplate DEFAULT_PROMPT_TEMPLATE = new PromptTemplate("You are an expert at information retrieval and search optimization.\nYour task is to generate {number} different versions of the given query.\n\nEach variant must cover different perspectives or aspects of the topic,\nwhile maintaining the core intent of the original query. The goal is to\nexpand the search space and improve the chances of finding relevant information.\n\nDo not explain your choices or add any other text.\nProvide the query variants separated by newlines.\n\nOriginal query: {query}\n\nQuery variants:\n");private final ChatClient chatClient;private final Integer NUMBER = 3;public ExpanderNode(ChatClient.Builder chatClientBuilder) {this.chatClient = chatClientBuilder.build();}@Overridepublic Map<String, Object> apply(OverAllState state) {//        OverAllState 中存儲的字段
//
//        query：用戶的問題
//        expandernumber：擴展的數量
//        expandercontent：擴展的內容
//        feedback：人類反饋的內容
//        humannextnode：人類反饋后的下一個節點
//        translatelanguage：翻譯的目標語言，默認為英文
//        translatecontent：翻譯的內容logger.info("expander node is running.");
//        擴展節點：AI 模型流式對問題進行擴展輸出String query = state.value("query", "");Integer expanderNumber = state.value("expander_number", this.NUMBER);Flux<ChatResponse> chatResponseFlux = this.chatClient.prompt().user((user) -> user.text(DEFAULT_PROMPT_TEMPLATE.getTemplate()).param("number", expanderNumber).param("query", query)).stream().chatResponse();AsyncGenerator<? extends NodeOutput> generator = StreamingChatGenerator.builder().startingNode("expander_llm_stream").startingState(state).mapResult(response -> {String text = response.getResult().getOutput().getText();List<String> queryVariants = Arrays.asList(text.split("\n"));return Map.of("expander_content", queryVariants);}).build(chatResponseFlux);return Map.of("expander_content", generator);}}
HumanFeedbackNode
package com.alibaba.cloud.ai.graph.node;import com.alibaba.cloud.ai.graph.OverAllState;
import com.alibaba.cloud.ai.graph.StateGraph;
import com.alibaba.cloud.ai.graph.action.NodeAction;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;import java.util.HashMap;
import java.util.Map;/*** @author yingzi* @since 2025/6/19*/public class HumanFeedbackNode implements NodeAction {private static final Logger logger = LoggerFactory.getLogger(HumanFeedbackNode.class);@Overridepublic Map<String, Object> apply(OverAllState state) {
//        人類節點：通過對用戶的反饋，決定是直接結束，還是接著執行翻譯節點logger.info("human_feedback node is running.");HashMap<String, Object> resultMap = new HashMap<>();String nextStep = StateGraph.END;Map<String, Object> feedBackData = state.humanFeedback().data();boolean feedback = (boolean) feedBackData.getOrDefault("feed_back", true);if (feedback) {nextStep = "translate";}resultMap.put("human_next_node", nextStep);logger.info("human_feedback node -> {} node", nextStep);return resultMap;}
}
TranslateNode
package com.alibaba.cloud.ai.graph.node;import com.alibaba.cloud.ai.graph.NodeOutput;
import com.alibaba.cloud.ai.graph.OverAllState;
import com.alibaba.cloud.ai.graph.action.NodeAction;
import com.alibaba.cloud.ai.graph.async.AsyncGenerator;
import com.alibaba.cloud.ai.graph.streaming.StreamingChatGenerator;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.chat.prompt.PromptTemplate;
import reactor.core.publisher.Flux;import java.util.Arrays;
import java.util.List;
import java.util.Map;/*** @author yingzi* @since 2025/6/13*/public class TranslateNode implements NodeAction {private static final Logger logger = LoggerFactory.getLogger(ExpanderNode.class);//用于提示模板的關鍵組件private static final PromptTemplate DEFAULT_PROMPT_TEMPLATE = new PromptTemplate("Given a user query, translate it to {targetLanguage}.\nIf the query is already in {targetLanguage}, return it unchanged.\nIf you don't know the language of the query, return it unchanged.\nDo not add explanations nor any other text.\n\nOriginal query: {query}\n\nTranslated query:\n");private final ChatClient chatClient;private final String TARGET_LANGUAGE = "English";public TranslateNode(ChatClient.Builder chatClientBuilder) {this.chatClient = chatClientBuilder.build();}@Overridepublic Map<String, Object> apply(OverAllState state) {logger.info("translate node is running.");//        翻譯節點：將問題翻譯為其他英文String query = state.value("query", "");String targetLanguage = state.value("translate_language", TARGET_LANGUAGE);Flux<ChatResponse> chatResponseFlux = this.chatClient.prompt().user((user) -> user.text(DEFAULT_PROMPT_TEMPLATE.getTemplate()).param("targetLanguage", targetLanguage).param("query", query)).stream().chatResponse();AsyncGenerator<? extends NodeOutput> generator = StreamingChatGenerator.builder().startingNode("translate_llm_stream").startingState(state).mapResult(response -> {String text = response.getResult().getOutput().getText();List<String> queryVariants = Arrays.asList(text.split("\n"));return Map.of("translate_content", queryVariants);}).build(chatResponseFlux);return Map.of("translate_content", generator);}
}

7、edge代碼

人類節點的下一個邊是條件邊，由 HumanFeedbackDispatcher 控制下一步跳轉到哪一個節點

package com.alibaba.cloud.ai.graph.dispatcher;import com.alibaba.cloud.ai.graph.OverAllState;
import com.alibaba.cloud.ai.graph.StateGraph;
import com.alibaba.cloud.ai.graph.action.EdgeAction;/*** @author yingzi* @since 2025/6/19*/public class HumanFeedbackDispatcher implements EdgeAction {@Overridepublic String apply(OverAllState state) throws Exception {
//        人類節點的下一個邊是條件邊，由 HumanFeedbackDispatcher 控制下一步跳轉到哪一個節點// state的human_next_node的value 默認 endreturn (String) state.value("human_next_node", StateGraph.END);}
}

?8、controller代碼

GraphHumanController
  • CompileConfig.builder().saverConfig(saverConfig).interruptBefore(“humanfeedback”）：在人類反饋節點前斷流
  • Sinks.Many<ServerSentEvent> sink：接收 Stream 數據
package com.alibaba.cloud.ai.graph.controller;import com.alibaba.cloud.ai.graph.CompileConfig;
import com.alibaba.cloud.ai.graph.CompiledGraph;
import com.alibaba.cloud.ai.graph.NodeOutput;
import com.alibaba.cloud.ai.graph.OverAllState;
import com.alibaba.cloud.ai.graph.RunnableConfig;
import com.alibaba.cloud.ai.graph.StateGraph;
import com.alibaba.cloud.ai.graph.async.AsyncGenerator;
import com.alibaba.cloud.ai.graph.checkpoint.config.SaverConfig;
import com.alibaba.cloud.ai.graph.checkpoint.constant.SaverConstant;
import com.alibaba.cloud.ai.graph.checkpoint.savers.MemorySaver;
import com.alibaba.cloud.ai.graph.controller.GraphProcess.GraphProcess;
import com.alibaba.cloud.ai.graph.exception.GraphRunnerException;
import com.alibaba.cloud.ai.graph.exception.GraphStateException;
import com.alibaba.cloud.ai.graph.state.StateSnapshot;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.http.MediaType;
import org.springframework.http.codec.ServerSentEvent;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import reactor.core.publisher.Flux;
import reactor.core.publisher.Sinks;import java.util.HashMap;
import java.util.Map;/*** @author yingzi* @since 2025/6/13*/
@RestController
public class GraphHumanController {private static final Logger logger = LoggerFactory.getLogger(GraphHumanController.class);private final CompiledGraph compiledGraph;@Autowiredpublic GraphHumanController(@Qualifier("humanGraph") StateGraph stateGraph) throws GraphStateException {SaverConfig saverConfig = SaverConfig.builder().register(SaverConstant.MEMORY, new MemorySaver()).build();
//        CompileConfig.builder().saverConfig(saverConfig).interruptBefore("human_feedback").build() 在人類反饋節點前斷流this.compiledGraph = stateGraph.compile(CompileConfig.builder().saverConfig(saverConfig).interruptBefore("human_feedback").build());    }@GetMapping(value = "/graph/human/expand", produces = MediaType.TEXT_EVENT_STREAM_VALUE)public Flux<ServerSentEvent<String>> expand(@RequestParam(value = "query", defaultValue = "你好，很高興認識你，能簡單介紹一下自己嗎？", required = false) String query,@RequestParam(value = "expander_number", defaultValue = "3", required = false) Integer expanderNumber,@RequestParam(value = "thread_id", defaultValue = "yingzi", required = false) String threadId) throws GraphRunnerException {RunnableConfig runnableConfig = RunnableConfig.builder().threadId(threadId).build();Map<String, Object> objectMap = new HashMap<>();objectMap.put("query", query);objectMap.put("expander_number", expanderNumber);GraphProcess graphProcess = new GraphProcess(this.compiledGraph);
//        Sinks.Many<ServerSentEvent> sink：接收 Stream 數據Sinks.Many<ServerSentEvent<String>> sink = Sinks.many().unicast().onBackpressureBuffer();AsyncGenerator<NodeOutput> resultFuture = compiledGraph.stream(objectMap, runnableConfig);graphProcess.processStream(resultFuture, sink);return sink.asFlux().doOnCancel(() -> logger.info("Client disconnected from stream")).doOnError(e -> logger.error("Error occurred during streaming", e));}@GetMapping(value = "/graph/human/resume", produces = MediaType.TEXT_EVENT_STREAM_VALUE)public Flux<ServerSentEvent<String>> resume(@RequestParam(value = "thread_id", defaultValue = "yingzi", required = false) String threadId,@RequestParam(value = "feed_back", defaultValue = "true", required = false) boolean feedBack) throws GraphRunnerException {RunnableConfig runnableConfig = RunnableConfig.builder().threadId(threadId).build();StateSnapshot stateSnapshot = this.compiledGraph.getState(runnableConfig);OverAllState state = stateSnapshot.state();state.withResume();Map<String, Object> objectMap = new HashMap<>();objectMap.put("feed_back", feedBack);state.withHumanFeedback(new OverAllState.HumanFeedback(objectMap, ""));// Create a unicast sink to emit ServerSentEventsSinks.Many<ServerSentEvent<String>> sink = Sinks.many().unicast().onBackpressureBuffer();GraphProcess graphProcess = new GraphProcess(this.compiledGraph);AsyncGenerator<NodeOutput> resultFuture = compiledGraph.streamFromInitialNode(state, runnableConfig);graphProcess.processStream(resultFuture, sink);return sink.asFlux().doOnCancel(() -> logger.info("Client disconnected from stream")).doOnError(e -> logger.error("Error occurred during streaming", e));    }
}

9、GraphProcess代碼

  • ExecutorService executor：配置線程池，獲取 stream 流

將結果寫入到 sink 中

package com.alibaba.cloud.ai.graph.controller.GraphProcess;import com.alibaba.cloud.ai.graph.CompiledGraph;
import com.alibaba.cloud.ai.graph.NodeOutput;
import com.alibaba.cloud.ai.graph.async.AsyncGenerator;
import com.alibaba.cloud.ai.graph.streaming.StreamingOutput;
import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.http.codec.ServerSentEvent;
import reactor.core.publisher.Sinks;import java.util.Map;
import java.util.concurrent.CompletionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;/*** @author yingzi* @since 2025/6/13*/public class GraphProcess {private static final Logger logger = LoggerFactory.getLogger(GraphProcess.class);
//    配置線程池，獲取 stream 流將結果寫入到 sink 中private final ExecutorService executor = Executors.newSingleThreadExecutor();private CompiledGraph compiledGraph;public GraphProcess(CompiledGraph compiledGraph) {this.compiledGraph = compiledGraph;}public void processStream(AsyncGenerator<NodeOutput> generator, Sinks.Many<ServerSentEvent<String>> sink) {executor.submit(() -> {generator.forEachAsync(output -> {try {logger.info("output = {}", output);String nodeName = output.node();String content;if (output instanceof StreamingOutput streamingOutput) {content = JSON.toJSONString(Map.of(nodeName, streamingOutput.chunk()));} else {JSONObject nodeOutput = new JSONObject();nodeOutput.put("data", output.state().data());nodeOutput.put("node", nodeName);content = JSON.toJSONString(nodeOutput);}sink.tryEmitNext(ServerSentEvent.builder(content).build());} catch (Exception e) {throw new CompletionException(e);}}).thenAccept(v -> {// 正常完成sink.tryEmitComplete();}).exceptionally(e -> {sink.tryEmitError(e);return null;});});}
}

?10 、效果

調用?http://127.0.0.1:8080/graph/human/expand?

?調用 expand 接口，流式輸出 && 斷流得到最終結果

再調用 resume 接口，狀態恢復續上流，接著走后續邏輯

http://127.0.0.1:8080/graph/human/resume?

?

http://127.0.0.1:8080/graph/human/resume?feed_back=false

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