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spring-ai 工作流

news 2025/7/1 13:52:26

工作流概念

工作流是以相对固化的模式来人为地拆解任务，将一个大任务拆解为包含多个分支的固化流程。工作流的优势是确定性强，模型作为流程中的一个节点起到的更多是一个分类决策、内容生成的职责，因此它更适合意图识别等类别属性强的应用场景。

参考文档：https://java2ai.com/docs/1.0.0.2/get-started/workflow/?spm=4347728f.7cee0e64.0.0.39076dd1jbppqZ

工作流程图

商品评价分类流程图：
在这里插入图片描述

如用户反馈

This product is excellent, I love it!
则输出：Praise, no action taken.
说明：很好，不需要改进措施
The product broke after one day, very disappointed."
则输出：product quality
说明：有问题，产品质量问题

spring-boot 编码

使用：Spring AI Alibaba Graph

附maven的pom.xml

<?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 https://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><parent><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-parent</artifactId><version>3.4.6</version><relativePath/> <!-- lookup parent from repository --></parent><groupId>com.example</groupId><artifactId>demo-spring-test</artifactId><version>0.0.1-SNAPSHOT</version><name>demo-spring-test</name><description>Demo project for Spring Boot</description><url/><licenses><license/></licenses><developers><developer/></developers><scm><connection/><developerConnection/><tag/><url/></scm><properties><java.version>17</java.version><spring-ai.version>1.0.0</spring-ai.version></properties><dependencies><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><!-- Spring AI Alibaba（通义大模型支持） --><dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-starter</artifactId><version>1.0.0-M6.1</version></dependency><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-core</artifactId><version>1.0.0-M6</version></dependency><dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-autoconfigure</artifactId><version>1.0.0-M6.1</version></dependency><!-- 引入 Graph 核心依赖 --><dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-graph-core</artifactId><version>1.0.0.2</version></dependency><dependency><groupId>com.alibaba.cloud.ai</groupId><artifactId>spring-ai-alibaba-starter-document-parser-tika</artifactId><version>1.0.0.2</version></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-test</artifactId><scope>test</scope></dependency></dependencies><build><plugins><plugin><groupId>org.springframework.boot</groupId><artifactId>spring-boot-maven-plugin</artifactId></plugin></plugins></build></project>

定义节点 (Node)

创建工作流中的核心节点，包括两个文本分类节点和一个记录节点

分类

// 评价正负分类节点
QuestionClassifierNode feedbackClassifier = QuestionClassifierNode.builder().chatClient(chatClient).inputTextKey("input").categories(List.of("positive feedback", "negative feedback")).classificationInstructions(List.of("Try to understand the user's feeling when he/she is giving the feedback.")).build();
// 负面评价具体问题分类节点
QuestionClassifierNode specificQuestionClassifier = QuestionClassifierNode.builder().chatClient(chatClient).inputTextKey("input").categories(List.of("after-sale service", "transportation", "product quality", "others")).classificationInstructions(List.of("What kind of service or help the customer is trying to get from us? " +"Classify the question based on your understanding.")).build();

记录节点 RecordingNode：


import com.alibaba.cloud.ai.graph.OverAllState;
import com.alibaba.cloud.ai.graph.action.NodeAction;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;import java.util.HashMap;
import java.util.Map;public class RecordingNode implements NodeAction {private static final Logger logger = LoggerFactory.getLogger(RecordingNode.class);@Overridepublic Map<String, Object> apply(OverAllState state) throws Exception {String feedback = (String) state.value("classifier_output").get();Map<String, Object> updatedState = new HashMap<>();if (feedback.contains("positive")) {logger.info("Received positive feedback: {}", feedback);updatedState.put("solution", "Praise, no action taken.");} else {logger.info("Received negative feedback: {}", feedback);updatedState.put("solution", feedback);}return updatedState;}}

定义节点图StateGraph

StateGraph graph = new StateGraph("Consumer Service Workflow Demo", stateFactory)// 添加节点.addNode("feedback_classifier", node_async(feedbackClassifier)).addNode("specific_question_classifier", node_async(specificQuestionClassifier)).addNode("recorder", node_async(recordingNode))// 定义边（流程顺序）.addEdge(START, "feedback_classifier")  // 起始节点.addConditionalEdges("feedback_classifier",edge_async(new CustomerServiceController.FeedbackQuestionDispatcher()),Map.of("positive", "recorder", "negative", "specific_question_classifier")).addConditionalEdges("specific_question_classifier",edge_async(new CustomerServiceController.SpecificQuestionDispatcher()),Map.of("after-sale", "recorder", "transportation", "recorder","quality", "recorder", "others", "recorder")).addEdge("recorder", END);  // 结束节点System.out.println("\n");return graph;

完整代码：

import com.alibaba.cloud.ai.graph.OverAllState;
import com.alibaba.cloud.ai.graph.OverAllStateFactory;
import com.alibaba.cloud.ai.graph.StateGraph;
import com.alibaba.cloud.ai.graph.exception.GraphStateException;
import com.alibaba.cloud.ai.graph.node.QuestionClassifierNode;
import com.alibaba.cloud.ai.graph.state.strategy.ReplaceStrategy;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;import java.util.List;
import java.util.Map;import static com.alibaba.cloud.ai.graph.StateGraph.END;
import static com.alibaba.cloud.ai.graph.StateGraph.START;
import static com.alibaba.cloud.ai.graph.action.AsyncEdgeAction.edge_async;
import static com.alibaba.cloud.ai.graph.action.AsyncNodeAction.node_async;@Configuration
public class WorkflowAutoconfiguration {@Beanpublic StateGraph workflowGraph(ChatModel chatModel) throws GraphStateException {ChatClient chatClient = ChatClient.builder(chatModel).defaultAdvisors(new SimpleLoggerAdvisor()).build();RecordingNode recordingNode = new RecordingNode();// 评价正负分类节点QuestionClassifierNode feedbackClassifier = QuestionClassifierNode.builder().chatClient(chatClient).inputTextKey("input").categories(List.of("positive feedback", "negative feedback")).classificationInstructions(List.of("Try to understand the user's feeling when he/she is giving the feedback.")).build();// 负面评价具体问题分类节点QuestionClassifierNode specificQuestionClassifier = QuestionClassifierNode.builder().chatClient(chatClient).inputTextKey("input").categories(List.of("after-sale service", "transportation", "product quality", "others")).classificationInstructions(List.of("What kind of service or help the customer is trying to get from us? " +"Classify the question based on your understanding.")).build();// 定义一个 OverAllStateFactory，用于在每次执行工作流时创建初始的全局状态对象OverAllStateFactory stateFactory = () -> {OverAllState state = new OverAllState();state.registerKeyAndStrategy("input", new ReplaceStrategy());state.registerKeyAndStrategy("classifier_output", new ReplaceStrategy());state.registerKeyAndStrategy("solution", new ReplaceStrategy());return state;};StateGraph graph = new StateGraph("Consumer Service Workflow Demo", stateFactory).addNode("feedback_classifier", node_async(feedbackClassifier)).addNode("specific_question_classifier", node_async(specificQuestionClassifier)).addNode("recorder", node_async(recordingNode))// 定义边（流程顺序）.addEdge(START, "feedback_classifier")  // 起始节点.addConditionalEdges("feedback_classifier",edge_async(new CustomerServiceController.FeedbackQuestionDispatcher()),Map.of("positive", "recorder", "negative", "specific_question_classifier")).addConditionalEdges("specific_question_classifier",edge_async(new CustomerServiceController.SpecificQuestionDispatcher()),Map.of("after-sale", "recorder", "transportation", "recorder","quality", "recorder", "others", "recorder")).addEdge("recorder", END);  // 结束节点System.out.println("\n");return graph;}}

controller测试

CustomerServiceController 完整代码

import java.util.HashMap;
import java.util.Map;import com.alibaba.cloud.ai.graph.CompiledGraph;
import com.alibaba.cloud.ai.graph.exception.GraphStateException;
import com.alibaba.cloud.ai.graph.OverAllState;
import com.alibaba.cloud.ai.graph.StateGraph;
import com.alibaba.cloud.ai.graph.action.EdgeAction;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;@RestController
@RequestMapping("/customer")
public class CustomerServiceController {private static final Logger logger = LoggerFactory.getLogger(CustomerServiceController.class);private CompiledGraph compiledGraph;public CustomerServiceController(@Qualifier("workflowGraph") StateGraph stateGraph) throws GraphStateException {this.compiledGraph = stateGraph.compile();}/*** localhost:8080/customer/chat?query=The product broke after one day, very disappointed.*/@GetMapping("/chat")public String simpleChat(String query) throws Exception {logger.info("simpleChat: {}", query);return compiledGraph.invoke(Map.of("input", query)).get().value("solution").get().toString();}public static class FeedbackQuestionDispatcher implements EdgeAction {@Overridepublic String apply(OverAllState state) throws Exception {/*** 反馈的是商品的负面内容* 分类为:negative*/String classifierOutput = (String) state.value("classifier_output").orElse("");logger.info("classifierOutput: {}", classifierOutput);if (classifierOutput.contains("positive")) {return "positive";}return "negative";}}public static class SpecificQuestionDispatcher implements EdgeAction {@Overridepublic String apply(OverAllState state) throws Exception {/*** 反馈的是产品的质量* 分类为：quality*/String classifierOutput = (String) state.value("classifier_output").orElse("");logger.info("classifierOutput: {}", classifierOutput);Map<String, String> classifierMap = new HashMap<>();classifierMap.put("after-sale", "after-sale");classifierMap.put("quality", "quality");classifierMap.put("transportation", "transportation");for (Map.Entry<String, String> entry : classifierMap.entrySet()) {if (classifierOutput.contains(entry.getKey())) {return entry.getValue();}}return "others";}}}