玉石电商网站建设方案,三亚旅游网站建设,网站权重降低,wordpress模版仿制本文中将阐述下 AI 流程编排框架和 Spring AI Alibaba Graph 以及如何使用。
1. Agent 智能体
结合 Google 和 Authropic 对 Agent 的定义#xff1a;Agent 的定义为#xff1a;智能体#xff08;Agent#xff09;是能够独立运行#xff0c;感知和理解现实世界并使用工具…本文中将阐述下 AI 流程编排框架和 Spring AI Alibaba Graph 以及如何使用。
1. Agent 智能体
结合 Google 和 Authropic 对 Agent 的定义Agent 的定义为智能体Agent是能够独立运行感知和理解现实世界并使用工具来实现最终目标的应用程序。
从架构上可以将 Agent 分为两类
Workflows 系统人类干预做整体决策LLMs 作为 workflows 链路的节点。 具有明确语义的系统预先定义好 workflows 流程LLMs 通过各个 Node 节点对 Workflows 路径编排来达到最终效果。 智能体系统AgentsLLMs 作为大脑决策自驱动完成任务。 LLMs 自己编排和规划工具调用适用于模型驱动决策的场景。
以上两种架构都在 Spring AI Alibaba 项目中有体现一是 JManus 系统。二是基于 spring ai alibaba graph 构建的 DeepResearch 系统。
1. AI 智能体框架介绍
在过去一年中AI Infra 快速发展涌现了一系列以 LangChain 为代码的 AI 应用开发框架到最基础的应用开发框架到智能体编排AI 应用观测等。此章节中主要介绍下 AI 应用的智能体编排框架。
1.1 Microsoft AutoGen
Github 地址https://github.com/microsoft/autogen
由微软开源的智能体开发框架AutoGen 是一个用于创建可自主行动或与人类协同工作的多智能体 AI 应用程序的框架。
1.2 LangGraph
Github 地址https://github.com/langchain-ai/langgraph
以 LangGraph 为基础使用图结构的 AI 应用编排框架。由 LangChain 社区开发社区活跃。
1.3 CrewAI
Github 地址https://github.com/crewAIInc/crewAI
CrewAI 是一个精简、快速的 Python 框架完全从零构建完全独立于 LangChain 或其他代理框架。它为开发人员提供了高级的简洁性和精确的底层控制非常适合创建适合任何场景的自主 AI 代理。
2. Spring AI Alibaba Graph
Github 地址https://github.com/alibaba/spring-ai-alibaba/tree/main/spring-ai-alibaba-graph Spring AI Alibaba Graph 是一款面向 Java 开发者的工作流、多智能体框架用于构建由多个 AI 模型或步骤组成的复杂应用。通过图结构的定义来描述智能体中的状态流转逻辑。 框架核心包括StateGraph状态图用于定义节点和边、Node节点封装具体操作或模型调用、Edge边表示节点间的跳转关系以及 OverAllState全局状态贯穿流程共享数据 2.1 快速入门
Demo 地址https://github.com/deigmata-paideias/deigmata-paideias/tree/main/ai/exmaple/spring-ai-alibaba-graph-demo
pom.xml
dependenciesdependencygroupIdcom.alibaba.cloud.ai/groupIdartifactIdspring-ai-alibaba-starter-dashscope/artifactId/dependencydependencygroupIdcom.alibaba.cloud.ai/groupIdartifactIdspring-ai-alibaba-graph-core/artifactIdversion1.0.0.2/version/dependencydependencygroupIdorg.springframework.boot/groupIdartifactIdspring-boot-starter-web/artifactId/dependencydependencygroupIdcom.google.code.gson/groupIdartifactIdgson/artifactId/dependency
/dependenciesdependencyManagementdependenciesdependencygroupIdorg.springframework.boot/groupIdartifactIdspring-boot-dependencies/artifactIdversion3.4.5/versiontypepom/typescopeimport/scope/dependencydependencygroupIdcom.alibaba.cloud.ai/groupIdartifactIdspring-ai-alibaba-bom/artifactIdversion1.0.0.2/versiontypepom/typescopeimport/scope/dependency/dependencies
/dependencyManagementapplication.yml
server:port: 8081spring:ai:dashscope:api-key: ${AI_DASHSCOPE_API_KEY}
Config import com.alibaba.cloud.ai.graph.GraphRepresentation;
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.action.EdgeAction;
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 indi.yuluo.graph.customnode.RecordingNode;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;import java.util.HashMap;
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;/*** Graph Demo首先判断评价正负其次细分负面问题最后输出处理方案。** author yuluo* author a hrefmailto:yuluo08290126gmail.comyuluo/a*/Configuration
public class GraphAutoConfiguration {private static final Logger logger LoggerFactory.getLogger(GraphAutoConfiguration.class);/*** 定义一个工作流 StateGraph Bean.*/Beanpublic StateGraph workflowGraph(ChatClient.Builder builder) throws GraphStateException {// LLMs BeanChatClient chatClient builder.defaultAdvisors(new SimpleLoggerAdvisor()).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。通过注册若干 Key 及其更新策略来管理上下文数据// 注册三个状态 key 分别为// 1. input用户输入的文本// 2. classifier_output分类器的输出结果// 3. solution最终输出结论// 使用 ReplaceStrategy每次写入替换旧值策略处理上下文状态对象中的数据用于在节点中传递数据OverAllStateFactory stateFactory () - {OverAllState state new OverAllState();state.registerKeyAndStrategy(input, new ReplaceStrategy());state.registerKeyAndStrategy(classifier_output, new ReplaceStrategy());state.registerKeyAndStrategy(solution, new ReplaceStrategy());return state;};// 创建 workflows 节点// 使用 Graph 框架预定义的 QuestionClassifierNode 来处理文本分类任务// 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 users 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();// 编排 Node 节点使用 StateGraph 的 API将上述节点加入图中并设置节点间的跳转关系// 首先将节点注册到图并使用 node_async(...) 将每个 NodeAction 包装为异步节点执行提高吞吐或防止阻塞具体实现框架已封装StateGraph stateGraph new StateGraph(Consumer Service Workflow Demo, stateFactory)// 定义节点.addNode(feedback_classifier, node_async(feedbackClassifier)).addNode(specific_question_classifier, node_async(specificQuestionClassifier)).addNode(recorder, node_async(new RecordingNode()))// 定义边流程顺序.addEdge(START, feedback_classifier).addConditionalEdges(feedback_classifier,edge_async(new FeedbackQuestionDispatcher()),Map.of(positive, recorder, negative, specific_question_classifier)).addConditionalEdges(specific_question_classifier,edge_async(new SpecificQuestionDispatcher()),Map.of(after-sale, recorder, transportation, recorder, quality, recorder, others,recorder))// 图的结束节点.addEdge(recorder, END);GraphRepresentation graphRepresentation stateGraph.getGraph(GraphRepresentation.Type.PLANTUML,workflow graph);System.out.println(\n\n);System.out.println(graphRepresentation.content());System.out.println(\n\n);return stateGraph;}public static class FeedbackQuestionDispatcher implements EdgeAction {Overridepublic String apply(OverAllState state) {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) {String classifierOutput (String) state.value(classifier_output).orElse();logger.info(classifierOutput: {}, classifierOutput);MapString, String classifierMap new HashMap();classifierMap.put(after-sale, after-sale);classifierMap.put(quality, quality);classifierMap.put(transportation, transportation);for (Map.EntryString, String entry : classifierMap.entrySet()) {if (classifierOutput.contains(entry.getKey())) {return entry.getValue();}}return others;}}}自定义 RecordingNode 节点
public class RecordingNode implements NodeAction {private static final Logger logger LoggerFactory.getLogger(RecordingNode.class);Overridepublic MapString, Object apply(OverAllState state) {String feedback (String) state.value(classifier_output).get();MapString, 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;}}Controller
RestController
RequestMapping(/graph/demo)
public class GraphController {private final CompiledGraph compiledGraph;public GraphController(Qualifier(workflowGraph) StateGraph stateGraph) throws GraphStateException {this.compiledGraph stateGraph.compile();}GetMapping(/chat)public String simpleChat(RequestParam(query) String query) {return compiledGraph.invoke(Map.of(input, query)).flatMap(input - input.value(solution)).get().toString();}}2.2 访问测试
### 正面
GET http://localhost:8081/graph/demo/chat?queryThis product is excellent, I love it!# Praise, no action taken.### 负面 1
GET http://localhost:8081/graph/demo/chat?query这东西真垃圾啊天呐太难用了# json
# {keywords: [东西, 垃圾, 难用], category_name: product quality}
# ### 负面 2
GET http://localhost:8081/graph/demo/chat?queryThe product broke after one day, very disappointed.# json
# {keywords: [product, broke, one day, disappointed], category_name: product quality}
# 3. 参考资料
Google Agent 白皮书https://www.kaggle.com/whitepaper-agentsAuthropic Agenthttps://www.anthropic.com/engineering/building-effective-agentsIBM Agents 智能体编排 https://www.ibm.com/cn-zh/think/topics/ai-agent-orchestrationSpring AI Alibaba Graphhttps://github.com/alibaba/spring-ai-alibaba/blob/main/spring-ai-alibaba-graph/README-zh.md
