智能体完成

pom.xml

@@ -20,15 +20,6 @@
     <developers>
         <developer/>
     </developers>
-    <scm>
-        <connection/>
-        <developerConnection/>
-        <tag/>
-        <url/>
-    </scm>
-    <modules>
-        <module>long-image-search-mcp-server</module>
-    </modules>
     <properties>
         <java.version>21</java.version>
     </properties>

src/main/java/com/huangge1199/aiagent/agent/BaseAgent.java

@@ -0,0 +1,108 @@
+package com.huangge1199.aiagent.agent;
+
+import cn.hutool.core.util.StrUtil;
+import com.huangge1199.aiagent.agent.model.AgentState;
+import lombok.Data;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.messages.Message;
+import org.springframework.ai.chat.messages.UserMessage;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * BaseAgent
+ * <p>
+ * 抽象基础代理类，用于管理代理状态和执行流程。
+ * <p>
+ * 提供状态转换、内存管理和基于步骤的执行循环的基础功能。
+ * 子类必须实现step方法。
+ *
+ * @author huangge1199
+ * @since 2025/6/4 17:01:45
+ */
+@Data
+@Slf4j
+public abstract class BaseAgent {
+
+    // 核心属性
+    private String name;
+
+    // 提示
+    private String systemPrompt;
+    private String nextStepPrompt;
+
+    // 状态
+    private AgentState state = AgentState.IDLE;
+
+    // 执行控制
+    private int maxSteps = 10;
+    private int currentStep = 0;
+
+    // LLM
+    private ChatClient chatClient;
+
+    // Memory（需要自主维护会话上下文）
+    private List<Message> messageList = new ArrayList<>();
+
+    /**
+     * 运行代理
+     *
+     * @param userPrompt 用户提示词
+     * @return 执行结果
+     */
+    public String run(String userPrompt) {
+        if (this.state != AgentState.IDLE) {
+            throw new RuntimeException("Cannot run agent from state: " + this.state);
+        }
+        if (StrUtil.isBlank(userPrompt)) {
+            throw new RuntimeException("Cannot run agent with empty user prompt");
+        }
+        // 更改状态
+        state = AgentState.RUNNING;
+        // 记录消息上下文
+        messageList.add(new UserMessage(userPrompt));
+        // 保存结果列表
+        List<String> results = new ArrayList<>();
+        try {
+            for (int i = 0; i < maxSteps && state != AgentState.FINISHED; i++) {
+                int stepNumber = i + 1;
+                currentStep = stepNumber;
+                log.info("Executing step {}/{}", stepNumber, maxSteps);
+                // 单步执行
+                String stepResult = step();
+                String result = "Step " + stepNumber + ": " + stepResult;
+                results.add(result);
+            }
+            // 检查是否超出步骤限制
+            if (currentStep >= maxSteps) {
+                state = AgentState.FINISHED;
+                results.add("Terminated: Reached max steps (" + maxSteps + ")");
+            }
+            return String.join("\n", results);
+        } catch (Exception e) {
+            state = AgentState.ERROR;
+            log.error("Error executing agent", e);
+            return "执行错误" + e.getMessage();
+        } finally {
+            // 清理资源
+            this.cleanup();
+        }
+    }
+
+    /**
+     * 执行单个步骤
+     *
+     * @return 步骤执行结果
+     */
+    public abstract String step();
+
+    /**
+     * 清理资源
+     */
+    protected void cleanup() {
+        // 子类可以重写此方法来清理资源
+    }
+}
+

src/main/java/com/huangge1199/aiagent/agent/LongManus.java

@@ -0,0 +1,41 @@
+package com.huangge1199.aiagent.agent;
+
+import com.huangge1199.aiagent.config.MyLoggerAdvisor;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.tool.ToolCallback;
+import org.springframework.stereotype.Component;
+
+/**
+ * LongManus
+ * AI 超级智能体（拥有自主规划能力，可以直接使用）
+ *
+ * @author huangge1199
+ * @since 2025/6/4 17:08:52
+ */
+@Component
+public class LongManus extends ToolCallAgent {
+
+    public LongManus(ToolCallback[] allTools, ChatModel dashscopeChatModel) {
+        super(allTools);
+        this.setName("longManus");
+        String SYSTEM_PROMPT = """
+                You are LongManus, an all-capable AI assistant, aimed at solving any task presented by the user.
+                You have various tools at your disposal that you can call upon to efficiently complete complex requests.
+                """;
+        this.setSystemPrompt(SYSTEM_PROMPT);
+        String NEXT_STEP_PROMPT = """
+                Based on user needs, proactively select the most appropriate tool or combination of tools.
+                For complex tasks, you can break down the problem and use different tools step by step to solve it.
+                After using each tool, clearly explain the execution results and suggest the next steps.
+                If you want to stop the interaction at any point, use the `terminate` tool/function call.
+                """;
+        this.setNextStepPrompt(NEXT_STEP_PROMPT);
+        this.setMaxSteps(5);
+        // 初始化 AI 对话客户端
+        ChatClient chatClient = ChatClient.builder(dashscopeChatModel)
+                .defaultAdvisors(new MyLoggerAdvisor())
+                .build();
+        this.setChatClient(chatClient);
+    }
+}

src/main/java/com/huangge1199/aiagent/agent/ReActAgent.java

@@ -0,0 +1,57 @@
+package com.huangge1199.aiagent.agent;
+
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+import lombok.extern.slf4j.Slf4j;
+
+/**
+ * ReActAgent
+ * ReAct (Reasoning and Acting) 模式的代理抽象类
+ * 实现了思考-行动的循环模式
+ *
+ * @author huangge1199
+ * @since 2025/6/4 17:04:56
+ */
+@EqualsAndHashCode(callSuper = true)
+@Data
+@Slf4j
+public abstract class ReActAgent extends BaseAgent {
+
+    /**
+     * 处理当前状态并决定下一步行动
+     *
+     * @return 是否需要执行行动，true表示需要执行，false表示不需要执行
+     */
+    public abstract boolean think();
+
+    /**
+     * 执行决定的行动
+     *
+     * @return 行动执行结果
+     */
+    public abstract String act();
+
+    /**
+     * 执行单个步骤：思考和行动
+     *
+     * @return 步骤执行结果
+     */
+    @Override
+    public String step() {
+        try {
+            // 先思考
+            boolean shouldAct = think();
+            if (!shouldAct) {
+                return "思考完成 - 无需行动";
+            }
+            // 再行动
+            return act();
+        } catch (Exception e) {
+            // 记录异常日志
+            e.printStackTrace();
+            return "步骤执行失败：" + e.getMessage();
+        }
+    }
+
+}
+

src/main/java/com/huangge1199/aiagent/agent/ToolCallAgent.java

@@ -0,0 +1,140 @@
+package com.huangge1199.aiagent.agent;
+
+import cn.hutool.core.collection.CollUtil;
+import cn.hutool.core.util.StrUtil;
+import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatOptions;
+import com.huangge1199.aiagent.agent.model.AgentState;
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.ai.chat.messages.AssistantMessage;
+import org.springframework.ai.chat.messages.Message;
+import org.springframework.ai.chat.messages.ToolResponseMessage;
+import org.springframework.ai.chat.messages.UserMessage;
+import org.springframework.ai.chat.model.ChatResponse;
+import org.springframework.ai.chat.prompt.ChatOptions;
+import org.springframework.ai.chat.prompt.Prompt;
+import org.springframework.ai.model.tool.ToolCallingManager;
+import org.springframework.ai.model.tool.ToolExecutionResult;
+import org.springframework.ai.tool.ToolCallback;
+
+import java.util.List;
+import java.util.stream.Collectors;
+
+/**
+ * ToolCallAgent
+ * 处理工具调用的基础代理类，具体实现了 think 和 act 方法，可以用作创建实例的父类
+ *
+ * @author huangge1199
+ * @since 2025/6/4 17:05:58
+ */
+@EqualsAndHashCode(callSuper = true)
+@Data
+@Slf4j
+public class ToolCallAgent extends ReActAgent {
+
+    // 可用的工具
+    private final ToolCallback[] availableTools;
+
+    // 保存工具调用信息的响应结果（要调用那些工具）
+    private ChatResponse toolCallChatResponse;
+
+    // 工具调用管理者
+    private final ToolCallingManager toolCallingManager;
+
+    // 禁用 Spring AI 内置的工具调用机制，自己维护选项和消息上下文
+    private final ChatOptions chatOptions;
+
+    public ToolCallAgent(ToolCallback[] availableTools) {
+        super();
+        this.availableTools = availableTools;
+        this.toolCallingManager = ToolCallingManager.builder().build();
+        // 禁用 Spring AI 内置的工具调用机制，自己维护选项和消息上下文
+        this.chatOptions = DashScopeChatOptions.builder()
+                .withProxyToolCalls(true)
+                .build();
+    }
+
+    /**
+     * 处理当前状态并决定下一步行动
+     *
+     * @return 是否需要执行行动
+     */
+    @Override
+    public boolean think() {
+        // 1、校验提示词，拼接用户提示词
+        if (StrUtil.isNotBlank(getNextStepPrompt())) {
+            UserMessage userMessage = new UserMessage(getNextStepPrompt());
+            getMessageList().add(userMessage);
+        }
+        // 2、调用 AI 大模型，获取工具调用结果
+        List<Message> messageList = getMessageList();
+        Prompt prompt = new Prompt(messageList, this.chatOptions);
+        try {
+            ChatResponse chatResponse = getChatClient().prompt(prompt)
+                    .system(getSystemPrompt())
+                    .tools(availableTools)
+                    .call()
+                    .chatResponse();
+            // 记录响应，用于等下 Act
+            this.toolCallChatResponse = chatResponse;
+            // 3、解析工具调用结果，获取要调用的工具
+            // 助手消息
+            AssistantMessage assistantMessage = chatResponse.getResult().getOutput();
+            // 获取要调用的工具列表
+            List<AssistantMessage.ToolCall> toolCallList = assistantMessage.getToolCalls();
+            // 输出提示信息
+            String result = assistantMessage.getText();
+            log.info("{}的思考：{}", getName(), result);
+            log.info("{}选择了 {} 个工具来使用", getName(), toolCallList.size());
+            String toolCallInfo = toolCallList.stream()
+                    .map(toolCall -> String.format("工具名称：%s，参数：%s", toolCall.name(), toolCall.arguments()))
+                    .collect(Collectors.joining("\n"));
+            log.info(toolCallInfo);
+            // 如果不需要调用工具，返回 false
+            if (toolCallList.isEmpty()) {
+                // 只有不调用工具时，才需要手动记录助手消息
+                getMessageList().add(assistantMessage);
+                return false;
+            } else {
+                // 需要调用工具时，无需记录助手消息，因为调用工具时会自动记录
+                return true;
+            }
+        } catch (Exception e) {
+            log.error("{}的思考过程遇到了问题：{}", getName(), e.getMessage());
+            getMessageList().add(new AssistantMessage("处理时遇到了错误：" + e.getMessage()));
+            return false;
+        }
+    }
+
+    /**
+     * 执行工具调用并处理结果
+     *
+     * @return 执行结果
+     */
+    @Override
+    public String act() {
+        if (!toolCallChatResponse.hasToolCalls()) {
+            return "没有工具需要调用";
+        }
+        // 调用工具
+        Prompt prompt = new Prompt(getMessageList(), this.chatOptions);
+        ToolExecutionResult toolExecutionResult = toolCallingManager.executeToolCalls(prompt, toolCallChatResponse);
+        // 记录消息上下文，conversationHistory 已经包含了助手消息和工具调用返回的结果
+        setMessageList(toolExecutionResult.conversationHistory());
+        ToolResponseMessage toolResponseMessage = (ToolResponseMessage) CollUtil.getLast(toolExecutionResult.conversationHistory());
+        // 判断是否调用了终止工具
+        boolean terminateToolCalled = toolResponseMessage.getResponses().stream()
+                .anyMatch(response -> "doTerminate".equals(response.name()));
+        if (terminateToolCalled) {
+            // 任务结束，更改状态
+            setState(AgentState.FINISHED);
+        }
+        String results = toolResponseMessage.getResponses().stream()
+                .map(response -> "工具 " + response.name() + " 返回的结果：" + response.responseData())
+                .collect(Collectors.joining("\n"));
+        log.info(results);
+        return results;
+    }
+}
+

src/main/java/com/huangge1199/aiagent/agent/model/AgentState.java

@@ -0,0 +1,33 @@
+package com.huangge1199.aiagent.agent.model;
+
+/**
+ * AgentState
+ *
+ * @author huangge1199
+ * @since 2025/6/4 17:00:54
+ */
+/**
+ * 代理执行状态的枚举类
+ */
+public enum AgentState {
+
+    /**
+     * 空闲状态
+     */
+    IDLE,
+
+    /**
+     * 运行中状态
+     */
+    RUNNING,
+
+    /**
+     * 已完成状态
+     */
+    FINISHED,
+
+    /**
+     * 错误状态
+     */
+    ERROR
+}

src/main/java/com/huangge1199/aiagent/config/ChatModelConfig.java

@@ -1,5 +1,6 @@
 package com.huangge1199.aiagent.config;
 
+import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatModel;
 import org.springframework.ai.chat.model.ChatModel;
 import org.springframework.ai.ollama.OllamaChatModel;
 import org.springframework.context.annotation.Bean;
@@ -16,16 +17,16 @@ import org.springframework.context.annotation.Primary;
 public class ChatModelConfig {
 
     // 选择DashScope作为默认模型
-//    @Bean
+    @Bean
-//    @Primary
+    @Primary
-//    public ChatModel primaryChatModel(DashScopeChatModel dashscopeChatModel) {
+    public ChatModel primaryChatModel(DashScopeChatModel dashscopeChatModel) {
-//        return dashscopeChatModel;
+        return dashscopeChatModel;
-//    }
+    }
 
-    // 或者选择Ollama：
+//    // 或者选择Ollama：
-     @Bean
+//     @Bean
-     @Primary
+//     @Primary
-     public ChatModel primaryChatModel(OllamaChatModel ollamaChatModel) {
+//     public ChatModel primaryChatModel(OllamaChatModel ollamaChatModel) {
-         return ollamaChatModel;
+//         return ollamaChatModel;
-     }
+//     }
 }

src/main/java/com/huangge1199/aiagent/controller/ResController.java

@@ -1,5 +1,6 @@
 package com.huangge1199.aiagent.controller;
 
+import com.huangge1199.aiagent.agent.LongManus;
 import com.huangge1199.aiagent.common.R;
 import com.huangge1199.aiagent.config.MyLoggerAdvisor;
 import io.swagger.v3.oas.annotations.Operation;
@@ -42,6 +43,9 @@ public class ResController {
     @Resource
     private ToolCallbackProvider toolCallbackProvider;
 
+    @Resource
+    private LongManus longManus;
+
     @PostMapping("/chatRes")
     @Operation(summary = "返回 ChatResponse")
     public R<ChatResponse> chatResRes(@RequestBody String question) {
@@ -108,4 +112,11 @@ public class ResController {
         String content = chatResponse.getResult().getOutput().getText();
         return R.ok(content);
     }
+
+    @PostMapping("/manus")
+    @Operation(summary = "智能体测试")
+    public R<String> manus(@RequestBody String question) {
+        String content = longManus.run(question);
+        return R.ok(content);
+    }
 }

src/main/resources/application.yml

@@ -9,7 +9,7 @@ spring:
         options:
           model: qwen-plus
     ollama:
-      base-url: http://192.168.188.2:11435
+      base-url: http://192.168.188.2:11434
       chat:
         model: llama3.2:3b
     vectorstore: