fix(ai-web): 完成feedback AI流程

service-ai/pom.xml

@@ -29,12 +29,18 @@
         <eclipse-collections.version>11.1.0</eclipse-collections.version>
         <curator.version>5.1.0</curator.version>
         <hutool.version>5.8.27</hutool.version>
+        <mapstruct.version>1.6.3</mapstruct.version>
     </properties>
 
     <dependencies>
+        <dependency>
+            <groupId>org.mapstruct</groupId>
+            <artifactId>mapstruct</artifactId>
+        </dependency>
         <dependency>
             <groupId>org.projectlombok</groupId>
             <artifactId>lombok</artifactId>
+            <optional>true</optional>
         </dependency>
     </dependencies>
 
@@ -154,12 +160,22 @@
                 <artifactId>solon-ai-dialect-openai</artifactId>
                 <version>${solon-ai.version}</version>
             </dependency>
+            <dependency>
+                <groupId>org.mapstruct</groupId>
+                <artifactId>mapstruct</artifactId>
+                <version>${mapstruct.version}</version>
+            </dependency>
         </dependencies>
     </dependencyManagement>
 
     <build>
         <pluginManagement>
             <plugins>
+                <plugin>
+                    <groupId>org.apache.maven.plugins</groupId>
+                    <artifactId>maven-compiler-plugin</artifactId>
+                    <version>3.14.0</version>
+                </plugin>
                 <plugin>
                     <groupId>org.apache.maven.plugins</groupId>
                     <artifactId>maven-source-plugin</artifactId>
@@ -168,7 +184,7 @@
                 <plugin>
                     <groupId>org.apache.maven.plugins</groupId>
                     <artifactId>maven-shade-plugin</artifactId>
-                    <version>3.3.0</version>
+                    <version>3.6.0</version>
                 </plugin>
                 <plugin>
                     <groupId>org.springframework.boot</groupId>

service-ai/service-ai-web/pom.xml

@@ -70,6 +70,32 @@
 
     <build>
         <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <configuration>
+                    <annotationProcessorPaths>
+                        <path>
+                            <groupId>org.mapstruct</groupId>
+                            <artifactId>mapstruct-processor</artifactId>
+                            <version>${mapstruct.version}</version>
+                        </path>
+                        <path>
+                            <groupId>org.projectlombok</groupId>
+                            <artifactId>lombok</artifactId>
+                        </path>
+                        <path>
+                            <groupId>org.projectlombok</groupId>
+                            <artifactId>lombok-mapstruct-binding</artifactId>
+                            <version>0.2.0</version>
+                        </path>
+                    </annotationProcessorPaths>
+                    <compilerArgs>
+                        <arg>-Amapstruct.defaultComponentModel=spring</arg>
+                        <arg>-Amapstruct.defaultInjectionStrategy=constructor</arg>
+                    </compilerArgs>
+                </configuration>
+            </plugin>
             <plugin>
                 <groupId>org.springframework.boot</groupId>
                 <artifactId>spring-boot-maven-plugin</artifactId>

service-ai/service-ai-web/src/main/java/com/lanyuanxiaoyao/service/ai/web/configuration/FileStoreProperties.java

@@ -11,7 +11,7 @@ import org.springframework.context.annotation.Configuration;
 @Data
 @Configuration
 @ConfigurationProperties(prefix = "file-store")
-public class FileStoreConfiguration {
+public class FileStoreProperties {
   private String downloadPrefix;
   private String uploadPath;
 }

service-ai/service-ai-web/src/main/java/com/lanyuanxiaoyao/service/ai/web/configuration/LlmConfiguration.java

@@ -63,7 +63,7 @@ public class LlmConfiguration {
             .openAiApi(apiBuilder.build())
             .defaultOptions(
                 OpenAiChatOptions.builder()
-                    .model(llmProperties.getChat().getModel())
+                    .model(llmProperties.getVisual().getModel())
                     .build()
             )
             .build()

service-ai/service-ai-web/src/main/java/com/lanyuanxiaoyao/service/ai/web/controller/DataFileController.java

@@ -7,7 +7,7 @@ import cn.hutool.core.util.URLUtil;
 import cn.hutool.crypto.SecureUtil;
 import com.fasterxml.jackson.annotation.JsonProperty;
 import com.lanyuanxiaoyao.service.ai.core.entity.amis.AmisResponse;
-import com.lanyuanxiaoyao.service.ai.web.configuration.FileStoreConfiguration;
+import com.lanyuanxiaoyao.service.ai.web.configuration.FileStoreProperties;
 import com.lanyuanxiaoyao.service.ai.web.entity.vo.DataFileVO;
 import com.lanyuanxiaoyao.service.ai.web.service.DataFileService;
 import jakarta.servlet.http.HttpServletResponse;
@@ -41,17 +41,17 @@ import org.springframework.web.multipart.MultipartFile;
 @RestController
 @RequestMapping("/upload")
 public class DataFileController {
-  private final FileStoreConfiguration fileStoreConfiguration;
+  private final FileStoreProperties fileStoreProperties;
   private final DataFileService dataFileService;
   private final String uploadFolderPath;
   private final String cacheFolderPath;
   private final String sliceFolderPath;
 
-  public DataFileController(FileStoreConfiguration fileStoreConfiguration, DataFileService dataFileService) {
-    this.fileStoreConfiguration = fileStoreConfiguration;
+  public DataFileController(FileStoreProperties fileStoreProperties, DataFileService dataFileService) {
+    this.fileStoreProperties = fileStoreProperties;
     this.dataFileService = dataFileService;
 
-    this.uploadFolderPath = fileStoreConfiguration.getUploadPath();
+    this.uploadFolderPath = fileStoreProperties.getUploadPath();
     this.cacheFolderPath = StrUtil.format("{}/cache", uploadFolderPath);
     this.sliceFolderPath = StrUtil.format("{}/slice", uploadFolderPath);
   }
@@ -60,7 +60,7 @@ public class DataFileController {
   public AmisResponse<FinishResponse> upload(@RequestParam("file") MultipartFile file) throws IOException {
     String filename = file.getOriginalFilename();
     Long id = dataFileService.initialDataFile(filename);
-    String url = StrUtil.format("{}/upload/download/{}", fileStoreConfiguration.getDownloadPrefix(), id);
+    String url = StrUtil.format("{}/upload/download/{}", fileStoreProperties.getDownloadPrefix(), id);
     byte[] bytes = file.getBytes();
     String originMd5 = SecureUtil.md5(new ByteArrayInputStream(bytes));
     File targetFile = new File(StrUtil.format("{}/{}", uploadFolderPath, originMd5));
@@ -80,7 +80,7 @@ public class DataFileController {
   }
 
   @GetMapping("/download/{id}")
-  public void download(@PathVariable Long id, HttpServletResponse response) throws IOException {
+  public void download(@PathVariable("id") Long id, HttpServletResponse response) throws IOException {
     DataFileVO dataFile = dataFileService.downloadFile(id);
     File targetFile = new File(dataFile.getPath());
     response.setHeader("Content-Type", dataFile.getType());
@@ -157,7 +157,7 @@ public class DataFileController {
             request.uploadId,
             request.filename,
             request.uploadId.toString(),
-            StrUtil.format("{}/upload/download/{}", fileStoreConfiguration.getDownloadPrefix(), request.uploadId)
+            StrUtil.format("{}/upload/download/{}", fileStoreProperties.getDownloadPrefix(), request.uploadId)
         ));
       } else {
         throw new RuntimeException("合并文件失败");

service-ai/service-ai-web/src/main/java/com/lanyuanxiaoyao/service/ai/web/controller/feedback/FeedbackController.java

@@ -1,13 +1,19 @@
 package com.lanyuanxiaoyao.service.ai.web.controller.feedback;
 
 import cn.hutool.core.util.ObjectUtil;
+import cn.hutool.core.util.StrUtil;
+import com.lanyuanxiaoyao.service.ai.core.entity.amis.AmisCrudResponse;
 import com.lanyuanxiaoyao.service.ai.core.entity.amis.AmisResponse;
+import com.lanyuanxiaoyao.service.ai.web.configuration.FileStoreProperties;
+import com.lanyuanxiaoyao.service.ai.web.entity.Feedback;
 import com.lanyuanxiaoyao.service.ai.web.service.feedback.FeedbackService;
+import lombok.Data;
 import lombok.extern.slf4j.Slf4j;
 import org.eclipse.collections.api.factory.Lists;
 import org.eclipse.collections.api.list.ImmutableList;
 import org.springframework.web.bind.annotation.GetMapping;
 import org.springframework.web.bind.annotation.PostMapping;
+import org.springframework.web.bind.annotation.RequestBody;
 import org.springframework.web.bind.annotation.RequestMapping;
 import org.springframework.web.bind.annotation.RequestParam;
 import org.springframework.web.bind.annotation.RestController;
@@ -16,27 +22,52 @@ import org.springframework.web.bind.annotation.RestController;
 @RestController
 @RequestMapping("feedback")
 public class FeedbackController {
+  private final FileStoreProperties fileStoreProperties;
   private final FeedbackService feedbackService;
 
-  public FeedbackController(FeedbackService feedbackService) {
+  public FeedbackController(FileStoreProperties fileStoreProperties, FeedbackService feedbackService) {
+    this.fileStoreProperties = fileStoreProperties;
     this.feedbackService = feedbackService;
   }
 
   @PostMapping("add")
-  public void add(
-      @RequestParam("source") String source,
-      @RequestParam(value = "pictures", required = false) ImmutableList<Long> pictures
-  ) {
-    feedbackService.add(source, ObjectUtil.defaultIfNull(pictures, Lists.immutable.empty()));
+  public void add(@RequestBody CreateItem item) {
+    feedbackService.add(item.source, ObjectUtil.defaultIfNull(item.pictures, Lists.immutable.empty()));
   }
 
   @GetMapping("list")
-  public AmisResponse<?> list() {
-    return AmisResponse.responseCrudData(feedbackService.list());
+  public AmisCrudResponse list() {
+    return AmisResponse.responseCrudData(feedbackService.list().collect(feedback -> new ListItem(fileStoreProperties, feedback)));
   }
 
   @GetMapping("delete")
   public void delete(@RequestParam("id") Long id) {
     feedbackService.remove(id);
   }
+
+  @Data
+  public static final class CreateItem {
+    private String source;
+    private ImmutableList<Long> pictures;
+  }
+
+  @Data
+  public static final class ListItem {
+    private Long id;
+    private String source;
+    private ImmutableList<String> pictures;
+    private Feedback.Status status;
+    private String analysis;
+    private String analysisShort;
+
+    public ListItem(FileStoreProperties fileStoreProperties, Feedback feedback) {
+      this.id = feedback.getId();
+      this.source = feedback.getSource();
+      this.pictures = feedback.getPictureIds()
+          .collect(id -> StrUtil.format("{}/upload/download/{}", fileStoreProperties.getDownloadPrefix(), id));
+      this.status = feedback.getStatus();
+      this.analysis = feedback.getAnalysis();
+      this.analysisShort = feedback.getAnalysisShort();
+    }
+  }
 }

service-ai/service-ai-web/src/main/java/com/lanyuanxiaoyao/service/ai/web/entity/Feedback.java

@@ -9,7 +9,7 @@ public class Feedback {
   private String source;
   private String analysisShort;
   private String analysis;
-  private ImmutableList<String> pictureIds;
+  private ImmutableList<Long> pictureIds;
   private Status status;
   private Long createdTime;
   private Long modifiedTime;

service-ai/service-ai-web/src/main/java/com/lanyuanxiaoyao/service/ai/web/entity/context/FeedbackContext.java

@@ -0,0 +1,16 @@
+package com.lanyuanxiaoyao.service.ai.web.entity.context;
+
+import com.lanyuanxiaoyao.service.ai.web.entity.Feedback;
+import java.util.List;
+import lombok.Data;
+import org.eclipse.collections.api.factory.Lists;
+
+/**
+ * @author lanyuanxiaoyao
+ * @version 20250616
+ */
+@Data
+public class FeedbackContext {
+  private Feedback feedback;
+  private List<String> pictureDescriptions = Lists.mutable.empty();
+}

service-ai/service-ai-web/src/main/java/com/lanyuanxiaoyao/service/ai/web/service/feedback/FeedbackService.java

@@ -1,18 +1,24 @@
 package com.lanyuanxiaoyao.service.ai.web.service.feedback;
 
 import club.kingon.sql.builder.SqlBuilder;
+import cn.hutool.core.lang.Assert;
 import cn.hutool.core.util.EnumUtil;
 import cn.hutool.core.util.ObjectUtil;
 import cn.hutool.core.util.StrUtil;
 import com.lanyuanxiaoyao.service.ai.core.configuration.SnowflakeId;
 import com.lanyuanxiaoyao.service.ai.web.entity.Feedback;
+import com.lanyuanxiaoyao.service.ai.web.entity.context.FeedbackContext;
 import com.lanyuanxiaoyao.service.common.Constants;
+import com.yomahub.liteflow.core.FlowExecutor;
+import java.util.List;
+import java.util.concurrent.TimeUnit;
 import java.util.stream.Collectors;
 import lombok.extern.slf4j.Slf4j;
 import org.eclipse.collections.api.factory.Lists;
 import org.eclipse.collections.api.list.ImmutableList;
 import org.springframework.jdbc.core.JdbcTemplate;
 import org.springframework.jdbc.core.RowMapper;
+import org.springframework.scheduling.annotation.Scheduled;
 import org.springframework.stereotype.Service;
 import org.springframework.transaction.annotation.Transactional;
 
@@ -20,6 +26,7 @@ import org.springframework.transaction.annotation.Transactional;
 @Service
 public class FeedbackService {
   public static final String FEEDBACK_TABLE_NAME = Constants.DATABASE_NAME + ".service_ai_feedback";
+  public static final String[] FEEDBACK_COLUMNS = new String[]{"id", "source", "analysis_short", "analysis", "pictures", "status", "created_time", "modified_time"};
   private static final RowMapper<Feedback> feedbackMapper = (rs, row) -> {
     Feedback feedback = new Feedback();
     feedback.setId(rs.getLong(1));
@@ -29,7 +36,7 @@ public class FeedbackService {
     feedback.setPictureIds(
         StrUtil.isBlank(rs.getString(5))
             ? Lists.immutable.empty()
-            : Lists.immutable.ofAll(StrUtil.split(rs.getString(5), ","))
+            : Lists.immutable.ofAll(StrUtil.split(rs.getString(5), ",")).collect(Long::parseLong)
     );
     feedback.setStatus(EnumUtil.fromString(Feedback.Status.class, rs.getString(6)));
     feedback.setCreatedTime(rs.getTimestamp(7).getTime());
@@ -37,9 +44,38 @@ public class FeedbackService {
     return feedback;
   };
   private final JdbcTemplate template;
+  private final FlowExecutor executor;
 
-  public FeedbackService(JdbcTemplate template) {
+  @SuppressWarnings("SpringJavaInjectionPointsAutowiringInspection")
+  public FeedbackService(JdbcTemplate template, FlowExecutor executor) {
     this.template = template;
+    this.executor = executor;
+  }
+
+  @Scheduled(initialDelay = 1, fixedDelay = 1, timeUnit = TimeUnit.MINUTES)
+  public void analysis() {
+    List<Feedback> feedbacks = template.query(
+        SqlBuilder.select(FEEDBACK_COLUMNS)
+            .from(FEEDBACK_TABLE_NAME)
+            .whereEq("status", Feedback.Status.ANALYSIS_PROCESSING.name())
+            .build(),
+        feedbackMapper
+    );
+    for (Feedback feedback : feedbacks) {
+      FeedbackContext context = new FeedbackContext();
+      context.setFeedback(feedback);
+      executor.execute2Resp("feedback_analysis", null, context);
+    }
+  }
+
+  public Feedback get(Long id) {
+    return template.queryForObject(
+        SqlBuilder.select(FEEDBACK_COLUMNS)
+            .from(FEEDBACK_TABLE_NAME)
+            .whereEq("id", id)
+            .build(),
+        feedbackMapper
+    );
   }
 
   @Transactional(rollbackFor = Exception.class)
@@ -51,15 +87,41 @@ public class FeedbackService {
             .precompileSql(),
         SnowflakeId.next(),
         source,
-        ObjectUtil.isEmpty(pictureIds)
-            ? null
-            : pictureIds.makeString(",")
+        ObjectUtil.isEmpty(pictureIds) ? null : pictureIds.makeString(",")
+    );
+  }
+
+  @Transactional(rollbackFor = Exception.class)
+  public void updateAnalysis(Long id, String analysis, String analysisShort) {
+    Assert.notNull(id, "ID cannot be null");
+    template.update(
+        SqlBuilder.update(FEEDBACK_TABLE_NAME)
+            .set("analysis", "?")
+            .addSet("analysis_short", "?")
+            .whereEq("id", "?")
+            .precompileSql(),
+        analysis,
+        analysisShort,
+        id
+    );
+  }
+
+  @Transactional(rollbackFor = Exception.class)
+  public void updateStatus(Long id, Feedback.Status status) {
+    Assert.notNull(id, "ID cannot be null");
+    template.update(
+        SqlBuilder.update(FEEDBACK_TABLE_NAME)
+            .set("status", "?")
+            .whereEq("id", "?")
+            .precompileSql(),
+        status.name(),
+        id
     );
   }
 
   public ImmutableList<Feedback> list() {
     return template.query(
-            SqlBuilder.select("id", "source", "analysis_short", "analysis", "pictures", "status", "created_time", "modified_time")
+            SqlBuilder.select(FEEDBACK_COLUMNS)
                 .from(FEEDBACK_TABLE_NAME)
                 .orderByDesc("created_time")
                 .build(),

service-ai/service-ai-web/src/main/java/com/lanyuanxiaoyao/service/ai/web/service/node/FeedbackNodes.java

@@ -1,14 +1,181 @@
 package com.lanyuanxiaoyao.service.ai.web.service.node;
 
+import cn.hutool.core.lang.Assert;
+import cn.hutool.core.util.ObjectUtil;
+import cn.hutool.core.util.StrUtil;
+import com.lanyuanxiaoyao.service.ai.web.entity.Feedback;
+import com.lanyuanxiaoyao.service.ai.web.entity.context.FeedbackContext;
+import com.lanyuanxiaoyao.service.ai.web.entity.vo.DataFileVO;
+import com.lanyuanxiaoyao.service.ai.web.service.DataFileService;
+import com.lanyuanxiaoyao.service.ai.web.service.feedback.FeedbackService;
 import com.yomahub.liteflow.annotation.LiteflowComponent;
+import com.yomahub.liteflow.annotation.LiteflowMethod;
+import com.yomahub.liteflow.core.NodeComponent;
+import com.yomahub.liteflow.enums.LiteFlowMethodEnum;
+import com.yomahub.liteflow.enums.NodeTypeEnum;
+import lombok.extern.slf4j.Slf4j;
 import org.springframework.ai.chat.client.ChatClient;
 import org.springframework.beans.factory.annotation.Qualifier;
+import org.springframework.core.io.FileSystemResource;
+import org.springframework.util.MimeType;
+import org.springframework.util.MimeTypeUtils;
 
+@Slf4j
 @LiteflowComponent
 public class FeedbackNodes {
   private final ChatClient.Builder chatClientBuilder;
+  private final ChatClient.Builder visualChatClientBuilder;
+  private final DataFileService dataFileService;
+  private final FeedbackService feedbackService;
 
-  public FeedbackNodes(@Qualifier("chat") ChatClient.Builder chatClientBuilder) {
+  public FeedbackNodes(
+      @Qualifier("chat") ChatClient.Builder chatClientBuilder,
+      @Qualifier("visual") ChatClient.Builder visualClientBuilder, DataFileService dataFileService, FeedbackService feedbackService
+  ) {
     this.chatClientBuilder = chatClientBuilder;
+    this.visualChatClientBuilder = visualClientBuilder;
+    this.dataFileService = dataFileService;
+    this.feedbackService = feedbackService;
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS_BOOLEAN, nodeId = "feedback_check_if_picture_needed", nodeName = "判断有图片进行识别", nodeType = NodeTypeEnum.BOOLEAN)
+  public boolean checkIfPictureReadNeeded(NodeComponent node) {
+    FeedbackContext context = node.getContextBean(FeedbackContext.class);
+    Feedback feedback = context.getFeedback();
+    return ObjectUtil.isNotEmpty(feedback.getPictureIds());
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "image_read", nodeName = "读取图片", nodeType = NodeTypeEnum.COMMON)
+  public void imageRead(NodeComponent node) {
+    FeedbackContext context = node.getContextBean(FeedbackContext.class);
+    Feedback feedback = context.getFeedback();
+    ChatClient client = visualChatClientBuilder
+        // language=TEXT
+        .defaultSystem("""
+            你是一个专业的OCR解析助手。请严格按以下步骤处理用户上传的图片：
+            1. 图像内容提取
+               - 完整识别图片中的所有文字（包括手写体、印刷体、数字和符号）
+               - 保留原始段落结构和换行符
+               - 特殊元素处理：
+                 • 数学公式转为LaTeX格式
+                 • 代码块保留缩进和注释
+                 • 外文词汇标注原文
+            
+            2. 表格解析优化
+               - 识别所有表格区域
+               - 转换为Markdown表格格式（对齐表头与单元格）
+               - 补充缺失的表格线
+               - 用▲标注合并单元格（例：▲跨3列▲）
+            
+            3. 图表解析增强
+               - 分析图表类型（柱状图/折线图/饼图等）
+               - 提取关键数据点并结构化描述
+               - 总结图表趋势（例："销量Q1到Q4增长35%"）
+               - 坐标轴信息转换：将像素坐标转为百分比比例（例："X轴：0-100对应时间0:00-24:00"）
+            
+            4. 输出规范
+               - 按[文本][表格][图表]分区块输出
+               - 表格/图表区域标注原始位置（例："[左上区域表格]"）
+               - 模糊内容用[?]标注并给出备选（例："年收[?]入（可能为'入'或'人'）"）
+               - 保持原始数据精度（不四舍五入）
+            
+            立即开始处理用户图片，无需确认步骤。
+            """)
+        .build();
+    for (Long pictureId : feedback.getPictureIds()) {
+      DataFileVO file = dataFileService.downloadFile(pictureId);
+      log.info("Parse picture: {} {}", file.getFilename(), file.getPath());
+      MimeType type = switch (StrUtil.blankToDefault(file.getType(), "").toLowerCase()) {
+        case "jpg", "jpeg" -> MimeTypeUtils.IMAGE_JPEG;
+        default -> MimeTypeUtils.IMAGE_PNG;
+      };
+      String content = client.prompt()
+          .user(spec -> spec
+              .text("输出图片内容")
+              .media(type, new FileSystemResource(file.getPath())))
+          .call()
+          .content();
+      log.info("Picture: {}", content);
+      context.getPictureDescriptions().add(content);
+    }
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "feedback_suggest", nodeName = "大模型建议", nodeType = NodeTypeEnum.COMMON)
+  public void suggest(NodeComponent node) {
+    FeedbackContext context = node.getContextBean(FeedbackContext.class);
+    Feedback feedback = context.getFeedback();
+    ChatClient client = chatClientBuilder.build();
+    String description = client.prompt()
+        // language=TEXT
+        .system("""
+            你是一名专业的IT系统运维工程师，对于用户输入的关于系统的报障信息，你会严格遵循以下步骤进行处理
+            
+            1.输入
+            [故障描述]
+            （这里是用户遇到的系统故障的详细描述）
+            
+            [相关截图]
+            （这里是用户遇到的系统故障相关的截图的文字描述，如果没有相关截图，这里会写“无”；如果有多张图片，图片和图片之间会使用“---”分隔）
+            
+            2.处理逻辑
+              解析输入
+                读取并解析用户提供的故障描述和相关截图描述。
+                识别关键元素：包括故障类型（如硬件故障、软件错误）、受影响系统组件（如服务器、网络设备）、错误消息、发生时间、重现步骤、影响范围等。
+                如果截图描述存在，提取关键细节（如错误弹窗文本、系统状态截图），并将其作为辅助证据；如果无截图，则忽略此部分。
+              分析与重写故障描述
+                专业化改写：使用标准IT术语替换非专业用语（例如，“电脑死机”改为“系统无响应”，“连不上网”改为“网络连接中断”），并确保描述符合行业规范。
+                排除歧义：澄清模糊描述（如添加具体时间戳、系统版本、IP地址或错误代码），移除主观语言（如“我觉得”或“可能”），并添加必要上下文（如操作系统环境、相关服务运行状态）。
+                结构化组织：将故障描述重写为逻辑段落，格式包括：
+                  问题概述：简明总结故障本质（例如，“数据库服务异常导致应用无法访问”）。
+                  详细症状：描述具体现象，包括错误消息、发生频率和影响范围（如“影响用户登录功能，错误代码500”）。
+                  重现步骤：列出可复现故障的操作序列（如“1. 访问URL X；2. 触发操作 Y”）。
+                  相关环境：添加系统细节（如“运行在Linux Ubuntu 20.04, Java 11环境”）。
+                整合截图信息：如果截图描述存在，将其嵌入重写中作为证据（例如，“根据截图，错误弹窗显示‘Connection timeout’”）。
+              质量校验
+                检查重写后的内容是否完整、一致且无歧义：确保所有用户输入细节都被涵盖，添加缺失信息（如建议的故障分类），并验证专业术语的准确性。
+                如果输入信息不足（如缺少时间戳或系统版本），在输出中添加注释提示用户补充。
+            
+            3.输出
+              输出一个专业、结构化的故障报告，格式清晰，可直接用于运维团队诊断。
+              重写的故障描述，以结构化段落呈现，涵盖问题概述、详细症状、重现步骤和相关环境。
+              输出将使用中性、客观语言，避免任何个人意见或建议，以确保报告专注于事实描述。
+            """)
+        .call()
+        .content();
+    Assert.notBlank(description, "Description cannot be blank");
+    String analysis = client.prompt()
+        .system("""
+            你是一名专业的IT系统运维工程师，对于用户输入的报障信息，你会给出专业的意见
+            """)
+        .user(StrUtil.format("""
+                [故障描述]
+                {}
+                
+                [相关截图]
+                {}
+                """,
+            description,
+            ObjectUtil.isEmpty(context.getPictureDescriptions()) ? "无" : StrUtil.join(",", context.getPictureDescriptions())
+        ))
+        .call()
+        .content();
+    feedback.setAnalysis(analysis);
+    Assert.notBlank(description, "Analysis cannot be blank");
+    String analysisShort = client.prompt()
+        .system("""
+            你是一名专业的文字编辑，对用户输入的内容，用一段话总结内容
+            """)
+        .user(analysis)
+        .call()
+        .content();
+    feedback.setAnalysisShort(analysisShort);
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "feedback_save", nodeName = "保存分析内容", nodeType = NodeTypeEnum.COMMON)
+  public void saveFeedback(NodeComponent node) {
+    FeedbackContext context = node.getContextBean(FeedbackContext.class);
+    Feedback feedback = context.getFeedback();
+    feedbackService.updateAnalysis(feedback.getId(), feedback.getAnalysis(), feedback.getAnalysisShort());
+    feedbackService.updateStatus(feedback.getId(), Feedback.Status.ANALYSIS_SUCCESS);
   }
 }

service-ai/service-ai-web/src/main/resources/application-build.yml

@@ -1,20 +0,0 @@
-spring:
-  profiles:
-    include: random-port,common,discovery,metrics,forest
-  ai:
-    vectorstore:
-      qdrant:
-        host: 132.121.206.65
-        port: 29463
-        api-key: ENC(0/0UkIKeAvyV17yNqSU3v04wmm8CdWKe4BYSSJa2FuBtK12TcZRJPdwk+ZpYnpISv+KmVTUrrmFBzAYrDR3ysA==)
-  llm:
-    base-url: http://132.121.206.65:10086
-    api-key: ENC(K+Hff9QGC+fcyi510VIDd9CaeK/IN5WBJ9rlkUsHEdDgIidW+stHHJlsK0lLPUXXREha+ToQZqqDXJrqSE+GUKCXklFhelD8bRHFXBIeP/ZzT2cxhzgKUXgjw3S0Qw2R)
-    chat:
-      model: 'Qwen3/qwen3-1.7b'
-    visual:
-      model: 'Qwen2.5/qwen2.5-vl-7b'
-    embedding:
-      model: 'Qwen3/qwen3-embedding-4b'
-    reranker:
-      model: 'Bge-reranker-v2'

service-ai/service-ai-web/src/main/resources/application-local.yml

@@ -1,39 +0,0 @@
-server:
-  port: 8080
-spring:
-  ai:
-    vectorstore:
-      qdrant:
-        host: 192.168.100.140
-        port: 6334
-  llm:
-    base-url: https://api.siliconflow.cn
-    api-key: sk-xrguybusoqndpqvgzgvllddzgjamksuecyqdaygdwnrnqfwo
-    chat:
-      model: 'Qwen/Qwen3-8B'
-    visual:
-      base-url: https://open.bigmodel.cn/api/paas/v4
-      endpoint: /chat/completions
-      model: 'glm-4v-flash'
-    embedding:
-      model: 'BAAI/bge-m3'
-    reranker:
-      model: 'BAAI/bge-reranker-v2-m3'
-  cloud:
-    discovery:
-      enabled: false
-    zookeeper:
-      enabled: false
-  datasource:
-    url: jdbc:mysql://192.168.100.140:3306/hudi_collect_build_b12?useSSL=false&allowPublicKeyRetrieval=true
-    username: test
-    password: test
-    driver-class-name: com.mysql.cj.jdbc.Driver
-  security:
-    meta:
-      authority: ENC(GXKnbq1LS11U2HaONspvH+D/TkIx13aWTaokdkzaF7HSvq6Z0Rv1+JUWFnYopVXu)
-      username: ENC(moIO5mO39V1Z+RDwROK9JXY4GfM8ZjDgM6Si7wRZ1MPVjbhTpmLz3lz28rAiw7c2LeCmizfJzHkEXIwGlB280g==)
-      darkcode: ENC(0jzpQ7T6S+P7bZrENgYsUoLhlqGvw7DA2MN3BRqEOwq7plhtg72vuuiPQNnr3DaYz0CpyTvxInhpx11W3VZ1trD6NINh7O3LN70ZqO5pWXk=)
-jasypt:
-  encryptor:
-    password: 'r#(R,P"Dp^A47>WSn:Wn].gs/+"v:q_Q*An~zF*g-@j@jtSTv5H/,S-3:R?r9R}.'

service-ai/service-ai-web/src/main/resources/application.yml

@@ -1,6 +1,8 @@
 spring:
   application:
     name: service-ai-web
+  profiles:
+    include: random-port,common,discovery,metrics,forest
   mvc:
     async:
       request-timeout: 3600000
@@ -13,6 +15,24 @@ spring:
       org.springframework.ai.model.openai.autoconfigure.OpenAiEmbeddingAutoConfiguration,
       org.springframework.ai.model.openai.autoconfigure.OpenAiModerationAutoConfiguration,
       org.springframework.ai.model.deepseek.autoconfigure.DeepSeekChatAutoConfiguration
+  ai:
+    vectorstore:
+      qdrant:
+        host: 132.121.206.65
+        port: 29463
+        api-key: ENC(0/0UkIKeAvyV17yNqSU3v04wmm8CdWKe4BYSSJa2FuBtK12TcZRJPdwk+ZpYnpISv+KmVTUrrmFBzAYrDR3ysA==)
+  llm:
+    base-url: http://132.121.206.65:10086
+    api-key: ENC(K+Hff9QGC+fcyi510VIDd9CaeK/IN5WBJ9rlkUsHEdDgIidW+stHHJlsK0lLPUXXREha+ToQZqqDXJrqSE+GUKCXklFhelD8bRHFXBIeP/ZzT2cxhzgKUXgjw3S0Qw2R)
+    chat:
+      base-url: ${spring.llm.base-url}/v1
+      model: 'Qwen3/qwen3-1.7b'
+    visual:
+      model: 'Qwen2.5/qwen2.5-vl-7b-q4km'
+    embedding:
+      model: 'Qwen3/qwen3-embedding-4b'
+    reranker:
+      model: 'BGE/beg-reranker-v2'
 liteflow:
   rule-source: liteflow.xml
   print-banner: false

service-ai/service-ai-web/src/main/resources/liteflow.xml

@@ -23,4 +23,14 @@
     <chain id="embedding_submit_directly">
         SER(import_vector_source)
     </chain>
+    <chain id="feedback_analysis">
+        SER(
+            IF(
+                feedback_check_if_picture_needed,
+                image_read
+            ),
+            feedback_suggest,
+            feedback_save
+        )
+    </chain>
 </flow>

service-ai/service-ai-web/src/test/java/com/lanyuanxiaoyao/service/ai/web/TestLlm.java

@@ -1,114 +1,68 @@
 package com.lanyuanxiaoyao.service.ai.web;
 
-import java.net.http.HttpClient;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
+import com.lanyuanxiaoyao.service.ai.core.configuration.WebClientConfiguration;
+import com.lanyuanxiaoyao.service.ai.web.configuration.LlmConfiguration;
+import com.lanyuanxiaoyao.service.ai.web.configuration.LlmProperties;
+import lombok.extern.slf4j.Slf4j;
 import org.springframework.ai.chat.client.ChatClient;
-import org.springframework.ai.openai.OpenAiChatModel;
-import org.springframework.ai.openai.OpenAiChatOptions;
-import org.springframework.ai.openai.api.OpenAiApi;
-import org.springframework.http.client.JdkClientHttpRequestFactory;
-import org.springframework.http.client.reactive.JdkClientHttpConnector;
-import org.springframework.web.client.RestClient;
-import org.springframework.web.reactive.function.client.WebClient;
+import org.springframework.core.io.FileSystemResource;
+import org.springframework.util.MimeTypeUtils;
 
 /**
  * @author lanyuanxiaoyao
  * @version 20250526
  */
+@Slf4j
 public class TestLlm {
-  private static final Logger log = LoggerFactory.getLogger(TestLlm.class);
-
   public static void main(String[] args) {
-    HttpClient httpClient = HttpClient.newBuilder()
-        .version(HttpClient.Version.HTTP_1_1)
-        .build();
-    ChatClient client = ChatClient.builder(
-            OpenAiChatModel.builder()
-                .openAiApi(
-                    OpenAiApi.builder()
-                        .baseUrl("http://132.121.206.65:10086")
-                        .apiKey("*XMySqV%>hR&v>>g*NwCs3tpQ5FVMFEF2VHVTj<MYQd$&@$sY7CgqNyea4giJi4")
-                        .webClientBuilder(WebClient.builder().clientConnector(new JdkClientHttpConnector(httpClient)))
-                        .restClientBuilder(RestClient.builder().requestFactory(new JdkClientHttpRequestFactory(httpClient)))
-                        .build()
-                )
-                .defaultOptions(
-                    OpenAiChatOptions.builder()
-                        .model("Qwen3-1.7-vllm")
-                        .build()
-                )
-                .build()
-        )
-        .build();
+    LlmConfiguration configuration = new LlmConfiguration();
+    LlmProperties properties = new LlmProperties();
+    LlmProperties.ChatProperties chatProperties = new LlmProperties.ChatProperties();
+    chatProperties.setBaseUrl("http://132.121.206.65:10086");
+    chatProperties.setApiKey("*XMySqV%>hR&v>>g*NwCs3tpQ5FVMFEF2VHVTj<MYQd$&@$sY7CgqNyea4giJi4");
+    chatProperties.setModel("Qwen2.5/qwen2.5-vl-7b");
+    properties.setVisual(chatProperties);
+    ChatClient client = configuration.visualClientBuilder(
+        properties,
+        WebClientConfiguration.generateWebClientBuilder(),
+        WebClientConfiguration.generateRestClientBuilder()
+    ).build();
     String content = client.prompt()
+        // language=TEXT
         .system("""
-            对用户输入的文本，生成多组高质量的问答对。请遵循以下指南：
-            1. 问题部分：
-              为同一个主题创建尽可能多的不同表述的问题，确保问题的多样性。
-              每个问题应考虑用户可能的多种问法，例如：
-              直接询问（如“什么是...？”）
-              请求确认（如“是否可以说...？”）
-              寻求解释（如“请解释一下...的含义。”）
-              假设性问题（如“如果...会怎样？”）
-              例子请求（如“能否举个例子说明...？”）
-              问题应涵盖文本中的关键信息、主要概念和细节，确保不遗漏重要内容。
-            2. 答案部分：
-              提供一个全面、信息丰富的答案，涵盖问题的所有可能角度，确保逻辑连贯。
-              答案应直接基于给定文本，确保准确性和一致性。
-              包含相关的细节，如日期、名称、职位等具体信息，必要时提供背景信息以增强理解。
-            3. 格式：
-              使用"问："标记问题集合的开始，所有问题应在一个段落内，问题之间用空格分隔。
-              使用"答："标记答案的开始，答案应清晰分段，便于阅读。
-              问答对之间用“---”分隔，以提高可读性。
-            4. 内容要求：
-              确保问答对紧密围绕文本主题，避免偏离主题。
-              避免添加文本中未提及的信息，确保信息的真实性。
-              一个问题搭配一个答案，避免一组问答对中同时涉及多个问题。
-              如果文本信息不足以回答某个方面，可以在答案中说明 "根据给定信息无法确定"，并尽量提供相关的上下文。
-            """)
-        .user("""
-            Apache Hudi 是一款开源的数据湖管理框架，专注于高效处理大规模数据集的增量更新和实时操作。以下是其核心要点介绍：
-            核心功能与特性
-            
-            增量数据处理：支持插入（Insert）、更新（Update）、删除（Delete）操作，并通过“Upsert”（插入或更新）机制高效处理变更数据，避免重写整个数据集。
-            
-            事务支持：提供 ACID 事务保证，确保数据一致性和可靠性，支持原子提交和回滚。
-            
-            存储优化：自动合并小文件并维护文件最佳大小，减少存储碎片化问题，提升查询性能。
-            
-            多查询类型：
-            
-            快照查询：获取最新数据状态（对 MoR 表合并基础文件和增量文件）。
-            
-            增量查询：捕获自某次提交后的变更数据流，适用于增量管道。
-            
-            读取优化查询：针对 MoR 表展示最新压缩后的数据。
-            存储模型
-            
-            写入时复制（Copy-on-Write, CoW）：数据以列式格式（如 Parquet）存储，每次更新生成新版本文件，适合读多写少的场景。
-            
-            读取时合并（Merge-on-Read, MoR）：结合列式（Parquet）和行式（Avro）存储，更新写入增量文件后异步合并，适合写频繁的场景。
-            适用场景
-            
-            合规性需求：支持 GDPR、CCPA 等法规要求的数据删除和更新。
-            
-            实时数据流处理：如 IoT 设备数据、CDC（变更数据捕获）系统，实现近实时分析。
-            
-            数据湖管理：优化大规模数据集的存储和查询效率，支持时间旅行查询和历史版本回溯。
-            技术生态与兼容性
-            
-            多引擎支持：与 Spark、Flink、Hive、Presto、Trino 等计算和查询引擎集成。
-            
-            存储格式：基于 Parquet 和 Avro，兼容 Hadoop 生态及云存储（如 Amazon S3）。
-            优势对比
-            
-            相较于同类产品（如 Apache Iceberg、Delta Lake），Hudi 在实时更新和增量处理方面表现突出，尤其适合需要频繁数据变更的场景。其独特的 MoR 模型在写入性能上优于传统批处理方案。
-            
-            总结
-            
-            Apache Hudi 通过灵活的存储模型、高效的事务管理和广泛的生态系统集成，成为构建现代化数据湖的核心工具，适用于金融、物联网、实时分析等对数据新鲜度和操作效率要求高的领域。
+            你是一个专业的OCR解析助手。请严格按以下步骤处理用户上传的图片：
+            1. 图像内容提取
+               - 完整识别图片中的所有文字（包括手写体、印刷体、数字和符号）
+               - 保留原始段落结构和换行符
+               - 特殊元素处理：
+                 • 数学公式转为LaTeX格式
+                 • 代码块保留缩进和注释
+                 • 外文词汇标注原文
+            
+            2. 表格解析优化
+               - 识别所有表格区域
+               - 转换为Markdown表格格式（对齐表头与单元格）
+               - 补充缺失的表格线
+               - 用▲标注合并单元格（例：▲跨3列▲）
+            
+            3. 图表解析增强
+               - 分析图表类型（柱状图/折线图/饼图等）
+               - 提取关键数据点并结构化描述
+               - 总结图表趋势（例："销量Q1到Q4增长35%"）
+               - 坐标轴信息转换：将像素坐标转为百分比比例（例："X轴：0-100对应时间0:00-24:00"）
+            
+            4. 输出规范
+               - 按[文本][表格][图表]分区块输出
+               - 表格/图表区域标注原始位置（例："[左上区域表格]"）
+               - 模糊内容用[?]标注并给出备选（例："年收[?]入（可能为'入'或'人'）"）
+               - 保持原始数据精度（不四舍五入）
+            
+            立即开始处理用户图片，无需确认步骤。
             """)
+        .user(spec -> spec
+            // language=TEXT
+            .text("输出图片内容")
+            .media(MimeTypeUtils.IMAGE_PNG, new FileSystemResource("/Users/lanyuanxiaoyao/Downloads/图片_002_07.png")))
         .call()
         .content();
     log.info(content);

service-cli/service-cli-runner/src/main/resources/application.yml

@@ -161,23 +161,15 @@ deploy:
         - "service"
       source-jar: service-monitor-1.0.0-SNAPSHOT.jar
       replicas: 1
-    service-ai-chat:
+    service-ai-web:
       order: 6
       groups:
         - "ai"
-      source-jar: service-ai-chat-1.0.0-SNAPSHOT.jar
-      jdk: "jdk17"
-      replicas: 1
-    service-ai-knowledge:
-      order: 6
-      groups:
-        - "ai"
-      source-jar: service-ai-knowledge-1.0.0-SNAPSHOT.jar
+      source-jar: service-ai-web-1.0.0-SNAPSHOT.jar
       jdk: "jdk17"
       replicas: 1
       arguments:
-        "[spring.profiles.active]": 'build'
-        "[file-store.download-prefix]": 'http://132.126.207.130:35690/hudi_services/ai_knowledge'
+        "[file-store.download-prefix]": 'http://132.126.207.130:35690/hudi_services/service_ai_web'
         "[file-store.upload-path]": ${deploy.runtime.data-path}/knowledge
         "[spring.datasource.url]": ${deploy.runtime.database.config.url}
         "[spring.datasource.username]": ${deploy.runtime.database.config.username}

service-web/client/src/pages/ai/feedback/Feedback.tsx

@@ -1,9 +1,25 @@
 import React from 'react'
-import {amisRender, commonInfo, crudCommonOptions} from '../../../util/amis.tsx'
+import styled from 'styled-components'
+import {amisRender, commonInfo, crudCommonOptions, mappingField, mappingItem} from '../../../util/amis.tsx'
+
+const FeedbackDiv = styled.div`
+    .feedback-list-images {
+        .antd-Img-container {
+            width: 32px;
+            height: 32px;
+        }
+    }
+`
+
+const statusMapping = [
+  mappingItem('分析中', 'ANALYSIS_PROCESSING', 'label-warning'),
+  mappingItem('分析完成', 'ANALYSIS_SUCCESS', 'label-primary'),
+  mappingItem('处理完成', 'FINISHED', 'label-success'),
+]
 
 const Feedback: React.FC = () => {
   return (
-    <div className="feedback">
+    <FeedbackDiv className="feedback">
       {amisRender(
         {
           type: 'page',
@@ -28,10 +44,7 @@ const Feedback: React.FC = () => {
                     body: {
                       debug: commonInfo.debug,
                       type: 'form',
-                      api: {
-                        url: `${commonInfo.baseAiUrl}/feedback/add`,
-                        dataType: 'form',
-                      },
+                      api: `${commonInfo.baseAiUrl}/feedback/add`,
                       body: [
                         {
                           type: 'editor',
@@ -50,12 +63,14 @@ const Feedback: React.FC = () => {
                           label: '相关截图',
                           autoUpload: false,
                           multiple: true,
+                          joinValues: false,
+                          extractValue: true,
                           // 5MB 5242880
                           // 100MB 104857600
                           // 500MB 524288000
                           // 1GB 1073741824
                           maxSize: 5242880,
-                          receiver: `${commonInfo.baseAiUrl}/upload`
+                          receiver: `${commonInfo.baseAiUrl}/upload`,
                         },
                       ],
                     },
@@ -66,11 +81,26 @@ const Feedback: React.FC = () => {
                 {
                   name: 'id',
                   label: '编号',
+                  width: 150,
+                },
+                {
+                  name: 'source',
+                  label: '故障描述',
+                },
+                {
+                  name: 'pictures',
+                  label: '相关截图',
+                  width: 200,
+                  className: 'feedback-list-images',
+                  type: 'images',
+                  enlargeAble: true,
+                  enlargeWithGallary: true,
                 },
                 {
-                  name: 'status',
                   label: '状态',
                   width: 80,
+                  align: 'center',
+                  ...mappingField('status', statusMapping),
                 },
                 {
                   type: 'operation',
@@ -101,7 +131,7 @@ const Feedback: React.FC = () => {
           ],
         },
       )}
-    </div>
+    </FeedbackDiv>
   )
 }
 

service-web/client/src/util/amis.tsx

@@ -10,8 +10,8 @@ import {isEqual} from 'licia'
 export const commonInfo = {
   debug: isEqual(import.meta.env.MODE, 'development'),
   baseUrl: 'http://132.126.207.130:35690/hudi_services/service_web',
-  // baseAiUrl: 'http://132.126.207.130:35690/hudi_services/service_ai_web',
-  baseAiUrl: 'http://localhost:8080',
+  baseAiUrl: 'http://132.126.207.130:35690/hudi_services/service_ai_web',
+  // baseAiUrl: 'http://localhost:8080',
   authorizationHeaders: {
     'Authorization': 'Basic QXhoRWJzY3dzSkRiWU1IMjpjWXhnM2I0UHRXb1ZENVNqRmF5V3h0blNWc2p6UnNnNA==',
     'Content-Type': 'application/json',
@@ -903,7 +903,7 @@ export function simpleYarnDialog(cluster: string, title: string, filterField: st
         type: 'crud',
         api: {
           method: 'get',
-          url: `\${base}/yarn/job_list`,
+          url: `${commonInfo.baseUrl}/yarn/job_list`,
           data: {
             clusters: `${cluster}`,
             page: '${page|default:undefined}',