refactor(ai-web): 优化流程编排

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

@@ -12,5 +12,6 @@ import org.eclipse.collections.api.factory.Lists;
 @Data
 public class FeedbackContext {
   private Feedback feedback;
+  private String optimizedSource;
   private List<String> pictureDescriptions = Lists.mutable.empty();
 }

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

@@ -19,8 +19,6 @@ import java.util.Map;
 import java.util.Optional;
 import java.util.stream.Collectors;
 import lombok.extern.slf4j.Slf4j;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
 import org.springframework.ai.chat.client.ChatClient;
 import org.springframework.ai.document.Document;
 import org.springframework.ai.document.DocumentReader;
@@ -42,12 +40,12 @@ import org.springframework.core.io.PathResource;
 @Slf4j
 @LiteflowComponent
 public class EmbeddingNodes {
-  private final ChatClient chatClient;
+  private final ChatClient.Builder chatClientBuilder;
   private final QdrantClient qdrantClient;
   private final EmbeddingModel embeddingModel;
 
   public EmbeddingNodes(@Qualifier("chat") ChatClient.Builder builder, VectorStore vectorStore, EmbeddingModel embeddingModel) {
-    this.chatClient = builder.build();
+    this.chatClientBuilder = builder;
     this.qdrantClient = (QdrantClient) vectorStore.getNativeClient().orElseThrow();
     this.embeddingModel = embeddingModel;
   }
@@ -190,7 +188,8 @@ public class EmbeddingNodes {
   }
 
   private List<Document> llmSplit(String prompt, String content, Map<String, Object> metadata) {
-    String response = chatClient.prompt()
+    ChatClient client = chatClientBuilder.build();
+    String response = client.prompt()
         .system(prompt)
         .user(content)
         .call()

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

@@ -100,12 +100,12 @@ public class FeedbackNodes {
     }
   }
 
-  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "feedback_suggest", nodeName = "大模型建议", nodeType = NodeTypeEnum.COMMON)
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "optimize_source", nodeName = "报障信息优化", nodeType = NodeTypeEnum.COMMON)
-  public void suggest(NodeComponent node) {
+  public void optimizeSource(NodeComponent node) {
     FeedbackContext context = node.getContextBean(FeedbackContext.class);
     Feedback feedback = context.getFeedback();
-    ChatClient client = chatClientBuilder.build();
+    String optimizedSource = chatClientBuilder.build()
-    String description = client.prompt()
+        .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
@@ -140,27 +140,39 @@ public class FeedbackNodes {
               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()
+        .user(StrUtil.format(
-        .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,
+            feedback.getSource(),
             ObjectUtil.isEmpty(context.getPictureDescriptions()) ? "无" : StrUtil.join(",", context.getPictureDescriptions())
         ))
         .call()
         .content();
+    if (StrUtil.isBlank(optimizedSource)) {
+      log.warn("Optimized source is blank");
+    }
+    context.setOptimizedSource(optimizedSource);
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "feedback_suggest", nodeName = "大模型建议", nodeType = NodeTypeEnum.COMMON)
+  public void suggestFeedback(NodeComponent node) {
+    FeedbackContext context = node.getContextBean(FeedbackContext.class);
+    Feedback feedback = context.getFeedback();
+    ChatClient client = chatClientBuilder.build();
+    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(context.getOptimizedSource())
+        .call()
+        .content();
     feedback.setAnalysis(analysis);
-    Assert.notBlank(description, "Analysis cannot be blank");
+    Assert.notBlank(analysis, "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

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

@@ -29,6 +29,7 @@
                 feedback_check_if_picture_needed,
                 image_read
             ),
+            optimize_source,
             feedback_suggest,
             feedback_save
         )