show optimized prompt in agent replies

app/(chat)/api/chat/route.ts

@@ -192,13 +192,8 @@ function applyOptimizedPromptToLatestUserMessage(
       (part): part is { type: "text"; text: string } => part.type === "text",
     );
     const originalText = textPart?.text?.trim() || "";
-    const normalizedOriginal = hiddenPromptPrefix && originalText.startsWith(hiddenPromptPrefix)
-      ? originalText.slice(hiddenPromptPrefix.length).trim()
-      : originalText;
-    const hasOriginalDemand = normalizedOriginal.length > 0;
-    const hasOriginalInOptimized =
-      hasOriginalDemand &&
-      (prompt.includes(normalizedOriginal) || normalizedOriginal.includes(prompt));
+    const { normalizedOriginal, hasOriginalDemand, hasOriginalInOptimized } =
+      analyzeOptimizedPromptMerge(originalText, prompt, hiddenPromptPrefix);
     const finalPrompt = hasOriginalDemand && !hasOriginalInOptimized
       ? [
           hiddenPromptPrefix.trim(),
@@ -227,6 +222,97 @@ function applyOptimizedPromptToLatestUserMessage(
   return cloned;
 }
 
+function analyzeOptimizedPromptMerge(
+  originalText: string,
+  optimizedPrompt?: string,
+  hiddenPromptPrefix = "",
+) {
+  const prompt = optimizedPrompt?.trim() || "";
+  const trimmedOriginal = String(originalText || "").trim();
+  const normalizedOriginal =
+    hiddenPromptPrefix && trimmedOriginal.startsWith(hiddenPromptPrefix)
+      ? trimmedOriginal.slice(hiddenPromptPrefix.length).trim()
+      : trimmedOriginal;
+  const hasOriginalDemand = normalizedOriginal.length > 0;
+  const hasOriginalInOptimized =
+    hasOriginalDemand &&
+    (prompt.includes(normalizedOriginal) || normalizedOriginal.includes(prompt));
+
+  return {
+    prompt,
+    normalizedOriginal,
+    hasOriginalDemand,
+    hasOriginalInOptimized,
+  };
+}
+
+function buildOptimizedPromptPreview(params: {
+  originalText: string;
+  optimizedPrompt?: string;
+  hiddenPromptPrefix?: string;
+}): string | undefined {
+  const { prompt, normalizedOriginal, hasOriginalDemand, hasOriginalInOptimized } =
+    analyzeOptimizedPromptMerge(
+      params.originalText,
+      params.optimizedPrompt,
+      params.hiddenPromptPrefix || "",
+    );
+
+  if (!prompt) return undefined;
+
+  const normalizedPrompt = prompt.replace(/\s+/g, " ").trim();
+  if (!normalizedPrompt) return undefined;
+
+  if (hasOriginalDemand && !hasOriginalInOptimized) {
+    return [
+      `- 核心目标：${normalizedOriginal.replace(/\s+/g, " ").trim()}`,
+      `- 优化提示词：${normalizedPrompt}`,
+    ].join("\n");
+  }
+
+  return `- ${normalizedPrompt}`;
+}
+
+function injectOptimizedPromptSection(params: {
+  message?: string;
+  originalText: string;
+  optimizedPrompt?: string;
+  hiddenPromptPrefix?: string;
+}): string | undefined {
+  const { message, originalText, optimizedPrompt, hiddenPromptPrefix } = params;
+  if (!message?.trim()) return message;
+
+  const preview = buildOptimizedPromptPreview({
+    originalText,
+    optimizedPrompt,
+    hiddenPromptPrefix,
+  });
+  if (!preview) return message;
+
+  const optimizedPromptSection = `【优化后提示词】\n${preview}`;
+  const optimizedSectionRegex =
+    /(?:###\s*优化后提示词|【\s*优化后提示词\s*】)[\s\S]*?(?=\n(?:###\s*|【\s*[^】]+\s*】)|$)/;
+  if (optimizedSectionRegex.test(message)) {
+    return message.replace(optimizedSectionRegex, optimizedPromptSection);
+  }
+
+  const outputSectionRegex = /(?:###\s*输出设置|【\s*输出设置\s*】)/;
+  if (outputSectionRegex.test(message)) {
+    return message.replace(outputSectionRegex, `${optimizedPromptSection}\n$&`);
+  }
+
+  const executionSectionRegex =
+    /(?:###\s*执行计划|【\s*执行计划\s*】)[\s\S]*?(?=\n(?:###\s*|【\s*[^】]+\s*】)|$)/;
+  if (executionSectionRegex.test(message)) {
+    return message.replace(executionSectionRegex, (matchedSection) => {
+      const trimmedSection = matchedSection.replace(/\s+$/, "");
+      return `${trimmedSection}\n${optimizedPromptSection}`;
+    });
+  }
+
+  return `${message.trim()}\n\n${optimizedPromptSection}`;
+}
+
 type StreamWriter = {
   write: (chunk: any) => void;
 };
@@ -1435,6 +1521,7 @@ function normalizeAgentReportHeadingLine(line: string): string {
     "需求理解",
     "图片分析",
     "执行计划",
+    "优化后提示词",
     "输出设置",
     "脚本规划",
     "分镜规划",
@@ -1580,7 +1667,7 @@ function buildAutoModeFallbackMessage(params: {
 
 function hasStructuredAgentSections(message?: string): boolean {
   if (!message) return false;
-  return /(?:###\s*需求理解|【\s*需求理解\s*】|###\s*图片分析|【\s*图片分析\s*】|###\s*执行计划|【\s*执行计划\s*】)/.test(
+  return /(?:###\s*需求理解|【\s*需求理解\s*】|###\s*图片分析|【\s*图片分析\s*】|###\s*执行计划|【\s*执行计划\s*】|###\s*优化后提示词|【\s*优化后提示词\s*】)/.test(
     message,
   );
 }
@@ -3578,6 +3665,11 @@ export async function POST(request: Request) {
         userImageParams: requestBody.imageParams,
         hasUserCustomImageParams,
       });
+      smartAutoFriendlyMessage = injectOptimizedPromptSection({
+        message: smartAutoFriendlyMessage,
+        originalText: requestTextContent,
+        optimizedPrompt: smartAutoOptimizedPrompt,
+      });
     }
 
     if (shouldRunAgentConversation) {

components/canvas/CanvasChatPanel.tsx

@@ -585,6 +585,7 @@ const CANVAS_AGENT_SECTION_TITLES = new Set([
   "需求理解",
   CANVAS_IMAGE_ANALYSIS_TITLE,
   "执行计划",
+  "优化后提示词",
   "输出设置",
 ]);
 const CANVAS_IMAGE_TASK_POLL_INTERVAL_MS = 1600;

components/elements/response.tsx

@@ -34,6 +34,7 @@ const AGENT_SECTION_TITLES = new Set([
   "需求理解",
   IMAGE_ANALYSIS_TITLE,
   "执行计划",
+  "优化后提示词",
   "输出设置",
 ]);
 

components/message.tsx

@@ -174,7 +174,7 @@ function filterAgentRoutingNoise(text: string): string {
 
 const IMAGE_DONE_SUMMARY = "已完成本次生成，如需微调可继续告诉我。";
 const ASSISTANT_TYPEWRITER_SECTION_REGEX =
-  /(?:###\s*需求理解|【\s*需求理解\s*】|###\s*图片分析|【\s*图片分析\s*】|###\s*执行计划|【\s*执行计划\s*】|###\s*输出设置|【\s*输出设置\s*】)/;
+  /(?:###\s*需求理解|【\s*需求理解\s*】|###\s*图片分析|【\s*图片分析\s*】|###\s*执行计划|【\s*执行计划\s*】|###\s*优化后提示词|【\s*优化后提示词\s*】|###\s*输出设置|【\s*输出设置\s*】)/;
 const ASSISTANT_TYPEWRITER_STORAGE_KEY = "assistant-typewriter-completed-keys-v1";
 const MODEL_BADGE_LINE_REGEX =
   /^\s*[◈◇◆🔹⬡✦•·]\s*([^\n]+?)\s*$/im;