improve agent planning brief
This commit is contained in:
134
parent
6c89df0f8b
commit
77531aa7b5
@ -52,6 +52,8 @@ export function normalizeAgentReportHeadingLine(line: string): string {
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.trim();
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const knownTitles = [
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"意图分析",
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"任务规划",
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"需求理解",
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"图片分析",
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"执行计划",
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@ -138,14 +138,14 @@ export function buildAutoModeFallbackMessage(params: {
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: "基于当前上传图片处理。";
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return [
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"### 需求理解",
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"### 意图分析",
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`- ${demand}`,
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"### 图片分析",
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params.imageSummary
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? `- ${params.imageSummary.replace(/\s+/g, " ").trim()}`
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: `- ${sourceLine}`,
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"- 我会优先保持主体、构图和整体风格一致，只处理你指定的改动。",
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"### 执行计划",
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"### 任务规划",
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"- 先完成本轮核心改动，再检查边缘与光影一致性。",
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"- 若结果偏差，我会按“保留A，修改B”的方式继续迭代。",
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"### 输出设置",
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@ -154,9 +154,9 @@ export function buildAutoModeFallbackMessage(params: {
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}
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return [
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"### 需求理解",
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"### 意图分析",
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`- ${demand}`,
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"### 执行计划",
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"### 任务规划",
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"- 先锁定主体与场景，再细化风格、材质与光影。",
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"### 输出设置",
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"- 默认单图输出，优先稳定命中。",
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@ -222,7 +222,7 @@ export function buildPromptOnlyReply(params: {
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export function hasStructuredAgentSections(message?: string): boolean {
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if (!message) return false;
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return /(?:###\s*需求理解|【\s*需求理解\s*】|###\s*图片分析|【\s*图片分析\s*】|###\s*执行计划|【\s*执行计划\s*】|###\s*优化后提示词|【\s*优化后提示词\s*】)/.test(
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return /(?:###\s*意图分析|【\s*意图分析\s*】|###\s*任务规划|【\s*任务规划\s*】|###\s*需求理解|【\s*需求理解\s*】|###\s*图片分析|【\s*图片分析\s*】|###\s*执行计划|【\s*执行计划\s*】|###\s*优化后提示词|【\s*优化后提示词\s*】)/.test(
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message,
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);
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}
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@ -253,6 +253,13 @@ export function ensureStructuredAgentFriendlyMessage(params: {
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return fallback;
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}
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if (fallback.includes("### 任务规划\n")) {
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return fallback.replace(
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"### 任务规划\n",
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`### 任务规划\n- 补充说明：${normalizedSupplement}\n`,
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);
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}
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return fallback.replace(
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"### 执行计划\n",
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`### 执行计划\n- 补充说明：${normalizedSupplement}\n`,
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@ -545,7 +545,7 @@ function writeCanvasOperationShadowChunk(params: {
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}
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const AGENT_EXECUTION_PREFACE_INTERNAL_PATTERN =
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/(?:^|\n)\s*(?:#{1,6}\s*)?(?:需求理解|图片分析|执行计划|输出设置|优化后提示词|使用建议)(?:\s|[:：]|$)|【\s*(?:需求理解|图片分析|执行计划|输出设置|优化后提示词|使用建议)\s*】|提示词|自动优化|模型|提交生成|回写|流程|步骤/;
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/(?:^|\n)\s*(?:#{1,6}\s*)?(?:意图分析|任务规划|需求理解|图片分析|执行计划|输出设置|优化后提示词|使用建议)(?:\s|[:：]|$)|【\s*(?:意图分析|任务规划|需求理解|图片分析|执行计划|输出设置|优化后提示词|使用建议)\s*】|提示词|自动优化|模型|提交生成|回写|流程|步骤/;
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function buildAgentExecutionPrefaceText(params: {
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friendlyMessage?: string | null;
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@ -791,6 +791,8 @@ function buildPreviewImageUrl(url: string, width: number): string {
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const CANVAS_IMAGE_ANALYSIS_TITLE = "图片分析";
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const CANVAS_IMAGE_DONE_SUMMARY = "已完成本次生成，如需微调可继续告诉我。";
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const CANVAS_AGENT_SECTION_TITLES = new Set([
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"意图分析",
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"任务规划",
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"需求理解",
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CANVAS_IMAGE_ANALYSIS_TITLE,
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"执行计划",
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@ -47,6 +47,8 @@ type ResponseProps = ComponentProps<typeof Streamdown> & {
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const IMAGE_ANALYSIS_TITLE = "图片分析";
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const AGENT_SECTION_TITLES = new Set([
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"意图分析",
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"任务规划",
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"需求理解",
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IMAGE_ANALYSIS_TITLE,
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"执行计划",
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@ -174,7 +174,7 @@ function filterAgentRoutingNoise(text: string): string {
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const IMAGE_DONE_SUMMARY = "已完成本次生成，如需微调可继续告诉我。";
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const ASSISTANT_TYPEWRITER_SECTION_REGEX =
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/(?:###\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*】)/;
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/(?:###\s*意图分析|【\s*意图分析\s*】|###\s*任务规划|【\s*任务规划\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*】)/;
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const ASSISTANT_TYPEWRITER_STORAGE_KEY = "assistant-typewriter-completed-keys-v1";
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const MODEL_BADGE_LINE_REGEX =
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/^\s*[◈◇◆🔹⬡✦•·]\s*([^\n]+?)\s*$/im;
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@ -19,6 +19,10 @@ import {
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normalizeAgentThreadState,
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type AgentThreadState,
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} from "@/lib/ai/runtime/agent-thread-state";
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import {
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buildAgentPlanningBriefMarkdown,
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isAgentPlanningBriefMarkdown,
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} from "@/lib/ai/runtime/agent-planning-brief";
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import {
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getAiModelAgentCodingModelId,
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isAiModelMultimodal,
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@ -1153,17 +1157,55 @@ function buildStrategicFriendlyMessage(
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if (existingReply) {
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const isStructuredReply =
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isAgentPlanningBriefMarkdown(existingReply) ||
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/###\s*意图分析/.test(existingReply) ||
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/###\s*任务规划/.test(existingReply) ||
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/###\s*需求理解/.test(existingReply) ||
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/###\s*图片分析/.test(existingReply) ||
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/###\s*执行计划/.test(existingReply) ||
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/【\s*意图分析\s*】/.test(existingReply) ||
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/【\s*任务规划\s*】/.test(existingReply) ||
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/【\s*需求理解\s*】/.test(existingReply) ||
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/【\s*图片分析\s*】/.test(existingReply) ||
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/【\s*执行计划\s*】/.test(existingReply);
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if (isStructuredReply || existingReply.length >= 24) {
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if (isStructuredReply) {
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return Promise.resolve(existingReply);
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}
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}
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if (
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analysis.intent === "image_edit" ||
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analysis.intent === "image_gen" ||
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analysis.intent === "video_gen"
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) {
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const imageParams = heuristicImageParams(state.text, analysis);
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const planningBrief = buildAgentPlanningBriefMarkdown({
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userText: state.text,
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intent: analysis.intent,
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subIntent: analysis.subIntent,
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nextAction: state.plan?.nextAction,
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preferredCapability: state.plan?.preferredCapability,
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targetSource: state.plan?.targetSource || state.targetResolutionMode,
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hasImage: state.hasImage,
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imageFromHistory: state.imageFromHistory,
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selectedImageCount: state.selectedImageCount,
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currentUploadImageCount: state.currentUploadImageCount,
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historyImageCount: state.historyImageCount,
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imageCount: imageParams?.imageCount,
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aspectRatio: imageParams?.aspectRatio,
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imageSize: imageParams?.imageSize,
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planGoal: state.plan?.goal,
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});
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if (process.env.AGENT_LLM_PLANNING_BRIEF_ENABLED !== "true") {
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return Promise.resolve(planningBrief);
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}
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return withTimeoutFallback(
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generateAgentConversation(analysis, state),
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AGENT_OPERATION_TIMEOUT_MS,
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planningBrief,
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);
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}
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return withTimeoutFallback(
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generateAgentConversation(analysis, state),
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AGENT_OPERATION_TIMEOUT_MS,
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302
lib/ai/runtime/agent-planning-brief.ts
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302
lib/ai/runtime/agent-planning-brief.ts
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@ -0,0 +1,302 @@
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export type AgentPlanningBriefIntent =
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| "image_edit"
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| "vision"
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| "image_gen"
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| "video_gen"
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| "chat";
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export type AgentPlanningBriefAction =
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| "respond"
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| "clarify"
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| "analyze_image"
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| "generate_image"
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| "edit_image"
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| "generate_video";
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export type AgentPlanningBriefTargetSource =
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| "explicit_selection"
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| "current_upload"
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| "text_reference"
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| "working_memory"
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| "latest_result"
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| "clarification"
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| "none";
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export type AgentPlanningBriefCapability =
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| "respond"
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| "analyze"
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| "generic_edit"
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| "remove_background"
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| "remove_watermark"
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| "enhance"
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| "extend"
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| "resize"
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| "angle_variant"
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| "local_edit"
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| "box_cutout"
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| "generate_image"
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| "generate_video"
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| "publish_canvas";
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export interface AgentPlanningBriefInput {
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userText: string;
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intent: AgentPlanningBriefIntent;
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subIntent?: string;
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nextAction?: AgentPlanningBriefAction;
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preferredCapability?: AgentPlanningBriefCapability;
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targetSource?: AgentPlanningBriefTargetSource;
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hasImage?: boolean;
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imageFromHistory?: boolean;
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selectedImageCount?: number;
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currentUploadImageCount?: number;
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historyImageCount?: number;
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imageCount?: number;
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aspectRatio?: string;
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imageSize?: string;
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planGoal?: string;
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}
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export interface AgentTaskGroup {
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label: string;
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count?: number;
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description: string;
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}
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const CHINESE_NUMBER_VALUES: Record<string, number> = {
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一: 1,
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二: 2,
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两: 2,
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三: 3,
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四: 4,
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五: 5,
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六: 6,
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七: 7,
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八: 8,
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九: 9,
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十: 10,
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};
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function compactLine(value: string, maxLength: number) {
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const normalized = String(value || "").replace(/\s+/g, " ").trim();
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if (normalized.length <= maxLength) return normalized;
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return `${normalized.slice(0, maxLength)}...`;
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}
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function parseCountToken(token?: string | null) {
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const value = String(token || "").trim();
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if (!value) return undefined;
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if (/^\d+$/.test(value)) {
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const numeric = Number(value);
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return Number.isFinite(numeric) && numeric > 0 ? Math.round(numeric) : undefined;
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}
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return CHINESE_NUMBER_VALUES[value];
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}
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function countFromText(text: string) {
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const match = String(text || "").match(/([1-9]\d?|一|二|两|三|四|五|六|七|八|九|十)\s*张/);
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return parseCountToken(match?.[1]);
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}
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function normalizeGroupDescription(description: string) {
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return String(description || "")
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.replace(/^[为是:：，,\s]+/, "")
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.replace(/[。；;，,]\s*$/, "")
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.trim();
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}
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export function extractAgentTaskGroups(text: string): AgentTaskGroup[] {
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const normalized = String(text || "").replace(/\s+/g, " ").trim();
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if (!normalized) return [];
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const groups: AgentTaskGroup[] = [];
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const groupPattern =
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/第([一二两三四五六七八九十]|\d{1,2})组(?:为|是|:|：)?([\s\S]*?)(?=(?:第[一二两三四五六七八九十\d]{1,2}组)|[。；;]|$)/g;
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let match: RegExpExecArray | null;
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while ((match = groupPattern.exec(normalized)) !== null) {
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const labelToken = match[1];
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const description = normalizeGroupDescription(match[2] || "");
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if (!description) continue;
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const labelNumber = parseCountToken(labelToken);
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groups.push({
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label: `第${labelNumber || labelToken}组`,
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count: countFromText(description),
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description: compactLine(description, 96),
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});
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}
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return groups.slice(0, 8);
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}
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function getTargetSourceLabel(source?: AgentPlanningBriefTargetSource) {
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switch (source) {
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case "explicit_selection":
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return "画布选中图片";
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case "current_upload":
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return "当前上传图片";
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case "text_reference":
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return "文本指定图片";
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case "working_memory":
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return "会话工作记忆图片";
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case "latest_result":
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return "最近生成结果";
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case "clarification":
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return "待确认目标";
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case "none":
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default:
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return "无需现有图片";
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}
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}
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function getCapabilityLabel(capability?: AgentPlanningBriefCapability, subIntent?: string) {
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switch (capability || subIntent) {
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case "remove_background":
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return "主体抠图/去背景";
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case "remove_watermark":
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return "去水印";
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case "enhance":
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return "清晰度增强";
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case "extend":
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return "扩图/画幅延展";
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case "resize":
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return "尺寸调整";
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case "angle_variant":
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case "generate_angle_variant":
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return "多视角变体";
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case "local_edit":
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case "inpaint_region":
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return "局部编辑";
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case "box_cutout":
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return "框选抠图";
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case "generate_video":
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return "视频生成";
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case "generate_image":
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return "图片生成";
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case "analyze":
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return "图片分析";
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default:
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return "图片编辑";
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}
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}
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function inferTotalCount(params: AgentPlanningBriefInput, groups: AgentTaskGroup[]) {
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if (typeof params.imageCount === "number" && Number.isFinite(params.imageCount)) {
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return Math.max(1, Math.round(params.imageCount));
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}
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const groupedTotal = groups.reduce((sum, group) => sum + (group.count || 0), 0);
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return groupedTotal > 0 ? groupedTotal : undefined;
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}
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function buildIntentLine(params: AgentPlanningBriefInput, groups: AgentTaskGroup[]) {
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const target = getTargetSourceLabel(params.targetSource);
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const capability = getCapabilityLabel(params.preferredCapability, params.subIntent);
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const totalCount = inferTotalCount(params, groups);
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const demand = compactLine(params.planGoal || params.userText || "", 120);
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if (params.intent === "image_gen") {
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const countText = totalCount ? `，共 ${totalCount} 张` : "";
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const groupText = groups.length > 0 ? `，拆成 ${groups.length} 组` : "";
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const referenceText = params.hasImage ? "，并保持参考图主体核心特征一致" : "";
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return `用户需要生成新图片${groupText}${countText}${referenceText}；核心目标是：${demand || "完成本轮生图任务"}。`;
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}
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if (params.intent === "image_edit") {
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return `用户需要基于${target}执行${capability}；核心目标是：${demand || "完成本轮图片处理"}。`;
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}
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if (params.intent === "video_gen") {
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return `用户需要生成视频；先拆解镜头、主体连续性和运动节奏，再提交视频生成。`;
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}
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if (params.intent === "vision") {
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return `用户需要理解或分析图片内容；先观察主体、场景、构图和潜在问题，再给出结论。`;
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}
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return `用户当前主要是在对话或咨询；先回答问题，不主动触发生图或修图工具。`;
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}
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function buildImageGenerationPlan(params: AgentPlanningBriefInput, groups: AgentTaskGroup[]) {
|
||||
if (groups.length > 0) {
|
||||
return groups.map((group, index) => {
|
||||
const countText = group.count ? `${group.count}张` : "一组";
|
||||
return `${index + 1}. ${group.label}：生成${countText}${group.description.replace(/^\d+\s*张/, "")}，保持主体识别一致。`;
|
||||
});
|
||||
}
|
||||
|
||||
const count = inferTotalCount(params, groups) || 1;
|
||||
return [
|
||||
`1. 锁定主体、场景、风格和构图约束，整理成可执行提示词。`,
|
||||
`2. 生成 ${count} 张候选图，优先保证主体一致性和画面完成度。`,
|
||||
`3. 输出结果并保留可继续追改的上下文。`,
|
||||
];
|
||||
}
|
||||
|
||||
function buildImageEditPlan(params: AgentPlanningBriefInput) {
|
||||
const target = getTargetSourceLabel(params.targetSource);
|
||||
const capability = getCapabilityLabel(params.preferredCapability, params.subIntent);
|
||||
return [
|
||||
`1. 先锁定${target}作为本轮处理目标，避免误处理其他图片。`,
|
||||
`2. 调用${capability}能力执行核心改动，同时尽量保留主体结构和画质。`,
|
||||
`3. 输出处理结果，并保留原图用于对比或继续微调。`,
|
||||
];
|
||||
}
|
||||
|
||||
function buildGenericPlan(params: AgentPlanningBriefInput) {
|
||||
if (params.intent === "video_gen") {
|
||||
return [
|
||||
"1. 拆解视频主体、场景、动作节奏和镜头关系。",
|
||||
"2. 生成脚本与分镜，并整理可直接提交的视频提示词。",
|
||||
"3. 按规划提交视频生成，返回结果后支持继续调整。",
|
||||
];
|
||||
}
|
||||
if (params.intent === "vision") {
|
||||
return [
|
||||
"1. 先识别图片主体、背景、构图和关键信息。",
|
||||
"2. 结合用户问题输出判断、建议或可执行修改方向。",
|
||||
];
|
||||
}
|
||||
return ["1. 先直接回答用户问题；如果需要执行工具，再根据明确指令进入下一步。"];
|
||||
}
|
||||
|
||||
function buildOutputSettings(params: AgentPlanningBriefInput, groups: AgentTaskGroup[]) {
|
||||
const lines: string[] = [];
|
||||
const totalCount = inferTotalCount(params, groups);
|
||||
if (params.intent === "image_gen" || params.intent === "image_edit") {
|
||||
lines.push(`- 输出数量：${params.intent === "image_edit" ? "1 张处理结果" : `${totalCount || 1} 张`}`);
|
||||
lines.push(`- 图片比例：${params.aspectRatio && params.aspectRatio !== "auto" ? params.aspectRatio : "按需求自适应"}`);
|
||||
lines.push(`- 清晰度：${params.imageSize || (params.intent === "image_edit" ? "4K优先" : "2K优先")}`);
|
||||
if (params.intent === "image_edit") {
|
||||
lines.push(`- 目标来源：${getTargetSourceLabel(params.targetSource)}`);
|
||||
}
|
||||
} else if (params.intent === "video_gen") {
|
||||
lines.push("- 输出形式：视频结果 + 可继续追改的分镜上下文");
|
||||
} else {
|
||||
lines.push("- 输出形式：直接回答，不主动触发生成工具");
|
||||
}
|
||||
return lines;
|
||||
}
|
||||
|
||||
export function buildAgentPlanningBriefMarkdown(params: AgentPlanningBriefInput) {
|
||||
const groups = extractAgentTaskGroups(params.userText);
|
||||
const planLines =
|
||||
params.intent === "image_gen"
|
||||
? buildImageGenerationPlan(params, groups)
|
||||
: params.intent === "image_edit"
|
||||
? buildImageEditPlan(params)
|
||||
: buildGenericPlan(params);
|
||||
const outputSettings = buildOutputSettings(params, groups);
|
||||
|
||||
return [
|
||||
"### 意图分析",
|
||||
`- ${buildIntentLine(params, groups)}`,
|
||||
"### 任务规划",
|
||||
...planLines.map((line) => (line.startsWith("- ") ? line : `- ${line}`)),
|
||||
"### 输出设置",
|
||||
...outputSettings,
|
||||
].join("\n");
|
||||
}
|
||||
|
||||
export function isAgentPlanningBriefMarkdown(text: string) {
|
||||
const normalized = String(text || "");
|
||||
return /###\s*意图分析|【\s*意图分析\s*】/.test(normalized) &&
|
||||
/###\s*任务规划|【\s*任务规划\s*】/.test(normalized);
|
||||
}
|
||||
@ -54,6 +54,11 @@ import {
|
||||
} from "@/lib/ai/runtime/canvas-agent-kernel";
|
||||
import { isExplicitImageGenerationRequest } from "@/lib/ai/smart-analyzer";
|
||||
import { inferAgentImageCountFromText } from "@/lib/ai/runtime/image-count-intent";
|
||||
import {
|
||||
buildAgentPlanningBriefMarkdown,
|
||||
extractAgentTaskGroups,
|
||||
isAgentPlanningBriefMarkdown,
|
||||
} from "@/lib/ai/runtime/agent-planning-brief";
|
||||
import {
|
||||
buildRecentDialogueMemory,
|
||||
isRecentDialogueRecallRequest,
|
||||
@ -385,6 +390,39 @@ test.describe("canvas chat pure helpers", () => {
|
||||
expect(inferAgentImageCountFromText("再出30张", 12)).toBe(12);
|
||||
});
|
||||
|
||||
test("builds market-grade planning brief for grouped image generation requests", () => {
|
||||
const request =
|
||||
"基于上传的潮玩角色图片，生成3组共12张图片，要求保持角色核心特征一致。第一组为4张动作表情夸张的半身表情包；第二组为4张不同场景的角色活动照；第三组为4张服装发型差异巨大的造型照，所有图片比例保持3:4。";
|
||||
|
||||
const groups = extractAgentTaskGroups(request);
|
||||
expect(groups).toHaveLength(3);
|
||||
expect(groups.map((group) => group.count)).toEqual([4, 4, 4]);
|
||||
expect(groups[0].description).toContain("半身表情包");
|
||||
expect(groups[1].description).toContain("不同场景");
|
||||
expect(groups[2].description).toContain("服装发型");
|
||||
|
||||
const markdown = buildAgentPlanningBriefMarkdown({
|
||||
userText: request,
|
||||
intent: "image_gen",
|
||||
nextAction: "generate_image",
|
||||
preferredCapability: "generate_image",
|
||||
targetSource: "current_upload",
|
||||
hasImage: true,
|
||||
imageCount: 12,
|
||||
aspectRatio: "3:4",
|
||||
imageSize: "2K",
|
||||
});
|
||||
|
||||
expect(isAgentPlanningBriefMarkdown(markdown)).toBe(true);
|
||||
expect(markdown).toContain("### 意图分析");
|
||||
expect(markdown).toContain("拆成 3 组，共 12 张");
|
||||
expect(markdown).toContain("### 任务规划");
|
||||
expect(markdown).toContain("第1组：生成4张");
|
||||
expect(markdown).toContain("第2组：生成4张");
|
||||
expect(markdown).toContain("第3组：生成4张");
|
||||
expect(markdown).toContain("图片比例：3:4");
|
||||
});
|
||||
|
||||
test("keeps canvas message empty-history access scoped and explicit", () => {
|
||||
const canvasChatId = "11111111-1111-4111-8111-111111111111";
|
||||
const agentScopedChatId = "11111111-1111-4111-8111-a93e17a60001";
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user