119 lines
3.9 KiB
TypeScript
119 lines
3.9 KiB
TypeScript
import { z } from "zod";
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import type { ProviderApiKeys } from "./keys";
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import { withAgentSystem } from "./agent";
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import { generateStructuredObject } from "./generate-structured";
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import { getModel } from "./provider";
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import { describePostImages } from "./vision";
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import type { ViralAnalysis } from "@/lib/types/viral";
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const viralSchema = z.object({
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whyViral: z.array(z.string()).min(2).max(6),
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hookPattern: z.string(),
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structurePattern: z.string(),
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emotionalTrigger: z.string(),
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timingAngle: z.string(),
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commentInsights: z.object({
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themes: z.array(z.string()),
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sentiment: z.string(),
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topReactions: z.array(z.string()),
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audienceQuestions: z.array(z.string()),
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whyPeopleEngage: z.string(),
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}),
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visualAnalysis: z.object({
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hasImages: z.boolean(),
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layout: z.string(),
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colorMood: z.string(),
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textOnImage: z.boolean(),
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typographyStyle: z.string(),
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visualHook: z.string(),
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replicationTips: z.array(z.string()),
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imageGenPrompt: z.string(),
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}),
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replicationStrategy: z.string(),
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keyTakeaways: z.array(z.string()),
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});
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export interface AnalyzeViralInput {
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postText: string;
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authorName?: string | null;
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likeCount?: number | null;
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replyCount?: number | null;
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searchTag?: string | null;
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mediaUrls?: string[];
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mediaType?: string | null;
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topicLabel: string;
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topicBrief?: string | null;
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persona?: string | null;
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aiProvider: string;
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aiModel: string;
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apiKeys?: ProviderApiKeys;
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replies: Array<{ text: string; authorName?: string | null; likeCount?: number | null }>;
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}
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export async function analyzeViralPost(input: AnalyzeViralInput): Promise<ViralAnalysis> {
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const model = getModel(input.aiProvider, input.aiModel, input.apiKeys ?? {});
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let visualDescription: string | null = null;
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if (input.mediaUrls && input.mediaUrls.length > 0) {
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visualDescription = await describePostImages(
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input.mediaUrls,
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input.postText,
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input.apiKeys ?? {}
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);
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}
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const repliesBlock = input.replies
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.slice(0, 15)
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.map(
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(r, i) =>
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`${i + 1}. @${r.authorName ?? "匿名"}（${r.likeCount ?? 0}讚）：${r.text.slice(0, 200)}`
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)
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.join("\n");
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const visualBlock = visualDescription
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? `\n\nAI 圖文視覺分析：\n${visualDescription}`
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: input.mediaUrls && input.mediaUrls.length > 0
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? `\n\n（附圖 ${input.mediaUrls.length} 張，未能進行視覺辨識，請依貼文文字推測圖文策略）`
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: "\n\n（純文字貼文，無附圖）";
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const object = await generateStructuredObject({
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model,
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provider: input.aiProvider,
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modelId: input.aiModel,
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schema: viralSchema,
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system: withAgentSystem(`你是 Threads 爆款內容分析師。任務是拆解「為什麼這篇會紅」，並從留言中理解受眾為什麼買單。
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分析重點：
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- 開頭 hook 用了什麼手法（反差、提問、數字、故事、痛點）
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- 內文結構（幾段、節奏、轉折、CTA）
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- 留言反映的共鳴點（不是摘要留言，是找「為什麼大家願意互動」）
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- 若有圖文，分析圖文如何配合文字放大傳播
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- imageGenPrompt 要寫成可直接給 AI 繪圖的英文 prompt，描述類似風格的圖
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除 imageGenPrompt 外，全部繁體中文台灣用語。`),
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prompt: `主題：${input.topicLabel}
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${input.topicBrief ? `Brief：${input.topicBrief}` : ""}
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${input.persona ? `創作者人設：${input.persona}` : ""}
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搜尋標籤：${input.searchTag ?? "—"}
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媒體類型：${input.mediaType ?? "unknown"}
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爆款貼文：
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@${input.authorName ?? "匿名"} · ${input.likeCount ?? 0} 讚 · ${input.replyCount ?? 0} 留言
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${input.postText}
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${visualBlock}
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熱門留言（理解受眾為什麼互動）：
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${repliesBlock || "（無留言資料）"}
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請深度分析這篇為什麼會爆款，以及如何複製其成功模式。`,
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});
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return {
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...object,
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visualAnalysis: {
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...object.visualAnalysis,
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hasImages: (input.mediaUrls?.length ?? 0) > 0,
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},
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analyzedAt: new Date().toISOString(),
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};
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} |