141 lines
5.4 KiB
TypeScript
141 lines
5.4 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 type { QualityAssessment, QualityTier, ResearchMap } from "@/lib/types/research";
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import { buildTopicAnchorPromptBlock } from "@/lib/topic-anchor";
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const filterSchema = z.object({
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items: z.array(
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z.object({
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index: z.number().int().min(0),
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relevanceScore: z.number().min(0).max(100),
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qualityTier: z.enum(["GOOD", "OK", "EXCLUDE"]),
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qualityReason: z.string(),
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})
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),
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});
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export interface FilterQualityInput {
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topicLabel: string;
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query: string;
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brief?: string | null;
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researchMap?: ResearchMap | null;
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aiProvider: string;
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aiModel: string;
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apiKeys?: ProviderApiKeys;
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posts: Array<{
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id: string;
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text: string;
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authorName?: string | null;
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searchTag?: string | null;
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likeCount?: number | null;
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replyCount?: number | null;
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replies?: string[] | null;
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}>;
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}
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function normalizeEngagement(likeCount?: number | null, replyCount?: number | null): number {
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const likes = likeCount ?? 0;
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const replies = replyCount ?? 0;
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const raw = likes * 1.0 + replies * 2.0;
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return Math.min(100, Math.log10(raw + 1) * 25);
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}
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export function computeCombinedScore(relevanceScore: number, engagementNorm: number): number {
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return relevanceScore * 0.7 + engagementNorm * 0.3;
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}
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export function computePlacementCombinedScore(
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placementScore: number,
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engagementNorm: number,
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replyCount?: number | null,
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recencyScore?: number | null
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): number {
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const replySignal = Math.min(100, (replyCount ?? 0) * 6);
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const recency = recencyScore ?? 35;
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return placementScore * 0.5 + recency * 0.28 + engagementNorm * 0.12 + replySignal * 0.1;
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}
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export async function filterPostQuality(input: FilterQualityInput): Promise<QualityAssessment[]> {
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if (input.posts.length === 0) return [];
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const model = getModel(input.aiProvider, input.aiModel, input.apiKeys ?? {});
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const similarAccounts = (input.researchMap?.similarAccounts ?? [])
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.slice(0, 8)
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.map((a) => `@${a.username}`)
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.join("、");
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const researchBlock = input.researchMap
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? `
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研究地圖：
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- 受眾：${input.researchMap.audienceSummary}
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- 目標：${input.researchMap.contentGoal}
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- 想了解的問題：${input.researchMap.questions.join("、")}
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- 內容支柱：${input.researchMap.pillars.join("、")}
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- 排除：${input.researchMap.exclusions.join("、")}
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${similarAccounts ? `- 相似帳號（同領域標竿，其貼文通常更值得參考）：${similarAccounts}` : ""}
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`
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: "";
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const postsBlock = input.posts
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.map((post, i) => {
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const replyLines = (post.replies ?? []).filter(Boolean).slice(0, 2);
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const replyBlock =
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replyLines.length > 0
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? `\n熱門留言（觀眾真實反應）：${replyLines.map((r) => `「${r.slice(0, 60)}」`).join(" ")}`
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: "";
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return `[${i}] 標籤：${post.searchTag ?? input.query} | @${post.authorName ?? "匿名"} | ${post.likeCount ?? 0}讚 ${post.replyCount ?? 0}留言
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${post.text.slice(0, 280)}${replyBlock}`;
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})
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.join("\n\n");
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const object = await generateStructuredObject({
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model,
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provider: input.aiProvider,
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modelId: input.aiModel,
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schema: filterSchema,
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system: withAgentSystem(`你是 Threads 內容品質審核員。評估每篇貼文是否值得改寫成「新型小語」——觀眾想看的、盡量正確、有故事感的一兩句話。
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評分標準：
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- relevanceScore 0-100：與主題、受眾需求的相關程度（嚴格扣緊種子關鍵字，略偏題即降分）
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- 語錄型、故事感、情緒共鳴、一兩句說中人心 → 提高 relevanceScore
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- qualityTier：
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- GOOD：有資訊或故事、結構清楚、值得參考改寫成新型小語
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- OK：角度可參考但內容薄弱、略偏題、或太長不像小語
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- EXCLUDE：業配、偏方、純宣洩無觀點、無關、低質量
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- 若有提供「熱門留言」，代表觀眾真實反應：留言越能呼應、討論越熱烈、共鳴越強 → 適度提高 relevanceScore；留言冷淡、跑題或反感 → 降低分數。沒有提供留言時，僅依貼文內容與讚數判斷即可，不要因為缺留言而扣分。
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- 帳號只是來源，不是相關性的保證。searchTag 為 @帳號時仍須逐篇審核；個人抒發、生活閒聊、家庭日常、政治或其他主題，即使作者是相似帳號也一律 EXCLUDE。
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- 來自 @帳號的貼文，只有內容本身明確命中主題與受眾問題才能給 GOOD/OK；不得因作者是網紅或相似帳號而加分。
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請為每篇貼文（用 index 對應）給出評分。qualityReason 用台灣用語繁體中文。`),
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prompt: `主題：${input.topicLabel}
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種子關鍵字：${input.query}
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${input.brief ? `Brief：${input.brief}` : ""}
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${buildTopicAnchorPromptBlock({
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label: input.topicLabel,
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query: input.query,
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brief: input.brief,
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exclusions: input.researchMap?.exclusions,
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})}
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${researchBlock}
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待評估貼文：
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${postsBlock}`,
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});
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return object.items
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.filter((item) => item.index >= 0 && item.index < input.posts.length)
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.map((item) => ({
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id: input.posts[item.index].id,
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relevanceScore: item.relevanceScore,
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qualityTier: item.qualityTier as QualityTier,
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qualityReason: item.qualityReason,
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}));
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}
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export function engagementNormForPost(likeCount?: number | null, replyCount?: number | null): number {
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return normalizeEngagement(likeCount, replyCount);
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}
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