315 lines
10 KiB
TypeScript
315 lines
10 KiB
TypeScript
import { generateObject, generateText } from "ai";
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import { z } from "zod";
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import type { ProviderApiKeys } from "./keys";
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import type { MatrixRow, ResearchMap } from "@/lib/types/research";
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import { withAgentSystem } from "./agent";
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import { buildPersonaPromptBlock } from "./persona";
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import { getModel } from "./provider";
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import { extractJsonFromText } from "./coerce-research-map";
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import {
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getOpenCodeGenerationSettings,
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prefersOpenCodeTextFirst,
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} from "./opencode-go-settings";
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import {
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coerceMatrixRaw,
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formatMatrixError,
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matrixLooseSchema,
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} from "./coerce-matrix";
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import { HASHTAG_USER_REMINDER, HASHTAG_WRITING_RULES } from "./hashtag-rules";
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import { sanitizePromptText, THREADS_MAX_CHARS } from "@/lib/utils";
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const matrixSchema = z.object({
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rows: z.array(
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z.object({
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sortOrder: z.number().int().min(1),
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searchTag: z.string(),
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angle: z.string(),
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hook: z.string(),
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text: z.string().max(THREADS_MAX_CHARS),
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referenceNotes: z.string(),
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sourcePermalinks: z.array(z.string()),
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rationale: z.string(),
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})
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),
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});
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const TEXT_FIRST_MODELS = new Set(["grok-3", "grok-3-fast"]);
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const BATCH_COUNT = 3;
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export interface GenerateMatrixInput {
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topicLabel: string;
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query: string;
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brief?: string | null;
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persona?: 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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count: number;
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posts: Array<{
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text: string;
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authorName?: string | null;
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permalink?: 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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qualityReason?: string | null;
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replies?: Array<{ text: string; authorName?: string | null; likeCount?: number | null }>;
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}>;
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}
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function distributePosts<T>(posts: T[], n: number): T[][] {
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const batches: T[][] = Array.from({ length: n }, () => []);
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posts.forEach((post, i) => batches[i % n].push(post));
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return batches;
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}
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function buildMaterialsBlock(posts: GenerateMatrixInput["posts"], defaultQuery: string): string {
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return posts
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.map((post, i) => {
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const repliesBlock =
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post.replies && post.replies.length > 0
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? `\n 留言：${post.replies
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.slice(0, 3)
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.map(
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(r) =>
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`@${sanitizePromptText(r.authorName) || "匿名"}：${sanitizePromptText(r.text).slice(0, 80)}`
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)
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.join(" | ")}`
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: "";
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return `${i + 1}. [${sanitizePromptText(post.searchTag) || sanitizePromptText(defaultQuery)}] @${sanitizePromptText(post.authorName) || "匿名"}（${post.likeCount ?? 0}讚）
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${sanitizePromptText(post.text).slice(0, 300)}
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連結：${sanitizePromptText(post.permalink) || "無"}${repliesBlock}${post.qualityReason ? `\n 品質說明：${sanitizePromptText(post.qualityReason)}` : ""}`;
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})
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.join("\n\n");
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}
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function buildResearchBlock(researchMap?: ResearchMap | null): string {
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if (!researchMap) return "";
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const similarAccounts = (researchMap.similarAccounts ?? [])
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.slice(0, 10)
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.map((a) => ` @${sanitizePromptText(a.username)}${a.reason ? `（${sanitizePromptText(a.reason)}）` : ""}`)
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.join("\n");
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return `
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受眾：${sanitizePromptText(researchMap.audienceSummary)}
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內容目標：${sanitizePromptText(researchMap.contentGoal)}
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想回答的問題：${researchMap.questions.map(sanitizePromptText).join("、")}
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內容支柱：${researchMap.pillars.map(sanitizePromptText).join("、")}
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${similarAccounts ? `同領域參考帳號：\n${similarAccounts}` : ""}
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`;
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}
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function buildSystemPrompt(persona?: string | null) {
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return withAgentSystem(`你是 Threads 內容企劃師。根據參考素材（含優質與中等品質），產出「內容矩陣」——一週可發的貼文企劃表。
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${buildPersonaPromptBlock(persona)}
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規則：
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- 每篇必須原創，不可抄襲參考貼文
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- hook 是一句吸引人的開頭（可獨立於正文），風格符合人設開場習慣
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- text 是完整 Threads 貼文（≤500字，含 #標籤），語感要像創作者本人親筆
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${HASHTAG_WRITING_RULES}
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- referenceNotes 是從參考素材摘出的 2-3 個重點（簡短條列感，用換行分隔）
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- 每篇對應一個 searchTag（主題標籤）
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- sortOrder 從 1 開始遞增
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- 繁體中文台灣用語
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- 知識型貼文：不寫未查證的醫療／數據斷言，rationale 標註需網路查證的關鍵句
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- 部分素材附有［品質說明］：品質較低的仍有參考價值（角度、切點可用），產出時不要直接沿用其內容`);
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}
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function buildBatchPrompt(
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input: GenerateMatrixInput,
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researchBlock: string,
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materials: string,
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batchIndex: number,
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totalBatches: number,
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thisBatchCount: number
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) {
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return `主題：${sanitizePromptText(input.topicLabel)}
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種子關鍵字：${sanitizePromptText(input.query)}
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${input.brief ? `Brief：${sanitizePromptText(input.brief)}` : ""}
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${researchBlock}
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【批次 ${batchIndex + 1}/${totalBatches}】
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以下為第 ${batchIndex + 1} 批素材（共 ${materials.split("\n\n").length} 篇），請產出 ${thisBatchCount} 篇不同切角，注意與其他批次做出區隔，避免重複。
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參考素材：
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${materials}
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寫作提醒：矩陣裡的 text 必須像創作者親筆，有代表句範例時語感要向範例靠攏。${HASHTAG_USER_REMINDER}`;
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}
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function buildJsonPromptSuffix(count: number) {
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return `
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請只回傳 JSON（不要 markdown），格式：
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{
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"rows": [
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{
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"sortOrder": 1,
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"searchTag": "標籤",
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"angle": "切角說明",
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"hook": "開頭一句",
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"text": "完整貼文（結尾含 1～3 個 #話題標籤）",
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"referenceNotes": "參考重點（多行以換行分隔）",
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"sourcePermalinks": ["https://..."],
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"rationale": "為什麼這篇有效"
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}
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]
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}
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請產出 ${count} 篇，sortOrder 從 1 遞增，每篇 text 不超過 ${THREADS_MAX_CHARS} 字。`;
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}
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function normalizeRows(rows: MatrixRow[]): MatrixRow[] {
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return rows
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.sort((a, b) => a.sortOrder - b.sortOrder)
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.map((row) => ({
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...row,
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text: row.text.slice(0, THREADS_MAX_CHARS),
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}));
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}
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function mergeBatches(batches: MatrixRow[][]): MatrixRow[] {
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const all = batches.flat();
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const seen = new Set<string>();
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const merged: MatrixRow[] = [];
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for (const row of all.sort((a, b) => a.sortOrder - b.sortOrder)) {
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const key = row.angle.slice(0, 30).toLowerCase().replace(/\s+/g, "");
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if (!seen.has(key)) {
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seen.add(key);
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merged.push(row);
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}
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}
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return merged.map((row, i) => ({ ...row, sortOrder: i + 1 }));
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}
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async function generateWithText(
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model: ReturnType<typeof getModel>,
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system: string,
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prompt: string,
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fallback: { query: string; count: number },
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provider: string,
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modelId: string
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) {
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const settings = getOpenCodeGenerationSettings(provider, modelId);
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const { text } = await generateText({
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model,
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system: sanitizePromptText(`${system}${buildJsonPromptSuffix(fallback.count)}`),
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prompt: sanitizePromptText(prompt),
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...settings,
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});
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const parsed = extractJsonFromText(text);
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matrixLooseSchema.parse(parsed);
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return coerceMatrixRaw(parsed, fallback);
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}
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async function generateWithObject(
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model: ReturnType<typeof getModel>,
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system: string,
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prompt: string,
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fallback: { query: string; count: number },
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provider: string,
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modelId: string
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) {
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const settings = getOpenCodeGenerationSettings(provider, modelId);
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const { object } = await generateObject({
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model,
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schema: matrixSchema,
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system: sanitizePromptText(system),
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prompt: sanitizePromptText(`${prompt}${buildJsonPromptSuffix(fallback.count)}`),
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...settings,
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});
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return normalizeRows(object.rows);
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}
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async function attemptBatch(
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model: ReturnType<typeof getModel>,
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system: string,
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prompt: string,
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fallback: { query: string; count: number },
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provider: string,
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modelId: string,
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preferText: boolean
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): Promise<MatrixRow[]> {
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const attempts: Array<() => Promise<MatrixRow[]>> = preferText
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? [
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() => generateWithText(model, system, prompt, fallback, provider, modelId),
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() => generateWithObject(model, system, prompt, fallback, provider, modelId),
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() =>
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generateWithText(
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model,
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system,
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`${prompt}\n\n上次格式不完整，請務必補齊 ${fallback.count} 篇 rows。`,
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fallback,
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provider,
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modelId
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),
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]
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: [
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() => generateWithObject(model, system, prompt, fallback, provider, modelId),
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() => generateWithText(model, system, prompt, fallback, provider, modelId),
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() =>
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generateWithText(
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model,
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system,
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`${prompt}\n\n上次格式不完整，請務必補齊 ${fallback.count} 篇 rows。`,
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fallback,
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provider,
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modelId
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),
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];
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let rows: MatrixRow[] | null = null;
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let lastError: unknown;
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for (const attempt of attempts) {
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try {
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rows = await attempt();
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break;
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} catch (error) {
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lastError = error;
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}
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}
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if (!rows) {
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throw new Error(formatMatrixError(lastError));
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}
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return normalizeRows(rows);
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}
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export async function generateContentMatrix(input: GenerateMatrixInput): Promise<MatrixRow[]> {
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if (input.posts.length === 0) {
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throw new Error("此海巡沒有素材，請重新海巡");
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}
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const model = getModel(input.aiProvider, input.aiModel, input.apiKeys ?? {});
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const researchBlock = buildResearchBlock(input.researchMap);
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const system = buildSystemPrompt(input.persona);
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const preferText =
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prefersOpenCodeTextFirst(input.aiProvider, input.aiModel) ||
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TEXT_FIRST_MODELS.has(input.aiModel);
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const batches = distributePosts(input.posts, BATCH_COUNT);
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const perBatch = Math.ceil(input.count / BATCH_COUNT);
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const results = await Promise.all(
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batches.map((batchPosts, batchIndex) => {
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if (batchPosts.length === 0) return Promise.resolve([] as MatrixRow[]);
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const materials = buildMaterialsBlock(batchPosts, input.query);
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const prompt = buildBatchPrompt(input, researchBlock, materials, batchIndex, BATCH_COUNT, perBatch);
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const fallback = { query: input.query, count: perBatch };
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return attemptBatch(model, system, prompt, fallback, input.aiProvider, input.aiModel, preferText);
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})
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);
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const merged = mergeBatches(results);
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return normalizeRows(merged).slice(0, input.count);
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}
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