152 lines
4.4 KiB
TypeScript
152 lines
4.4 KiB
TypeScript
import { z } from "zod";
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import type { ProviderApiKeys } from "./keys";
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import { generateStructuredObject } from "./generate-structured";
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import { getModel } from "./provider";
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import { filterSuggestedTags, preserveTagsFromPreviousMap } from "./normalize-suggested-tags";
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import { buildResearchMapSystemPrompt } from "./prompts/research-map";
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import { buildTopicAnchorPromptBlock } from "@/lib/topic-anchor";
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import type { ResearchMap } from "@/lib/types/research";
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const searchIntentSchema = z.enum([
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"痛點",
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"知識",
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"經驗",
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"對比",
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"工具",
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"語錄",
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"需求",
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"求助",
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]);
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const searchTypeSchema = z.enum(["短詞", "情境", "帳號", "語錄"]);
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const researchMapSchema = z.object({
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audienceSummary: z.string(),
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contentGoal: z.string(),
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questions: z.array(z.string()).min(1).max(12),
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pillars: z.array(z.string()).min(1).max(8),
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suggestedTags: z
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.array(
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z.object({
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tag: z.string(),
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reason: z.string(),
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searchIntent: searchIntentSchema,
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searchType: searchTypeSchema.optional(),
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})
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)
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.min(1)
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.max(15),
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similarAccounts: z
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.array(
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z.object({
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username: z.string(),
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reason: z.string(),
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postUrl: z.string().optional(),
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profileUrl: z.string().optional(),
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source: z.enum(["web", "threads", "scan"]).optional(),
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confidence: z.enum(["high", "medium", "low"]).optional(),
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lastActiveAt: z.string().optional(),
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})
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)
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.optional(),
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exclusions: z.array(z.string()).min(1).max(12),
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});
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const refineResponseSchema = z.object({
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reply: z.string(),
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researchMap: researchMapSchema,
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});
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export interface RefineChatMessage {
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role: "user" | "assistant";
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content: string;
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}
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export interface RefineResearchMapInput {
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label: 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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currentMap: ResearchMap;
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message: string;
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history?: RefineChatMessage[];
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aiProvider: string;
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aiModel: string;
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apiKeys?: ProviderApiKeys;
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}
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function buildRefineUserPrompt(input: RefineResearchMapInput): string {
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const historyBlock =
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input.history && input.history.length > 0
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? `\n【先前對話】\n${input.history
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.map((m) => `${m.role === "user" ? "用戶" : "助理"}：${m.content}`)
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.join("\n")}\n`
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: "";
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return `【主題】${input.label}（種子字：${input.query}）
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【Brief】${input.brief?.trim() || "（無）"}
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${buildTopicAnchorPromptBlock({ label: input.label, query: input.query, brief: input.brief })}
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${historyBlock}
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【目前研究地圖草稿】
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${JSON.stringify(input.currentMap, null, 2)}
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【用戶這次的調整請求】
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${input.message}
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請依請求微調研究地圖。只改用戶提到的部分，其餘盡量保留。reply 用 2～4 句繁體中文說明改了什麼。`;
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}
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export async function refineResearchMap(input: RefineResearchMapInput): Promise<{
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reply: string;
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researchMap: ResearchMap;
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}> {
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const model = getModel(input.aiProvider, input.aiModel, input.apiKeys ?? {});
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const system = `${buildResearchMapSystemPrompt()}
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你現在進入「微調模式」。用戶已有一份研究地圖，會告訴你想改什麼、補什麼、刪什麼。
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- 精準執行用戶意圖，不要整份重寫除非用戶明確要求
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- 維持 Threads 搜尋標籤品質（短詞優先、像真人會搜的字、扣緊種子關鍵字）
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- 用戶沒要求刪的標籤盡量保留；複合主題不可拆成過寬單字（如寵物洗毛精≠只搜寵物或洗碗精）
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- reply 簡潔說明變更重點，繁體中文台灣用語
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回傳 JSON 格式，須包含兩個欄位：
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1. reply：字串，2~4 句繁體中文說明本次變更重點
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2. researchMap：研究地圖物件，格式與「目前研究地圖草稿」相同`;
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const prompt = buildRefineUserPrompt(input);
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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: refineResponseSchema,
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system,
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prompt,
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});
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const tagInput = {
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label: input.label,
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query: input.query,
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brief: input.brief,
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exclusions: input.currentMap.exclusions,
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};
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let researchMap = preserveTagsFromPreviousMap(
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object.researchMap as ResearchMap,
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input.currentMap,
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tagInput
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);
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researchMap = {
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...researchMap,
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suggestedTags: filterSuggestedTags(researchMap.suggestedTags, tagInput),
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similarAccounts: input.currentMap.similarAccounts,
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};
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return {
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reply: object.reply,
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researchMap,
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};
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}
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