373 lines
12 KiB
TypeScript
373 lines
12 KiB
TypeScript
import { describe, expect, it } from "vitest";
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import {
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articleInputSchema,
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confirmedFactCardSchema,
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candidateFactCardSchema,
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copyOptimizationRequestSchema,
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copyOptimizationResultSchema,
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optimizationFactCardSchema,
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optimizedArticleSchema,
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qaReportSchema,
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} from "../validation";
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describe("domain validation", () => {
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it("accepts article input with title, body, images, platform, and instructions", () => {
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const parsed = articleInputSchema.parse({
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title: "How GEO Optimization Improves Brand Visibility",
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body: "A practical overview of GEO optimization for marketing teams.",
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images: [
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{ type: "description", content: "Dashboard screenshot" },
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{ type: "link", content: "https://example.com/image.png" },
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],
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platform: "official_site",
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user_instructions: "Keep the article factual and concise.",
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});
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expect(parsed.images).toHaveLength(2);
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expect(parsed.platform).toBe("official_site");
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});
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it("accepts article input with an empty optional title", () => {
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const parsed = articleInputSchema.parse({
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title: " ",
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body: "完整文章正文可以直接粘贴在这里。",
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images: [],
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platform: "official_site",
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user_instructions: "",
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});
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expect(parsed.title).toBe("");
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expect(parsed.body).toBe("完整文章正文可以直接粘贴在这里。");
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});
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it("still rejects article input with an empty body", () => {
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expect(() =>
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articleInputSchema.parse({
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title: "",
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body: " ",
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images: [],
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platform: "official_site",
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user_instructions: "",
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}),
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).toThrow();
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});
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it("accepts a trimmed copy optimization request", () => {
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const parsed = copyOptimizationRequestSchema.parse({
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source_text: " 我写了一段有点卡的文案 ",
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goal: "",
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intensity: "light",
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user_instructions: " 保留口语感 ",
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});
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expect(parsed).toEqual({
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source_text: "我写了一段有点卡的文案",
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goal: "保留原意，减少 AI 味",
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intensity: "light",
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user_instructions: "保留口语感",
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});
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});
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it("rejects empty copy optimization source text", () => {
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expect(() =>
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copyOptimizationRequestSchema.parse({
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source_text: " ",
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intensity: "light",
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}),
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).toThrow();
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});
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it("accepts structured copy optimization results", () => {
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const parsed = copyOptimizationResultSchema.parse({
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optimized_text: "我把句子顺了一下。",
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change_notes: [
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{
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original: "我把句子顺顺。",
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revised: "我把句子顺了一下。",
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reason: "修正口语里不顺的重复。",
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confidence: "confident",
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revertible: false,
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},
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],
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ai_taste_checks: [
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{
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rule_id: "promotion_tone",
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status: "pass",
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evidence: "没有新增宣传词。",
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suggestion: "",
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},
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],
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warnings: [],
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});
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expect(parsed.optimized_text).toBe("我把句子顺了一下。");
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expect(parsed.change_notes[0]?.confidence).toBe("confident");
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expect(parsed.ai_taste_checks[0]?.rule_id).toBe("promotion_tone");
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});
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it("accepts an unconfirmed optimization fact card with unresolved items", () => {
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const parsed = optimizationFactCardSchema.parse({
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company_full_name: "",
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company_short_names: ["示例科技"],
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brand_names: [],
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product_names: ["GEO内容优化平台"],
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target_industry: "",
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target_audience: "市场团队",
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experience_years: "",
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core_claims: ["提供GEO内容优化服务"],
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forbidden_claims: [],
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image_topics: [],
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uncertain_items: ["客户案例需要确认"],
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confirmed_by_user: false,
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});
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expect(parsed.company_full_name).toBe("");
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expect(parsed.experience_years).toBeNull();
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expect(parsed.confirmed_by_user).toBe(false);
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expect(parsed.is_ready_for_optimization).toBe(false);
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expect(parsed.uncertain_items).toEqual(["客户案例需要确认"]);
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});
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it("marks a fact card with unresolved uncertain items as not ready for optimization", () => {
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const parsed = candidateFactCardSchema.parse({
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company_full_name: "Example Technology Co., Ltd.",
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company_short_names: ["Example Tech"],
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brand_names: ["Example"],
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product_names: ["Example GEO"],
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target_industry: "GEO optimization",
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target_audience: "Marketing teams",
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experience_years: 8,
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core_claims: ["Eight years of GEO optimization experience"],
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forbidden_claims: [],
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image_topics: ["Product dashboard"],
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uncertain_items: ["Conflicting product names found"],
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});
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expect(parsed.is_ready_for_optimization).toBe(false);
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});
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it("normalizes near-valid LLM fact card field types", () => {
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const emptyExperience = candidateFactCardSchema.parse({
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company_full_name: "Example Technology Co., Ltd.",
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company_short_names: ["Example Tech"],
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brand_names: ["Example"],
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product_names: ["Example GEO"],
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target_industry: "GEO optimization",
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target_audience: ["Marketing teams", "Brand teams"],
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experience_years: "",
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core_claims: ["GEO optimization experience"],
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forbidden_claims: [],
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image_topics: [],
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uncertain_items: [],
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});
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const numericExperience = candidateFactCardSchema.parse({
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company_full_name: "Example Technology Co., Ltd.",
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company_short_names: ["Example Tech"],
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brand_names: ["Example"],
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product_names: ["Example GEO"],
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target_industry: "GEO optimization",
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target_audience: "Marketing teams",
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experience_years: "8年",
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core_claims: ["GEO optimization experience"],
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forbidden_claims: [],
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image_topics: [],
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uncertain_items: [],
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});
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expect(emptyExperience.target_audience).toBe("Marketing teams、Brand teams");
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expect(emptyExperience.experience_years).toBeNull();
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expect(numericExperience.experience_years).toBe(8);
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});
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it("normalizes object-shaped and single-string LLM fact card fields", () => {
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const parsed = candidateFactCardSchema.parse({
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company_full_name: { name: "示例科技有限公司" },
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company_short_names: "示例科技",
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brand_names: [{ name: "示例品牌" }],
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product_names: [{ product: "GEO内容优化平台" }],
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target_industry: { industry: "GEO内容优化" },
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target_audience: { audience: "市场团队" },
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experience_years: { years: "8年" },
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core_claims: [
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{ claim: "提供GEO内容优化服务", source: "原文明确出现" },
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],
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forbidden_claims: [
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{ claim: "行业第一", reason: "缺少第三方依据" },
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],
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image_topics: [{ topic: "产品后台截图" }],
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uncertain_items: [
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{ item: "客户案例", reason: "原文没有给出客户名称" },
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{ claim: "出海能力", evidence: "只出现营销表述" },
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],
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});
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expect(parsed.company_full_name).toBe("示例科技有限公司");
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expect(parsed.company_short_names).toEqual(["示例科技"]);
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expect(parsed.brand_names).toEqual(["示例品牌"]);
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expect(parsed.product_names).toEqual(["GEO内容优化平台"]);
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expect(parsed.target_industry).toBe("GEO内容优化");
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expect(parsed.target_audience).toBe("市场团队");
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expect(parsed.experience_years).toBe(8);
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expect(parsed.core_claims).toEqual(["提供GEO内容优化服务"]);
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expect(parsed.forbidden_claims).toEqual(["行业第一"]);
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expect(parsed.image_topics).toEqual(["产品后台截图"]);
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expect(parsed.uncertain_items).toEqual(["客户案例", "出海能力"]);
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expect(parsed.is_ready_for_optimization).toBe(false);
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});
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it("keeps incomplete candidate fact cards editable and ready", () => {
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const parsed = candidateFactCardSchema.parse({
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company_full_name: "",
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company_short_names: [],
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brand_names: [],
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product_names: [],
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target_industry: "",
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target_audience: "",
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experience_years: "",
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core_claims: [],
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forbidden_claims: [],
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image_topics: [],
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uncertain_items: [],
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});
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expect(parsed.target_industry).toBe("");
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expect(parsed.target_audience).toBe("");
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expect(parsed.experience_years).toBeNull();
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expect(parsed.uncertain_items).toEqual([]);
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expect(parsed.is_ready_for_optimization).toBe(true);
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});
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it("rejects a confirmed fact card with an empty company full name", () => {
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expect(() =>
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confirmedFactCardSchema.parse({
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company_full_name: "",
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company_short_names: ["Example"],
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brand_names: ["Example"],
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product_names: ["Example GEO"],
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target_industry: "GEO optimization",
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target_audience: "Marketing teams",
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experience_years: 8,
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core_claims: ["Eight years of GEO optimization experience"],
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forbidden_claims: [],
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image_topics: [],
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uncertain_items: [],
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confirmed_by_user: true,
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}),
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).toThrow();
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});
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it("accepts QA reports only with pass, warn, or fail statuses", () => {
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const valid = qaReportSchema.parse({
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job_id: "job_123",
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revision: 1,
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overall_status: "warn",
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checks: [
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{
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rule_id: "title_quality",
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status: "pass",
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evidence: "Title reads naturally.",
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reason: "No grammar issue detected.",
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suggested_fix: "",
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target_agent: null,
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},
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],
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});
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expect(valid.checks[0]?.status).toBe("pass");
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expect(() =>
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qaReportSchema.parse({
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job_id: "job_123",
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revision: 1,
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overall_status: "blocked",
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checks: [],
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}),
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).toThrow();
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});
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it("normalizes near-valid LLM QA report values", () => {
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const parsed = qaReportSchema.parse({
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overall_status: "警告",
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checks: [
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{
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rule_id: "标题质量",
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status: "警告",
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evidence: { detail: "标题仍然偏营销化" },
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reason: { reason: "官网标题需要更克制" },
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suggested_fix: null,
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target_agent: "",
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},
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{
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rule_id: "company_name_integrity",
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status: "通过",
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evidence: "公司全称一致",
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reason: "正文保留了事实卡中的公司全称",
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target_agent: "无",
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},
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],
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});
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expect(parsed.overall_status).toBe("warn");
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expect(parsed.checks[0]).toEqual({
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rule_id: "title_quality",
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status: "warn",
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evidence: "标题仍然偏营销化",
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reason: "官网标题需要更克制",
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suggested_fix: "",
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target_agent: null,
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});
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expect(parsed.checks[1]?.target_agent).toBeNull();
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});
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it("normalizes object-shaped changed sections from LLM output", () => {
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const parsed = optimizedArticleSchema.parse({
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title: "Optimized article",
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summary: "Summary constrained by the confirmed fact card.",
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body_markdown: "## Body\nUpdated body.",
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image_suggestions: [],
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changed_sections: [
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{ section: "title", change: "Improved keyword clarity." },
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{ name: "body", reason: "Fixed sentence flow." },
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],
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requires_user_confirmation: [],
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});
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expect(parsed.changed_sections).toEqual([
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"title: Improved keyword clarity.",
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"body: Fixed sentence flow.",
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]);
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});
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it("normalizes near-valid optimized article LLM optional fields", () => {
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const parsed = optimizedArticleSchema.parse({
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title: { text: "示例科技 GEO 内容优化方案" },
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summary: ["围绕事实卡重写官网文章摘要"],
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body_markdown: {
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markdown: "## 服务能力\n示例科技有限公司提供GEO内容优化服务。",
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},
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image_suggestions: [
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"当前版本不生成图片建议",
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{ source: "image_1" },
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{ suggestion: "使用产品后台截图" },
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{ source: "image_2", suggestion: "保留原文截图说明" },
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],
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changed_sections: [
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{ section: "title", change: "改成中文官网标题" },
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],
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requires_user_confirmation: [
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{ claim: "客户案例", reason: "原文没有给出客户名称" },
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],
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});
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expect(parsed.title).toBe("示例科技 GEO 内容优化方案");
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expect(parsed.summary).toBe("围绕事实卡重写官网文章摘要");
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expect(parsed.body_markdown).toContain("## 服务能力");
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expect(parsed.image_suggestions).toEqual([
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{ source: "image_2", suggestion: "保留原文截图说明" },
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]);
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expect(parsed.changed_sections).toEqual(["title: 改成中文官网标题"]);
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expect(parsed.requires_user_confirmation).toEqual(["客户案例"]);
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});
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});
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