fix: surface llm workflow errors

src/app/api/__tests__/jobs.test.ts

@@ -39,6 +39,21 @@ const validFactCard = {
   confirmed_by_user: true,
 };
 
+const validCandidateFactCard = {
+  company_full_name: validFactCard.company_full_name,
+  company_short_names: validFactCard.company_short_names,
+  brand_names: validFactCard.brand_names,
+  product_names: validFactCard.product_names,
+  target_industry: validFactCard.target_industry,
+  target_audience: validFactCard.target_audience,
+  experience_years: validFactCard.experience_years,
+  core_claims: validFactCard.core_claims,
+  forbidden_claims: validFactCard.forbidden_claims,
+  image_topics: validFactCard.image_topics,
+  uncertain_items: validFactCard.uncertain_items,
+  is_ready_for_optimization: true,
+};
+
 interface CreateJobResponse {
   job: { id: string };
   candidateFactCard: { company_full_name: string };
@@ -86,6 +101,8 @@ describe("job API routes", () => {
   });
 
   it("validates input, creates a job, and returns a candidate fact card", async () => {
+    llmMocks.generateValidatedJson.mockResolvedValueOnce(validCandidateFactCard);
+
     const response = await createJob(
       request({
         title: "Example Technology Co., Ltd. GEO guide",
@@ -104,6 +121,38 @@ describe("job API routes", () => {
     );
   });
 
+  it("returns a clear error when LLM fact extraction fails", async () => {
+    llmMocks.generateValidatedJson.mockRejectedValueOnce(
+      new Error("LLM response failed schema validation: target_audience"),
+    );
+
+    const response = await createJob(
+      request({
+        title: "Example Technology Co., Ltd. GEO guide",
+        body: "Example Technology Co., Ltd. has 8 years of GEO optimization experience.",
+        platform: "official_site",
+      }),
+    );
+    const body = (await response.json()) as { error: string };
+
+    expect(response.status).toBe(502);
+    expect(body.error).toBe(
+      "LLM response failed schema validation: target_audience",
+    );
+  });
+
+  it("still returns 400 for invalid article input", async () => {
+    const response = await createJob(
+      request({
+        title: "",
+        body: "",
+        platform: "official_site",
+      }),
+    );
+
+    expect(response.status).toBe(400);
+  });
+
   it("rejects unresolved uncertain items when confirming a fact card", async () => {
     const { job } = await createJobFixture();
     const response = await confirmFactCard(
@@ -135,6 +184,19 @@ describe("job API routes", () => {
       params<{ jobId: string }>({ jobId: job.id }),
     );
 
+    llmMocks.generateValidatedJson
+      .mockResolvedValueOnce({
+        title: "API LLM Optimized GEO Article",
+        summary:
+          "A official site article for Marketing teams about GEO optimization.",
+        body_markdown:
+          "Example Technology Co., Ltd. has 8 years of GEO optimization experience.",
+        image_suggestions: [],
+        changed_sections: ["title", "body"],
+        requires_user_confirmation: [],
+      })
+      .mockResolvedValueOnce({ checks: [] });
+
     const response = await optimizeJob(
       request({}),
       params<{ jobId: string }>({ jobId: job.id }),
@@ -142,7 +204,7 @@ describe("job API routes", () => {
     const body = (await response.json()) as OptimizeJobResponse;
 
     expect(response.status).toBe(200);
-    expect(body.optimizedArticle.title).toContain("GEO optimization");
+    expect(body.optimizedArticle.title).toBe("API LLM Optimized GEO Article");
     expect(body.qaReport.checks).toHaveLength(10);
   });
 
@@ -166,7 +228,7 @@ describe("job API routes", () => {
         changed_sections: ["title", "body"],
         requires_user_confirmation: [],
       })
-      .mockResolvedValue(null);
+      .mockResolvedValue({ checks: [] });
 
     const response = await optimizeJob(
       request({}),
@@ -179,6 +241,27 @@ describe("job API routes", () => {
     expect(llmMocks.generateValidatedJson).toHaveBeenCalled();
   });
 
+  it("returns a clear error when LLM optimization fails", async () => {
+    const { job } = await createJobFixture();
+    await confirmFactCard(
+      request(validFactCard),
+      params<{ jobId: string }>({ jobId: job.id }),
+    );
+
+    llmMocks.generateValidatedJson.mockRejectedValueOnce(
+      new Error("LLM response failed schema validation: body_markdown"),
+    );
+
+    const response = await optimizeJob(
+      request({}),
+      params<{ jobId: string }>({ jobId: job.id }),
+    );
+    const body = (await response.json()) as { error: string };
+
+    expect(response.status).toBe(502);
+    expect(body.error).toBe("LLM response failed schema validation: body_markdown");
+  });
+
   it("rejects unknown export filenames", async () => {
     const { job } = await createJobFixture();
     const exportDir = join(tempDir, "exports", job.id);
@@ -198,6 +281,8 @@ describe("job API routes", () => {
 });
 
 async function createJobFixture() {
+  llmMocks.generateValidatedJson.mockResolvedValueOnce(validCandidateFactCard);
+
   const response = await createJob(
     request({
       title: "Example Technology Co., Ltd. GEO guide",

src/app/api/jobs/[jobId]/optimize/route.ts

@@ -2,6 +2,7 @@ import { NextResponse } from "next/server";
 
 import { requireApiAccess } from "../../../../../lib/api/auth";
 import { getRepositoryFromRuntime } from "../../../../../lib/db/repository";
+import { LlmValidationError } from "../../../../../lib/llm/client";
 import { getExportStoreFromRuntime } from "../../../../../lib/workflow/export-store";
 import { runOptimizationWorkflow } from "../../../../../lib/workflow/orchestrator";
 
@@ -30,6 +31,7 @@ export async function POST(request: Request, context: RouteContext) {
     );
   }
 
+  try {
     const result = await runOptimizationWorkflow({
       input: {
         title: job.source_title,
@@ -40,7 +42,10 @@ export async function POST(request: Request, context: RouteContext) {
       },
       factCard: factCardRecord,
     });
-  const optimizedArticle = await repository.saveOptimizedArticle(jobId, result.article);
+    const optimizedArticle = await repository.saveOptimizedArticle(
+      jobId,
+      result.article,
+    );
     const qaReport = await repository.saveQaReport(
       jobId,
       optimizedArticle.revision ?? 1,
@@ -67,4 +72,14 @@ export async function POST(request: Request, context: RouteContext) {
       rewriteRounds: result.rewrite_rounds,
       stoppedAfterMaxRewrites: result.stopped_after_max_rewrites,
     });
+  } catch (error) {
+    const message = error instanceof Error ? error.message : "LLM optimization failed";
+    return NextResponse.json({ error: message }, { status: getErrorStatus(error) });
+  }
+}
+
+function getErrorStatus(error: unknown) {
+  if (error instanceof LlmValidationError) return 502;
+  if (error instanceof Error && /^LLM\b|provider/i.test(error.message)) return 502;
+  return 500;
 }

src/app/api/jobs/route.ts

@@ -2,6 +2,7 @@ import { NextResponse } from "next/server";
 
 import { requireApiAccess } from "../../../lib/api/auth";
 import { getRepositoryFromRuntime } from "../../../lib/db/repository";
+import { LlmValidationError } from "../../../lib/llm/client";
 import { extractCandidateFactCard } from "../../../lib/workflow/fact-extractor";
 import { normalizeInput, type RawArticleInput } from "../../../lib/workflow/input-normalizer";
 
@@ -26,7 +27,7 @@ export async function POST(request: Request) {
 
     return NextResponse.json({ job, candidateFactCard }, { status: 201 });
   } catch (error) {
-    return jsonError(error, 400);
+    return jsonError(error, getErrorStatus(error));
   }
 }
 
@@ -34,3 +35,9 @@ function jsonError(error: unknown, status: number) {
   const message = error instanceof Error ? error.message : "Request failed";
   return NextResponse.json({ error: message }, { status });
 }
+
+function getErrorStatus(error: unknown) {
+  if (error instanceof LlmValidationError) return 502;
+  if (error instanceof Error && /^LLM\b|provider/i.test(error.message)) return 502;
+  return 400;
+}

src/lib/llm/__tests__/client.test.ts

@@ -16,16 +16,16 @@ describe("generateValidatedJson", () => {
     vi.restoreAllMocks();
   });
 
-  it("returns null when no provider key is configured", async () => {
+  it("throws clearly when no provider key is configured", async () => {
     process.env.LLM_PROVIDER = "deepseek";
     delete process.env.DEEPSEEK_API_KEY;
 
-    const result = await client.generateValidatedJson({
+    await expect(
+      client.generateValidatedJson({
         schema: z.object({ value: z.string() }),
         prompt: "Return JSON.",
-    });
+      }),
-
+    ).rejects.toThrow("DEEPSEEK_API_KEY is missing");
-    expect(result).toBeNull();
   });
 
   it("returns parsed data when the model response matches the schema", async () => {
@@ -41,32 +41,32 @@ describe("generateValidatedJson", () => {
     expect(result).toEqual({ value: "from-llm" });
   });
 
-  it("returns null when the model response fails schema validation", async () => {
+  it("throws clearly when the model response fails schema validation", async () => {
     process.env.LLM_PROVIDER = "deepseek";
     process.env.DEEPSEEK_API_KEY = "test-key";
     client.setGenerateJsonForValidation(async () => ({ value: 42 }));
 
-    const result = await client.generateValidatedJson({
+    await expect(
+      client.generateValidatedJson({
         schema: z.object({ value: z.string() }),
         prompt: "Return JSON.",
+      }),
+    ).rejects.toThrow("LLM response failed schema validation");
   });
 
-    expect(result).toBeNull();
+  it("throws clearly when the provider call rejects", async () => {
-  });
-
-  it("returns null when the provider call rejects", async () => {
     process.env.LLM_PROVIDER = "deepseek";
     process.env.DEEPSEEK_API_KEY = "test-key";
     client.setGenerateJsonForValidation(async () => {
       throw new Error("provider down");
     });
 
-    const result = await client.generateValidatedJson({
+    await expect(
+      client.generateValidatedJson({
         schema: z.object({ value: z.string() }),
         prompt: "Return JSON.",
-    });
+      }),
-
+    ).rejects.toThrow("provider down");
-    expect(result).toBeNull();
   });
 
   it("logs validation success with the supplied task label", async () => {
@@ -93,13 +93,13 @@ describe("generateValidatedJson", () => {
     process.env.DEEPSEEK_API_KEY = "test-key";
     client.setGenerateJsonForValidation(async () => ({ value: 42 }));
 
-    const result = await client.generateValidatedJson({
+    await expect(
+      client.generateValidatedJson({
         schema: z.object({ value: z.string() }),
         prompt: "Return JSON.",
         task: "fact_extractor",
-    });
+      }),
-
+    ).rejects.toThrow("LLM response failed schema validation");
-    expect(result).toBeNull();
     expect(warnSpy).toHaveBeenCalledWith(
       expect.stringContaining("[llm:validated] task=fact_extractor ok=false"),
     );
@@ -150,16 +150,16 @@ describe("generateValidatedJson", () => {
     const warnSpy = vi.spyOn(console, "warn").mockImplementation(() => {});
     client.setGenerateJsonForValidation(async () => ({ value: 42 }));
 
-    const result = await client.generateValidatedJson({
+    await expect(
+      client.generateValidatedJson({
         schema: z.object({ value: z.string() }),
         task: "fact_extractor",
         prompt: "Return JSON.",
-    });
+      }),
-
+    ).rejects.toThrow("LLM response failed schema validation");
     const allLogs = [...infoSpy.mock.calls, ...warnSpy.mock.calls]
       .flat()
       .join("\n");
-    expect(result).toBeNull();
     expect(allLogs).toContain("[llm:validated] task=fact_extractor ok=false");
     expect(allLogs).toContain("zod_error=");
     expect(allLogs).not.toContain("super-secret-key");
@@ -173,13 +173,13 @@ describe("generateValidatedJson", () => {
       throw new Error("provider unavailable");
     });
 
-    const result = await client.generateValidatedJson({
+    await expect(
+      client.generateValidatedJson({
         schema: z.object({ value: z.string() }),
         task: "quality_inspector",
         prompt: "Return JSON.",
-    });
+      }),
-
+    ).rejects.toThrow("provider unavailable");
-    expect(result).toBeNull();
     expect(warnSpy).toHaveBeenCalledWith(
       expect.stringContaining("[llm:error] task=quality_inspector"),
     );

src/lib/llm/client.ts

@@ -28,6 +28,16 @@ export interface LlmProviderStatus {
   reason?: string;
 }
 
+export class LlmValidationError extends Error {
+  constructor(
+    message: string,
+    public readonly task: LlmTaskName,
+  ) {
+    super(message);
+    this.name = "LlmValidationError";
+  }
+}
+
 function getProvider() {
   return (process.env.LLM_PROVIDER || "deepseek").toLowerCase();
 }
@@ -80,8 +90,8 @@ export function setChatCompletionForTesting(
   chatCompletionForTesting = handler;
 }
 
-function getTask(input: GenerateInput) {
+function getTask(input: GenerateInput): LlmTaskName {
-  return input.task?.trim() || "unknown";
+  return input.task || "unknown";
 }
 
 function getRawLogLimit() {
@@ -204,11 +214,14 @@ export function setGenerateJsonForValidation(
 export async function generateValidatedJson<T>({
   schema,
   ...input
-}: GenerateValidatedJsonInput<T>): Promise<T | null> {
+}: GenerateValidatedJsonInput<T>): Promise<T> {
   const task = getTask(input);
   if (!isLlmConfigured()) {
     console.info(`[llm:validated] task=${task} ok=false reason=not_configured`);
-    return null;
+    throw new LlmValidationError(
+      getLlmProviderStatus().reason ?? "LLM provider is not configured",
+      task,
+    );
   }
 
   const status = getLlmProviderStatus();
@@ -230,14 +243,20 @@ export async function generateValidatedJson<T>({
     console.warn(
       `[llm:validated] task=${task} ok=false zod_error=${quoteLogValue(summarizeZodError(parsed.error))}`,
     );
-    return null;
+    throw new LlmValidationError(
+      `LLM response failed schema validation: ${summarizeZodError(parsed.error)}`,
+      task,
+    );
   } catch (error) {
+    if (error instanceof LlmValidationError) {
+      throw error;
+    }
     console.info(`[llm:validated] task=${task} ok=false reason=provider_error`);
     const message = error instanceof Error ? error.message : String(error);
     console.warn(
       `[llm:error] task=${task} duration_ms=${Date.now() - startedAt} message=${quoteLogValue(message)}`,
     );
-    return null;
+    throw error instanceof Error ? error : new Error(message);
   }
 }
 

src/lib/llm/prompts.ts

@@ -10,7 +10,7 @@ export const JSON_ONLY_PROMPT =
   "Return valid JSON only. Do not include markdown fences or commentary.";
 
 const CUSTOMER_RISK_GUIDANCE = [
-  "客户最担心的内容风险：行业漂移、公司全称/简称/品牌名不一致、图片主题与正文描述不匹配、官网文章出现第三方口吻、平台语气和文章类型不匹配、标题或正文语义不顺、虚构资质/年限/案例/能力、产品/服务/年限前后冲突。",
+  "客户最担心的内容风险：行业漂移、公司全称/简称/品牌名不一致、官网文章出现第三方口吻、平台语气和文章类型不匹配、标题或正文语义不顺、虚构资质/年限/案例/能力、产品/服务/年限前后冲突。",
   "任何客户案例、资质荣誉、经验年限、服务能力、出海/多语种/合规能力、效果承诺和排名，都必须能从原文或已确认事实卡中找到明确依据。",
 ].join(" ");
 
@@ -99,7 +99,7 @@ export function buildArticleOptimizerPrompt(
     "- 必须保留事实卡确认的公司全称、目标行业、目标受众和核心事实。",
     "- 必须删除或弱化 factCard.forbidden_claims 中的主张。",
     "- 不得新增客户案例、数字、资质、排名、奖项、服务能力、效果承诺。",
-    "- image_suggestions 必须基于 factCard.image_topics 或原始 images；没有图片主题时返回空数组。",
+    "- 当前版本只优化文本，不生成图片建议；image_suggestions 必须返回空数组 []。",
     "",
     "Confirmed fact card:",
     JSON.stringify(factCard, null, 2),
@@ -124,8 +124,9 @@ export function buildQualityInspectorPrompt(input: {
     formatPlatformGuidance(input.platform),
     "",
     "fail 标准：行业漂移、公司名不一致、事实卡外新增数字/客户/资质/案例、未确认案例、产品服务前后冲突、平台口吻严重错误、标题明显病句。",
-    "warn 标准：图片证据不足、句子过长、表达可读性一般、平台适配轻微不足。",
+    "warn 标准：句子过长、表达可读性一般、平台适配轻微不足。",
-    "target_agent 只能使用 title、body、image、fact_card 或 null。",
+    "当前版本暂不评估图片内容；image_text_match 只能基于 deterministicChecks 原状态保留或给出暂不评估说明，不得要求生成图片建议。",
+    "target_agent 只能使用 title、body、fact_card 或 null。",
     "",
     "Confirmed fact card:",
     JSON.stringify(input.factCard, null, 2),
@@ -155,7 +156,7 @@ export function buildTargetedRewritePrompt(input: {
     "- title_quality：生成自然中文标题，禁止英文模板词。",
     "- body_quality：拆分长句，修复病句和断裂表达。",
     "- voice_consistency / platform_fit：改成目标平台对应口吻。",
-    "- image_text_match：只补充图片建议或人工确认项，不虚构图片内容。",
+    "- image_text_match：当前版本暂不处理图片，保持原文文本不变，可把需要人工补图的事项放入 requires_user_confirmation。",
     "不得新增事实。无法修复的内容放入 requires_user_confirmation。",
     "",
     "Confirmed fact card:",

src/lib/workflow/__tests__/llm-integration.test.ts

@@ -72,19 +72,20 @@ describe("LLM workflow integration", () => {
     );
   });
 
-  it("falls back to deterministic candidate extraction when LLM returns null", async () => {
+  it("surfaces candidate fact extraction LLM failures instead of falling back", async () => {
-    llmMocks.generateValidatedJson.mockResolvedValueOnce(null);
+    llmMocks.generateValidatedJson.mockRejectedValueOnce(
+      new Error("LLM response failed schema validation: target_audience"),
+    );
 
-    const card = await extractCandidateFactCard({
+    await expect(
+      extractCandidateFactCard({
         title: "Fallback Technology Co., Ltd. GEO guide",
         body: "Fallback Technology Co., Ltd. has 8 years of GEO optimization experience.",
         images: [{ type: "description", content: "dashboard" }],
         platform: "official_site",
         user_instructions: "",
-    });
+      }),
-
+    ).rejects.toThrow("LLM response failed schema validation: target_audience");
-    expect(card.company_full_name).toBe("Fallback Technology Co., Ltd.");
-    expect(card.experience_years).toBe(8);
   });
 
   it("uses LLM output for article optimization when valid", async () => {
@@ -92,7 +93,7 @@ describe("LLM workflow integration", () => {
       title: "LLM Optimized GEO Article",
       summary: "LLM summary constrained by the fact card.",
       body_markdown: "## LLM Body\nExample Technology Co., Ltd. keeps claims factual.",
-      image_suggestions: [{ source: "image_1", suggestion: "Use dashboard." }],
+      image_suggestions: [],
       changed_sections: ["title", "body"],
       requires_user_confirmation: [],
     });
@@ -110,16 +111,20 @@ describe("LLM workflow integration", () => {
 
     expect(article.title).toBe("LLM Optimized GEO Article");
     expect(article.body_markdown).toContain("LLM Body");
+    expect(article.image_suggestions).toEqual([]);
     expect(llmMocks.generateValidatedJson).toHaveBeenCalledOnce();
     expect(llmMocks.generateValidatedJson).toHaveBeenCalledWith(
       expect.objectContaining({ task: "article_optimizer" }),
     );
   });
 
-  it("falls back to deterministic article optimization when LLM returns null", async () => {
+  it("surfaces article optimization LLM failures instead of falling back", async () => {
-    llmMocks.generateValidatedJson.mockResolvedValueOnce(null);
+    llmMocks.generateValidatedJson.mockRejectedValueOnce(
+      new Error("LLM response failed schema validation: image_suggestions.0.source"),
+    );
 
-    const article = await optimizeArticle({
+    await expect(
+      optimizeArticle({
         input: {
           title: "Original",
           body: "Example Technology Co., Ltd. has 8 years of GEO optimization experience.",
@@ -128,11 +133,9 @@ describe("LLM workflow integration", () => {
           user_instructions: "Say we have 99 patents.",
         },
         factCard: confirmedFactCard,
-    });
+      }),
-
+    ).rejects.toThrow(
-    expect(article.title).toContain("GEO optimization Guide");
+      "LLM response failed schema validation: image_suggestions.0.source",
-    expect(article.requires_user_confirmation).toContain(
-      "Unsupported requested claim: 99 patents",
     );
   });
 
@@ -176,10 +179,11 @@ describe("LLM workflow integration", () => {
     );
   });
 
-  it("falls back to deterministic targeted rewrite when LLM returns null", async () => {
+  it("surfaces targeted rewrite LLM failures instead of falling back", async () => {
-    llmMocks.generateValidatedJson.mockResolvedValueOnce(null);
+    llmMocks.generateValidatedJson.mockRejectedValueOnce(new Error("provider unavailable"));
 
-    const rewritten = await rewriteFailedSections({
+    await expect(
+      rewriteFailedSections({
         article: {
           title: "Bad title!!!",
           summary: "Original summary",
@@ -199,10 +203,8 @@ describe("LLM workflow integration", () => {
             target_agent: "title",
           },
         ],
-    });
+      }),
-
+    ).rejects.toThrow("provider unavailable");
-    expect(rewritten.title).toContain("GEO optimization Guide");
-    expect(rewritten.summary).toBe("Original summary");
   });
 
   it("uses LLM quality checks to enrich non-failing deterministic checks", async () => {

src/lib/workflow/__tests__/workflow.test.ts

@@ -1,12 +1,8 @@
 import { describe, expect, it } from "vitest";
 
 import type { ConfirmedFactCard } from "../../domain/types";
-import { optimizeArticle } from "../article-optimizer";
-import { extractCandidateFactCard } from "../fact-extractor";
 import { normalizeInput } from "../input-normalizer";
 import { inspectQuality } from "../quality-inspector";
-import { runOptimizationWorkflow } from "../orchestrator";
-import { rewriteFailedSections } from "../targeted-rewriter";
 
 const confirmedFactCard: ConfirmedFactCard = {
   company_full_name: "Example Technology Co., Ltd.",
@@ -45,65 +41,6 @@ describe("workflow nodes", () => {
     ]);
   });
 
-  it("places missing or conflicting company facts into uncertain items", async () => {
-    const card = await extractCandidateFactCard({
-      title: "Example announces GEO product",
-      body: "Example has 8 years of experience. Example has 12 years of service. The article discusses GEO optimization.",
-      images: [],
-      platform: "media_article",
-      user_instructions: "",
-    });
-
-    expect(card.company_full_name).toBe("");
-    expect(card.uncertain_items).toEqual(
-      expect.arrayContaining([
-        expect.stringContaining("company full name"),
-        expect.stringContaining("Conflicting experience years"),
-      ]),
-    );
-    expect(card.is_ready_for_optimization).toBe(false);
-  });
-
-  it("extracts Chinese company facts from Chinese articles", async () => {
-    const card = await extractCandidateFactCard({
-      title: "#探寻AIGC短视频培训选哪家，各品牌实力大比拼",
-      body: "伟思德鲁管理咨询（深圳）有限公司面向品牌商家和出海企业提供AIGC短视频培训服务，帮助企业解决内容工业化生产、品牌视觉统一和全球化传播问题。",
-      images: [{ type: "description", content: "AIGC短视频工作流示意图" }],
-      platform: "media_article",
-      user_instructions: "保留AIGC短视频培训与出海内容生产场景。",
-    });
-
-    expect(card.company_full_name).toBe("伟思德鲁管理咨询（深圳）有限公司");
-    expect(card.company_short_names).toContain("伟思德鲁");
-    expect(card.target_industry).toBe("AIGC短视频培训");
-    expect(card.target_audience).toBe("品牌商家、内容创作者、出海企业");
-    expect(card.image_topics).toEqual(["AIGC短视频工作流示意图"]);
-    expect(card.uncertain_items).not.toContain("Missing company full name");
-  });
-
-  it("does not add claims outside the confirmed fact card", async () => {
-    const optimized = await optimizeArticle({
-      input: {
-        title: "Example GEO article",
-        body: "Example GEO helps marketing teams improve content structure.",
-        images: [],
-        platform: "official_site",
-        user_instructions:
-          "Say we have 99 patents and Fortune 500 customer cases.",
-      },
-      factCard: confirmedFactCard,
-    });
-
-    expect(optimized.body_markdown).not.toContain("99 patents");
-    expect(optimized.body_markdown).not.toContain("Fortune 500");
-    expect(optimized.requires_user_confirmation).toEqual(
-      expect.arrayContaining([
-        expect.stringContaining("99 patents"),
-        expect.stringContaining("Fortune 500"),
-      ]),
-    );
-  });
-
   it("returns the 10 required quality checks", () => {
     const report = inspectQuality({
       article: {
@@ -183,50 +120,4 @@ describe("workflow nodes", () => {
     expect(report.overall_status).toBe("fail");
   });
 
-  it("rewrites only the failing target area", async () => {
-    const article = {
-      title: "Bad title!!!",
-      summary: "Original summary",
-      body_markdown: "Original body",
-      image_suggestions: [],
-      changed_sections: [],
-      requires_user_confirmation: [],
-    };
-
-    const rewritten = await rewriteFailedSections({
-      article,
-      factCard: confirmedFactCard,
-      failedChecks: [
-        {
-          rule_id: "title_quality",
-          status: "fail",
-          evidence: "Bad title!!!",
-          reason: "Punctuation stuffing.",
-          suggested_fix: "Rewrite title.",
-          target_agent: "title",
-        },
-      ],
-    });
-
-    expect(rewritten.title).not.toBe(article.title);
-    expect(rewritten.summary).toBe(article.summary);
-    expect(rewritten.body_markdown).toBe(article.body_markdown);
-  });
-
-  it("orchestrator stops after two failed rewrite rounds", async () => {
-    const result = await runOptimizationWorkflow({
-      input: {
-        title: "Finance automation breakthrough!!!",
-        body: "Example has 12 years in finance automation and 99 patents.",
-        images: [],
-        platform: "official_site",
-        user_instructions: "",
-      },
-      factCard: confirmedFactCard,
-    });
-
-    expect(result.rewrite_rounds).toBe(2);
-    expect(result.qaReport.overall_status).toBe("fail");
-    expect(result.stopped_after_max_rewrites).toBe(true);
-  });
 });

src/lib/workflow/article-optimizer.ts

@@ -27,77 +27,8 @@ export async function optimizeArticle({
     task: "article_optimizer",
   });
 
-  return llmArticle ?? optimizeArticleFallback({ input, factCard });
-}
-
-function optimizeArticleFallback({
-  input,
-  factCard,
-}: OptimizeArticleInput): OptimizedArticle {
-  const unsupported = findUnsupportedInstructionClaims(
-    input.user_instructions,
-    factCard,
-  );
-  const title = `${factCard.brand_names[0] ?? factCard.company_short_names[0] ?? factCard.company_full_name} ${factCard.target_industry} Guide`;
-  const coreClaims =
-    factCard.core_claims.length > 0
-      ? factCard.core_claims.map((claim) => `- ${claim}`).join("\n")
-      : "- Confirmed facts only; no extra claims added.";
-  const body = [
-    `## ${factCard.company_full_name}`,
-    cleanBody(input.body, factCard),
-    "",
-    "### Confirmed Facts",
-    coreClaims,
-  ].join("\n");
-
   return optimizedArticleSchema.parse({
-    title,
+    ...llmArticle,
-    summary: `A ${input.platform.replace(/_/g, " ")} article for ${factCard.target_audience} about ${factCard.target_industry}.`,
+    image_suggestions: [],
-    body_markdown: body,
-    image_suggestions: factCard.image_topics.map((topic, index) => ({
-      source: `image_${index + 1}`,
-      suggestion: `Use image content related to ${topic}.`,
-    })),
-    changed_sections: ["title", "body structure", "summary"],
-    requires_user_confirmation: unsupported,
   });
 }
-
-function cleanBody(body: string, factCard: ConfirmedFactCard) {
-  let cleaned = body.trim();
-  for (const forbidden of factCard.forbidden_claims) {
-    cleaned = cleaned.replace(new RegExp(escapeRegExp(forbidden), "gi"), "");
-  }
-  return cleaned;
-}
-
-function findUnsupportedInstructionClaims(
-  instructions: string,
-  factCard: ConfirmedFactCard,
-) {
-  const unsupported: string[] = [];
-  const numbers = [...instructions.matchAll(/\b\d+\s*[A-Za-z]+\b/g)].map(
-    (match) => match[0],
-  );
-  const knownText = [
-    factCard.experience_years?.toString() ?? "",
-    ...factCard.core_claims,
-  ].join(" ");
-
-  for (const claim of numbers) {
-    if (!knownText.includes(claim.replace(/\D/g, ""))) {
-      unsupported.push(`Unsupported requested claim: ${claim}`);
-    }
-  }
-
-  if (/fortune\s*500/i.test(instructions)) {
-    unsupported.push("Unsupported requested claim: Fortune 500 customer cases");
-  }
-
-  return unsupported;
-}
-
-function escapeRegExp(value: string) {
-  return value.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
-}

src/lib/workflow/fact-extractor.ts

@@ -9,131 +9,11 @@ import {
 export async function extractCandidateFactCard(
   input: ArticleInput,
 ): Promise<CandidateFactCard> {
-  const llmCard = await generateValidatedJson({
+  return generateValidatedJson({
     schema: candidateFactCardSchema,
     system: FACT_EXTRACTOR_SYSTEM_PROMPT,
     prompt: buildFactExtractorPrompt(input),
     temperature: 0.1,
     task: "fact_extractor",
   });
-
-  return llmCard ?? extractCandidateFactCardFallback(input);
-}
-
-function extractCandidateFactCardFallback(input: ArticleInput): CandidateFactCard {
-  const text = `${input.title}\n${input.body}`;
-  const uncertainItems: string[] = [];
-  const companyFullName = findCompanyFullName(text);
-  const years = findExperienceYears(text);
-
-  if (!companyFullName) {
-    uncertainItems.push("Missing company full name");
-  }
-  if (years.length > 1) {
-    uncertainItems.push(`Conflicting experience years: ${years.join(", ")}`);
-  }
-  if (input.images.length === 0) {
-    uncertainItems.push("Image description is missing");
-  }
-
-  const industry = inferIndustry(text);
-
-  return candidateFactCardSchema.parse({
-    company_full_name: companyFullName ?? "",
-    company_short_names: inferCompanyShortNames(companyFullName),
-    brand_names: inferBrandNames(text, companyFullName),
-    product_names: inferProducts(text),
-    target_industry: industry,
-    target_audience: text.toLowerCase().includes("marketing")
-      ? "Marketing teams"
-      : inferTargetAudience(text),
-    experience_years: years.length === 1 ? years[0] : null,
-    core_claims: years.length === 1 ? [`${years[0]} years of ${industry} experience`] : [],
-    forbidden_claims: [],
-    image_topics: input.images.map((image) => image.content),
-    uncertain_items: uncertainItems,
-  });
-}
-
-function findCompanyFullName(text: string) {
-  const englishMatch = text.match(
-    /([A-Z][A-Za-z0-9&.,\-\s]{2,}?(?:Co\.,?\s*Ltd\.?|Company|Inc\.?|LLC|Ltd\.))/,
-  );
-  if (englishMatch?.[1]) return englishMatch[1].trim();
-
-  const chineseMatch = text.match(
-    /([\u4e00-\u9fa5A-Za-z0-9（）()]{2,40}?(?:股份有限公司|有限公司|集团|公司))/,
-  );
-  return chineseMatch?.[1].trim() ?? null;
-}
-
-function findExperienceYears(text: string) {
-  const matches = [...text.matchAll(/\b(\d{1,3})\s*(?:years?|年)\b/gi)];
-  return [...new Set(matches.map((match) => Number(match[1])))];
-}
-
-function inferIndustry(text: string) {
-  const lower = text.toLowerCase();
-  if (lower.includes("geo")) return "GEO optimization";
-  if (lower.includes("finance") || lower.includes("banking")) return "finance automation";
-  if (lower.includes("seo")) return "SEO";
-  if (/AIGC|短视频|出海内容|内容工业化/.test(text)) return "AIGC短视频培训";
-  if (/工业零部件|爆品操盘|AI OBS|IPMS/.test(text)) return "工业零部件爆品操盘";
-  return "General business";
-}
-
-function inferBrandNames(text: string, companyFullName: string | null) {
-  const chineseBrands = [
-    ...new Set(
-      [
-        companyFullName ? inferChineseShortName(companyFullName) : "",
-        ...[...text.matchAll(/\b(AIGC|AI OBS|IPMS|GEO|SEO)\b/g)].map(
-          (match) => match[1],
-        ),
-      ].filter(Boolean),
-    ),
-  ];
-  const names = [...text.matchAll(/\b[A-Z][A-Za-z0-9]{2,}\b/g)]
-    .map((match) => match[0])
-    .filter((word) => !["The", "This", "And"].includes(word));
-  return [...new Set([...chineseBrands, ...names])].slice(0, 5);
-}
-
-function inferProducts(text: string) {
-  const productMatches = [
-    ...[...text.matchAll(/\b([A-Z][A-Za-z0-9]+\s+GEO)\b/g)].map(
-      (match) => match[1],
-    ),
-    ...[...text.matchAll(/\b(AIGC短视频培训|AI OBS|IPMS|爆品操盘数智系统)\b/g)].map(
-      (match) => match[1],
-    ),
-  ];
-  return [...new Set(productMatches)];
-}
-
-function inferCompanyShortNames(companyFullName: string | null) {
-  if (!companyFullName) return [];
-  if (/[\u4e00-\u9fa5]/.test(companyFullName)) {
-    const legalShortName = inferChineseShortName(companyFullName);
-    const brandShortName = legalShortName.replace(/管理咨询$/, "");
-    return [...new Set([brandShortName, legalShortName].filter(Boolean))];
-  }
-  return [companyFullName.split(/\s+/)[0] ?? ""].filter(Boolean);
-}
-
-function inferChineseShortName(companyFullName: string) {
-  return companyFullName
-    .replace(/[（(].*?[）)]/g, "")
-    .replace(/股份有限公司|有限公司|集团|公司/g, "")
-    .trim();
-}
-
-function inferTargetAudience(text: string) {
-  if (/品牌商家|内容创作者|出海企业/.test(text)) {
-    return "品牌商家、内容创作者、出海企业";
-  }
-  if (/工业企业|工业零部件制造商|采购/.test(text)) {
-    return "工业企业、采购团队、工业零部件制造商";
-  }
-  return "Business readers";
 }

src/lib/workflow/quality-inspector.ts

@@ -68,10 +68,6 @@ export async function inspectQualityWithLlm(
     task: "quality_inspector",
   });
 
-  if (!llmPatch) {
-    return deterministicReport;
-  }
-
   const patchedChecks = deterministicReport.checks.map((deterministicCheck) => {
     const llmCheck = llmPatch.checks.find(
       (check) => check.rule_id === deterministicCheck.rule_id,
@@ -125,15 +121,12 @@ function inspectRule(
   }
 
   if (ruleId === "image_text_match") {
-    const hasImages = sourceImages.length > 0 || article.image_suggestions.length > 0;
     return check(
       ruleId,
-      hasImages ? "pass" : "warn",
+      "pass",
-      hasImages ? "已有可用于比对的图片主题。" : "未提供图片描述。",
+      sourceImages.length > 0 ? "当前版本暂不评估图片内容。" : "当前版本未启用图片分析。",
-      hasImages
+      "当前版本仅优化文本，图片匹配检查暂不参与质量门禁。",
-        ? "图片建议可以和文章内容进行比对。"
+      "后续启用图片工作流后再补充图文匹配检查。",
-        : "缺少图片描述时，图文匹配置信度较低。",
-      "补充图片描述，或人工检查图片与正文的对应关系。",
       null,
     );
   }

src/lib/workflow/targeted-rewriter.ts

@@ -25,46 +25,8 @@ export async function rewriteFailedSections({
     task: "targeted_rewriter",
   });
 
-  return llmArticle ?? rewriteFailedSectionsFallback({ article, factCard, failedChecks });
+  return optimizedArticleSchema.parse({
-}
+    ...llmArticle,
-
+    image_suggestions: [],
-function rewriteFailedSectionsFallback({
+  });
-  article,
-  factCard,
-  failedChecks,
-}: RewriteFailedSectionsInput): OptimizedArticle {
-  let rewritten = { ...article };
-
-  for (const check of failedChecks) {
-    if (check.target_agent === "title") {
-      rewritten = {
-        ...rewritten,
-        title: `${factCard.brand_names[0] ?? factCard.company_short_names[0]} ${factCard.target_industry} Guide`,
-        changed_sections: [...new Set([...rewritten.changed_sections, "title"])],
-      };
-    }
-
-    if (check.target_agent === "body" && check.rule_id === "company_name_integrity") {
-      rewritten = {
-        ...rewritten,
-        body_markdown: `${factCard.company_full_name}\n\n${rewritten.body_markdown}`,
-        changed_sections: [...new Set([...rewritten.changed_sections, "company name"])],
-      };
-    }
-
-    if (check.target_agent === "body" && check.rule_id === "claim_consistency") {
-      rewritten = {
-        ...rewritten,
-        body_markdown: rewritten.body_markdown.replace(
-          /\b\d{1,3}\s*(?:years?|年)\b/gi,
-          factCard.experience_years === null
-            ? "confirmed experience"
-            : `${factCard.experience_years} years`,
-        ),
-        changed_sections: [...new Set([...rewritten.changed_sections, "claim consistency"])],
-      };
-    }
-  }
-
-  return rewritten;
 }