接入文章优化案例自动保存

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

@@ -30,6 +30,7 @@ import { GET as getJobProgress } from "../jobs/[jobId]/progress/route";
 import { POST as optimizeStream } from "../jobs/optimize-stream/route";
 import { POST as createJob } from "../jobs/route";
 import { POST as recordPerformance } from "../publications/[publicationId]/performance/route";
+import { createSqliteRepository } from "../../../lib/db/sqlite-repository";
 
 const validFactCard = {
   company_full_name: "Example Technology Co., Ltd.",
@@ -89,6 +90,8 @@ interface StreamEventResponse {
   type: string;
   job_id?: string;
   job?: { id: string };
+  case?: { id: string; case_type: string };
+  result_version?: { id: string; version: number };
   fact_card?: { company_full_name: string; confirmed_by_user?: boolean };
   article?: { title: string; body_markdown?: string };
   optimized_article?: { title: string };
@@ -244,6 +247,44 @@ describe("job API routes", () => {
     ).toBe("流式优化标题");
   });
 
+  it("auto-saves stream article optimization as a case and result version", async () => {
+    llmMocks.generateValidatedJson
+      .mockResolvedValueOnce(validCandidateFactCard)
+      .mockResolvedValueOnce({
+        title: "流式优化标题",
+        summary: "流式优化摘要。",
+        body_markdown:
+          "## 服务能力\nExample Technology Co., Ltd. 提供 GEO optimization 服务。",
+        image_suggestions: [],
+        changed_sections: ["title", "body"],
+        requires_user_confirmation: [],
+      })
+      .mockResolvedValueOnce({ checks: [] });
+
+    const response = await optimizeStream(
+      request({
+        body: "Example Technology Co., Ltd. has 8 years of GEO optimization experience.",
+        platform: "official_site",
+      }),
+    );
+    const events = await streamEvents(response);
+    const finalEvent = events.find((event) => event.type === "final_ready");
+
+    expect(finalEvent?.case?.case_type).toBe("article");
+    expect(finalEvent?.result_version?.version).toBe(1);
+
+    const repository = createSqliteRepository();
+    await expect(
+      repository.listOptimizationCases({ include_archived: false }),
+    ).resolves.toEqual([
+      expect.objectContaining({
+        id: finalEvent?.case?.id,
+        case_type: "article",
+        status: "optimized",
+      }),
+    ]);
+  });
+
   it("uses an edited fact card without extracting a new one", async () => {
     llmMocks.generateValidatedJson
       .mockResolvedValueOnce({
@@ -325,6 +366,40 @@ describe("job API routes", () => {
     );
   });
 
+  it("auto-saves stream article LLM failure as a failed case", async () => {
+    llmMocks.generateValidatedJson
+      .mockResolvedValueOnce(validCandidateFactCard)
+      .mockRejectedValueOnce(new Error("LLM provider error: timeout"));
+
+    const response = await optimizeStream(
+      request({
+        body: "Example Technology Co., Ltd. has 8 years of GEO optimization experience.",
+        platform: "official_site",
+      }),
+    );
+    const events = await streamEvents(response);
+    const failedEvent = events[events.length - 1];
+
+    expect(failedEvent.type).toBe("failed");
+    expect(failedEvent.case?.id).toMatch(/^case_/);
+    expect(failedEvent.result_version?.version).toBe(1);
+
+    const repository = createSqliteRepository();
+    const detail = await repository.getOptimizationCaseDetail(
+      failedEvent.case?.id ?? "",
+    );
+
+    expect(detail).toMatchObject({
+      case: { status: "failed", last_error_stage: "draft" },
+      versions: [
+        expect.objectContaining({
+          status: "failed",
+          error_summary: "LLM provider error: timeout",
+        }),
+      ],
+    });
+  });
+
   it("rejects unresolved uncertain items when confirming a fact card", async () => {
     const { job } = await createJobFixture();
     const response = await confirmFactCard(
@@ -387,6 +462,52 @@ describe("job API routes", () => {
     expect(body.timing.steps.every((step) => step.duration_ms >= 0)).toBe(true);
   });
 
+  it("auto-saves non-stream article optimization as a case result version", async () => {
+    const { job } = await createJobFixture();
+    await confirmFactCard(
+      request(validFactCard),
+      params<{ jobId: string }>({ jobId: job.id }),
+    );
+
+    llmMocks.generateValidatedJson
+      .mockResolvedValueOnce({
+        title: "非流式优化标题",
+        summary:
+          "Example Technology Co., Ltd. 面向市场团队提供 GEO optimization 服务。",
+        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 }),
+    );
+
+    expect(response.status).toBe(200);
+
+    const repository = createSqliteRepository();
+    const savedJob = await repository.getArticleJob(job.id);
+    expect(savedJob?.case_id).toMatch(/^case_/);
+
+    const detail = await repository.getOptimizationCaseDetail(
+      savedJob?.case_id ?? "",
+    );
+    expect(detail).toMatchObject({
+      case: { case_type: "article", status: "optimized" },
+      versions: [
+        expect.objectContaining({
+          version: 1,
+          status: "optimized",
+          article_job_id: job.id,
+        }),
+      ],
+    });
+  });
+
   it("scores a revision, registers publication, and records manual performance", async () => {
     const { job } = await createJobFixture();
     await confirmFactCard(

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

@@ -1,7 +1,10 @@
 import { NextResponse } from "next/server";
 
 import { requireApiAccess } from "../../../../../lib/api/auth";
-import { getRepositoryFromRuntime } from "../../../../../lib/db/repository";
+import { buildArticleCaseSummary, createProcessStep } from "../../../../../lib/cases/summaries";
+import { getRepositoryFromRuntime, type AppRepository } from "../../../../../lib/db/repository";
+import type { ArticleJob } from "../../../../../lib/db/repositories";
+import type { LlmAuditSummary } from "../../../../../lib/llm/audit";
 import { LlmValidationError } from "../../../../../lib/llm/client";
 import { getExportStoreFromRuntime } from "../../../../../lib/workflow/export-store";
 import { runOptimizationWorkflow } from "../../../../../lib/workflow/orchestrator";
@@ -36,6 +39,8 @@ export async function POST(request: Request, context: RouteContext) {
   }
 
   const requestStartedAt = Date.now();
+  const caseId = await ensureArticleCase(repository, job);
+  const llmAuditSummary: LlmAuditSummary[] = [];
   startWorkflowProgress(jobId);
   try {
     const result = await runOptimizationWorkflow({
@@ -50,6 +55,7 @@ export async function POST(request: Request, context: RouteContext) {
       onProgress: (event) => {
         recordWorkflowProgress(jobId, event);
       },
+      onAuditSummary: (summary) => llmAuditSummary.push(summary),
     });
     const optimizedArticle = await repository.saveOptimizedArticle(
       jobId,
@@ -70,8 +76,27 @@ export async function POST(request: Request, context: RouteContext) {
       status: "optimized",
       export_paths: exportPaths,
     });
+    const resultVersion = await repository.createOptimizationResultVersion({
+      case_id: caseId,
+      case_type: "article",
+      status: "optimized",
+      article_job_id: jobId,
+      article_revision: optimizedArticle.revision ?? 1,
+      result_summary: optimizedArticle.summary,
+      payload: {
+        article: optimizedArticle,
+        qa_report: qaReport,
+        export_paths: exportPaths,
+      },
+      process_summary: [],
+      llm_audit_summary: llmAuditSummary,
+      error_stage: null,
+      error_summary: null,
+    });
 
     return NextResponse.json({
+      case: { id: caseId, case_type: "article" },
+      resultVersion: { id: resultVersion.id, version: resultVersion.version },
       optimizedArticle,
       qaReport,
       exportPaths,
@@ -81,6 +106,28 @@ export async function POST(request: Request, context: RouteContext) {
     });
   } catch (error) {
     const message = error instanceof Error ? error.message : "LLM optimization failed";
+    await repository.createOptimizationResultVersion({
+      case_id: caseId,
+      case_type: "article",
+      status: "failed",
+      article_job_id: jobId,
+      article_revision: null,
+      result_summary: "",
+      payload: null,
+      process_summary: [
+        createProcessStep({
+          stage: "optimize",
+          startedAt: requestStartedAt,
+          endedAt: Date.now(),
+          status: "failed",
+          errorSummary: message,
+          producedResultVersion: false,
+        }),
+      ],
+      llm_audit_summary: llmAuditSummary,
+      error_stage: "optimize",
+      error_summary: message,
+    });
     return NextResponse.json(
       {
         error: message,
@@ -94,6 +141,36 @@ export async function POST(request: Request, context: RouteContext) {
   }
 }
 
+async function ensureArticleCase(
+  repository: AppRepository,
+  job: ArticleJob,
+) {
+  if (job.case_id) return job.case_id;
+
+  const caseSummary = buildArticleCaseSummary({
+    source_title: job.source_title,
+    source_body: job.source_body,
+    publish_platform: job.publish_platform,
+  });
+  const optimizationCase = await repository.createOptimizationCase({
+    case_type: "article",
+    ...caseSummary,
+  });
+  await repository.saveCaseInput({
+    case_id: optimizationCase.id,
+    case_type: "article",
+    article_job_id: job.id,
+    payload: {
+      source_title: job.source_title,
+      source_body: job.source_body,
+      image_inputs: job.image_inputs,
+      publish_platform: job.publish_platform,
+      user_instructions: job.user_instructions,
+    },
+  });
+  return optimizationCase.id;
+}
+
 function getErrorStatus(error: unknown) {
   if (error instanceof LlmValidationError) return 502;
   if (error instanceof Error && /^LLM\b|provider/i.test(error.message)) return 502;

src/app/api/jobs/optimize-stream/route.ts

@@ -1,8 +1,11 @@
 import { NextResponse } from "next/server";
 
 import { requireApiAccess } from "../../../../lib/api/auth";
+import type { ProcessSummaryStep } from "../../../../lib/cases/types";
+import { buildArticleCaseSummary, createProcessStep } from "../../../../lib/cases/summaries";
 import { getRepositoryFromRuntime } from "../../../../lib/db/repository";
 import { optimizationFactCardSchema } from "../../../../lib/domain/validation";
+import type { LlmAuditSummary } from "../../../../lib/llm/audit";
 import { LlmValidationError } from "../../../../lib/llm/client";
 import { getExportStoreFromRuntime } from "../../../../lib/workflow/export-store";
 import { extractCandidateFactCard } from "../../../../lib/workflow/fact-extractor";
@@ -56,11 +59,26 @@ export async function POST(request: Request) {
       };
 
       let jobId: string | undefined;
+      let caseId: string | undefined;
       let stage: OptimizationStreamStage = "job";
+      const llmAuditSummary: LlmAuditSummary[] = [];
+      const processSummary: ProcessSummaryStep[] = [];
+      const requestStartedAt = Date.now();
 
       try {
         const repository = getRepositoryFromRuntime();
+        const caseSummary = buildArticleCaseSummary({
+          source_title: normalized.articleInput.title,
+          source_body: normalized.articleInput.body,
+          publish_platform: normalized.articleInput.platform,
+        });
+        const optimizationCase = await repository.createOptimizationCase({
+          case_type: "article",
+          ...caseSummary,
+        });
+        caseId = optimizationCase.id;
         const job = await repository.createArticleJob({
+          case_id: optimizationCase.id,
           source_title: normalized.articleInput.title,
           source_body: normalized.articleInput.body,
           image_inputs: normalized.articleInput.images,
@@ -68,12 +86,40 @@ export async function POST(request: Request) {
           user_instructions: normalized.articleInput.user_instructions,
         });
         jobId = job.id;
-        send({ type: "job_created", job: { id: job.id } });
+        await repository.saveCaseInput({
+          case_id: optimizationCase.id,
+          case_type: "article",
+          article_job_id: job.id,
+          payload: {
+            source_title: normalized.articleInput.title,
+            source_body: normalized.articleInput.body,
+            image_inputs: normalized.articleInput.images,
+            publish_platform: normalized.articleInput.platform,
+            user_instructions: normalized.articleInput.user_instructions,
+          },
+        });
+        send({
+          type: "job_created",
+          job: { id: job.id },
+          case: { id: optimizationCase.id, case_type: "article" },
+        });
 
         stage = "fact_card";
+        const factCardStartedAt = Date.now();
         const factCard = optimizationFactCardSchema.parse(
           payload.fact_card ??
-            (await extractCandidateFactCard(normalized.articleInput)),
+            (await extractCandidateFactCard(normalized.articleInput, {
+              onAuditSummary: (summary) => llmAuditSummary.push(summary),
+            })),
+        );
+        processSummary.push(
+          createProcessStep({
+            stage: "fact_card",
+            startedAt: factCardStartedAt,
+            endedAt: Date.now(),
+            status: "success",
+            producedResultVersion: false,
+          }),
         );
         const savedFactCard = await repository.saveFactCard(job.id, factCard);
         send({
@@ -90,6 +136,7 @@ export async function POST(request: Request) {
             stage = stageForEvent(event, stage);
             send(event);
           },
+          onAuditSummary: (summary) => llmAuditSummary.push(summary),
         });
 
         stage = "final";
@@ -112,19 +159,75 @@ export async function POST(request: Request) {
           status: "optimized",
           export_paths: exportPaths,
         });
+        const resultVersion = await repository.createOptimizationResultVersion({
+          case_id: optimizationCase.id,
+          case_type: "article",
+          status: "optimized",
+          article_job_id: job.id,
+          article_revision: optimizedArticle.revision ?? 1,
+          result_summary: optimizedArticle.summary,
+          payload: {
+            article: optimizedArticle,
+            qa_report: qaReport,
+            export_paths: exportPaths,
+          },
+          process_summary: [...processSummary, ...result.processSummary],
+          llm_audit_summary: llmAuditSummary,
+          error_stage: null,
+          error_summary: null,
+        });
         send({
           type: "final_ready",
           job_id: job.id,
+          case: { id: optimizationCase.id, case_type: "article" },
+          result_version: { id: resultVersion.id, version: resultVersion.version },
           optimized_article: optimizedArticle,
           qa_report: qaReport,
           export_paths: exportPaths,
         });
       } catch (error) {
+        const message = error instanceof Error ? error.message : "优化失败";
+        let failedVersion:
+          | { id: string; version: number }
+          | undefined;
+        if (caseId) {
+          try {
+            const repository = getRepositoryFromRuntime();
+            const version = await repository.createOptimizationResultVersion({
+              case_id: caseId,
+              case_type: "article",
+              status: "failed",
+              article_job_id: jobId ?? null,
+              article_revision: null,
+              result_summary: "",
+              payload: null,
+              process_summary: [
+                ...processSummary,
+                createProcessStep({
+                  stage,
+                  startedAt: requestStartedAt,
+                  endedAt: Date.now(),
+                  status: "failed",
+                  errorSummary: message,
+                  producedResultVersion: false,
+                }),
+              ],
+              llm_audit_summary: llmAuditSummary,
+              error_stage: stage,
+              error_summary: message,
+            });
+            failedVersion = { id: version.id, version: version.version };
+          } catch {
+            failedVersion = undefined;
+          }
+        }
         send({
           type: "failed",
           job_id: jobId,
+          case: caseId ? { id: caseId, case_type: "article" } : undefined,
+          result_version: failedVersion,
           stage,
-          error: error instanceof Error ? error.message : "优化失败",
+          error: message,
         });
       } finally {
         controller.close();

src/app/api/jobs/route.ts

@@ -1,7 +1,9 @@
 import { NextResponse } from "next/server";
 
 import { requireApiAccess } from "../../../lib/api/auth";
+import { buildArticleCaseSummary, createProcessStep } from "../../../lib/cases/summaries";
 import { getRepositoryFromRuntime } from "../../../lib/db/repository";
+import type { LlmAuditSummary } from "../../../lib/llm/audit";
 import { LlmValidationError } from "../../../lib/llm/client";
 import { extractCandidateFactCard } from "../../../lib/workflow/fact-extractor";
 import { normalizeInput, type RawArticleInput } from "../../../lib/workflow/input-normalizer";
@@ -16,29 +18,78 @@ export async function POST(request: Request) {
     const payload = (await request.json()) as RawArticleInput;
     const normalized = normalizeInput(payload);
     const repository = getRepositoryFromRuntime();
+    const caseSummary = buildArticleCaseSummary({
+      source_title: normalized.articleInput.title,
+      source_body: normalized.articleInput.body,
+      publish_platform: normalized.articleInput.platform,
+    });
+    const optimizationCase = await repository.createOptimizationCase({
+      case_type: "article",
+      ...caseSummary,
+    });
     const job = await repository.createArticleJob({
+      case_id: optimizationCase.id,
       source_title: normalized.articleInput.title,
       source_body: normalized.articleInput.body,
       image_inputs: normalized.articleInput.images,
       publish_platform: normalized.articleInput.platform,
       user_instructions: normalized.articleInput.user_instructions,
     });
+    await repository.saveCaseInput({
+      case_id: optimizationCase.id,
+      case_type: "article",
+      article_job_id: job.id,
+      payload: {
+        source_title: normalized.articleInput.title,
+        source_body: normalized.articleInput.body,
+        image_inputs: normalized.articleInput.images,
+        publish_platform: normalized.articleInput.platform,
+        user_instructions: normalized.articleInput.user_instructions,
+      },
+    });
     const factStartedAt = Date.now();
+    const llmAuditSummary: LlmAuditSummary[] = [];
     const timing = {
       total_ms: 0,
       steps: [] as Array<{ label: string; duration_ms: number }>,
     };
     try {
-      const candidateFactCard = await extractCandidateFactCard(normalized.articleInput);
+      const candidateFactCard = await extractCandidateFactCard(
+        normalized.articleInput,
+        { onAuditSummary: (summary) => llmAuditSummary.push(summary) },
+      );
       const duration = Date.now() - factStartedAt;
       timing.total_ms = duration;
       timing.steps.push({ label: "事实卡提取", duration_ms: duration });
 
       return NextResponse.json({ job, candidateFactCard, timing }, { status: 201 });
     } catch (error) {
+      const message = error instanceof Error ? error.message : "Request failed";
       const duration = Date.now() - factStartedAt;
       timing.total_ms = duration;
       timing.steps.push({ label: "事实卡提取", duration_ms: duration });
+      await repository.createOptimizationResultVersion({
+        case_id: optimizationCase.id,
+        case_type: "article",
+        status: "failed",
+        article_job_id: job.id,
+        article_revision: null,
+        result_summary: "",
+        payload: null,
+        process_summary: [
+          createProcessStep({
+            stage: "fact_card",
+            startedAt: factStartedAt,
+            endedAt: Date.now(),
+            status: "failed",
+            errorSummary: message,
+            producedResultVersion: false,
+          }),
+        ],
+        llm_audit_summary: llmAuditSummary,
+        error_stage: "fact_card",
+        error_summary: message,
+      });
       return jsonError(error, getErrorStatus(error), timing);
     }
   } catch (error) {

src/lib/workflow/article-optimizer.ts

@@ -4,7 +4,7 @@ import type {
   OptimizedArticle,
 } from "../domain/types";
 import { optimizedArticleSchema } from "../domain/validation";
-import { generateValidatedJson } from "../llm/client";
+import { generateValidatedJson, type GenerateInput } from "../llm/client";
 import {
   ARTICLE_OPTIMIZER_SYSTEM_PROMPT,
   buildArticleOptimizerPrompt,
@@ -13,11 +13,13 @@ import {
 export interface OptimizeArticleInput {
   input: ArticleInput;
   factCard: OptimizationFactCard;
+  onAuditSummary?: GenerateInput["onAuditSummary"];
 }
 
 export async function optimizeArticle({
   input,
   factCard,
+  onAuditSummary,
 }: OptimizeArticleInput): Promise<OptimizedArticle> {
   const llmArticle = await generateValidatedJson({
     schema: optimizedArticleSchema,
@@ -25,6 +27,7 @@ export async function optimizeArticle({
     prompt: buildArticleOptimizerPrompt(input, factCard),
     temperature: 0.2,
     task: "article_optimizer",
+    onAuditSummary,
   });
 
   return optimizedArticleSchema.parse({

src/lib/workflow/fact-extractor.ts

@@ -1,6 +1,6 @@
 import type { ArticleInput, CandidateFactCard } from "../domain/types";
 import { candidateFactCardSchema } from "../domain/validation";
-import { generateValidatedJson } from "../llm/client";
+import { generateValidatedJson, type GenerateInput } from "../llm/client";
 import {
   FACT_EXTRACTOR_SYSTEM_PROMPT,
   buildFactExtractorPrompt,
@@ -8,6 +8,7 @@ import {
 
 export async function extractCandidateFactCard(
   input: ArticleInput,
+  options: { onAuditSummary?: GenerateInput["onAuditSummary"] } = {},
 ): Promise<CandidateFactCard> {
   return generateValidatedJson({
     schema: candidateFactCardSchema,
@@ -15,5 +16,6 @@ export async function extractCandidateFactCard(
     prompt: buildFactExtractorPrompt(input),
     temperature: 0.1,
     task: "fact_extractor",
+    onAuditSummary: options.onAuditSummary,
   });
 }

src/lib/workflow/orchestrator.ts

@@ -1,4 +1,5 @@
 import type { ArticleInput, OptimizationFactCard } from "../domain/types";
+import type { GenerateInput } from "../llm/client";
 
 import { optimizeArticle } from "./article-optimizer";
 import { inspectQualityWithLlm } from "./quality-inspector";
@@ -8,6 +9,7 @@ export interface RunOptimizationWorkflowInput {
   input: ArticleInput;
   factCard: OptimizationFactCard;
   onProgress?: (event: WorkflowProgressEvent) => void | Promise<void>;
+  onAuditSummary?: GenerateInput["onAuditSummary"];
 }
 
 export interface WorkflowTimingStep {
@@ -68,13 +70,14 @@ export async function runOptimizationWorkflow({
   input,
   factCard,
   onProgress,
+  onAuditSummary,
 }: RunOptimizationWorkflowInput) {
   const startedAt = Date.now();
   const timingSteps: WorkflowTimingStep[] = [];
   let article = await timedStep(
     "生成优化稿",
     timingSteps,
-    () => optimizeArticle({ input, factCard }),
+    () => optimizeArticle({ input, factCard, onAuditSummary }),
     onProgress,
   );
   let qaReport = await timedStep(
@@ -86,6 +89,7 @@ export async function runOptimizationWorkflow({
         factCard,
         platform: input.platform,
         sourceImages: input.images,
+        onAuditSummary,
       }),
     onProgress,
   );
@@ -97,7 +101,7 @@ export async function runOptimizationWorkflow({
     article = await timedStep(
       `定向修复第 ${nextRound} 轮`,
       timingSteps,
-      () => rewriteFailedSections({ article, factCard, failedChecks }),
+      () => rewriteFailedSections({ article, factCard, failedChecks, onAuditSummary }),
       onProgress,
     );
     rewriteRounds = nextRound;
@@ -110,6 +114,7 @@ export async function runOptimizationWorkflow({
           factCard,
           platform: input.platform,
           sourceImages: input.images,
+          onAuditSummary,
         }),
       onProgress,
     );

src/lib/workflow/quality-inspector.ts

@@ -9,7 +9,7 @@ import type {
   QualityRuleId,
 } from "../domain/types";
 import { qaCheckSchema, qaReportSchema } from "../domain/validation";
-import { generateValidatedJson } from "../llm/client";
+import { generateValidatedJson, type GenerateInput } from "../llm/client";
 import {
   QUALITY_INSPECTOR_SYSTEM_PROMPT,
   buildQualityInspectorPrompt,
@@ -43,6 +43,7 @@ export interface InspectQualityInput {
   factCard: OptimizationFactCard;
   platform: PublishPlatform;
   sourceImages: ImageInput[];
+  onAuditSummary?: GenerateInput["onAuditSummary"];
 }
 
 export function inspectQuality(input: InspectQualityInput): QaReport {
@@ -71,6 +72,7 @@ export async function inspectQualityWithLlm(
     }),
     temperature: 0.1,
     task: "quality_inspector",
+    onAuditSummary: input.onAuditSummary,
   });
 
   const patchedChecks = deterministicReport.checks.map((deterministicCheck) => {

src/lib/workflow/stream-events.ts

@@ -14,7 +14,11 @@ export type OptimizationStreamStage =
   | "final";
 
 export type OptimizationStreamEvent =
-  | { type: "job_created"; job: { id: string } }
+  | {
+      type: "job_created";
+      job: { id: string };
+      case?: { id: string; case_type: "article" };
+    }
   | {
       type: "fact_card_ready";
       job_id: string;
@@ -34,6 +38,8 @@ export type OptimizationStreamEvent =
   | {
       type: "final_ready";
       job_id: string;
+      case?: { id: string; case_type: "article" };
+      result_version?: { id: string; version: number };
       optimized_article: OptimizedArticle;
       qa_report: QaReport;
       export_paths: Record<string, string>;
@@ -41,6 +47,8 @@ export type OptimizationStreamEvent =
   | {
       type: "failed";
       job_id?: string;
+      case?: { id: string; case_type: "article" };
+      result_version?: { id: string; version: number };
       stage: OptimizationStreamStage;
       error: string;
     };

src/lib/workflow/streaming-optimizer.ts

@@ -1,4 +1,7 @@
 import type { ArticleInput, OptimizationFactCard } from "../domain/types";
+import type { ProcessSummaryStep } from "../cases/types";
+import { createProcessStep } from "../cases/summaries";
+import type { GenerateInput } from "../llm/client";
 
 import { optimizeArticle } from "./article-optimizer";
 import { inspectQualityWithLlm } from "./quality-inspector";
@@ -10,6 +13,7 @@ export interface RunStreamingOptimizationWorkflowInput {
   input: ArticleInput;
   factCard: OptimizationFactCard;
   onEvent: (event: OptimizationStreamEvent) => void | Promise<void>;
+  onAuditSummary?: GenerateInput["onAuditSummary"];
 }
 
 export async function runStreamingOptimizationWorkflow({
@@ -17,13 +21,26 @@ export async function runStreamingOptimizationWorkflow({
   input,
   factCard,
   onEvent,
+  onAuditSummary,
 }: RunStreamingOptimizationWorkflowInput) {
+  const processSummary: ProcessSummaryStep[] = [];
+
   await onEvent({
     type: "draft_started",
     job_id: jobId,
     message: "正在生成优化草稿",
   });
-  let article = await optimizeArticle({ input, factCard });
+  let stageStartedAt = Date.now();
+  let article = await optimizeArticle({ input, factCard, onAuditSummary });
+  processSummary.push(
+    createProcessStep({
+      stage: "draft",
+      startedAt: stageStartedAt,
+      endedAt: Date.now(),
+      status: "success",
+      producedResultVersion: false,
+    }),
+  );
   await onEvent({ type: "draft_ready", job_id: jobId, article });
 
   await onEvent({
@@ -31,12 +48,23 @@ export async function runStreamingOptimizationWorkflow({
     job_id: jobId,
     message: "正在检查质量",
   });
+  stageStartedAt = Date.now();
   let qaReport = await inspectQualityWithLlm({
     article,
     factCard,
     platform: input.platform,
     sourceImages: input.images,
+    onAuditSummary,
   });
+  processSummary.push(
+    createProcessStep({
+      stage: "qa",
+      startedAt: stageStartedAt,
+      endedAt: Date.now(),
+      status: "success",
+      producedResultVersion: false,
+    }),
+  );
   await onEvent({ type: "qa_ready", job_id: jobId, qa_report: qaReport });
 
   let rewriteRounds = 0;
@@ -48,8 +76,24 @@ export async function runStreamingOptimizationWorkflow({
       job_id: jobId,
       round: nextRound,
     });
-    article = await rewriteFailedSections({ article, factCard, failedChecks });
+    stageStartedAt = Date.now();
+    article = await rewriteFailedSections({
+      article,
+      factCard,
+      failedChecks,
+      onAuditSummary,
+    });
     rewriteRounds = nextRound;
+    processSummary.push(
+      createProcessStep({
+        stage: "rewrite",
+        startedAt: stageStartedAt,
+        endedAt: Date.now(),
+        status: "success",
+        rewriteRound: nextRound,
+        producedResultVersion: false,
+      }),
+    );
     await onEvent({
       type: "rewrite_ready",
       job_id: jobId,
@@ -62,12 +106,24 @@ export async function runStreamingOptimizationWorkflow({
       job_id: jobId,
       message: `正在复检第 ${nextRound} 轮修复`,
     });
+    stageStartedAt = Date.now();
     qaReport = await inspectQualityWithLlm({
       article,
       factCard,
       platform: input.platform,
       sourceImages: input.images,
+      onAuditSummary,
     });
+    processSummary.push(
+      createProcessStep({
+        stage: "qa",
+        startedAt: stageStartedAt,
+        endedAt: Date.now(),
+        status: "success",
+        rewriteRound: nextRound,
+        producedResultVersion: false,
+      }),
+    );
     await onEvent({ type: "qa_ready", job_id: jobId, qa_report: qaReport });
   }
 
@@ -77,5 +133,6 @@ export async function runStreamingOptimizationWorkflow({
     rewriteRounds,
     stoppedAfterMaxRewrites:
       qaReport.overall_status === "fail" && rewriteRounds >= 2,
+    processSummary,
   };
 }

src/lib/workflow/targeted-rewriter.ts

@@ -1,6 +1,6 @@
 import type { OptimizationFactCard, OptimizedArticle, QaCheck } from "../domain/types";
 import { optimizedArticleSchema } from "../domain/validation";
-import { generateValidatedJson } from "../llm/client";
+import { generateValidatedJson, type GenerateInput } from "../llm/client";
 import {
   TARGETED_REWRITER_SYSTEM_PROMPT,
   buildTargetedRewritePrompt,
@@ -10,12 +10,14 @@ export interface RewriteFailedSectionsInput {
   article: OptimizedArticle;
   factCard: OptimizationFactCard;
   failedChecks: QaCheck[];
+  onAuditSummary?: GenerateInput["onAuditSummary"];
 }
 
 export async function rewriteFailedSections({
   article,
   factCard,
   failedChecks,
+  onAuditSummary,
 }: RewriteFailedSectionsInput): Promise<OptimizedArticle> {
   const llmArticle = await generateValidatedJson({
     schema: optimizedArticleSchema,
@@ -23,6 +25,7 @@ export async function rewriteFailedSections({
     prompt: buildTargetedRewritePrompt({ article, factCard, failedChecks }),
     temperature: 0.15,
     task: "targeted_rewriter",
+    onAuditSummary,
   });
 
   return optimizedArticleSchema.parse({