接入文章优化LLM追踪

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

@@ -205,9 +205,8 @@ describe("job API routes", () => {
   });
 
   it("streams a one-click optimization from body-only input", async () => {
-    llmMocks.generateValidatedJson
+    mockTrackedLlmResult(validCandidateFactCard, "llmcall_fact");
-      .mockResolvedValueOnce(validCandidateFactCard)
+    mockTrackedLlmResult({
-      .mockResolvedValueOnce({
       title: "流式优化标题",
       summary: "流式优化摘要。",
       body_markdown:
@@ -215,8 +214,8 @@ describe("job API routes", () => {
       image_suggestions: [],
       changed_sections: ["title", "body"],
       requires_user_confirmation: [],
-      })
+    }, "llmcall_draft");
-      .mockResolvedValueOnce({ checks: [] });
+    mockTrackedLlmResult({ checks: [] }, "llmcall_qa");
 
     const response = await optimizeStream(
       request({
@@ -231,13 +230,25 @@ describe("job API routes", () => {
     expect(response.status).toBe(200);
     expect(events.map((event) => event.type)).toEqual([
       "job_created",
+      "llm_call_started",
+      "llm_call_responded",
+      "llm_call_validated",
       "fact_card_ready",
       "draft_started",
+      "llm_call_started",
+      "llm_call_responded",
+      "llm_call_validated",
       "draft_ready",
       "qa_started",
+      "llm_call_started",
+      "llm_call_responded",
+      "llm_call_validated",
       "qa_ready",
       "final_ready",
     ]);
+    expect(JSON.stringify(events)).not.toContain("messages");
+    expect(JSON.stringify(events)).not.toContain("choices");
+    expect(JSON.stringify(events)).not.toContain("test-key");
     expect(
       events.find((event) => event.type === "fact_card_ready")?.fact_card,
     ).toEqual(expect.objectContaining({ confirmed_by_user: false }));
@@ -784,6 +795,47 @@ async function createJobFixture() {
   return response.json() as Promise<{ job: { id: string } }>;
 }
 
+function mockTrackedLlmResult(value: unknown, callId: string) {
+  llmMocks.generateValidatedJson.mockImplementationOnce(async (input) => {
+    const onTraceEvent = input.onTraceEvent as
+      | ((event: Record<string, unknown>) => void | Promise<void>)
+      | undefined;
+    await onTraceEvent?.({
+      type: "started",
+      call_id: callId,
+      task: input.task ?? "unknown",
+      context: {
+        workflow_stage: input.traceStage ?? "unknown",
+        rewrite_round: input.rewriteRound,
+        schema_name: input.schemaName,
+      },
+      provider: "deepseek",
+      model: "deepseek-v4-pro",
+      request: { model: "deepseek-v4-pro", messages: [] },
+      started_at: "2026-07-16T00:00:00.000Z",
+    });
+    await onTraceEvent?.({
+      type: "responded",
+      call_id: callId,
+      response: {
+        choices: [{ message: { content: JSON.stringify(value) } }],
+        usage: { prompt_tokens: 10, completion_tokens: 4, total_tokens: 14 },
+      },
+      duration_ms: 1200,
+      responded_at: "2026-07-16T00:00:01.200Z",
+    });
+    await onTraceEvent?.({
+      type: "validated",
+      call_id: callId,
+      schema_name: input.schemaName ?? "anonymousSchema",
+      schema_valid: true,
+      validation_issues: [],
+      validated_at: "2026-07-16T00:00:01.300Z",
+    });
+    return value;
+  });
+}
+
 function request(body: unknown, options: { apiKey?: string | null } = {}) {
   const headers: Record<string, string> = { "content-type": "application/json" };
   const apiKey = options.apiKey === undefined ? "test-key" : options.apiKey;

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

@@ -7,6 +7,13 @@ 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 { getLlmTracePayloadStoreFromRuntime } from "../../../../lib/llm/trace-payload-store";
+import {
+  createLlmTraceRecorder,
+  createNoopLlmTraceRecorder,
+  safeTraceError,
+} from "../../../../lib/llm/trace-recorder";
+import { getLlmTraceRepositoryFromRuntime } from "../../../../lib/llm/trace-repository";
 import { getExportStoreFromRuntime } from "../../../../lib/workflow/export-store";
 import { extractCandidateFactCard } from "../../../../lib/workflow/fact-extractor";
 import {
@@ -61,6 +68,7 @@ export async function POST(request: Request) {
       let jobId: string | undefined;
       let caseId: string | undefined;
       let stage: OptimizationStreamStage = "job";
+      let traceRecorder = createNoopLlmTraceRecorder();
       const llmAuditSummary: LlmAuditSummary[] = [];
       const processSummary: ProcessSummaryStep[] = [];
       const requestStartedAt = Date.now();
@@ -104,6 +112,23 @@ export async function POST(request: Request) {
           case: { id: optimizationCase.id, case_type: "article" },
         });
 
+        try {
+          traceRecorder = await createLlmTraceRecorder({
+            jobId: job.id,
+            caseId: optimizationCase.id,
+            repository: getLlmTraceRepositoryFromRuntime(),
+            payloadStore: getLlmTracePayloadStoreFromRuntime(),
+            publish: (event) => send(event),
+          });
+        } catch (error) {
+          send({
+            type: "trace_warning",
+            job_id: job.id,
+            trace_completeness: "incomplete",
+            error_summary: safeTraceError(error),
+          });
+        }
+
         stage = "fact_card";
         const factCardStartedAt = Date.now();
         const factCard = optimizationFactCardSchema.parse(
@@ -112,6 +137,7 @@ export async function POST(request: Request) {
               onAuditSummary: (summary) => {
                 llmAuditSummary.push(summary);
               },
+              onTraceEvent: traceRecorder.onLlmEvent,
             })),
         );
         processSummary.push(
@@ -124,20 +150,24 @@ export async function POST(request: Request) {
           }),
         );
         const savedFactCard = await repository.saveFactCard(job.id, factCard);
-        send({
+        const factCardReadyEvent: OptimizationStreamEvent = {
           type: "fact_card_ready",
           job_id: job.id,
           fact_card: savedFactCard,
-        });
+        };
+        await traceRecorder.onWorkflowEvent(factCardReadyEvent);
+        send(factCardReadyEvent);
 
         const result = await runStreamingOptimizationWorkflow({
           jobId: job.id,
           input: normalized.articleInput,
           factCard: savedFactCard,
-          onEvent: (event) => {
+          onEvent: async (event) => {
             stage = stageForEvent(event, stage);
+            await traceRecorder.onWorkflowEvent(event);
             send(event);
           },
+          onTraceEvent: traceRecorder.onLlmEvent,
           onAuditSummary: (summary) => {
             llmAuditSummary.push(summary);
           },
@@ -180,7 +210,7 @@ export async function POST(request: Request) {
           error_stage: null,
           error_summary: null,
         });
-        send({
+        const finalReadyEvent: OptimizationStreamEvent = {
           type: "final_ready",
           job_id: job.id,
           case: { id: optimizationCase.id, case_type: "article" },
@@ -188,7 +218,10 @@ export async function POST(request: Request) {
           optimized_article: optimizedArticle,
           qa_report: qaReport,
           export_paths: exportPaths,
-        });
+        };
+        await traceRecorder.onWorkflowEvent(finalReadyEvent);
+        await traceRecorder.finish({ status: "completed" });
+        send(finalReadyEvent);
       } catch (error) {
         const message = error instanceof Error ? error.message : "优化失败";
         let failedVersion:
@@ -225,14 +258,21 @@ export async function POST(request: Request) {
             failedVersion = undefined;
           }
         }
-        send({
+        const failedEvent: OptimizationStreamEvent = {
           type: "failed",
           job_id: jobId,
           case: caseId ? { id: caseId, case_type: "article" } : undefined,
           result_version: failedVersion,
           stage,
           error: message,
+        };
+        await traceRecorder.onWorkflowEvent(failedEvent);
+        await traceRecorder.finish({
+          status: "failed",
+          errorStage: stage,
+          errorSummary: message,
         });
+        send(failedEvent);
       } finally {
         controller.close();
       }

src/lib/llm/d1-trace-repository.ts

@@ -7,7 +7,6 @@ import type {
   LlmTraceCallStatus,
   LlmTraceCompleteness,
   LlmTraceErrorType,
-  LlmTraceRun,
   LlmTraceRunStatus,
   LlmTraceWorkflowStage,
 } from "./trace-types";

src/lib/llm/sqlite-trace-repository.ts

@@ -9,7 +9,6 @@ import type {
   LlmTraceCallStatus,
   LlmTraceCompleteness,
   LlmTraceErrorType,
-  LlmTraceRun,
   LlmTraceRunStatus,
   LlmTraceWorkflowStage,
 } from "./trace-types";

src/lib/workflow/article-optimizer.ts

@@ -13,12 +13,14 @@ import {
 export interface OptimizeArticleInput {
   input: ArticleInput;
   factCard: OptimizationFactCard;
+  onTraceEvent?: GenerateInput["onTraceEvent"];
   onAuditSummary?: GenerateInput["onAuditSummary"];
 }
 
 export async function optimizeArticle({
   input,
   factCard,
+  onTraceEvent,
   onAuditSummary,
 }: OptimizeArticleInput): Promise<OptimizedArticle> {
   const llmArticle = await generateValidatedJson({
@@ -27,6 +29,9 @@ export async function optimizeArticle({
     prompt: buildArticleOptimizerPrompt(input, factCard),
     temperature: 0.2,
     task: "article_optimizer",
+    schemaName: "optimizedArticleSchema",
+    traceStage: "draft",
+    onTraceEvent,
     onAuditSummary,
   });
 

src/lib/workflow/fact-extractor.ts

@@ -8,7 +8,10 @@ import {
 
 export async function extractCandidateFactCard(
   input: ArticleInput,
-  options: { onAuditSummary?: GenerateInput["onAuditSummary"] } = {},
+  options: {
+    onAuditSummary?: GenerateInput["onAuditSummary"];
+    onTraceEvent?: GenerateInput["onTraceEvent"];
+  } = {},
 ): Promise<CandidateFactCard> {
   return generateValidatedJson({
     schema: candidateFactCardSchema,
@@ -16,6 +19,9 @@ export async function extractCandidateFactCard(
     prompt: buildFactExtractorPrompt(input),
     temperature: 0.1,
     task: "fact_extractor",
+    schemaName: "candidateFactCardSchema",
+    traceStage: "fact_card",
+    onTraceEvent: options.onTraceEvent,
     onAuditSummary: options.onAuditSummary,
   });
 }

src/lib/workflow/quality-inspector.ts

@@ -43,6 +43,8 @@ export interface InspectQualityInput {
   factCard: OptimizationFactCard;
   platform: PublishPlatform;
   sourceImages: ImageInput[];
+  rewriteRound?: number;
+  onTraceEvent?: GenerateInput["onTraceEvent"];
   onAuditSummary?: GenerateInput["onAuditSummary"];
 }
 
@@ -72,6 +74,10 @@ export async function inspectQualityWithLlm(
     }),
     temperature: 0.1,
     task: "quality_inspector",
+    schemaName: "llmQaPatchSchema",
+    traceStage: "qa",
+    rewriteRound: input.rewriteRound,
+    onTraceEvent: input.onTraceEvent,
     onAuditSummary: input.onAuditSummary,
   });
 

src/lib/workflow/streaming-optimizer.ts

@@ -13,6 +13,7 @@ export interface RunStreamingOptimizationWorkflowInput {
   input: ArticleInput;
   factCard: OptimizationFactCard;
   onEvent: (event: OptimizationStreamEvent) => void | Promise<void>;
+  onTraceEvent?: GenerateInput["onTraceEvent"];
   onAuditSummary?: GenerateInput["onAuditSummary"];
 }
 
@@ -21,6 +22,7 @@ export async function runStreamingOptimizationWorkflow({
   input,
   factCard,
   onEvent,
+  onTraceEvent,
   onAuditSummary,
 }: RunStreamingOptimizationWorkflowInput) {
   const processSummary: ProcessSummaryStep[] = [];
@@ -31,7 +33,12 @@ export async function runStreamingOptimizationWorkflow({
     message: "正在生成优化草稿",
   });
   let stageStartedAt = Date.now();
-  let article = await optimizeArticle({ input, factCard, onAuditSummary });
+  let article = await optimizeArticle({
+    input,
+    factCard,
+    onTraceEvent,
+    onAuditSummary,
+  });
   processSummary.push(
     createProcessStep({
       stage: "draft",
@@ -54,6 +61,8 @@ export async function runStreamingOptimizationWorkflow({
     factCard,
     platform: input.platform,
     sourceImages: input.images,
+    rewriteRound: 0,
+    onTraceEvent,
     onAuditSummary,
   });
   processSummary.push(
@@ -81,6 +90,8 @@ export async function runStreamingOptimizationWorkflow({
       article,
       factCard,
       failedChecks,
+      rewriteRound: nextRound,
+      onTraceEvent,
       onAuditSummary,
     });
     rewriteRounds = nextRound;
@@ -112,6 +123,8 @@ export async function runStreamingOptimizationWorkflow({
       factCard,
       platform: input.platform,
       sourceImages: input.images,
+      rewriteRound: nextRound,
+      onTraceEvent,
       onAuditSummary,
     });
     processSummary.push(

src/lib/workflow/targeted-rewriter.ts

@@ -10,6 +10,8 @@ export interface RewriteFailedSectionsInput {
   article: OptimizedArticle;
   factCard: OptimizationFactCard;
   failedChecks: QaCheck[];
+  rewriteRound?: number;
+  onTraceEvent?: GenerateInput["onTraceEvent"];
   onAuditSummary?: GenerateInput["onAuditSummary"];
 }
 
@@ -17,6 +19,8 @@ export async function rewriteFailedSections({
   article,
   factCard,
   failedChecks,
+  rewriteRound,
+  onTraceEvent,
   onAuditSummary,
 }: RewriteFailedSectionsInput): Promise<OptimizedArticle> {
   const llmArticle = await generateValidatedJson({
@@ -25,6 +29,10 @@ export async function rewriteFailedSections({
     prompt: buildTargetedRewritePrompt({ article, factCard, failedChecks }),
     temperature: 0.15,
     task: "targeted_rewriter",
+    schemaName: "optimizedArticleSchema",
+    traceStage: "rewrite",
+    rewriteRound,
+    onTraceEvent,
     onAuditSummary,
   });