Jesse_Chen
433 lines
15 KiB
TypeScript
433 lines
15 KiB
TypeScript
import assert from "node:assert/strict";
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import { randomUUID } from "node:crypto";
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import test from "node:test";
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import {
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diagnosticsSummarySchema,
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storedPublicMessageSchema,
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type DiagnosticsSummary,
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type QuestionOpportunity,
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} from "../src/lib/rectification-agent/contracts.ts";
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import { processRectificationAgentTurn } from "../src/lib/rectification-agent/orchestrator.ts";
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import { runBoundedReasoner, sanitizeReasoningSummary } from "../src/lib/rectification-agent/reasoner-agent.ts";
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import { createRectificationV4CaseService } from "../src/lib/rectification-v4/case-service.ts";
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import type { CandidateEngineResult } from "../src/lib/rectification-v4/candidate-engine.ts";
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import type {
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CalculationSpec,
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LifeEventRevision,
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RectificationAnalysisTrace,
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RectificationV4Case,
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RectificationV4Turn,
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} from "../src/lib/rectification-v4/contracts.ts";
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import { calculationSpecHash } from "../src/lib/rectification-v4/fingerprints.ts";
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import { createRectificationV4MemoryStore } from "../src/lib/rectification-v4/memory-store.ts";
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import { projectAnalysisMessages } from "../src/lib/rectification-v4/supabase-store.ts";
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import type { ClaimedRectificationV4Job } from "../src/lib/rectification-v4/store.ts";
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import { createRectificationV4Worker } from "../src/lib/rectification-v4/worker.ts";
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import { v5EngineResult, withV5Mode } from "./rectification-v5-test-support.ts";
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const now = "2026-07-29T00:00:00.000Z";
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const spec: CalculationSpec = {
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version: "rectification-calculation-spec-v4",
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birthDate: "1997-08-08",
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candidateRange: { start: "05:00", end: "06:00" },
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latitude: 36.419,
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longitude: 114.213,
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timezoneOffsetHours: 8,
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ayanamsa: "lahiri",
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nodeMode: "mean",
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minuteStep: 1,
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};
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function event(
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domain: LifeEventRevision["domain"],
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eventKind: LifeEventRevision["eventKind"],
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summary: string,
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date: string,
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): LifeEventRevision {
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return {
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id: randomUUID(),
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eventId: randomUUID(),
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revision: 1,
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domain,
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eventKind,
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subject: "self",
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relatedPerson: null,
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summary,
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rawText: `${date} ${summary}`,
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dateRange: { start: `${date}-01`, end: `${date}-28`, precision: "month", label: date },
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scoreability: "scoreable",
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supersedesRevisionId: null,
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createdAt: now,
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};
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}
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function makeClaimed(events: readonly LifeEventRevision[]): ClaimedRectificationV4Job {
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const caseId = randomUUID();
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const turn: RectificationV4Turn = {
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id: randomUUID(),
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caseId,
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caseVersion: 1,
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questionId: randomUUID(),
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questionDomain: "other",
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questionTargetEventId: null,
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question: "请换一个方向，补充一件时间较清楚的经历。",
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answer: "记不清了，换一个吧。",
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modelId: null,
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actionId: randomUUID(),
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createdAt: now,
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};
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const caseValue: RectificationV4Case = {
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id: caseId,
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userId: randomUUID(),
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protocol: "rectification-evidence-v5",
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version: 1,
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status: "processing",
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phase: "extracting_evidence",
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calculationSpec: spec,
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calculationSpecHash: calculationSpecHash(spec),
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evidenceSetHash: "e".repeat(64),
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currentQuestion: null,
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latestSnapshot: null,
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orchestrationModelId: null,
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narrationModelId: null,
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skillVersion: "birth-time-rectification-v6",
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promptVersion: "rectification-agent-v6-1",
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algorithmVersion: "rectification-v5-matrix-scoring-1",
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deploymentMode: "v5_agent",
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agentMode: "deterministic_fallback",
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featureSnapshotId: null,
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latestDiagnosticsId: null,
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acceptedRange: null,
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createdAt: now,
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updatedAt: now,
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};
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return {
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case: caseValue,
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turn,
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turns: [turn],
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events,
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attemptedRefinementEventIds: [],
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job: {
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id: randomUUID(),
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caseId,
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status: "processing",
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phase: "extracting_evidence",
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expectedCaseVersion: 1,
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evidenceSetHash: caseValue.evidenceSetHash,
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calculationSpecHash: caseValue.calculationSpecHash,
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errorCode: null,
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createdAt: now,
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updatedAt: now,
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},
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};
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}
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const opportunity: QuestionOpportunity = {
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contractVersion: "semantic-question-v2",
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opportunityId: randomUUID(),
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kind: "ask_new_event",
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domain: "career",
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targetEventId: null,
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goal: "收集一件有大致日期的新经历。",
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requestedFields: ["new_dated_event"],
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anchors: [],
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contextFacts: [],
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forbiddenMoves: [
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"switch_target_event",
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"ask_multiple_questions",
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"claim_exact_birth_minute",
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"invent_event",
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"invent_date",
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"expose_private_score",
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"expose_internal_id",
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"expose_technique_trace",
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],
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fallbackPrompt: "请再说一件时间比较明确的经历。",
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reason: "补充可区分的证据。",
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expectedInformationGain: .8,
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dateSensitivity: .5,
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candidateSplitRelevance: .5,
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domainCoverageGain: .8,
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recallEase: .8,
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novelty: 1,
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repetitionPenalty: 0,
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privacyCost: 0,
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utility: .85,
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active: true,
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};
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const diagnostics: DiagnosticsSummary = diagnosticsSummarySchema.parse({
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id: randomUUID(),
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caseId: randomUUID(),
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snapshotId: randomUUID(),
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primaryClusterRetentionRate: .8,
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leaveOneEventOutRetentionRate: .8,
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leaveOneDomainOutRetentionRate: .8,
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dateSensitivityRetentionRate: .8,
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neighborSupportMinutes: 3,
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primarySecondaryMarginPercent: 12,
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clusterMassRatio: .8,
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unstableEventIds: [],
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mostDiscriminatingLayers: [],
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eventDateSensitivity: [],
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candidateSplits: [],
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calculationHash: "d".repeat(64),
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createdAt: now,
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});
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function analysisToolLabels(trace: RectificationAnalysisTrace, category: RectificationAnalysisTrace["toolCalls"][number]["category"]): string[] {
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return trace.toolCalls.filter((call) => call.category === category).map((call) => call.label);
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}
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test("provider reasoning keeps safe summaries and rejects private or copied content", async () => {
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const safeSummary = "现有材料还不足，宜先补充一件时间较清楚的经历。";
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const reasoned = await runBoundedReasoner({
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caseValue: makeClaimed([]).case,
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snapshot: null,
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diagnostics,
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opportunities: [opportunity],
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generateDecision: async () => ({
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object: { action: "ask_question", opportunityId: opportunity.opportunityId, narrativeFocus: ["uncertainty"] },
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reasoningSummary: safeSummary,
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reasoningSource: "provider_summary",
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}),
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});
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assert.equal(reasoned.reasoningSummary, safeSummary);
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const unsafe = [
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"参考 00000000-0000-4000-8000-000000000901",
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"候选是 05:13",
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"score 较高",
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"snapshotId 已更新",
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"opportunityId 已选中",
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"执行 tool call",
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"采用 D9 继续判断",
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"候选更接近清晨五点十三分",
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"保留率低于百分之六十五",
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];
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for (const value of unsafe) assert.equal(sanitizeReasoningSummary(value), null, value);
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const userText = "2016年9月离家去外地上大学";
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assert.equal(sanitizeReasoningSummary(`用户提到${userText}，所以继续。`, [userText]), null);
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assert.equal(sanitizeReasoningSummary("母亲去世后需要继续收集证据。", ["母亲去世"]), null);
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assert.equal(sanitizeReasoningSummary("癌症使这项证据需要谨慎处理。", ["癌症"]), null);
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const unverifiedSource = await runBoundedReasoner({
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caseValue: makeClaimed([]).case,
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snapshot: null,
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diagnostics,
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opportunities: [opportunity],
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generateDecision: async () => ({
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object: { action: "ask_question", opportunityId: opportunity.opportunityId, narrativeFocus: [] },
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reasoningSummary: safeSummary,
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}),
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});
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assert.equal(unverifiedSource.reasoningSummary, null);
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});
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test("below the scoring gate does not claim candidate scanning, diagnostics, or techniques", async () => {
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let scoreCalls = 0;
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const result = await processRectificationAgentTurn({
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claimed: makeClaimed([
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event("education", "education_milestone", "离家去外地上大学", "2016-09"),
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event("career", "career_change", "开始第一份工作", "2020-04"),
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]),
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engine: { score: async () => { scoreCalls += 1; throw new Error("candidate_engine_should_not_run"); } },
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now: new Date(now),
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});
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const trace = result.publicMessage.analysisTrace;
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assert.ok(trace);
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assert.equal(scoreCalls, 0);
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assert.equal(result.snapshot, null);
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assert.equal(trace.stages.some((stage) => stage.phase === "scoring_candidates"), false);
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assert.equal(trace.stages.some((stage) => stage.phase === "checking_robustness"), false);
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assert.deepEqual(trace.toolCalls, []);
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assert.deepEqual(trace.techniques, []);
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});
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test("scoring trace records one real engine call and only matrix-confirmed techniques", async () => {
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const claimed = makeClaimed([
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event("education", "education_milestone", "离家去外地上大学", "2016-09"),
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event("relocation", "relocation", "搬到北京长期居住", "2018-08"),
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event("career", "career_change", "开始负责商业巡演公司", "2023-09"),
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]);
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let scoreCalls = 0;
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const result = await processRectificationAgentTurn({
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claimed,
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engine: {
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score: async ({ calculationSpec, events }) => {
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scoreCalls += 1;
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const base = v5EngineResult(calculationSpec, events);
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const contributionMatrix: CandidateEngineResult["contributionMatrix"] = Object.fromEntries(
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Object.entries(base.contributionMatrix).map(([eventId, candidates]) => [
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eventId,
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Object.fromEntries(Object.entries(candidates).map(([time, contribution]) => [time, {
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...contribution,
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rule_ids: ["vimshottari_dasha", "D60"],
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technique_layers: ["D2", "D60"],
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}])),
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]),
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);
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return { ...base, contributionMatrix };
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},
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},
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now: new Date(now),
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});
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const trace = result.publicMessage.analysisTrace;
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assert.ok(trace);
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assert.equal(scoreCalls, 1);
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assert.equal(result.snapshot?.canConfirmExactMinute, false);
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assert.equal(trace.stages.some((stage) => stage.phase === "scoring_candidates"), true);
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assert.equal(trace.stages.some((stage) => stage.phase === "checking_robustness"), true);
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assert.deepEqual(analysisToolLabels(trace, "candidate_engine"), ["候选分钟扫描与稳定性诊断"]);
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assert.deepEqual(analysisToolLabels(trace, "diagnostic"), []);
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assert.deepEqual(new Set(trace.techniques), new Set(["Vimshottari Dasha", "D2"]));
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assert.equal(trace.techniques.includes("D60"), false);
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assert.equal(trace.techniques.includes("D9"), false);
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assert.equal(trace.techniques.includes("D10"), false);
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assert.deepEqual(analysisToolLabels(trace, "agent_diagnostic"), []);
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});
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test("read-only Agent diagnostics are traced only when the reasoner actually requests one", async () => {
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const caseValue = makeClaimed([]).case;
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const direct = await runBoundedReasoner({
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caseValue,
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snapshot: null,
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diagnostics,
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opportunities: [opportunity],
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generateDecision: async () => ({ object: { action: "ask_question", opportunityId: opportunity.opportunityId, narrativeFocus: [] } }),
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});
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assert.deepEqual(direct.toolCalls, []);
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const phases: string[] = [];
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const diagnosticRun = await runBoundedReasoner({
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caseValue,
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snapshot: null,
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diagnostics,
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opportunities: [opportunity],
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generateDecision: async (_prompt, phase) => {
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phases.push(phase);
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return phase === "initial"
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? { object: { action: "run_diagnostic", diagnostic: "neighbor_stability" } }
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: { object: { action: "ask_question", opportunityId: opportunity.opportunityId, narrativeFocus: ["uncertainty"] } };
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},
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});
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assert.deepEqual(phases, ["initial", "after_diagnostic"]);
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assert.deepEqual(diagnosticRun.toolCalls.map((call) => [call.tool, call.diagnostic, call.outcome]), [
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["run_rectification_diagnostics", "neighbor_stability", "succeeded"],
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]);
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});
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test("old public messages without analysisTrace remain readable and are omitted from trace history", async () => {
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await withV5Mode("v5_agent", async () => {
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const store = createRectificationV4MemoryStore();
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const service = createRectificationV4CaseService(store, { now: () => new Date(now) });
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const worker = createRectificationV4Worker({
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store,
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now: () => new Date(now),
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engine: { score: async () => { throw new Error("candidate_engine_should_not_run"); } },
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});
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const userId = randomUUID();
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const created = await service.createCase({ userId, actionId: randomUUID(), calculationSpec: spec });
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const queued = await service.answer({
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userId,
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caseId: created.case.id,
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actionId: randomUUID(),
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expectedCaseVersion: created.case.version,
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answer: "2016年9月离家去外地上大学",
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});
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assert.ok(queued?.job);
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assert.equal(await worker.runOnce(), true);
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const legacyMessage = storedPublicMessageSchema.parse({
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acknowledgement: "你提到的是离家去外地上大学。",
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candidateUpdate: null,
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limitation: null,
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question: "请再说一件时间比较明确的经历。",
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});
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assert.equal(legacyMessage.analysisTrace, undefined);
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store.publicMessages.set(queued.job.id, legacyMessage);
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assert.deepEqual(await store.loadAnalysisMessages(userId, created.case.id), []);
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});
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});
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function storedMessageWithTrace(trace: unknown): Record<string, unknown> {
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return {
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acknowledgement: "承接这段经历。",
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candidateUpdate: null,
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limitation: null,
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question: "请再说一件时间比较明确的经历。",
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analysisTrace: trace,
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};
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}
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test("Supabase analysis projection orders multiple turns and maps job ids to turn ids", () => {
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const earlierJobId = randomUUID();
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const laterJobId = randomUUID();
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const earlierTurnId = randomUUID();
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const laterTurnId = randomUUID();
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const earlierTrace: RectificationAnalysisTrace = {
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status: "completed",
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stages: [],
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toolCalls: [],
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techniques: ["D2"],
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reasoningSummary: null,
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reasoningSource: "none",
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};
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const laterTrace: RectificationAnalysisTrace = {
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status: "completed",
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stages: [],
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toolCalls: [],
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techniques: ["D4"],
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reasoningSummary: null,
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reasoningSource: "none",
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};
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const projected = projectAnalysisMessages([
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{ job_id: laterJobId, message: storedMessageWithTrace(laterTrace), created_at: "2026-07-29T02:00:00.000Z" },
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{ job_id: earlierJobId, message: storedMessageWithTrace(earlierTrace), created_at: "2026-07-29T01:00:00.000Z" },
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], [
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{ id: laterJobId, turn_id: laterTurnId },
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{ id: earlierJobId, turn_id: earlierTurnId },
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]);
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assert.deepEqual(projected, [
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{ sourceTurnId: earlierTurnId, trace: earlierTrace },
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{ sourceTurnId: laterTurnId, trace: laterTrace },
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]);
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});
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test("Supabase analysis projection ignores legacy messages without analysisTrace", () => {
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const jobId = randomUUID();
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const turnId = randomUUID();
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assert.deepEqual(projectAnalysisMessages([{
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job_id: jobId,
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message: {
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acknowledgement: "承接这段经历。",
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candidateUpdate: null,
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limitation: null,
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question: "请再说一件时间比较明确的经历。",
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},
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created_at: "2026-07-29T01:00:00.000Z",
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}], [{ id: jobId, turn_id: turnId }]), []);
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});
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test("Supabase analysis projection ignores invalid traces", () => {
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const jobId = randomUUID();
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const turnId = randomUUID();
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assert.deepEqual(projectAnalysisMessages([{
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job_id: jobId,
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message: storedMessageWithTrace({
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status: "invented",
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stages: [],
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toolCalls: [],
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techniques: [],
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reasoningSummary: null,
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reasoningSource: "none",
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}),
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created_at: "2026-07-29T01:00:00.000Z",
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}], [{ id: jobId, turn_id: turnId }]), []);
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});
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