872 lines
32 KiB
TypeScript
872 lines
32 KiB
TypeScript
import assert from "node:assert/strict";
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import { readFileSync } from "node:fs";
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import test from "node:test";
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import { candidateSetId } from "../src/lib/rectification-agentic/core/build-state.ts";
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import { asInferenceState } from "../src/lib/rectification-agentic/core/compose-receipt.ts";
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import { INFERENCE_ALGORITHM_VERSION } from "../src/lib/rectification-agentic/core/types.ts";
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import type { ConflictProbe, InferenceState } from "../src/lib/rectification-agentic/core/types.ts";
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import {
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decideFromDossier,
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type DecisionDossier,
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} from "../src/lib/rectification-agentic/v9/decision-from-dossier.ts";
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import { RECTIFICATION_USER_COPY } from "../src/lib/rectification-agentic/user-copy.ts";
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import {
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applyRectificationChoice,
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ensureNonTerminalTurnExit,
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persistNextInterviewIfIdle,
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} from "../src/lib/rectification-agentic/v9/answer-choice.ts";
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import { RECTIFICATION_SKILL_NAME, RECTIFICATION_SKILL_VERSION } from "../src/lib/rectification-agentic/v9/case-status.ts";
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import { evidenceLedgerFingerprint } from "../src/lib/rectification-agentic/v9/tool-service.ts";
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import { CHOICE_STOP_LABEL } from "../src/lib/rectification-agentic/v9/choice-card.ts";
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import { CHOICE_ACTION } from "../src/lib/rectification-agentic/v9/choice-action.ts";
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import { runV9AgentTurn } from "../src/lib/rectification-agentic/v9/agent-run.ts";
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import { finalizeSuccessfulTurnExit } from "../src/lib/rectification-agentic/v9/turn-exit.ts";
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import {
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CASE_ID,
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FOCUS_ID,
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SESSION_ID,
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TURN_ID,
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USER_ID,
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activeFocusFixture,
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candidateSnapshotFixture,
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computeFixture,
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dossierFixture,
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fakeAccounting,
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receiptHandlers,
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} from "./rectification-v9-test-support.ts";
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const EXISTENCE_OPTIONS = [
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{ label: "明确发生且时间吻合", answer_class: "yes" as const },
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{ label: "发生过但程度较弱", answer_class: "weak_yes" as const },
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{ label: "明确没有发生", answer_class: "no" as const },
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{ label: "这段记不清楚", answer_class: "unsure" as const },
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];
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const TIMES = [
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"04:47", "04:51", "04:53", "04:59", "05:00", "05:07", "05:12", "05:14", "05:15",
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] as const;
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const ELIMINATED = new Set(["05:00", "05:07", "05:12", "05:14", "05:15"]);
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const ACTIVE = ["04:47", "04:51", "04:53", "04:59"] as const;
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const SCORES: Record<string, number> = {
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"04:47": 16,
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"04:51": 20,
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"04:53": 16,
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"04:59": 10,
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"05:00": 4,
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"05:07": 3,
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"05:12": 2,
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"05:14": 1,
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"05:15": 1,
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};
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const PROBABILITY: Record<string, number> = {
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"04:47": 0.25,
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"04:51": 0.4,
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"04:53": 0.25,
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"04:59": 0.1,
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};
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const EVIDENCE = [
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{
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id: "e-edu-start",
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status: "confirmed" as const,
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domain: "education",
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datePrecision: "month" as const,
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occurredFrom: "2016-09-01",
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occurredTo: "2016-09-30",
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eventKind: "education_start",
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summary: "education start",
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},
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{
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id: "e-edu-end",
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status: "confirmed" as const,
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domain: "education",
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datePrecision: "month" as const,
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occurredFrom: "2020-06-01",
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occurredTo: "2020-06-30",
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eventKind: "education_completion",
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summary: "education completion",
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},
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{
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id: "e-rel-start",
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status: "confirmed" as const,
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domain: "relationship",
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datePrecision: "month" as const,
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occurredFrom: "2024-05-01",
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occurredTo: null,
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eventKind: "relationship_start",
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summary: "relationship start",
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},
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{
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id: "e-rel-end",
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status: "confirmed" as const,
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domain: "relationship",
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datePrecision: "day" as const,
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occurredFrom: "2024-08-08",
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occurredTo: null,
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eventKind: "relationship_end",
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summary: "relationship end",
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},
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{
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id: "e-finance",
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status: "confirmed" as const,
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domain: "finance",
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datePrecision: "month" as const,
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occurredFrom: "2026-01-01",
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occurredTo: "2026-01-31",
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eventKind: "finance_loss",
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summary: "finance change",
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},
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{
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id: "e-reloc",
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status: "confirmed" as const,
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domain: "relocation",
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datePrecision: "month" as const,
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occurredFrom: "2023-07-01",
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occurredTo: "2023-07-31",
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eventKind: "relocation",
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summary: "relocation",
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},
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{
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id: "e-health",
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status: "confirmed" as const,
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domain: "health_pressure",
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datePrecision: "month" as const,
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occurredFrom: "2026-01-01",
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occurredTo: "2026-08-31",
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eventKind: "pressure_period",
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summary: "health pressure",
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},
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{
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id: "e-occupation",
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status: "confirmed" as const,
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domain: "occupation",
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datePrecision: "unknown" as const,
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occurredFrom: null,
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occurredTo: null,
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eventKind: "occupation_note",
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summary: "occupation note",
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},
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] as const;
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function uuidAt(index: number) {
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return `00000000-0000-4000-8000-${String(index + 1).padStart(12, "0")}`;
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}
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function existenceProbe(input: {
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key: string;
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domain: string;
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year: number;
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question: string;
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}): ConflictProbe {
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return {
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id: `probe:${input.key}`,
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semantic_key: input.key,
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candidate_split_hash: input.key,
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domain: input.domain,
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year: input.year,
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question: input.question,
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candidate_ids: [...ACTIVE],
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expected_outcomes: [
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{ answer_class: "yes", supports: ["04:51"], conflicts: ["04:47"] },
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{ answer_class: "weak_yes", supports: [], conflicts: [] },
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{ answer_class: "no", supports: ["04:47"], conflicts: ["04:51"] },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: 0.4,
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source: "dasha_boundary",
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choice_kind: "existence",
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style_options: EXISTENCE_OPTIONS,
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};
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}
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const D9: ConflictProbe = {
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id: "contrast:varga.d9.style",
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semantic_key: "varga.d9.style",
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candidate_split_hash: "varga.d9.style",
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domain: "relationship",
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year: 0,
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question: "亲密关系里更接近哪一种",
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candidate_ids: [...ACTIVE],
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expected_outcomes: [
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{ answer_class: "yes", supports: ["04:51"], conflicts: ["04:47"] },
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{ answer_class: "weak_yes", supports: ["04:47"], conflicts: ["04:51"] },
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{ answer_class: "no", supports: [], conflicts: [] },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: 0.3,
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source: "varga_contrast",
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choice_kind: "varga_style",
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};
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const D10: ConflictProbe = {
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id: "contrast:varga.d10.style",
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semantic_key: "varga.d10.style",
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candidate_split_hash: "varga.d10.style",
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domain: "career",
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year: 0,
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question: "平时做事更接近哪一种",
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candidate_ids: [...ACTIVE],
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expected_outcomes: [
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{ answer_class: "yes", supports: ["04:51"], conflicts: ["04:47"] },
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{ answer_class: "weak_yes", supports: ["04:47"], conflicts: ["04:51"] },
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{ answer_class: "no", supports: [], conflicts: [] },
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{ answer_class: "unsure", supports: [], conflicts: [] },
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],
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information_gain: 0.3,
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source: "varga_contrast",
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choice_kind: "varga_style",
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};
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const CAREER_MONTH = existenceProbe({
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key: "career.2023.05.dasha_boundary",
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domain: "career",
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year: 2023,
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question: "2023 年 5 月前后有没有入职或换工作",
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});
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const CAREER_YEAR = existenceProbe({
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key: "career.2023.dasha_activation",
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domain: "career",
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year: 2023,
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question: "2023 年前后工作上有没有入职或换工作",
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});
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const RELOC = existenceProbe({
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key: "relocation.2018.05.dasha_boundary",
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domain: "relocation",
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year: 2018,
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question: "2018 年 5 月前后有没有搬家",
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});
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const FINANCE = existenceProbe({
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key: "finance.2024.03.dasha_boundary",
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domain: "finance",
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year: 2024,
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question: "2024 年 3 月前后钱上有没有明显变化",
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});
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const ASKED_PROBES = [D9, D10, CAREER_MONTH, CAREER_YEAR, RELOC, FINANCE];
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const LEFTOVER_PROBE = existenceProbe({
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key: "career.2021.04.dasha_boundary",
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domain: "career",
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year: 2021,
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question: "2021 年 4 月前后有没有入职或换工作",
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});
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const ACTION_ID = "aaaaaaaa-aaaa-4aaa-8aaa-aaaaaaaaaaa1";
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const GATE_SENTENCE = /还差(?: \d+ 件)?带月份的经历|两件事的日期还没对清|当前还排不出可比较的候选时间|还差另一个领域的带月份经历/;
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function liveState(extraProbes: readonly ConflictProbe[] = []): InferenceState {
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const probes = [...ASKED_PROBES, ...extraProbes];
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const rankedActive = [...ACTIVE].sort((left, right) => (
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(PROBABILITY[right] ?? 0) - (PROBABILITY[left] ?? 0)
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|| (SCORES[right] ?? 0) - (SCORES[left] ?? 0)
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|| left.localeCompare(right)
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));
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const candidates = TIMES.map((time, index) => {
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const eliminated = ELIMINATED.has(time);
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const activeRank = (rankedActive as readonly string[]).indexOf(time);
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return {
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id: time,
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time,
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cluster_range: [time, time] as const,
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prior_score: SCORES[time] ?? 0,
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posterior_score: SCORES[time] ?? 0,
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probability: eliminated ? 0 : (PROBABILITY[time] ?? 0),
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status: eliminated ? "eliminated" as const : "active" as const,
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rank: eliminated ? ACTIVE.length + index : activeRank + 1,
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strong_conflict_count: eliminated ? 3 : 0,
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};
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});
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const answered = [
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{ probe: D9, answer_class: "yes" as const },
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{ probe: D10, answer_class: "yes" as const },
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{ probe: CAREER_MONTH, answer_class: "yes" as const },
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{ probe: CAREER_YEAR, answer_class: "no" as const },
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{ probe: RELOC, answer_class: "no" as const },
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{ probe: FINANCE, answer_class: "no" as const },
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].map((item) => ({
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probe_id: item.probe.id,
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semantic_key: item.probe.semantic_key,
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candidate_split_hash: item.probe.candidate_split_hash,
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answer_class: item.answer_class,
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classified_from: "choice" as const,
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}));
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const raw = {
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algorithm_version: INFERENCE_ALGORITHM_VERSION,
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candidate_set_id: candidateSetId("04:47", "05:15", TIMES),
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revision: 6,
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phase: "discrimination" as const,
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result_status: "discriminating" as const,
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range_start: "04:47",
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range_end: "05:15",
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candidates,
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events: [
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{ id: "e-edu-start", domain: "education", year: 2016, precision: "month" as const, usage: "training" as const },
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{ id: "e-edu-end", domain: "education", year: 2020, precision: "month" as const, usage: "training" as const },
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{ id: "e-rel-start", domain: "relationship", year: 2024, precision: "month" as const, usage: "training" as const },
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{ id: "e-rel-end", domain: "relationship", year: 2024, precision: "day" as const, usage: "holdout" as const },
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{ id: "e-finance", domain: "finance", year: 2026, precision: "month" as const, usage: "training" as const },
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{ id: "e-reloc", domain: "relocation", year: 2023, precision: "month" as const, usage: "training" as const },
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{ id: "e-health", domain: "health_pressure", year: 2026, precision: "month" as const, usage: "training" as const },
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],
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probes,
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answered_probes: answered,
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rounds: [],
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last_inference_round: null,
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entropy: 1.2,
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representative_time: "04:51",
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credible_range: ["04:47", "04:53"] as const,
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holdout_passed: null,
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};
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const loaded = asInferenceState(raw);
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assert.ok(loaded, "accident-shape inference must pass asInferenceState");
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return loaded;
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}
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function eventProbeRow(probe: ConflictProbe) {
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return {
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year: probe.year,
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year_label: probe.year > 0 ? `${probe.year} 年前后` : "",
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domain: probe.domain,
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event_family: probe.domain === "career" ? "入职、换工作或职责加重" : probe.domain,
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source: probe.source,
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tracks: ["vimshottari", "narayana"],
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tracks_agree: true,
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unique_minute_claim: false,
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user_meaning: probe.question,
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role: "distinguish",
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information_gain: probe.information_gain,
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semantic_key: probe.semantic_key,
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candidate_split_hash: probe.candidate_split_hash,
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candidate_ids: probe.candidate_ids,
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expected_outcomes: probe.expected_outcomes,
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choice_kind: probe.choice_kind,
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style_options: probe.style_options,
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};
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}
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function snapshotCandidates() {
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return TIMES.map((time, index) => ({
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candidate_id: uuidAt(index),
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rank: index + 1,
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time,
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relative_support: Math.round(SCORES[time] ?? 0),
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tied_minute_count: 1,
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}));
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}
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function accidentDossier(extra: {
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acceptanceAllowed?: boolean;
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acceptanceReasons?: string[];
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mismatchSnapshot?: boolean;
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leftoverProbe?: ConflictProbe;
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holdoutUnavailable?: boolean;
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} = {}): DecisionDossier {
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const base = liveState(extra.leftoverProbe ? [extra.leftoverProbe] : []);
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const evidence = extra.holdoutUnavailable
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? EVIDENCE.filter((item) => item.id !== "e-rel-end")
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: EVIDENCE;
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const state = {
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...base,
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events: extra.holdoutUnavailable
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? base.events.filter((item) => item.usage !== "holdout" && item.id !== "e-rel-end")
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: base.events,
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holdout_passed: extra.holdoutUnavailable ? true : base.holdout_passed,
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};
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const fingerprint = evidenceLedgerFingerprint(evidence as never);
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const acceptanceAllowed = extra.acceptanceAllowed !== false;
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return {
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evidence,
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conversationSummary: {
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activeFocus: null,
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declinedSkippedTopics: [{ target_domain: "family", status: "declined" }],
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},
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latestResult: {
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resultId: "55555555-5555-4555-8555-555555555555",
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selectionAllowed: acceptanceAllowed,
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confirmationAllowed: false,
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evidenceLedgerFingerprint: fingerprint,
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candidates: extra.mismatchSnapshot
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? [{ candidateId: uuidAt(0), time: "03:00", rank: 1, relativeSupport: 10 }]
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: TIMES.map((time, index) => ({
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candidateId: uuidAt(index),
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time,
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rank: index + 1,
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relativeSupport: Math.round(SCORES[time] ?? 0),
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})),
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representativeTime: "04:51",
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decisionReceipt: {
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accept_allowed: acceptanceAllowed,
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acceptance_allowed: acceptanceAllowed,
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propose_allowed: extra.acceptanceAllowed === false ? false : true,
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selection_allowed: extra.acceptanceAllowed === false ? false : true,
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confirmation_allowed: false,
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...(extra.acceptanceReasons ? { acceptance_reasons: extra.acceptanceReasons } : {}),
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inference_state: state,
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discriminating_event_probes: [
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...ASKED_PROBES.map(eventProbeRow),
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...(extra.leftoverProbe ? [eventProbeRow(extra.leftoverProbe)] : []),
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],
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oos_blind_prompts: extra.holdoutUnavailable
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? []
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: [{
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domain: "career",
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user_meaning: "工作这条线还没用过。有没有记得大概时间的入职或换工作？",
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used_for_scoring: false,
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}],
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},
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},
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case: { acceptedTime: null, status: "collecting_evidence" },
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};
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}
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function rpcDossier(decision: DecisionDossier, extra: { activeFocus?: ReturnType<typeof activeFocusFixture> } = {}) {
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const evidence = decision.evidence.map((item) => ({
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id: item.id ?? "e-unknown",
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source_turn_id: TURN_ID,
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subject: "self",
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event_kind: item.eventKind ?? item.domain,
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domain: item.domain,
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occurred_from: item.occurredFrom,
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occurred_to: item.occurredTo,
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date_precision: item.datePrecision,
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summary: item.summary ?? item.domain,
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status: item.status,
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supersedes_evidence_id: null,
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created_at: "2026-09-06T00:00:00.000Z",
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}));
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return dossierFixture({
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evidence,
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latestResult: candidateSnapshotFixture({
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selectionAllowed: decision.latestResult?.selectionAllowed ?? true,
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confirmationAllowed: false,
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representativeTime: "04:51",
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evidenceLedgerFingerprint: evidenceLedgerFingerprint(decision.evidence as never),
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candidates: decision.latestResult?.candidates?.map((item, index) => ({
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candidate_id: item.candidateId ?? uuidAt(index),
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time: item.time,
|
||
rank: item.rank ?? index + 1,
|
||
relative_support: Math.max(0, Math.min(100, item.relativeSupport ?? 0)),
|
||
tied_minute_count: 1,
|
||
})) ?? snapshotCandidates(),
|
||
decisionReceipt: { ...(decision.latestResult?.decisionReceipt ?? {}) },
|
||
}),
|
||
conversationSummary: {
|
||
confirmed_evidence_summary: [],
|
||
pending_revisions: [],
|
||
active_focus: extra.activeFocus ?? null,
|
||
declined_skipped_topics: decision.conversationSummary.declinedSkippedTopics,
|
||
candidate_divergence_summary: null,
|
||
missing_evidence_categories: [],
|
||
last_result_policy: null,
|
||
summary_version: 1,
|
||
updated_at: "2026-09-06T00:00:00.000Z",
|
||
},
|
||
});
|
||
}
|
||
|
||
function idleHandlers(decision: DecisionDossier, extra: { activeFocus?: ReturnType<typeof activeFocusFixture>; allowFocus?: boolean } = {}) {
|
||
return fakeAccounting({
|
||
...receiptHandlers,
|
||
get_agentic_rectification_case_dossier: () => rpcDossier(decision, extra),
|
||
get_agentic_rectification_case_compute: () => computeFixture(),
|
||
append_agentic_rectification_turn: () => ({ turn_id: TURN_ID, idempotent: false }),
|
||
apply_agentic_rectification_choice_action: (_fn, args) => ({
|
||
action_id: args.p_action_id,
|
||
status: "applied",
|
||
idempotent: false,
|
||
question_id: args.p_question_id,
|
||
option_id: args.p_option_id,
|
||
probe_id: args.p_inference && typeof args.p_inference === "object"
|
||
? (args.p_inference as { probe_id?: string }).probe_id ?? "p-cd"
|
||
: "p-cd",
|
||
revision: Number(args.p_expected_revision) + 1,
|
||
source_quote: args.p_source_quote,
|
||
derived_context: args.p_derived_context,
|
||
narration: args.p_narration,
|
||
focus_status: args.p_focus_status,
|
||
}),
|
||
set_agentic_rectification_conversation_focus: extra.allowFocus
|
||
? (_fn, args) => ({
|
||
focus: {
|
||
id: FOCUS_ID,
|
||
case_id: CASE_ID,
|
||
question_id: args.p_question_id,
|
||
intent: args.p_intent,
|
||
target_evidence_id: args.p_target_evidence_id,
|
||
target_domain: args.p_target_domain,
|
||
target_kind: args.p_target_kind,
|
||
expected_answer_schema: args.p_expected_answer_schema,
|
||
status: "active",
|
||
asked_at: "2026-09-06T00:00:00.000Z",
|
||
resolved_at: null,
|
||
asked_turn_id: args.p_asked_turn_id ?? null,
|
||
},
|
||
idempotent: false,
|
||
})
|
||
: (_fn, args) => {
|
||
throw new Error(`must not persist collect focus ${String(args.p_question_id ?? args.p_target_domain)}`);
|
||
},
|
||
finalize_agentic_rectification_turn: () => ({ turn_id: TURN_ID, status: "completed", idempotent: false }),
|
||
get_agentic_rectification_turn_receipt: () => null,
|
||
});
|
||
}
|
||
|
||
function leftoverFocus(probe: ConflictProbe) {
|
||
return activeFocusFixture({
|
||
intent: "distinguish_candidates",
|
||
targetDomain: probe.domain,
|
||
questionId: `probe:${probe.semantic_key}`,
|
||
expectedAnswerSchema: {
|
||
choice: {
|
||
prompt: probe.question,
|
||
option_a: EXISTENCE_OPTIONS[0].label,
|
||
option_b: EXISTENCE_OPTIONS[1].label,
|
||
option_c: EXISTENCE_OPTIONS[2].label,
|
||
option_d: EXISTENCE_OPTIONS[3].label,
|
||
options: EXISTENCE_OPTIONS.map((option, index) => ({
|
||
key: (["A", "B", "C", "D"] as const)[index]!,
|
||
label: option.label,
|
||
answer_class: option.answer_class,
|
||
})),
|
||
},
|
||
probe_id: probe.id,
|
||
semantic_key: probe.semantic_key,
|
||
candidate_split_hash: probe.candidate_split_hash,
|
||
},
|
||
});
|
||
}
|
||
|
||
function assistantAppendCalls(calls: Array<{ fn: string; args: Record<string, unknown> }>) {
|
||
return calls.filter((item) => (
|
||
item.fn === "append_agentic_rectification_turn"
|
||
&& typeof item.args.p_assistant_message === "string"
|
||
&& String(item.args.p_assistant_message).trim()
|
||
));
|
||
}
|
||
|
||
function gateAppendCalls(calls: Array<{ fn: string; args: Record<string, unknown> }>) {
|
||
return assistantAppendCalls(calls).filter((item) => GATE_SENTENCE.test(String(item.args.p_assistant_message)));
|
||
}
|
||
|
||
function gateBodyCount(text: string | null | undefined) {
|
||
return (text ?? "").match(new RegExp(GATE_SENTENCE.source, "g"))?.length ?? 0;
|
||
}
|
||
|
||
function fakeAgentStream(chunks: Array<{ type: string; payload?: Record<string, unknown> }>) {
|
||
const streamResult = {
|
||
fullStream: (async function* () {
|
||
for (const item of chunks) yield item;
|
||
})(),
|
||
totalUsage: Promise.resolve({ inputTokens: 10, outputTokens: 20 }),
|
||
};
|
||
return {
|
||
stream: async () => streamResult,
|
||
getSkill: async () => ({ name: RECTIFICATION_SKILL_NAME, instructions: "skill" }),
|
||
};
|
||
}
|
||
|
||
function warnLines(run: () => Promise<unknown> | unknown) {
|
||
const lines: string[] = [];
|
||
const original = console.warn;
|
||
console.warn = (...args: unknown[]) => {
|
||
lines.push(args.map((item) => String(item)).join(" "));
|
||
original.apply(console, args);
|
||
};
|
||
return Promise.resolve(run()).finally(() => {
|
||
console.warn = original;
|
||
}).then((result) => ({ result, lines }));
|
||
}
|
||
|
||
test("skill version is 10.0.19 after the delivery UI simplify bump", () => {
|
||
assert.equal(RECTIFICATION_SKILL_VERSION, "10.0.19");
|
||
});
|
||
|
||
test("USER_COLLECT_QUESTION no longer has an other fallback", () => {
|
||
const srcRoot = new URL("../src/", import.meta.url);
|
||
const files = [
|
||
"lib/rectification-agentic/v9/method-followup.ts",
|
||
"lib/rectification-agentic/v9/answer-choice.ts",
|
||
"mastra/rectification-v9-tools.ts",
|
||
"app/api/rectification/agent/route.ts",
|
||
"lib/rectification-agentic/user-copy.ts",
|
||
];
|
||
const hits: string[] = [];
|
||
for (const relative of files) {
|
||
const source = readFileSync(new URL(relative, srcRoot), "utf8");
|
||
for (const line of source.split("\n")) {
|
||
if (line.includes("USER_COLLECT_QUESTION.other") || line.includes("collectQuestionRetryByDomain.other")) {
|
||
hits.push(`${relative}: ${line.trim()}`);
|
||
}
|
||
}
|
||
}
|
||
assert.deepEqual(hits, []);
|
||
});
|
||
|
||
test("collect spoken stop stays on choice cards; range line is status only", () => {
|
||
// 原值: 口述停止按钮和范围小字共用 CHOICE_STOP_LABEL / onStop
|
||
// 新值: CHOICE_STOP_LABEL 只在选择题卡；composer-wrap 不再挂先这样
|
||
// 原因: BUG-595 决策 2
|
||
const chat = readFileSync(new URL("../src/components/rectification-agentic-chat.tsx", import.meta.url), "utf8");
|
||
const route = readFileSync(new URL("../src/app/api/rectification/agent/route.ts", import.meta.url), "utf8");
|
||
const rangeFn = chat.slice(
|
||
chat.indexOf("function RectificationReadonlyRange"),
|
||
chat.indexOf("type RectificationAgenticChatProps"),
|
||
);
|
||
const wrap = chat.slice(chat.indexOf("className=\"composer-wrap\""), chat.indexOf("className=\"composer-footer\""));
|
||
assert.doesNotMatch(wrap, /rectification-collect-stop/);
|
||
assert.match(chat, /CHOICE_STOP_LABEL/);
|
||
assert.match(chat, /kind === "collect_spoken"/);
|
||
assert.match(chat, /function submitStop/);
|
||
assert.match(chat, /RectificationReadonlyRange/);
|
||
assert.match(rangeFn, /role="status"/);
|
||
assert.doesNotMatch(rangeFn, /onStop/);
|
||
assert.doesNotMatch(rangeFn, /<button/);
|
||
assert.match(route, /ask_about_result/);
|
||
assert.match(route, /STOP_ACTION/);
|
||
assert.equal(CHOICE_STOP_LABEL, "先这样，先看当前范围");
|
||
});
|
||
|
||
test("covered accident shape adopts instead of collecting other", async () => {
|
||
const dossier = accidentDossier();
|
||
const decision = decideFromDossier(dossier, { birthDate: "1998-03-15" });
|
||
assert.notEqual(decision.sessionOutcome, "collect_evidence");
|
||
assert.equal(decision.sessionOutcome, "adopt_representative");
|
||
assert.match(decision.stopReason ?? "", /probe_pool_exhausted/);
|
||
|
||
const accounting = idleHandlers(dossier);
|
||
const { result: idle } = await warnLines(() => persistNextInterviewIfIdle({
|
||
accounting: accounting.client,
|
||
userId: USER_ID,
|
||
caseId: CASE_ID,
|
||
}));
|
||
const persisted = idle as Awaited<ReturnType<typeof persistNextInterviewIfIdle>>;
|
||
assert.equal(
|
||
accounting.calls.some((item) => item.fn === "set_agentic_rectification_conversation_focus"),
|
||
false,
|
||
);
|
||
assert.ok(persisted.hostNarration);
|
||
// 原值: probePoolExhaustedStop「能分开候选的问题已经问完…」
|
||
// 新值: 三句交付正文
|
||
// 原因: BUG-595 决策 4
|
||
assert.match(persisted.hostNarration ?? "", /这次给出的范围 04:47–04:53/);
|
||
assert.doesNotMatch(persisted.hostNarration ?? "", /排盘用/);
|
||
assert.match(persisted.hostNarration ?? "", /对照了 7 件经历/);
|
||
assert.equal((persisted.hostNarration ?? "").includes("也可以再" + "说一件"), false);
|
||
assert.match(persisted.hostNarration ?? "", /这只是代表性候选，不是已确认的唯一出生分钟/);
|
||
assert.equal(persisted.terminalNote, true);
|
||
});
|
||
|
||
test("closed ceiling translates the gate and does not persist a focus", async () => {
|
||
const dossier = accidentDossier({
|
||
acceptanceAllowed: false,
|
||
acceptanceReasons: ["insufficient_events"],
|
||
holdoutUnavailable: true,
|
||
});
|
||
const accounting = idleHandlers(dossier);
|
||
const { result: idle, lines } = await warnLines(() => persistNextInterviewIfIdle({
|
||
accounting: accounting.client,
|
||
userId: USER_ID,
|
||
caseId: CASE_ID,
|
||
}));
|
||
const persisted = idle as Awaited<ReturnType<typeof persistNextInterviewIfIdle>>;
|
||
assert.equal(
|
||
accounting.calls.some((item) => item.fn === "set_agentic_rectification_conversation_focus"),
|
||
false,
|
||
);
|
||
assert.ok(persisted.hostNarration);
|
||
assert.match(persisted.hostNarration ?? "", /还差|带月份的经历|日期还没对清|排不出可比较的候选/);
|
||
assert.equal((persisted.hostNarration ?? "").includes("也可以再" + "说一件"), false);
|
||
|
||
const repaired = await ensureNonTerminalTurnExit({
|
||
accounting: accounting.client,
|
||
userId: USER_ID,
|
||
caseId: CASE_ID,
|
||
});
|
||
assert.ok(repaired.hostNarration === null || repaired.terminalNote !== false);
|
||
|
||
const log = lines.map((line) => {
|
||
try {
|
||
return JSON.parse(line) as Record<string, unknown>;
|
||
} catch {
|
||
return null;
|
||
}
|
||
}).find((item) => item?.event === "rectification_exhaustion_collect");
|
||
assert.ok(log);
|
||
for (const key of [
|
||
"event", "case_id", "can_adopt", "ceiling", "training_gate",
|
||
"stop_class", "ranked_count", "probe_key", "budget", "next_domain", "next_source",
|
||
]) {
|
||
assert.ok(key in (log ?? {}), key);
|
||
}
|
||
assert.equal("summary" in (log ?? {}), false);
|
||
assert.equal("year" in (log ?? {}), false);
|
||
assert.equal("evidence" in (log ?? {}), false);
|
||
});
|
||
|
||
test("inconsistent projection logs ranked_count 0 and still delivers a gate", async () => {
|
||
const dossier = accidentDossier({ mismatchSnapshot: true, holdoutUnavailable: true });
|
||
const accounting = idleHandlers(dossier);
|
||
const { result: idle, lines } = await warnLines(() => persistNextInterviewIfIdle({
|
||
accounting: accounting.client,
|
||
userId: USER_ID,
|
||
caseId: CASE_ID,
|
||
}));
|
||
const persisted = idle as Awaited<ReturnType<typeof persistNextInterviewIfIdle>>;
|
||
assert.equal((persisted.hostNarration ?? "").includes("也可以再" + "说一件"), false);
|
||
assert.equal(
|
||
accounting.calls.some((item) => item.fn === "set_agentic_rectification_conversation_focus"),
|
||
false,
|
||
);
|
||
const log = lines.map((line) => {
|
||
try {
|
||
return JSON.parse(line) as Record<string, unknown>;
|
||
} catch {
|
||
return null;
|
||
}
|
||
}).find((item) => item?.event === "rectification_exhaustion_collect");
|
||
assert.equal(log?.ranked_count, 0);
|
||
assert.match(persisted.hostNarration ?? "", /还差|带月份|排不出可比较的候选|范围/);
|
||
});
|
||
|
||
test("finalizeSuccessfulTurnExit writes one gate body and repair does not add another", async () => {
|
||
const dossier = accidentDossier({
|
||
acceptanceAllowed: false,
|
||
acceptanceReasons: ["insufficient_events"],
|
||
holdoutUnavailable: true,
|
||
});
|
||
const accounting = idleHandlers(dossier);
|
||
await warnLines(() => finalizeSuccessfulTurnExit({
|
||
accounting: accounting.client,
|
||
userId: USER_ID,
|
||
caseId: CASE_ID,
|
||
action: "message",
|
||
}));
|
||
const first = gateAppendCalls(accounting.calls);
|
||
assert.equal(first.length, 1, JSON.stringify(first.map((item) => item.args.p_assistant_message)));
|
||
assert.equal(gateBodyCount(String(first[0]?.args.p_assistant_message)), 1);
|
||
|
||
const beforeRepair = accounting.calls.filter((item) => item.fn === "append_agentic_rectification_turn").length;
|
||
await ensureNonTerminalTurnExit({
|
||
accounting: accounting.client,
|
||
userId: USER_ID,
|
||
caseId: CASE_ID,
|
||
});
|
||
assert.equal(
|
||
accounting.calls.filter((item) => item.fn === "append_agentic_rectification_turn").length,
|
||
beforeRepair,
|
||
);
|
||
});
|
||
|
||
test("agent evidence path writes one gate body", async () => {
|
||
const dossier = accidentDossier({
|
||
acceptanceAllowed: false,
|
||
acceptanceReasons: ["insufficient_events"],
|
||
holdoutUnavailable: true,
|
||
});
|
||
const accounting = idleHandlers(dossier);
|
||
const result = await runV9AgentTurn({
|
||
userId: USER_ID,
|
||
caseId: CASE_ID,
|
||
sessionId: SESSION_ID,
|
||
requestId: "aaaaaaaa-bbbb-4ccc-8ddd-eeeeeeeeeeee",
|
||
action: "evidence",
|
||
message: "2016年9月离开家去北京工作",
|
||
modelName: "gpt-4o-mini",
|
||
accounting: accounting.client,
|
||
billing: {
|
||
reserve: async () => ({ success: true, status: 200 }),
|
||
complete: async () => true,
|
||
release: async () => true,
|
||
},
|
||
emit: async () => {},
|
||
buildAgent: async () => fakeAgentStream([
|
||
{ type: "start" },
|
||
{ type: "tool-call", payload: { toolName: "skill", args: { name: RECTIFICATION_SKILL_NAME } } },
|
||
{ type: "tool-result", payload: { toolName: "skill" } },
|
||
{ type: "tool-call", payload: { toolName: "rectification-read-case", args: { caseId: CASE_ID } } },
|
||
{ type: "tool-result", payload: { toolName: "rectification-read-case" } },
|
||
{ type: "text-delta", payload: { text: "先记下这件事。" } },
|
||
{ type: "finish" },
|
||
]) as never,
|
||
});
|
||
assert.equal(result.ok, true);
|
||
// 原值: persistExhaustionGateTurn 再写一条门槛句
|
||
// 新值: 本轮已有 agent 正文（askedTurnId）时不再写第二条确定性门槛 turn
|
||
// 原因: BUG-596
|
||
const gates = gateAppendCalls(accounting.calls);
|
||
assert.equal(gates.length, 0, JSON.stringify(gates.map((item) => item.args.p_assistant_message)));
|
||
});
|
||
|
||
test("last closed-ceiling card concatenates the gate into one append", async () => {
|
||
const dossier = accidentDossier({
|
||
acceptanceAllowed: false,
|
||
acceptanceReasons: ["insufficient_events"],
|
||
leftoverProbe: LEFTOVER_PROBE,
|
||
holdoutUnavailable: true,
|
||
});
|
||
const accounting = idleHandlers(dossier, {
|
||
activeFocus: leftoverFocus(LEFTOVER_PROBE),
|
||
});
|
||
const applied = await applyRectificationChoice(accounting.client, {
|
||
userId: USER_ID,
|
||
caseId: CASE_ID,
|
||
sessionId: SESSION_ID,
|
||
actionId: ACTION_ID,
|
||
action: CHOICE_ACTION,
|
||
focusId: FOCUS_ID,
|
||
questionId: `probe:${LEFTOVER_PROBE.semantic_key}`,
|
||
probeId: LEFTOVER_PROBE.id,
|
||
optionId: "A",
|
||
expectedRevision: 6,
|
||
});
|
||
const gates = gateAppendCalls(accounting.calls);
|
||
assert.equal(gates.length, 1, JSON.stringify(gates.map((item) => item.args.p_assistant_message)));
|
||
assert.equal(gateBodyCount(String(gates[0]?.args.p_assistant_message)), 1);
|
||
assert.match(applied.narration, GATE_SENTENCE);
|
||
assert.equal(gateBodyCount(applied.narration), 1);
|
||
});
|
||
|
||
test("closed ceiling with a leftover discriminator persists the card not the gate", async () => {
|
||
const dossier = accidentDossier({
|
||
acceptanceAllowed: false,
|
||
leftoverProbe: LEFTOVER_PROBE,
|
||
});
|
||
const decision = decideFromDossier(dossier, { birthDate: "1998-03-15" });
|
||
assert.equal(decision.nextAction, "ask_candidate_discriminator");
|
||
|
||
const accounting = idleHandlers(dossier, { allowFocus: true });
|
||
const { result: idle } = await warnLines(() => persistNextInterviewIfIdle({
|
||
accounting: accounting.client,
|
||
userId: USER_ID,
|
||
caseId: CASE_ID,
|
||
}));
|
||
const persisted = idle as Awaited<ReturnType<typeof persistNextInterviewIfIdle>>;
|
||
const focusCalls = accounting.calls.filter((item) => item.fn === "set_agentic_rectification_conversation_focus");
|
||
assert.equal(focusCalls.length > 0, true);
|
||
assert.match(String(focusCalls[0]?.args.p_question_id ?? ""), /career\.2021/);
|
||
assert.equal(focusCalls[0]?.args.p_intent, "distinguish_candidates");
|
||
assert.equal(gateAppendCalls(accounting.calls).length, 0);
|
||
assert.doesNotMatch(persisted.hostNarration ?? "", GATE_SENTENCE);
|
||
assert.match(persisted.hostNarration ?? "", /2021/);
|
||
});
|
||
|
||
test("closed ceiling with holdout still open persists holdout not the gate", async () => {
|
||
const dossier = accidentDossier({ acceptanceAllowed: false });
|
||
const decision = decideFromDossier(dossier, { birthDate: "1998-03-15" });
|
||
assert.equal(decision.nextAction, "ask_holdout_validation");
|
||
|
||
const accounting = idleHandlers(dossier, { allowFocus: true });
|
||
const { result: idle } = await warnLines(() => persistNextInterviewIfIdle({
|
||
accounting: accounting.client,
|
||
userId: USER_ID,
|
||
caseId: CASE_ID,
|
||
}));
|
||
const persisted = idle as Awaited<ReturnType<typeof persistNextInterviewIfIdle>>;
|
||
const focusCalls = accounting.calls.filter((item) => item.fn === "set_agentic_rectification_conversation_focus");
|
||
assert.equal(focusCalls.length > 0, true);
|
||
assert.equal(focusCalls[0]?.args.p_intent, "collect_method_evidence");
|
||
assert.equal(focusCalls[0]?.args.p_target_domain, "career");
|
||
assert.equal(gateAppendCalls(accounting.calls).length, 0);
|
||
assert.doesNotMatch(persisted.hostNarration ?? "", GATE_SENTENCE);
|
||
assert.match(persisted.hostNarration ?? "", /入职|换工作|工作/);
|
||
});
|