Jesse_ChenandCursor
1fa994ea63
Occupation collect was wiping year-month into an unscored note, and idle gap copy never joined the evidence turn. Co-authored-by: Cursor <cursoragent@cursor.com>
447 lines
15 KiB
TypeScript
447 lines
15 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 {
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applyOccupationCollectLedgerNorm,
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isOccupationCollectFocus,
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isPrimaryScoreableEvidence,
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trainingScoreableGate,
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type EvidenceKind,
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} from "../src/lib/rectification-agentic/v9/evidence-model.ts";
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import { createRectificationV9Tools } from "../src/mastra/rectification-v9-tools.ts";
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import {
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CASE_ID,
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CANDIDATE_ID,
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FOCUS_ID,
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SECOND_CANDIDATE_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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conversationSummaryFixture,
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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 OCCUPATION_FOCUS = {
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intent: "collect_method_evidence" as const,
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targetDomain: "occupation",
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targetKind: "occupation_note",
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questionId: "collect:occupation:collect_method_evidence",
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};
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const NOTE_ID = "44444444-4444-4444-8444-444444444451";
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const CAREER_ID = "44444444-4444-4444-8444-444444444452";
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const EDUCATION_ID = "44444444-4444-4444-8444-444444444441";
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const ENGINE_SCORE = {
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success: true,
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endpoint: "rectification_v5_score",
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result_id: "e4fbf2e0-85dc-5b42-a5a3-34e5dd4b7e62",
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algorithm_version: "rectification-event-contract-v2",
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event_contract_version: "rectification-event-contract-v2",
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decision_policy_version: "rectification-candidate-policy-v2",
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execution_ledger_version: "rectification-execution-ledger-v2",
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candidate_decisions: [
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{ candidate_id: CANDIDATE_ID, time: "04:50", rank: 1, relative_support: 57, tied_minute_count: 1 },
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{ candidate_id: SECOND_CANDIDATE_ID, time: "04:51", rank: 2, relative_support: 25, tied_minute_count: 2 },
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],
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decision_receipt: {
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receipt_version: "candidate-decision-receipt-v2",
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contract_version: "v2",
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event_contract_version: "rectification-event-contract-v2",
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policy_version: "rectification-candidate-policy-v2",
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decision_policy_version: "rectification-candidate-policy-v2",
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display_allowed: true,
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selection_allowed: true,
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acceptance_allowed: true,
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propose_allowed: true,
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confirmation_allowed: false,
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accept_allowed: true,
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confirm_allowed: false,
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representative_candidate_id: CANDIDATE_ID,
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representative_time: "04:50",
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overall_confidence: "high",
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margin_percent: 42.5,
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gates: {
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event_quality: { scoreable_event_count: 3, minimum: 3 },
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domain_diversity: { domains: ["education", "career"], count: 2, minimum: 2 },
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},
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},
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execution_ledger: [
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{ ledger_version: "rectification-execution-ledger-v2", stage: "technique_layer", method: "d1-rashi", status: "executed", source: "python-engine" },
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],
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diagnostics: {
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window_scan: {
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scanned: true,
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confirmation_allowed: false,
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unique_minute_claim: false,
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d9_lagna_count: 2,
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d10_lagna_count: 1,
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d9_candidates_differ: true,
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d10_candidates_differ: false,
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d9_sign_names: ["白羊座", "天蝎"],
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},
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},
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};
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function stubEngine(response: unknown) {
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const previous = globalThis.fetch;
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globalThis.fetch = (async () => ({
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ok: true,
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status: 200,
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json: async () => response,
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})) as unknown as typeof fetch;
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return () => {
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globalThis.fetch = previous;
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};
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}
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const educationEvidence = {
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id: EDUCATION_ID,
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source_turn_id: TURN_ID,
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subject: "self",
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event_kind: "education_start",
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domain: "education",
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occurred_from: "2016-09-01",
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occurred_to: "2016-09-30",
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date_precision: "month",
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summary: "2016年9月上大学",
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status: "confirmed",
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supersedes_evidence_id: null,
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created_at: "2026-09-11T00:00:00.000Z",
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};
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function datedOccupationInput(kind: EvidenceKind = "career_entry") {
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return {
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domain: "career" as const,
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eventKind: kind,
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datePrecision: "month" as const,
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occurredFrom: "2024-04-01",
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occurredTo: null,
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summary: "2024年4月开始做程序员",
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quote: "2024 年 4 月开始做程序员",
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subject: "self" as const,
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};
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}
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test("occupation collect focus is identified from question id, not body text", () => {
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assert.equal(isOccupationCollectFocus(OCCUPATION_FOCUS), true);
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assert.equal(isOccupationCollectFocus({
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intent: "collect_method_evidence",
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targetDomain: "career",
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targetKind: "anchor:education_completion:2020",
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questionId: "collect:anchor:education_completion:2020",
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}), false);
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});
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test("dated occupation collect writes occupation_note plus a scoreable career_entry", () => {
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const remapped = applyOccupationCollectLedgerNorm(OCCUPATION_FOCUS, [datedOccupationInput()]);
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assert.equal(remapped.length, 2);
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assert.equal(remapped[0]?.domain, "occupation");
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assert.equal(remapped[0]?.eventKind, "occupation_note");
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assert.equal(remapped[0]?.datePrecision, "unknown");
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assert.equal(remapped[0]?.occurredFrom, null);
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assert.equal(remapped[0]?.occurredTo, null);
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assert.equal(remapped[1]?.domain, "career");
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assert.equal(remapped[1]?.eventKind, "career_entry");
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assert.equal(remapped[1]?.datePrecision, "month");
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assert.equal(remapped[1]?.occurredFrom, "2024-04-01");
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assert.equal(isPrimaryScoreableEvidence({
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status: "confirmed",
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domain: remapped[0]!.domain,
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datePrecision: remapped[0]!.datePrecision,
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occurredFrom: remapped[0]!.occurredFrom,
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occurredTo: remapped[0]!.occurredTo,
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eventKind: remapped[0]!.eventKind,
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}), false);
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assert.equal(isPrimaryScoreableEvidence({
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status: "confirmed",
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domain: remapped[1]!.domain,
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datePrecision: remapped[1]!.datePrecision,
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occurredFrom: remapped[1]!.occurredFrom,
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occurredTo: remapped[1]!.occurredTo,
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eventKind: remapped[1]!.eventKind,
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}), true);
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});
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test("undated occupation collect still writes only occupation_note", () => {
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const remapped = applyOccupationCollectLedgerNorm(OCCUPATION_FOCUS, [{
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domain: "career",
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eventKind: "career_entry" as const,
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datePrecision: "unknown" as const,
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occurredFrom: null,
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occurredTo: null,
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summary: "程序员",
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}]);
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assert.equal(remapped.length, 1);
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assert.equal(remapped[0]?.eventKind, "occupation_note");
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assert.equal(remapped[0]?.occurredFrom, null);
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});
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test("model career kinds other than career_entry are kept on the dated row", () => {
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const remapped = applyOccupationCollectLedgerNorm(
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OCCUPATION_FOCUS,
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[datedOccupationInput("career_change")],
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);
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assert.equal(remapped[1]?.eventKind, "career_change");
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});
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test("unknown precision with leftover dates still writes only the note", () => {
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const remapped = applyOccupationCollectLedgerNorm(OCCUPATION_FOCUS, [{
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domain: "career",
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eventKind: "career_entry" as const,
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datePrecision: "unknown" as const,
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occurredFrom: "2024-04-01",
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occurredTo: null,
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}]);
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assert.equal(remapped.length, 1);
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assert.equal(remapped[0]?.occurredFrom, null);
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assert.equal(remapped[0]?.datePrecision, "unknown");
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});
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test("occupation-norm null dates are not restored by a ?? fallback in tools", () => {
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const source = readFileSync(new URL("../src/mastra/rectification-v9-tools.ts", import.meta.url), "utf8");
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assert.doesNotMatch(source, /normalized\?\.occurredFrom \?\?/);
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assert.doesNotMatch(source, /normalized\?\.occurredTo \?\?/);
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assert.doesNotMatch(source, /\?\? item\.occurredFrom/);
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assert.doesNotMatch(source, /\?\? occurredFrom/);
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assert.match(source, /occupationNormalizedLedgerRows/);
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});
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test("dated occupation collect raises the training gate by one career event", () => {
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const education = [{
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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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}, {
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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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}];
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const before = trainingScoreableGate(education);
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assert.equal(before.trainingCount, 2);
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assert.equal(before.open, false);
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const remapped = applyOccupationCollectLedgerNorm(OCCUPATION_FOCUS, [datedOccupationInput()]);
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const after = trainingScoreableGate([
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...education,
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...remapped.map((item) => ({
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status: "confirmed" as const,
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domain: item.domain,
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datePrecision: item.datePrecision,
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occurredFrom: item.occurredFrom,
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occurredTo: item.occurredTo,
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eventKind: item.eventKind,
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})),
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]);
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assert.equal(after.trainingCount, before.trainingCount + 1);
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assert.equal(after.trainingDomainCount, 2);
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assert.equal(after.open, true);
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});
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test("record-evidence-batch writes note plus career_entry and rescores", async () => {
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const restore = stubEngine(ENGINE_SCORE);
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let collectFocusResolved = false;
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try {
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const quote = "2024 年 4 月开始做程序员";
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const accounting = fakeAccounting({
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...receiptHandlers,
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get_agentic_rectification_case_dossier: () => dossierFixture({
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evidence: [educationEvidence],
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latestResult: null,
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conversationSummary: conversationSummaryFixture({
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activeFocus: collectFocusResolved
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? null
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: activeFocusFixture({
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intent: "collect_method_evidence",
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targetDomain: "occupation",
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targetKind: "occupation_note",
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questionId: OCCUPATION_FOCUS.questionId,
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expectedAnswerSchema: { collect: true, prompt: "你平时主要做什么工作？" },
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}),
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}),
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}),
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get_agentic_rectification_case_compute: () => computeFixture(),
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record_agentic_rectification_evidence_batch: (_fn, args) => {
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const items = Array.isArray(args.p_items) ? args.p_items as Array<Record<string, unknown>> : [];
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return {
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items: items.map((item, index) => ({
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index,
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outcome: "accepted",
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evidence_id: item.event_kind === "occupation_note" ? NOTE_ID : CAREER_ID,
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status: "confirmed",
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idempotent: false,
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clarification_fields: [],
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error_code: null,
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})),
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accepted_count: items.length,
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needs_clarification_count: 0,
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rejected_count: 0,
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focus_id: FOCUS_ID,
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};
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},
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resolve_agentic_rectification_conversation_focus: () => {
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collectFocusResolved = true;
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return {
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focus_id: FOCUS_ID,
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status: "resolved",
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evidence_id: NOTE_ID,
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idempotent: false,
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};
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},
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set_agentic_rectification_conversation_focus: (_fn, args) => ({
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focus: {
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id: FOCUS_ID,
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case_id: CASE_ID,
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question_id: args.p_question_id,
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intent: args.p_intent,
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target_evidence_id: args.p_target_evidence_id,
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target_domain: args.p_target_domain,
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target_kind: args.p_target_kind,
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expected_answer_schema: args.p_expected_answer_schema,
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status: "active",
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asked_at: "2026-09-11T00:00:00.000Z",
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resolved_at: null,
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asked_turn_id: args.p_asked_turn_id ?? null,
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},
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idempotent: false,
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}),
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persist_agentic_rectification_candidate_v2: () => ({
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...candidateSnapshotFixture({
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representativeTime: "04:50",
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selectionAllowed: true,
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confirmationAllowed: false,
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decisionReceipt: {
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gates: {
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event_quality: { scoreable_event_count: 3, minimum: 3 },
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domain_diversity: { domains: ["education", "career"], count: 2, minimum: 2 },
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},
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},
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}),
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cached: false,
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}),
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});
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const tools = createRectificationV9Tools({
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userId: USER_ID,
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caseId: CASE_ID,
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turnId: TURN_ID,
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userMessage: quote,
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accounting: accounting.client as never,
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});
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const result = await (tools["rectification-record-evidence-batch"] as unknown as {
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execute(input: unknown): Promise<{
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accepted_count: number;
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accepted_recaps: Array<{ display_date_label?: string }>;
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rescore: { status: string; executed_methods: string[] };
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}>;
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}).execute({
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caseId: CASE_ID,
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focusId: FOCUS_ID,
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items: [{
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quote,
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proposedKind: "career_entry",
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subject: "self",
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domain: "career",
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datePrecision: "month",
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occurredFrom: "2024-04",
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summary: "2024年4月开始做程序员",
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}],
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});
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const write = accounting.calls.find((call) => call.fn === "record_agentic_rectification_evidence_batch");
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const items = (write?.args.p_items ?? []) as Array<Record<string, unknown>>;
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assert.equal(items.length, 2);
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assert.equal(items[0]?.event_kind, "occupation_note");
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assert.equal(items[0]?.domain, "occupation");
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assert.equal(items[0]?.date_precision, "unknown");
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assert.equal(items[0]?.occurred_from, null);
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assert.equal(items[1]?.event_kind, "career_entry");
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assert.equal(items[1]?.domain, "career");
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assert.equal(items[1]?.date_precision, "month");
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assert.equal(items[1]?.occurred_from, "2024-04-01");
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assert.equal(result.accepted_count, 2);
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assert.equal(result.accepted_recaps.some((item) => item.display_date_label === "2024-04"), true);
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assert.ok(result.rescore.executed_methods.includes("d1-rashi"));
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} finally {
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restore();
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}
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});
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test("undated occupation collect batch writes only the note", async () => {
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const quote = "程序员";
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const accounting = fakeAccounting({
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...receiptHandlers,
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get_agentic_rectification_case_dossier: () => dossierFixture({
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evidence: [educationEvidence],
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latestResult: null,
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conversationSummary: conversationSummaryFixture({
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activeFocus: activeFocusFixture({
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intent: "collect_method_evidence",
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targetDomain: "occupation",
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targetKind: "occupation_note",
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questionId: OCCUPATION_FOCUS.questionId,
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expectedAnswerSchema: { collect: true, prompt: "你平时主要做什么工作？" },
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}),
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}),
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}),
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record_agentic_rectification_evidence_batch: (_fn, args) => {
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const items = Array.isArray(args.p_items) ? args.p_items as Array<Record<string, unknown>> : [];
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return {
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items: items.map((item, index) => ({
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index,
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outcome: "accepted",
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evidence_id: NOTE_ID,
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status: "confirmed",
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idempotent: false,
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clarification_fields: [],
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error_code: null,
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})),
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accepted_count: items.length,
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needs_clarification_count: 0,
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rejected_count: 0,
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focus_id: FOCUS_ID,
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};
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},
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resolve_agentic_rectification_conversation_focus: () => ({
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focus_id: FOCUS_ID,
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status: "resolved",
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evidence_id: NOTE_ID,
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idempotent: false,
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}),
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});
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const tools = createRectificationV9Tools({
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userId: USER_ID,
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caseId: CASE_ID,
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turnId: TURN_ID,
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userMessage: quote,
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accounting: accounting.client as never,
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});
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await (tools["rectification-record-evidence-batch"] as unknown as {
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execute(input: unknown): Promise<unknown>;
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}).execute({
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caseId: CASE_ID,
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focusId: FOCUS_ID,
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items: [{
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quote,
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proposedKind: "occupation_note",
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subject: "self",
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domain: "occupation",
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datePrecision: "unknown",
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summary: "程序员",
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}],
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});
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const write = accounting.calls.find((call) => call.fn === "record_agentic_rectification_evidence_batch");
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const items = (write?.args.p_items ?? []) as Array<Record<string, unknown>>;
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assert.equal(items.length, 1);
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assert.equal(items[0]?.event_kind, "occupation_note");
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assert.equal(items[0]?.occurred_from, null);
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});
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