Jyotisha/frontend/tests/rectification-server-focus.test.ts

import assert from "node:assert/strict";
import test from "node:test";

import { persistServerOwnedFocus, shouldSkipDiscriminatorFollowup, stableFollowupQuestionId } from "../src/lib/rectification-agentic/v9/server-focus.ts";
import { buildChoiceFrame, serverOwnedChoiceCopy } from "../src/lib/rectification-agentic/v9/choice-card.ts";
import type { MethodFollowup } from "../src/lib/rectification-agentic/v9/method-followup.ts";
import { fakeAccounting, CASE_ID, FOCUS_ID, USER_ID, activeFocusFixture } from "./rectification-v9-test-support.ts";

function discriminatorFollowup(overrides: Partial<MethodFollowup> = {}): MethodFollowup {
  const frame = buildChoiceFrame({
    method_id: "dasha_events",
    ask_theme: "dated_event",
    domain: "education",
    user_prompt_hint: "ask",
  }, {
    probes: [{
      year: 2016,
      year_label: "2016 年前后",
      domain: "education",
      event_family: "高考或重要考试发挥明显失常",
      source: "dasha_activation",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: true,
      unique_minute_claim: false,
      user_meaning: "2016 年前后高考或重要考试发挥明显失常",
      role: "distinguish",
      information_gain: 0.4,
      semantic_key: "education:2016",
    }],
  });
  return {
    method_id: "dasha_events",
    intent: "distinguish_candidates",
    ask_theme: "dated_event",
    domain: "education",
    kind_hint: null,
    user_prompt_hint: "ask",
    must_not_label: false,
    choice_frame: frame,
    source: "event_probe",
    information_gain: 0.4,
    semantic_key: "education:2016",
    probe_year: 2016,
    ...overrides,
  };
}

test("does not ask an already-answered discriminator probe again", async () => {
  const followup = discriminatorFollowup();
  const active = activeFocusFixture({
    expectedAnswerSchema: { probe_id: "education:2016", choice: serverOwnedChoiceCopy(followup.choice_frame!) },
  });
  const accounting = fakeAccounting({
    set_agentic_rectification_conversation_focus: () => {
      throw new Error("should not create a second focus");
    },
  });
  const result = await persistServerOwnedFocus({
    accounting: accounting.client,
    userId: USER_ID,
    caseId: CASE_ID,
    activeFocus: {
      ...active,
      id: FOCUS_ID,
      caseId: CASE_ID,
      questionId: stableFollowupQuestionId(followup),
      intent: "distinguish_candidates",
      targetEvidenceId: null,
      targetDomain: "education",
      targetKind: null,
      expectedAnswerSchema: { probe_id: "education:2016" },
      status: "active",
      askedAt: "2026-08-24T00:00:00.000Z",
      resolvedAt: null,
    },
    decisionReceipt: {
      inference_state: {
        answered_probes: [{ probe_id: "education:2016", semantic_key: "education:2016", answer_class: "yes" }],
        probes: [],
      },
    },
    followup,
  });
  assert.equal(result.status, "probe_already_answered");
  assert.equal(accounting.calls.length, 0);
});

test("zero information gain does not open a discriminator", () => {
  assert.equal(
    shouldSkipDiscriminatorFollowup(discriminatorFollowup({ information_gain: 0 })),
    "zero_information_gain",
  );
});

test("duplicate focus conflict does not throw", async () => {
  const followup = discriminatorFollowup({ source: "precision_stage", information_gain: 0.2 });
  const accounting = fakeAccounting({
    set_agentic_rectification_conversation_focus: () => {
      throw new Error("agentic_rectification_focus_idempotency_conflict");
    },
  });
  const result = await persistServerOwnedFocus({
    accounting: accounting.client,
    userId: USER_ID,
    caseId: CASE_ID,
    activeFocus: null,
    decisionReceipt: null,
    followup,
  });
  assert.equal(result.status, "duplicate_focus");
});

test("contrast probe is not replaced by an already-answered education quality probe", async () => {
  const followup = discriminatorFollowup({
    domain: "education",
    ask_theme: "education_style",
    information_gain: 0.16,
    semantic_key: "varga.d24.05:00/05:06|05:07",
    candidate_split_hash: "varga.d24.05:00/05:06|05:07",
    probe_year: undefined,
  });
  const accounting = fakeAccounting({
    set_agentic_rectification_conversation_focus: (_fn, args) => ({
      id: FOCUS_ID,
      case_id: CASE_ID,
      question_id: args.p_question_id,
      intent: args.p_intent,
      target_evidence_id: null,
      target_domain: args.p_target_domain,
      target_kind: args.p_target_kind,
      expected_answer_schema: args.p_expected_answer_schema,
      status: "active",
      asked_at: "2026-08-25T00:00:00.000Z",
      resolved_at: null,
      idempotent: false,
    }),
  });
  const result = await persistServerOwnedFocus({
    accounting: accounting.client,
    userId: USER_ID,
    caseId: CASE_ID,
    activeFocus: null,
    decisionReceipt: {
      inference_state: {
        answered_probes: [{
          probe_id: "probe:education.2016",
          semantic_key: "education.2016",
          answer_class: "yes",
        }],
        probes: [{
          id: "probe:education.2016",
          semantic_key: "education.2016",
          candidate_split_hash: "education:2016",
          domain: "education",
          year: 2016,
          question: "发挥失常",
          candidate_ids: [],
          expected_outcomes: [],
          information_gain: 0,
          source: "known_event_quality",
        }],
      },
    },
    followup,
  });
  assert.equal(result.status, "created");
  assert.ok(result.focus);
  const schema = result.focus.expectedAnswerSchema;
  assert.equal(schema.semantic_key, "varga.d24.05:00/05:06|05:07");
  assert.notEqual(schema.probe_id, "probe:education.2016");
  assert.match(String(schema.probe_id), /varga\.d24/);
});