Jyotisha/frontend/src/lib/rectification-agentic/v9/inference-adapter.ts

import {
  answersFromEvidence,
  applyAnswerToState,
  applyHoldoutAnswer,
  applySupersedeAnswer,
  buildInferenceState,
  classifyChoiceAnswer,
  type EngineEventInput,
} from "../core/build-state.ts";
import { isDuplicateProbe } from "../core/duplicate-probes.ts";
import { probeFromEngine } from "../core/probes-from-engine.ts";
import { selectHighestGainProbe } from "../core/select-probe.ts";
import { askedEventProbeKeysFromLedgerEvidence } from "../core/candidate-contrast-packet.ts";
import { rankActive } from "../core/convergence-evaluator.ts";
import { rangeFromTimes, unionStillValidRange } from "../core/credible-range.ts";
import {
  previousInferenceFromReceipt as parsePreviousInferenceFromReceipt,
} from "../core/compose-receipt.ts";
import type { AnswerClass, ConflictProbe, InferenceState, ProbeAnswer } from "../core/types.ts";
import { datedPrecision } from "./evidence-model.ts";
import {
  isHoldoutVerificationQuote,
  type ChoiceKey,
} from "./choice-card.ts";
import {
  parseNakshatraBoundary,
  type DiscriminatingEventProbe,
  type NakshatraBoundary,
} from "./refinement-packet.ts";

function yearFrom(value: string | null | undefined): number | null {
  if (!value || value.length < 4 || !/^\d{4}/.test(value)) return null;
  const year = Number(value.slice(0, 4));
  return year >= 1900 && year <= 2100 ? year : null;
}

function asPrecision(value: string | null | undefined): EngineEventInput["precision"] {
  if (!value) return "unknown";
  return datedPrecision(value);
}

export function askedProbeKeysFromReceipt(
  receipt: Readonly<Record<string, unknown>> | null | undefined,
): string[] {
  const inference = receipt?.inference_state;
  if (!inference || typeof inference !== "object" || Array.isArray(inference)) return [];
  const answers = (inference as { answered_probes?: unknown }).answered_probes;
  if (!Array.isArray(answers)) return [];
  const keys: string[] = [];
  for (const item of answers) {
    if (!item || typeof item !== "object") continue;
    const row = item as Record<string, unknown>;
    if (typeof row.probe_id === "string") keys.push(row.probe_id);
    if (typeof row.semantic_key === "string") keys.push(row.semantic_key);
    if (typeof row.candidate_split_hash === "string") keys.push(row.candidate_split_hash);
  }
  return keys;
}

export function askedDiscriminatorKeys(
  receipt: Readonly<Record<string, unknown>> | null | undefined,
  evidence: readonly Readonly<{
    status?: string | null;
    domain?: string | null;
    eventKind?: string | null;
    summary?: string | null;
    occurredFrom?: string | null;
    occurredTo?: string | null;
  }>[] = [],
): string[] {
  return [
    ...askedProbeKeysFromReceipt(receipt),
    ...askedEventProbeKeysFromLedgerEvidence(evidence),
  ];
}

const NAKSHATRA_BOUNDARY_SOURCE = "nakshatra_boundary";

export function nakshatraBoundaryProbe(
  state: InferenceState | null | undefined,
  boundary: NakshatraBoundary | null | undefined,
): ConflictProbe | null {
  if (!state || !boundary?.near_boundary) return null;
  const optionA = boundary.options.find((item) => item.key === "A") ?? null;
  const optionB = boundary.options.find((item) => item.key === "B") ?? null;
  if (
    !optionA?.traits.length
    || !optionB?.traits.length
    || optionA.time_bias === optionB.time_bias
  ) return null;

  const active = rankActive(state.candidates)
    .slice()
    .sort((left, right) => left.time.localeCompare(right.time));
  if (active.length < 2) return null;
  const pivot = Math.ceil(active.length / 2);
  const earlier = active.slice(0, pivot).map((item) => item.id);
  const later = active.slice(pivot).map((item) => item.id);
  if (earlier.length === 0 || later.length === 0) return null;

  const semanticKey = `nakshatra-boundary:${state.candidate_set_id}`;
  const candidateSplitHash = `${semanticKey}:${earlier.join(",")}|${later.join(",")}`;
  const candidatesFor = (bias: "earlier" | "later") => bias === "earlier" ? earlier : later;
  const conflictsFor = (bias: "earlier" | "later") => bias === "earlier" ? later : earlier;
  const optionLabel = (key: "A" | "B", traits: readonly string[]) => `${key} 组：${traits.join("、")}`;

  return {
    id: `probe:${semanticKey}`,
    semantic_key: semanticKey,
    candidate_split_hash: candidateSplitHash,
    domain: "appearance",
    year: 0,
    question: boundary.user_meaning
      ?? "升点靠近两段日常节奏的交界。平时做事时，哪一组更像你？这只用来偏置时间窗，不能确认唯一分钟。",
    candidate_ids: active.map((item) => item.id),
    expected_outcomes: [
      {
        answer_class: "yes",
        supports: candidatesFor(optionA.time_bias),
        conflicts: conflictsFor(optionA.time_bias),
      },
      {
        answer_class: "weak_yes",
        supports: candidatesFor(optionB.time_bias),
        conflicts: conflictsFor(optionB.time_bias),
      },
      { answer_class: "no", supports: [], conflicts: [] },
      { answer_class: "unsure", supports: [], conflicts: [] },
    ],
    information_gain: 0.01,
    source: NAKSHATRA_BOUNDARY_SOURCE,
    choice_kind: "varga_style",
    style_options: [
      {
        label: optionLabel("A", optionA.traits),
        answer_class: "yes",
        sign: optionA.time_bias === "earlier" ? "较早时间窗" : "较晚时间窗",
      },
      {
        label: optionLabel("B", optionB.traits),
        answer_class: "weak_yes",
        sign: optionB.time_bias === "earlier" ? "较早时间窗" : "较晚时间窗",
      },
      { label: "两组都不太像我", answer_class: "no" },
      { label: "一时说不好", answer_class: "unsure" },
    ],
  };
}

export function withNakshatraBoundaryProbe(
  state: InferenceState | null,
  boundary: NakshatraBoundary | null | undefined,
): InferenceState | null {
  if (!state) return null;
  const probe = nakshatraBoundaryProbe(state, boundary);
  const existingIndexes = state.probes.flatMap((item, index) => (
    item.source === NAKSHATRA_BOUNDARY_SOURCE ? [index] : []
  ));
  if (!probe || isDuplicateProbe(probe, state.answered_probes)) {
    if (existingIndexes.length === 0) return state;
    return {
      ...state,
      probes: state.probes.filter((item) => item.source !== NAKSHATRA_BOUNDARY_SOURCE),
    };
  }
  const existing = existingIndexes.length > 0 ? state.probes[existingIndexes[0]!] : null;
  if (
    existingIndexes.length === 1
    && existing?.id === probe.id
    && existing.semantic_key === probe.semantic_key
    && existing.candidate_split_hash === probe.candidate_split_hash
  ) return state;
  const probes = state.probes.filter((item) => item.source !== NAKSHATRA_BOUNDARY_SOURCE);
  const insertAt = existingIndexes[0] ?? probes.length;
  return {
    ...state,
    probes: [
      ...probes.slice(0, insertAt),
      probe,
      ...probes.slice(insertAt),
    ],
  };
}

export function previousInferenceFromReceipt(
  receipt: Readonly<Record<string, unknown>> | null | undefined,
): InferenceState | null {
  return withNakshatraBoundaryProbe(
    parsePreviousInferenceFromReceipt(receipt),
    parseNakshatraBoundary(receipt?.nakshatra_boundary),
  );
}

type CandidateSnapshotRow = Readonly<{
  candidateId?: string;
  time: string;
  rank?: number;
  relativeSupport?: number;
  posterior_score?: number;
  tiedMinuteCount?: number;
}>;

type CandidateSnapshotSource<T extends CandidateSnapshotRow> = Readonly<{
  decisionReceipt?: Readonly<Record<string, unknown>> | null;
  candidates?: readonly T[];
}> | null | undefined;

type ProjectedCandidate<T extends CandidateSnapshotRow> = T & Readonly<{
  rank?: number;
  relativeSupport?: number;
  posterior_score?: number;
}>;

type CandidateProjection<T extends CandidateSnapshotRow> = Readonly<{
  fromInference: boolean;
  consistent: boolean;
  candidates: readonly ProjectedCandidate<T>[];
  scores: readonly Readonly<{ id?: string; time: string; score: number }>[];
  representativeTime: string | null;
  credibleRange: readonly [string, string] | null;
}>;

export function authoritativeCandidateProjection<T extends CandidateSnapshotRow>(
  latest: CandidateSnapshotSource<T>,
): CandidateProjection<T> {
  const inference = previousInferenceFromReceipt(latest?.decisionReceipt ?? null);
  if (!inference || inference.candidates.length === 0) {
    return {
      fromInference: true,
      consistent: false,
      candidates: [],
      scores: [],
      representativeTime: null,
      credibleRange: null,
    };
  }
  const active = rankActive(inference.candidates);
  const persisted = latest?.candidates ?? [];
  const matchedPersistedIndexes = inference.candidates.map((item) => persisted.findIndex((row) => (
    row.candidateId === item.id || row.time === item.time
  )));
  const completeCandidateSet = inference.candidates.length === persisted.length
    && matchedPersistedIndexes.every((index) => index >= 0)
    && new Set(matchedPersistedIndexes).size === persisted.length
    && new Set(inference.candidates.map((item) => item.id)).size === inference.candidates.length
    && new Set(inference.candidates.map((item) => item.time)).size === inference.candidates.length;
  const candidates = active.flatMap((item, index) => {
    const source = persisted.find((row) => row.candidateId === item.id)
      ?? persisted.find((row) => row.time === item.time);
    if (!source) return [];
    return [{
      ...source,
      rank: index + 1,
      relativeSupport: Math.max(0, Math.min(100, Math.round(item.posterior_score))),
      posterior_score: item.posterior_score,
    }];
  });
  const representativeTime = active[0]?.time ?? null;
  const activePoints = active.flatMap((item) => [item.cluster_range[0], item.time, item.cluster_range[1]]);
  const activeSpan = rangeFromTimes(activePoints);
  const stillValidRange = unionStillValidRange(inference.candidates);
  const receiptRange = inference.credible_range;
  const activeRangesValid = Boolean(activeSpan) && active.every((item) => {
    const clusterRange = rangeFromTimes(item.cluster_range);
    return clusterRange?.[0] === item.cluster_range[0]
      && clusterRange[1] === item.cluster_range[1]
      && rangeFromTimes([item.time])?.[0] === item.time
      && item.time >= clusterRange[0]
      && item.time <= clusterRange[1]
      && item.cluster_range[0] >= activeSpan![0]
      && item.cluster_range[1] <= activeSpan![1];
  });
  const receiptRangeMatches = Boolean(stillValidRange && receiptRange)
    && receiptRange![0] <= receiptRange![1]
    && receiptRange![0] === stillValidRange![0]
    && receiptRange![1] === stillValidRange![1];
  const consistent = active.length > 0
    && completeCandidateSet
    && candidates.length === active.length
    && inference.representative_time === representativeTime
    && activeRangesValid
    && receiptRangeMatches;
  return {
    fromInference: true,
    consistent,
    candidates: consistent ? candidates : [],
    scores: consistent
      ? active.map((item) => ({ id: item.id, time: item.time, score: item.posterior_score }))
      : [],
    representativeTime: consistent ? representativeTime : null,
    credibleRange: consistent ? stillValidRange : null,
  };
}

export function compactInferenceProjection(state: InferenceState | null | undefined): Record<string, unknown> | null {
  if (!state) return null;
  const next = selectHighestGainProbe(state.probes, state.answered_probes);
  return {
    algorithm_version: state.algorithm_version,
    candidate_set_id: state.candidate_set_id,
    revision: state.revision,
    phase: state.result_status === "discriminating" && state.phase === "event_collection"
      ? "discrimination"
      : state.phase,
    result_status: state.result_status,
    entropy: state.entropy,
    representative_time: state.representative_time,
    credible_range: state.credible_range,
    candidates: state.candidates.map((item) => ({
      id: item.id,
      time: item.time,
      probability: item.probability,
      posterior_score: item.posterior_score,
      status: item.status,
      rank: item.rank,
      cluster_range: item.cluster_range,
    })),
    next_probe: next
      ? {
        semantic_key: next.semantic_key,
        information_gain: next.information_gain,
        question: next.question,
        domain: next.domain,
        year: next.year,
      }
      : null,
    answered_probe_count: state.answered_probes.length,
    last_inference_round: state.last_inference_round
      ? {
        kind: state.last_inference_round.kind,
        entropy_before: state.last_inference_round.entropy_before,
        entropy_after: state.last_inference_round.entropy_after,
        eliminated_ids: state.last_inference_round.eliminated_ids,
        score_deltas: state.last_inference_round.score_deltas ?? {},
      }
      : null,
    informative_round_count: state.rounds.filter((item) => item.kind === "informative").length,
  };
}

export function buildCaseInferenceState(input: {
  range: { start_time: string; end_time: string };
  candidates: readonly Readonly<{ candidateId: string; time: string; relativeSupport: number }>[];
  evidence: readonly Readonly<{
    id: string;
    domain: string;
    occurredFrom: string | null;
    datePrecision: string;
  }>[];
  probes: readonly DiscriminatingEventProbe[];
  extraProbes?: readonly ConflictProbe[];
  previous?: InferenceState | null;
  transitionTimes?: readonly string[];
  eventLedger?: Readonly<Record<string, Readonly<Record<string, number>>>>;
}): InferenceState {
  const events = input.evidence.map((item) => ({
    id: item.id,
    domain: item.domain,
    year: yearFrom(item.occurredFrom),
    precision: asPrecision(item.datePrecision),
  }));
  const probes = [
    ...input.probes.flatMap((probe) => {
      const mapped = probeFromEngine(probe);
      return mapped ? [mapped] : [];
    }),
    ...(input.extraProbes ?? []),
  ];
  return buildInferenceState({
    range_start: input.range.start_time,
    range_end: input.range.end_time,
    candidates: input.candidates.map((item) => ({
      id: item.time,
      time: item.time,
      relative_support: item.relativeSupport,
    })),
    events,
    probes,
    previous: input.previous,
    answered_probes: answersFromEvidence(probes, events),
    transition_times: input.transitionTimes,
    event_ledger: input.eventLedger,
  });
}

function asRecord(value: unknown): Readonly<Record<string, unknown>> | null {
  return value && typeof value === "object" && !Array.isArray(value)
    ? value as Readonly<Record<string, unknown>>
    : null;
}

function asText(value: unknown): string | null {
  return typeof value === "string" && value.trim() ? value.trim() : null;
}

export function hasChoiceSchema(schema: unknown): boolean {
  const row = asRecord(schema);
  if (!row) return false;
  if (asRecord(row.choice)) return true;
  return Boolean(asText(row.semantic_key) || asText(row.probe_id) || asText(row.candidate_split_hash));
}

export function isHoldoutChoiceSchema(
  schema: unknown,
  questionId?: string | null,
  userMessage?: string | null,
): boolean {
  if (userMessage && isHoldoutVerificationQuote(userMessage)) return true;
  if (questionId?.endsWith(":holdout")) return true;
  const row = asRecord(schema);
  return row?.scoring === false;
}

export function resolveChoiceKey(input: {
  choiceKey?: string | null;
}): ChoiceKey | null {
  const explicit = input.choiceKey?.trim().toUpperCase();
  if (explicit === "A" || explicit === "B" || explicit === "C" || explicit === "D") return explicit;
  return null;
}

export function matchProbeForChoice(
  state: InferenceState,
  schema: unknown,
  domain?: string | null,
): ConflictProbe | null {
  const row = asRecord(schema);
  const probeId = asText(row?.probe_id);
  const semanticKey = asText(row?.semantic_key);
  const splitHash = asText(row?.candidate_split_hash);
  const probes = state.probes;
  if (probeId || semanticKey || splitHash) {
    return probes.find((item) => (
      (!probeId || item.id === probeId || item.semantic_key === probeId || probeId === `probe:${item.semantic_key}`)
      && (!semanticKey || item.semantic_key === semanticKey || item.id === semanticKey)
      && (!splitHash || item.candidate_split_hash === splitHash)
    )) ?? null;
  }
  const unanswered = domain
    ? probes.filter((item) => item.domain === domain)
    : probes;
  return selectHighestGainProbe(unanswered.length > 0 ? unanswered : probes, state.answered_probes);
}

function probeMatchesPreferred(
  probe: ConflictProbe,
  preferred: { semantic_key?: string | null; candidate_split_hash?: string | null; probe_id?: string | null },
): boolean {
  const semanticKey = preferred.semantic_key?.trim() || null;
  const splitHash = preferred.candidate_split_hash?.trim() || null;
  const probeId = preferred.probe_id?.trim() || null;
  if (probeId && (probe.id === probeId || probe.semantic_key === probeId)) return true;
  if (semanticKey && (probe.semantic_key === semanticKey || probe.id === semanticKey)) return true;
  if (splitHash && probe.candidate_split_hash === splitHash) return true;
  return false;
}

export function stampChoiceSchemaWithProbe(
  schema: Readonly<Record<string, unknown>>,
  state: InferenceState | null,
  questionId: string,
  preferred?: {
    semantic_key?: string | null;
    candidate_split_hash?: string | null;
    probe_id?: string | null;
  },
): Record<string, unknown> {
  if (!hasChoiceSchema(schema)) return { ...schema };
  const scoring = schema.scoring === false || questionId.endsWith(":holdout") ? false : true;
  const preferredKey = preferred?.semantic_key?.trim() || asText(schema.semantic_key);
  const preferredSplit = preferred?.candidate_split_hash?.trim() || asText(schema.candidate_split_hash);
  const preferredId = preferred?.probe_id?.trim() || asText(schema.probe_id);
  const matched = state?.probes.find((probe) => probeMatchesPreferred(probe, {
    semantic_key: preferredKey,
    candidate_split_hash: preferredSplit,
    probe_id: preferredId,
  })) ?? null;
  if (preferredKey && !matched) {
    if (state) {
      const unstamped = { ...schema };
      delete unstamped.probe_id;
      delete unstamped.semantic_key;
      delete unstamped.candidate_split_hash;
      return { ...unstamped, scoring };
    }
    return {
      ...schema,
      probe_id: preferredId ?? `probe:${preferredKey}`,
      semantic_key: preferredKey,
      candidate_split_hash: preferredSplit ?? preferredKey,
      scoring,
    };
  }
  const next = matched ?? (state ? selectHighestGainProbe(state.probes, state.answered_probes) : null);
  if (!next) {
    if (!preferredKey) return { ...schema };
    return {
      ...schema,
      probe_id: preferredId ?? `probe:${preferredKey}`,
      semantic_key: preferredKey,
      candidate_split_hash: preferredSplit ?? preferredKey,
      scoring,
    };
  }
  return {
    ...schema,
    probe_id: next.id,
    semantic_key: next.semantic_key,
    candidate_split_hash: next.candidate_split_hash,
    scoring,
  };
}

export type ChoiceWithoutEvidenceResult = Readonly<{
  applied: boolean;
  reason: "applied" | "no_choice" | "holdout" | "no_probe" | "already_answered" | "stale_probe" | "superseded";
  state: InferenceState;
  answerClass: AnswerClass | null;
  probeId: string | null;
}>;

export function applyChoiceWithoutEvidence(
  state: InferenceState,
  input: {
    choiceKey?: string | null;
    userMessage?: string | null;
    schema?: unknown;
    questionId?: string | null;
    domain?: string | null;
    classifiedFrom?: Extract<ProbeAnswer["classified_from"], "choice" | "declined">;
  },
): ChoiceWithoutEvidenceResult {
  if (!hasChoiceSchema(input.schema) && !input.choiceKey) {
    return { applied: false, reason: "no_choice", state, answerClass: null, probeId: null };
  }
  if (isHoldoutChoiceSchema(input.schema, input.questionId, input.userMessage)) {
    const choiceKey = resolveChoiceKey(input);
    const answerClass = choiceKey ? classifyChoiceAnswer(choiceKey, input.schema) : null;
    if (!answerClass) {
      return { applied: false, reason: "holdout", state, answerClass: null, probeId: null };
    }
    return {
      applied: true,
      reason: "holdout",
      state: applyHoldoutAnswer(state, answerClass),
      answerClass,
      probeId: asText(asRecord(input.schema)?.probe_id),
    };
  }
  const choiceKey = resolveChoiceKey(input);
  const declined = input.classifiedFrom === "declined";
  if (!choiceKey && !declined) {
    return { applied: false, reason: "no_choice", state, answerClass: null, probeId: null };
  }
  const schema = asRecord(input.schema);
  const submittedProbeId = asText(schema?.probe_id);
  const hasSubmittedProbeIdentity = Boolean(
    submittedProbeId || asText(schema?.semantic_key) || asText(schema?.candidate_split_hash),
  );
  const probe = matchProbeForChoice(state, input.schema, input.domain);
  if (!probe) {
    return { applied: false, reason: hasSubmittedProbeIdentity ? "stale_probe" : "no_probe", state, answerClass: null, probeId: submittedProbeId };
  }
  const lastAnsweredId = state.answered_probes.at(-1)?.probe_id ?? null;
  const answerClass = declined ? "unsure" : classifyChoiceAnswer(choiceKey!, input.schema);
  if (!answerClass) {
    return { applied: false, reason: "no_choice", state, answerClass: null, probeId: probe.id };
  }
  const existing = state.answered_probes.find((item) => (
    item.probe_id === probe.id || item.semantic_key === probe.semantic_key
  ));
  if (existing) {
    if (existing.answer_class === answerClass) {
      return { applied: false, reason: "already_answered", state, answerClass, probeId: probe.id };
    }
    if (probe.id === lastAnsweredId) {
      return {
        applied: true,
        reason: "superseded",
        state: applySupersedeAnswer(state, probe.id, answerClass, input.classifiedFrom),
        answerClass,
        probeId: probe.id,
      };
    }
    return { applied: false, reason: "stale_probe", state, answerClass, probeId: probe.id };
  }
  if (isDuplicateProbe(probe, state.answered_probes)) {
    return { applied: false, reason: "already_answered", state, answerClass, probeId: probe.id };
  }
  return {
    applied: true,
    reason: "applied",
    state: applyAnswerToState(state, probe.id, answerClass, input.classifiedFrom),
    answerClass,
    probeId: probe.id,
  };
}