worldmonitor/scripts/shared/brief-llm-core.js

// @ts-check
/**
 * Edge-safe pure helpers for the brief LLM enrichment path. Shared by:
 *   - scripts/lib/brief-llm.mjs   (Railway cron, Node)
 *   - api/internal/brief-why-matters.ts  (Vercel edge)
 *
 * No `node:*` imports. Hashing via Web Crypto (`crypto.subtle.digest`),
 * which is available in both Edge and modern Node. Everything else is
 * pure string manipulation.
 *
 * Any change here MUST be mirrored byte-for-byte to
 * `scripts/shared/brief-llm-core.js` (enforced by the shared-mirror
 * parity test; see `feedback_shared_dir_mirror_requirement`).
 */

/**
 * System prompt for the one-sentence "why this matters" enrichment.
 * Moved verbatim from scripts/lib/brief-llm.mjs so the edge endpoint
 * and the cron fallback emit the identical editorial voice.
 */
export const WHY_MATTERS_SYSTEM =
  'You are the editor of WorldMonitor Brief, a geopolitical intelligence magazine. ' +
  'For each story below, write ONE concise sentence (18–30 words) explaining the ' +
  'regional or global stakes. Editorial, impersonal, serious. No preamble ' +
  '("This matters because…"), no questions, no calls to action, no markdown, ' +
  'no quotes. One sentence only.';

export const WHY_MATTERS_V1_MIN_CHARS = 30;
export const WHY_MATTERS_V1_MAX_CHARS = 400;
export const WHY_MATTERS_V2_MIN_CHARS = 100;
export const WHY_MATTERS_V2_MAX_CHARS = 500;

/**
 * Date-grounding line appended to every brief LLM system prompt.
 *
 * The brief's source stories are dated, but the system prompts are
 * static — without an explicit "today" the model fills date/year
 * gaps from its training-data priors. A May 2026 brief shipped a
 * whyMatters claiming a deploy "in 2024" (plan F6). The proper-noun
 * grounding gate does not catch numeric/date fabrication, so one
 * line stating the current date and forbidding contradictory years
 * is the cheap structural guard.
 *
 * `todayIso` is injectable so prompt-builder tests stay deterministic;
 * production call sites pass nothing and get the current UTC date.
 * A malformed override falls back to today rather than interpolating
 * garbage into the prompt.
 *
 * @param {string} [todayIso] ISO date `YYYY-MM-DD`. Defaults to today (UTC).
 * @returns {string}
 */
export function briefDateLine(todayIso) {
  const iso = typeof todayIso === 'string' && /^\d{4}-\d{2}-\d{2}$/.test(todayIso)
    ? todayIso
    : new Date().toISOString().slice(0, 10);
  return (
    `Today is ${iso}. Do not state any year or date that contradicts ` +
    'the dates in the stories below; when a date is not given, omit it ' +
    'rather than guess.'
  );
}

/**
 * @param {{
 *   headline: string;
 *   source: string;
 *   threatLevel: string;
 *   category: string;
 *   country: string;
 * }} story
 * @param {string} [todayIso] ISO date for the date-grounding line; defaults to today.
 * @returns {{ system: string; user: string }}
 */
export function buildWhyMattersUserPrompt(story, todayIso) {
  const user = [
    `Headline: ${story.headline}`,
    `Source: ${story.source}`,
    `Severity: ${story.threatLevel}`,
    `Category: ${story.category}`,
    `Country: ${story.country}`,
    '',
    'One editorial sentence on why this matters:',
  ].join('\n');
  return { system: `${WHY_MATTERS_SYSTEM}\n${briefDateLine(todayIso)}`, user };
}

/**
 * Whether a whyMatters candidate ends as a complete sentence. Accept closing
 * quote marks after terminal punctuation so cached and wire-format values are
 * safe to validate before parser-specific normalization.
 *
 * @param {unknown} text
 * @returns {boolean}
 */
export function hasTerminalPunctuation(text) {
  if (typeof text !== 'string') return false;
  const s = text.trim();
  const prose = s.replace(/["'\u2019\u201D]+$/, '');
  if (/(?:\.\.\.|\u2026)$/.test(prose)) return false;
  return /[.!?]$/.test(prose);
}

/**
 * Parse + validate the LLM response into complete editorial prose.
 * Returns null when the output is obviously wrong (empty, boilerplate
 * preamble that survived stripReasoningPreamble, too short / too long).
 *
 * @param {unknown} text
 * @returns {string | null}
 */
export function parseWhyMatters(text) {
  if (typeof text !== 'string') return null;
  let s = text.trim();
  if (!s) return null;
  s = s.replace(/^[\u201C"']+/, '').replace(/[\u201D"']+$/, '').trim();
  // Dotted abbreviations make sentence splitting intrinsically ambiguous
  // (`U.S. Navy` vs `U.S. Markets rallied`). The prompt owns sentence count;
  // provider finish_reason plus this punctuation gate own completeness.
  if (!hasTerminalPunctuation(s)) return null;
  if (s.length < WHY_MATTERS_V1_MIN_CHARS || s.length > WHY_MATTERS_V1_MAX_CHARS) return null;
  if (/^story flagged by your sensitivity/i.test(s)) return null;
  return s;
}

/**
 * Deterministic 16-char hex hash of the SIX story fields that flow
 * into the whyMatters prompt (5 core + description). Also consumed by
 * server/worldmonitor/intelligence/v1/get-country-intel-brief.ts
 * (citation verification + grounding telemetry, #4921). Cache identity
 * MUST cover every field that shapes the LLM output, or two requests
 * with the same core fields but different descriptions will share a
 * cache entry and the second caller gets prose grounded in the first
 * caller's description (P1 regression caught in PR #3269 review).
 *
 * History:
 *   - pre-v3: 5 fields, sync `node:crypto.createHash`.
 *   - v3: moved to Web Crypto (async), same 5 fields.
 *   - v5 (with endpoint cache bump to brief:llm:whymatters:v5:):
 *     6 fields — `description` added to match the analyst path's
 *     v2 prompt which interpolates `Description: <desc>` between
 *     headline and source.
 *
 * Uses Web Crypto so the module is edge-safe. Returns a Promise because
 * `crypto.subtle.digest` is async; cron call sites are already in an
 * async context so the await is free.
 *
 * @param {{
 *   headline?: string;
 *   source?: string;
 *   threatLevel?: string;
 *   category?: string;
 *   country?: string;
 *   description?: string;
 * }} story
 * @returns {Promise<string>}
 */
export async function hashBriefStory(story) {
  const material = [
    story.headline ?? '',
    story.source ?? '',
    story.threatLevel ?? '',
    story.category ?? '',
    story.country ?? '',
    // New in v5: description is a prompt input on the analyst path,
    // so MUST be part of cache identity. Absent on legacy paths →
    // empty string → deterministic; same-story-same-description pairs
    // still collide on purpose, different descriptions don't.
    story.description ?? '',
  ].join('||');
  const bytes = new TextEncoder().encode(material);
  const digest = await crypto.subtle.digest('SHA-256', bytes);
  let hex = '';
  const view = new Uint8Array(digest);
  for (let i = 0; i < view.length; i++) {
    hex += view[i].toString(16).padStart(2, '0');
  }
  return hex.slice(0, 16);
}

// ── Analyst-path prompt v2 (multi-sentence, grounded) ──────────────────────
//
// Shadow-diff on 12 prod stories (2026-04-21) showed the v1 analyst output
// was indistinguishable from the legacy Gemini-only output: identical
// single-sentence abstraction-speak ("destabilize / systemic / sovereign
// risk repricing") with no named actors, metrics, or dates. Root cause:
// the 18–30 word cap compressed the context's specifics out of the LLM's
// response. v2 first loosened to 40–70 words / 2–3 sentences; 2026-07-10
// retightens to 25–40 words / 1–2 sentences after the field read found the
// looser cap produced padded, formulaic prose — "much longer to say almost
// the same" as the concise stable path. Still REQUIRES the LLM to ground at
// least one specific reference from the story or materially relevant live
// context.

/**
 * System prompt for the analyst-path v2 (1–2 sentences, ~25–40 words,
 * grounded in a specific named actor / metric / date / place). It uses
 * varied plain prose rather than a fixed analytic sequence, with no
 * section labels in the output.
 */
export const WHY_MATTERS_ANALYST_SYSTEM_V2 =
  'You are the lead analyst at WorldMonitor Brief, a geopolitical intelligence magazine. ' +
  'Using the story as the primary source and the optional Live WorldMonitor Context only when it is materially connected, write 1–2 sentences (25–40 words total) ' +
  'on why the story matters.\n\n' +
  'VOICE:\n' +
  '- Be concise: high signal density, every word earns its place. Do not pad to fill the range or restate the headline.\n' +
  '- Vary sentence structure and emphasis across stories; choose the natural angle for this story rather than following a fixed sequence.\n' +
  '- Do not default to a second sentence beginning "This…" or a "Watch for…" closing construction.\n' +
  '- Ground the prose in a SPECIFIC named actor, metric, date, or place relevant to this story.\n\n' +
  'HARD CONSTRAINTS:\n' +
  '- Total length 25–40 words across 1–2 sentences.\n' +
  '- MUST reference at least ONE specific: named person / country / organization / ' +
  'number / percentage / date / city.\n' +
  '- No preamble ("This matters because…", "The importance of…").\n' +
  '- No markdown, no bullet points, no section labels in the output — plain prose.\n' +
  '- Editorial, impersonal, serious. No calls to action, no questions, no quotes.\n\n' +
  'RELEVANCE RULE (critical, read carefully):\n' +
  '- The context block may contain facts from world-brief, country-brief, risk scores, ' +
  'forecasts, macro signals, and market data. These are optional BACKGROUND, not a ' +
  "mandatory narrative — most stories should not mention the global context. Only cite what is directly relevant to this story's category and country.\n" +
  '- If NO context fact clearly fits, ground instead in a named actor, place, date, ' +
  'or figure drawn from the headline or description. That is a VALID grounding — do ' +
  'NOT invent a market reading, VIX value, or forecast probability to satisfy the rule.\n' +
  '- Treat internal forecast figures as private reasoning input. Do not quote raw forecast probabilities or present a WorldMonitor forecast as a user-facing fact.\n' +
  '- NEVER drag an off-topic market metric, FX reading, or probability into a ' +
  'humanitarian, aviation, diplomacy, or cyber story. A story about a refugee flow ' +
  'does not need a VIX number; a story about a drone incursion does not need an FX ' +
  "stress reading. If it isn't editorially connected to the story, leave it out.";

/**
 * Parse + validate the analyst-path v2 LLM response. Accepts
 * short multi-sentence output (1–2 sentences), 100–500 chars. Otherwise
 * same rejection semantics as v1 (stub echo, empty) plus explicit
 * rejection of preamble boilerplate and leaked section labels.
 *
 * Returns null when the output is obviously wrong so the caller can
 * fall through to the next layer.
 *
 * @param {unknown} text
 * @param {{
 *   publicStory?: { headline?: string, description?: string, source?: string },
 *   privateForecasts?: string,
 * }} [provenance]
 * @returns {string | null}
 */
export function parseWhyMattersV2(text, provenance) {
  if (typeof text !== 'string') return null;
  let s = text.trim();
  if (!s) return null;
  // Drop surrounding quotes if the model insisted.
  s = s.replace(/^[\u201C"']+/, '').replace(/[\u201D"']+$/, '').trim();
  if (s.length < WHY_MATTERS_V2_MIN_CHARS || s.length > WHY_MATTERS_V2_MAX_CHARS) return null;
  if (!hasTerminalPunctuation(s)) return null;
  // Reject the stub echo (same as v1).
  if (/^story flagged by your sensitivity/i.test(s)) return null;
  // Reject common preamble the system prompt explicitly banned.
  if (/^(this matters because|the importance of|it is important|importantly,|in summary,|to summarize)/i.test(s)) {
    return null;
  }
  // Reject markdown / section-label leakage (we told it to use plain prose).
  if (/^(#|-|\*|\d+\.\s)/.test(s)) return null;
  if (/^(situation|analysis|watch)\s*[:\-–—]/i.test(s)) return null;
  // Forecast context is private reasoning input. Compare normalized percentage
  // values instead of nearby wording so paraphrases, distance, and newlines do
  // not bypass the guard. A value present in the public story remains valid
  // even when the private forecast block happens to contain the same number.
  const percentageValues = (value) => {
    if (typeof value !== 'string' || value.length === 0) return new Set();
    const values = new Set();
    const percentage = /(?:^|[^\d.])(\d{1,3}(?:\.\d+)?)\s*(?:%|per\s*cent\b)/gi;
    for (const match of value.matchAll(percentage)) {
      const number = Number(match[1]);
      if (Number.isFinite(number)) values.add(String(number));
    }
    return values;
  };
  const privateValues = percentageValues(provenance?.privateForecasts);
  if (privateValues.size > 0) {
    const publicStory = provenance?.publicStory;
    const publicText = [publicStory?.headline, publicStory?.description, publicStory?.source]
      .filter((value) => typeof value === 'string')
      .join('\n');
    const publicValues = percentageValues(publicText);
    const outputValues = percentageValues(s);
    for (const value of outputValues) {
      if (privateValues.has(value) && !publicValues.has(value)) return null;
    }
  }
  return s;
}

// ─────────────────────────────────────────────────────────────────────
// Hallucination validator for the brief-paragraph LLM rewrite.
//
// The 2026-05-19 Pro brief shipped "Lebanese President Michel Aoun
// pledged..." against a source headline that contained NO name — just
// "Lebanese president vows to 'do the impossible'..." The current
// Lebanese president is Joseph Aoun, not Michel Aoun. The LLM invented
// the name despite the prompt explicitly forbidding invention.
//
// Prompts that say "do not invent proper nouns" are not enforceable on
// every output. This validator catches inventions mechanically: it
// extracts proper-noun sequences from the LLM summary and verifies
// each appears in the source headline (after acronym + demonym
// normalization). When a sequence in the summary has no matching
// contiguous subsequence in the headline, the summary is rejected.
//
// The caller decides what to do with a rejection. In `seed-insights.mjs`
// the integration path writes `worldBrief = sanitizeTitle(topHeadline)`
// (the headline IS the summary), preserving R1 of the plan:
// "falls back to a safe summary (headline-grounded template) rather
// than publishing the hallucination."
//
// Out of scope (documented in docs/plans/2026-05-19-001 Scope Boundaries):
//   - Source-level fact-checking. If the source headline already
//     contains the wrong name (wire-service typo), this validator OKs
//     the summary and the fallback ships the wrong fact verbatim.
//   - Full NER. The pinned heuristic rules below cover the surface
//     PR-2 needs; deeper NER is deferred.
// ─────────────────────────────────────────────────────────────────────

// Title-prefix stop list — when these words sit immediately before a
// capitalized word, they're consumed but not counted as a proper noun
// themselves. "former President Trump" → ['Trump'], not
// ['former', 'President', 'Trump']. The leading lowercase forms
// ('former', 'ex-') are caught by being lowercase in the first place;
// the capitalized title nouns ('President', 'Prime', 'Minister') need
// the explicit stop list because they LOOK like proper nouns.
const TITLE_PREFIX_STOP = new Set([
  'President', 'Prime', 'Minister', 'Senator', 'Representative',
  'Dr', 'Dr.', 'Mr', 'Mr.', 'Ms', 'Ms.', 'Mrs', 'Mrs.',
  'Acting', 'Interim', 'Former', 'Ex',
  'Chairman', 'Chairwoman', 'Chair', 'Speaker',
  'CEO', 'Secretary', 'Defense', 'Foreign',
  'Ambassador', 'General', 'Admiral', 'Colonel', 'Captain',
  'Pope', 'King', 'Queen', 'Prince', 'Princess',
  'Lord', 'Lady', 'Sir', 'Dame',
  'Judge', 'Justice',
]);

// Joiner words — lowercase tokens that bridge proper-noun sequences.
// "Democratic Republic of Congo" is ONE sequence, not three.
const PROPER_NOUN_JOINER = new Set(['of', 'the', 'and', 'for', 'de', 'du', 'der', 'van', 'el', 'al']);

// Acronym ↔ expansion table (bidirectional). When summary contains
// either form and headline contains the other, treated as equivalent.
// Each entry is a single canonical key + its variants. The lookup is
// hostname-style: build a Set of every form, then map each form to
// its canonical key for equivalence.
const ACRONYM_EXPANSIONS = [
  ['WHO', 'World Health Organization'],
  ['UN', 'United Nations'],
  ['US', 'USA', 'United States', 'United States of America', 'America'],
  ['UK', 'United Kingdom', 'Britain', 'Great Britain'],
  ['EU', 'European Union'],
  ['IDF', 'Israel Defense Forces', 'Israeli Defense Forces'],
  ['IMF', 'International Monetary Fund'],
  ['WTO', 'World Trade Organization'],
  ['NATO', 'North Atlantic Treaty Organization'],
  ['OECD'],
  ['OPEC', 'Organization of the Petroleum Exporting Countries'],
  ['IAEA', 'International Atomic Energy Agency'],
  ['ASEAN'],
  ['ECOWAS'],
  ['BRICS'],
  ['DOJ', 'Department of Justice', 'Justice Department'],
  ['FBI', 'Federal Bureau of Investigation'],
  ['SEC', 'Securities and Exchange Commission'],
  ['CIA', 'Central Intelligence Agency'],
  ['NSA', 'National Security Agency'],
  ['DOD', 'Department of Defense', 'Defense Department', 'Pentagon'],
  ['DR Congo', 'Democratic Republic of Congo', 'DRC'],
  ['UAE', 'United Arab Emirates'],
];

// Demonym ↔ nation table. "Israeli strikes" headline ↔ "Israel struck"
// summary — treated as equivalent.
const DEMONYM_TO_NATION = new Map([
  ['Israeli', 'Israel'], ['Israelis', 'Israel'],
  ['American', 'United States'], ['Americans', 'United States'],
  ['Iranian', 'Iran'], ['Iranians', 'Iran'],
  ['Russian', 'Russia'], ['Russians', 'Russia'],
  ['Chinese', 'China'],
  ['French', 'France'],
  ['German', 'Germany'], ['Germans', 'Germany'],
  ['Japanese', 'Japan'],
  ['Lebanese', 'Lebanon'],
  ['Syrian', 'Syria'], ['Syrians', 'Syria'],
  ['Saudi', 'Saudi Arabia'], ['Saudis', 'Saudi Arabia'],
  ['Egyptian', 'Egypt'], ['Egyptians', 'Egypt'],
  ['Turkish', 'Turkey'], ['Turks', 'Turkey'],
  ['Indian', 'India'], ['Indians', 'India'],
  ['Pakistani', 'Pakistan'], ['Pakistanis', 'Pakistan'],
  ['British', 'United Kingdom'], ['Briton', 'United Kingdom'], ['Britons', 'United Kingdom'],
  ['Ukrainian', 'Ukraine'], ['Ukrainians', 'Ukraine'],
  ['Palestinian', 'Palestine'], ['Palestinians', 'Palestine'],
  ['Yemeni', 'Yemen'], ['Yemenis', 'Yemen'],
  ['Iraqi', 'Iraq'], ['Iraqis', 'Iraq'],
  ['Afghan', 'Afghanistan'], ['Afghans', 'Afghanistan'],
  ['Spanish', 'Spain'],
  ['Italian', 'Italy'], ['Italians', 'Italy'],
  ['Korean', 'Korea'], ['Koreans', 'Korea'],
  ['Vietnamese', 'Vietnam'],
  ['Mexican', 'Mexico'], ['Mexicans', 'Mexico'],
  ['Brazilian', 'Brazil'], ['Brazilians', 'Brazil'],
  ['Canadian', 'Canada'], ['Canadians', 'Canada'],
  ['Australian', 'Australia'], ['Australians', 'Australia'],
  ['Cuban', 'Cuba'], ['Cubans', 'Cuba'],
  ['Venezuelan', 'Venezuela'], ['Venezuelans', 'Venezuela'],
  ['Argentine', 'Argentina'], ['Argentinian', 'Argentina'], ['Argentinians', 'Argentina'],
  ['Polish', 'Poland'],
  ['Dutch', 'Netherlands'],
  ['Greek', 'Greece'], ['Greeks', 'Greece'],
  ['Portuguese', 'Portugal'],
  ['Swiss', 'Switzerland'],
  ['Swedish', 'Sweden'], ['Swedes', 'Sweden'],
  ['Norwegian', 'Norway'], ['Norwegians', 'Norway'],
  ['Finnish', 'Finland'], ['Finns', 'Finland'],
  ['Danish', 'Denmark'], ['Danes', 'Denmark'],
  ['Belgian', 'Belgium'], ['Belgians', 'Belgium'],
  ['Austrian', 'Austria'], ['Austrians', 'Austria'],
  ['Filipino', 'Philippines'], ['Filipinos', 'Philippines'],
  ['Thai', 'Thailand'], ['Thais', 'Thailand'],
  ['Indonesian', 'Indonesia'], ['Indonesians', 'Indonesia'],
  ['Nigerian', 'Nigeria'], ['Nigerians', 'Nigeria'],
  ['Ethiopian', 'Ethiopia'], ['Ethiopians', 'Ethiopia'],
  ['Kenyan', 'Kenya'], ['Kenyans', 'Kenya'],
  ['South Korean', 'South Korea'], ['South Koreans', 'South Korea'],
  ['North Korean', 'North Korea'], ['North Koreans', 'North Korea'],
]);

// Build a normalization map from every variant (acronym OR expansion)
// to a single canonical key. Lookup uses lower-case keys for case-
// insensitive matching.
const ACRONYM_NORMALIZE = (() => {
  const map = new Map();
  for (const group of ACRONYM_EXPANSIONS) {
    const canonical = group[0].toLowerCase();
    for (const variant of group) map.set(variant.toLowerCase(), canonical);
  }
  return map;
})();

const DEMONYM_NORMALIZE = (() => {
  const map = new Map();
  for (const [demonym, nation] of DEMONYM_TO_NATION) {
    map.set(demonym.toLowerCase(), nation.toLowerCase());
  }
  return map;
})();

/**
 * Extract contiguous proper-noun sequences from a text. A sequence is
 * a run of capitalized tokens (length ≥ 1), bridged by joiner words
 * (lowercase 'of', 'the', 'and', etc.). All-caps acronyms (length 2-6)
 * count. Title-prefix stop-list words ('President', 'Senator', 'Dr.',
 * 'former', …) when immediately followed by a capitalized token are
 * consumed but not counted as proper nouns themselves.
 *
 * Returns an array of arrays — each inner array is the token sequence
 * of one proper noun, lower-cased.
 *
 * Examples:
 *   "former President Trump said..."  → [['trump']]
 *   "Democratic Republic of Congo declared"
 *                                     → [['democratic', 'republic', 'of', 'congo']]
 *   "The UN said the EU agreed"       → [['un'], ['eu']]
 *   "Lebanese President Michel Aoun"  → [['michel', 'aoun']]
 *
 * @param {string} text
 * @returns {string[][]}
 */
// Common sentence-start words that LOOK like proper nouns when
// capitalized (sentence-start) but aren't. Lowercase keys; matched
// case-insensitively. The list is conservative — words on it are
// EXCLUSIVELY common nouns / function words / discourse markers.
// Real proper nouns at sentence start ("Trump said...", "Israel
// announced...", "WHO declared...") MUST pass through unfiltered.
const SENTENCE_START_AMBIGUOUS = new Set([
  'the', 'a', 'an',
  'this', 'that', 'these', 'those',
  'it', 'he', 'she', 'they', 'we', 'you', 'i',
  'some', 'many', 'most', 'all', 'few', 'several', 'both', 'each', 'every',
  'other', 'another', 'such', 'any', 'either', 'neither',
  'there', 'here', 'now', 'today', 'yesterday', 'tomorrow',
  'when', 'where', 'while', 'as', 'after', 'before', 'during', 'since', 'until',
  'if', 'because', 'although', 'though', 'unless', 'whether',
  'how', 'why', 'what', 'which', 'whose',
  'no', 'not', 'yes',
  'breaking', 'live', 'updated', 'latest', 'exclusive', 'just',
  'meanwhile', 'however', 'moreover', 'additionally', 'furthermore', 'still',
  'with', 'without', 'on', 'in', 'at', 'by', 'for', 'over', 'under', 'about',
]);

/**
 * Collapse dotted acronyms (`U.S.`, `U.S.A.`, `D.O.J.`) into bare form
 * (`US`, `USA`, `DOJ`) so the tokenizer doesn't split them into single-
 * char tokens that fail the 2-6-char acronym rule. PR #3836 review:
 * a valid summary using common dotted style ("the U.S. announced...")
 * against an expanded headline ("United States imposed...") tokenized
 * to `['U', 'S']` vs `['us']` (canonical) and never matched —
 * false-flagging as hallucination.
 *
 * Match: a capital letter followed by a dot, then at least one more
 * capital-letter-then-optional-dot pair. The second pair commitment
 * prevents a sentence-final initial ("...J.") from false-positiving.
 *
 * Examples:
 *   "the U.S. announced"             → "the US announced"
 *   "U.S.A. delegation"              → "USA delegation"
 *   "FBI raided J.D. Vance's office" → "FBI raided JD Vance's office"
 *   "i.e., these things"             → "i.e., these things"  (lowercase, no match)
 *   "end of sentence."               → "end of sentence."    (single dot, no run)
 */
function normalizeDottedAcronyms(text) {
  return text.replace(/(\b[A-Z]\.(?:[A-Z]\.?)+)/g, (match) => match.replace(/\./g, ''));
}

export function extractProperNounSequences(text) {
  if (typeof text !== 'string' || text.length === 0) return [];

  // Normalize dotted acronyms BEFORE sentence-splitting so "U.S." isn't
  // misread as a sentence boundary or split into ['U', 'S'].
  const preprocessed = normalizeDottedAcronyms(text);

  // Split into sentences so sentence-start handling can run per-sentence.
  const sentences = preprocessed.split(/[.!?]+\s+|\n+/);

  const sequences = [];
  for (const rawSentence of sentences) {
    const sentence = rawSentence.trim();
    if (!sentence) continue;

    // Tokenize: keep alphanumeric runs + apostrophes + hyphens
    // (preserves "Mar-a-Lago", "O'Brien").
    const tokens = sentence.split(/[^\p{L}\p{N}'’-]+/u).filter(Boolean);
    if (tokens.length === 0) continue;

    let current = [];
    let bridgeBuffer = []; // joiners pending — kept only if another proper noun follows
    let firstToken = true;

    for (const token of tokens) {
      // Strip trailing punctuation and possessive 's / ’s so
      // "Beirut's" → "beirut" and "U.S." → "U.S" (handled below).
      let stripped = token.replace(/[.,;:'’]+$/g, '');
      stripped = stripped.replace(/['’]s$/i, '');
      const tokenForLookup = stripped || token;
      const isTitlePrefix = TITLE_PREFIX_STOP.has(stripped);
      const isJoiner = PROPER_NOUN_JOINER.has(token.toLowerCase());
      // Capitalized: at least 2 chars long. Single-letter capitalized
      // tokens are sentence-final initials ("...J.D. Vance was met by Smith
      // and J."), middle initials in names, or "I" (the pronoun, already
      // handled by SENTENCE_START_AMBIGUOUS). None should register as
      // a standalone proper noun.
      const isCapitalized = token.length >= 2 && /^[A-Z]/.test(token);
      const isAllCapsAcronym = /^[A-Z]{2,6}$/.test(token);
      const isAmbiguousSentenceStart = firstToken
        && !isAllCapsAcronym
        && SENTENCE_START_AMBIGUOUS.has(token.toLowerCase());

      // Title-prefix consume — even at sentence start, "Former" /
      // "President" / "Dr." consume without registering.
      if (isTitlePrefix && current.length === 0) {
        firstToken = false;
        continue;
      }

      // Skip ambiguous sentence-starters ("The", "It", "Breaking", …)
      // that LOOK like proper nouns but are common-word capitalization.
      if (isAmbiguousSentenceStart) {
        firstToken = false;
        continue;
      }
      firstToken = false;

      if (isJoiner) {
        if (current.length > 0) bridgeBuffer.push(token.toLowerCase());
        continue;
      }

      if (isCapitalized || isAllCapsAcronym) {
        // Flush pending bridge buffer into current sequence.
        if (current.length > 0 && bridgeBuffer.length > 0) {
          current.push(...bridgeBuffer);
        }
        bridgeBuffer = [];
        // Use the punctuation/possessive-stripped form so "Beirut's"
        // lands as "beirut", matching the headline's "Beirut".
        current.push(tokenForLookup.toLowerCase());
      } else {
        // Lowercase non-joiner — ends current sequence.
        if (current.length > 0) {
          sequences.push(current);
          current = [];
        }
        bridgeBuffer = [];
      }
    }
    if (current.length > 0) sequences.push(current);
  }

  return sequences;
}

/**
 * Normalize a token: if it's a known acronym variant, return the
 * canonical key; otherwise return it unchanged (lower-cased).
 */
function normalizeToken(token) {
  const lower = token.toLowerCase();
  if (ACRONYM_NORMALIZE.has(lower)) return ACRONYM_NORMALIZE.get(lower);
  if (DEMONYM_NORMALIZE.has(lower)) return DEMONYM_NORMALIZE.get(lower);
  return lower;
}

/**
 * Apply the same normalization to a token SEQUENCE: greedy match
 * multi-word acronym expansions ("Democratic Republic of Congo" → 'dr
 * congo' canonical) before single-token normalization. Returns a new
 * sequence (array of canonical tokens).
 */
function normalizeSequence(sequence) {
  const out = [];
  let i = 0;
  while (i < sequence.length) {
    // Try multi-word matches first, longest first (5 → 4 → 3 → 2 tokens).
    let matched = false;
    for (let span = Math.min(5, sequence.length - i); span >= 2; span--) {
      const candidate = sequence.slice(i, i + span).join(' ').toLowerCase();
      if (ACRONYM_NORMALIZE.has(candidate)) {
        out.push(ACRONYM_NORMALIZE.get(candidate));
        i += span;
        matched = true;
        break;
      }
    }
    if (matched) continue;
    // Fall through to single-token normalization.
    out.push(normalizeToken(sequence[i]));
    i += 1;
  }
  return out;
}

/**
 * Validate that every proper noun in `summary` has a matching
 * contiguous subsequence in `headline` (after acronym + demonym
 * normalization). The validator catches LLM-introduced invention.
 *
 * Returns `{ ok: true }` when every summary proper-noun sequence is
 * grounded in the headline, OR when either input is malformed (defensive
 * default — ship the LLM output rather than fall back on confusion).
 *
 * Returns `{ ok: false, hallucinated: [...] }` when at least one
 * summary sequence has no matching contiguous subsequence in the
 * headline. The first such sequence is returned; iterating to find
 * all of them is not necessary for the fallback decision.
 *
 * @param {string} summary - the LLM-rewritten brief paragraph
 * @param {string} headline - the source headline the LLM was given
 * @returns {{ ok: boolean, hallucinated?: string[] }}
 */
export function validateNoHallucinatedProperNouns(summary, headline) {
  // Defensive: malformed inputs return ok (ship the LLM output rather
  // than fall back on confusion). Catches null, undefined, empty
  // string, non-string, and weird unicode.
  if (typeof summary !== 'string' || summary.length === 0) return { ok: true };
  if (typeof headline !== 'string' || headline.length === 0) return { ok: true };

  let summarySequences, headlineSequences;
  try {
    summarySequences = extractProperNounSequences(summary).map(normalizeSequence);
    headlineSequences = extractProperNounSequences(headline).map(normalizeSequence);
  } catch {
    return { ok: true };
  }

  if (summarySequences.length === 0) return { ok: true };

  // For each summary sequence, check whether at least one contiguous
  // subsequence of it appears in some headline sequence.
  for (const summarySeq of summarySequences) {
    let found = false;
    for (const headlineSeq of headlineSequences) {
      if (containsSubsequence(headlineSeq, summarySeq)) {
        found = true;
        break;
      }
      // Also check the reverse: a single-token summary sequence
      // (e.g., ['us']) matches if it appears anywhere in a longer
      // headline sequence.
      if (summarySeq.length === 1 && headlineSeq.includes(summarySeq[0])) {
        found = true;
        break;
      }
    }
    if (!found) {
      return { ok: false, hallucinated: summarySeq };
    }
  }

  return { ok: true };
}

/**
 * Does `haystack` contain `needle` as a contiguous subsequence?
 * Both are token arrays. Order matters; positions must be adjacent.
 */
function containsSubsequence(haystack, needle) {
  if (needle.length === 0) return true;
  if (needle.length > haystack.length) return false;
  outer: for (let i = 0; i <= haystack.length - needle.length; i++) {
    for (let j = 0; j < needle.length; j++) {
      if (haystack[i + j] !== needle[j]) continue outer;
    }
    return true;
  }
  return false;
}

// ═══════════════════════════════════════════════════════════════════════════
// Grounding spine (#4921 — ported from scripts/lib/brief-llm.mjs so EVERY
// brief product shares one grounding implementation; brief-llm.mjs
// re-exports these for backcompat). Edge-safe: no node:crypto, no env reads.
// ═══════════════════════════════════════════════════════════════════════════


// Shared delimiter regex for tokenising both story headlines (anchor
// extraction) and synthesis prose (haystack lookup). Same delimiter
// set on both sides keeps the matching contract symmetric.
//
// Unicode quotes (U+2018, U+2019, U+201C, U+201D, U+00B4) are
// included alongside their ASCII counterparts. News headlines from
// Reuters/AP/Guardian use U+2019 for possessives ("China's",
// "Iran's", "DPRK's") and U+201C/U+201D for quoted phrases. Without
// splitting on them, "China's" becomes one token "china’s" that
// a lead saying "China" can never match — a false negative that
// would reject genuinely grounded leads. (PR #3667 review round 2
// finding #2.)
const GROUNDING_TOKEN_DELIMS = /[\s,.!?;:()'"‘’“”´\\/—–\-[\]{}]+/;

// Anchor-side stopword list. Story headlines often capitalise
// titles ("President Trump"), generic actors ("Officials confirmed"),
// quasi-adjectives ("Senior commander", "Federal court"), and
// sentence-start filler ("Following the announcement"). Without
// filtering, these enter storyTokens and a hallucinated lead like
// "President Biden announced..." passes the lead-anchor check via
// the shared word "President", then a teaser mentioning a real
// anchor satisfies the combined threshold — the visible top-of-
// email lead stays fabricated. (PR #3667 review round 2 finding #1.)
//
// Scope rule: only words that are commonly capitalised but do NOT
// discriminate a story. Specific entity names (people, places,
// orgs, brands) are NEVER on this list, even when common — "Iran",
// "Trump", "Israel", "EU", "UN" all stay in. "May" is also
// deliberately omitted (Theresa May, May Day, May = month all
// collide on it; safer to keep "may" matchable than to filter it
// and lose a real anchor).
//
// Maintenance heuristic (PR #3667 review round 5 #3): a capitalised
// token of length ≥4 belongs in this set if it appears in >~10% of
// real headlines without discriminating between stories. The cheap
// audit is: dump a week of headlines, tokenise with this same
// extractAnchorTokens function (with stopwords disabled), count
// frequencies, and inspect any token in >50 of ~500 headlines that
// isn't already a known proper noun. The "Prime"/"Chief"/"Cardinal"
// gaps caught on review rounds 2-3 would each have surfaced from
// such a frequency audit. Don't try to enumerate exhaustively up
// front; let production usage drive additions and capture each new
// ride-along bug class as a regression test.
const GROUNDING_ANCHOR_STOPWORDS = new Set([
  // Honorifics / titles
  'president', 'vice', 'senator', 'minister', 'secretary',
  'chairman', 'chairwoman', 'spokesman', 'spokeswoman',
  'director', 'general', 'admiral', 'colonel', 'captain',
  'mayor', 'governor', 'judge', 'justice', 'doctor',
  'professor', 'pope', 'rabbi', 'imam', 'sheikh', 'sultan',
  'emir', 'king', 'queen', 'prince', 'princess',
  // Round-3 review additions: bigram-leading titles ("Prime
  // Minister", "Chief Justice", "Cardinal Smith") whose first
  // word alone passes the cap+length filter and would otherwise
  // let a hallucinated "Prime Minister Trudeau announced..." lead
  // ride on a "Prime Minister Netanyahu says..." headline via the
  // shared "prime" token. PR #3667 review round 3.
  'prime', 'chief', 'premier', 'chancellor', 'speaker',
  'ambassador', 'envoy', 'commissioner', 'attorney',
  'cardinal', 'archbishop', 'monsignor', 'reverend',
  'pastor', 'bishop', 'lord', 'lady', 'dame',
  'congressman', 'congresswoman', 'congressperson',
  'representative', 'delegate', 'baron', 'baroness',
  // Generic role plurals / institutional collectives
  'officials', 'officers', 'leaders', 'members', 'people',
  'forces', 'police', 'troops', 'agents', 'authorities',
  'sources', 'rebels', 'militants', 'protesters', 'civilians',
  'residents', 'citizens', 'workers', 'voters',
  // Headline qualifiers / quasi-adjectives
  'senior', 'junior', 'former', 'acting', 'deputy', 'assistant',
  'federal', 'national', 'international', 'global', 'regional',
  'central', 'local', 'foreign', 'domestic', 'civil', 'public',
  'private', 'special', 'major', 'armed',
  // Sentence-start / common filler
  'after', 'before', 'during', 'while', 'despite', 'following',
  'amid', 'today', 'yesterday', 'tomorrow', 'this', 'these',
  'those', 'when', 'where', 'what', 'which', 'breaking',
  // News-headline glue
  'says', 'said', 'told', 'reports', 'analysis', 'opinion',
  'editorial', 'update', 'updates',
  // Calendar (May omitted — see scope rule above)
  'monday', 'tuesday', 'wednesday', 'thursday', 'friday',
  'saturday', 'sunday', 'january', 'february', 'march', 'april',
  'june', 'july', 'august', 'september', 'october', 'november',
  'december',
]);

/**
 * Anchor extraction from a story headline: capitalised + length ≥4 +
 * NOT in GROUNDING_ANCHOR_STOPWORDS. The capitalisation filter makes
 * this a "proper noun" heuristic; the stopword filter strips
 * honorifics, role labels, bigram-leading titles, and sentence-start
 * filler that would otherwise be shared anchors between any
 * "President X..." headline and any "President Y..." hallucinated
 * lead. File-level so the closure isn't re-instantiated per
 * checkLeadGrounding call (PR #3667 review round 4 P2).
 *
 * @param {string} s
 * @returns {string[]} lowercased anchor tokens
 */
export function extractAnchorTokens(s) {
  if (typeof s !== 'string' || s.length === 0) return [];
  const out = [];
  for (const w of s.split(GROUNDING_TOKEN_DELIMS)) {
    if (w.length < 4 || !/^[A-Z]/.test(w)) continue;
    const lower = w.toLowerCase();
    if (!GROUNDING_ANCHOR_STOPWORDS.has(lower)) out.push(lower);
  }
  return out;
}

/**
 * Tokenise synthesis prose into a Set of lowercased words for
 * membership lookup. NO capitalisation filter — the synthesis can
 * mention the entity in any case (sentence-medial, possessive form,
 * etc.) and we still want it to count. File-level for the same
 * reason as extractAnchorTokens (PR #3667 review round 4 P2).
 *
 * @param {string} text
 * @returns {Set<string>}
 */
export function groundingTokenSet(text) {
  const set = new Set();
  if (typeof text !== 'string' || text.length === 0) return set;
  for (const w of text.toLowerCase().split(GROUNDING_TOKEN_DELIMS)) {
    if (w.length >= 4) set.add(w);
  }
  return set;
}

/**
 * Cheap content-grounding check: the canonical lead MUST reference
 * proper-noun tokens that actually appear in the input story
 * headlines. Without this, the LLM is free to confabulate even with
 * shape-valid output — e.g. the 2026-05-12 incident where a Trump-
 * era geopolitics pool (Iran/Israel/Sudan/Cuba/Ukraine) shipped a
 * "President Biden announced a crypto executive order" lead. Shape
 * was valid; content was a complete fabrication the model produced
 * from training-data priors instead of grounding.
 *
 * Two independent grounding requirements (BOTH must pass):
 *
 *   1. **Lead anchor**: the lead alone must hit ≥1 anchor token.
 *      Without this, a hallucinated lead can sneak through when the
 *      threads happen to mention real entities — the visible lead
 *      stays fabricated even though the combined check passes.
 *      (Code-review finding on PR #3667 #1.)
 *   2. **Combined coverage**: the lead + thread teasers together
 *      must hit ≥2 anchors (relaxed to 1 when the corpus itself has
 *      <4 anchor tokens, so single-named-actor briefs aren't
 *      false-positives).
 *
 * Matching is **token-set membership** — both sides are split on
 * the same delimiter regex and lowercased into Sets. Substring
 * matching (the v1 implementation) was rejected on PR #3667 review:
 * it accepts unrelated entities like `iran` inside `tirana`,
 * `oman` inside `romania`, `india` inside `indiana`. Token-set
 * matching avoids that class of false positive cleanly.
 * (Code-review finding on PR #3667 #2.)
 *
 * Length cap of 4 deliberately filters out 2-letter ISO country
 * codes (`IR`, `PS`, `US`) and short-form orgs (`UN`, `EU`, `RSF`)
 * which are too generic to be discriminating anchors. The check is
 * about whether the lead names a SPECIFIC entity — not whether it
 * uses any capitalised token at all.
 *
 * Returns true (grounded, or check-skipped because corpus lacks
 * signal / no stories supplied) → accept. Returns false → reject.
 *
 * @param {{ lead?: string; threads?: Array<{tag?:string;teaser?:string}> }} synthesis
 * @param {Array<{ headline?: string }>} stories
 * @returns {boolean}
 */
// Default story cap for grounding: mirrors the digest's default
// MAX_STORIES_PER_USER without dragging its env read into this
// edge-safe module — callers with a different cap pass it explicitly.
const DEFAULT_GROUNDING_STORY_CAP = 8;

export function checkLeadGrounding(synthesis, stories, storyCap = DEFAULT_GROUNDING_STORY_CAP) {
  if (!Array.isArray(stories) || stories.length === 0) return true;

  const storyTokens = new Set();
  for (const s of stories.slice(0, storyCap)) {
    for (const tok of extractAnchorTokens(s?.headline ?? '')) {
      storyTokens.add(tok);
    }
  }
  // Corpus has no proper-noun anchors — can't validate, skip.
  // Genuine input (2026-era stories) reliably has >0 such tokens;
  // the empty branch is for synthetic / single-headline tests.
  //
  // Lowercase-headline blind spot (PR #3667 review round 5 #2):
  // if a feed ever produces all-lowercase or all-≤3-char headlines,
  // every story contributes zero anchors and the gate silently
  // skips. Emit a warn so ops can detect the regression — but only
  // when stories.length is meaningful (≥3) so the synthetic
  // single-headline test corpora don't spam logs.
  if (storyTokens.size === 0) {
    if (stories.length >= 3) {
      console.warn(
        `[brief-llm-core] grounding gate skipped: storyTokens empty for stories.length=${stories.length} — likely all-lowercase or <4-char headlines from a feed regression`,
      );
    }
    return true;
  }

  const leadTokens = groundingTokenSet(typeof synthesis?.lead === 'string' ? synthesis.lead : '');

  // Requirement 1: the lead alone must hit ≥1 anchor. A hallucinated
  // lead with grounded teasers would otherwise pass — the user still
  // sees the fabricated text at the top of the email.
  let leadHasAnchor = false;
  for (const tok of leadTokens) {
    if (storyTokens.has(tok)) { leadHasAnchor = true; break; }
  }
  if (!leadHasAnchor) return false;

  // Requirement 2: combined lead + teasers hit ≥threshold anchors.
  // Threshold relaxes to 1 when the corpus is sparse so single-
  // story briefs don't false-positive.
  const combinedTokens = new Set(leadTokens);
  for (const t of (Array.isArray(synthesis?.threads) ? synthesis.threads : [])) {
    for (const w of groundingTokenSet(typeof t?.teaser === 'string' ? t.teaser : '')) {
      combinedTokens.add(w);
    }
  }
  const threshold = storyTokens.size >= 4 ? 2 : 1;
  let combinedHits = 0;
  for (const tok of storyTokens) {
    if (combinedTokens.has(tok)) {
      combinedHits++;
      if (combinedHits >= threshold) return true;
    }
  }
  return false;
}

/**
 * Lead ↔ single-story coherence check (F4). Returns true iff `lead`
 * shares ≥1 proper-noun anchor with `headline`. Reuses the same
 * anchor machinery as `checkLeadGrounding` (capitalised, length ≥4,
 * stopword-filtered headline anchors; token-set membership against
 * the lead) but with a FIXED threshold of 1 — coherence asks only
 * "is the lead about the same story?", not "how well-grounded is it?".
 *
 * `checkLeadGrounding` itself is the wrong fit here: scoped to one
 * story, a single headline can carry ≥4 anchor tokens, which trips
 * its `size >= 4 ? 2 : 1` threshold up to 2 — too strict for
 * coherence, where a lead legitimately about card #1 may name only
 * one of its entities.
 *
 * Used by the cron's lead/card-#1 coherence telemetry
 * (`composeAndStoreBriefForUser`) — see plan
 * docs/plans/2026-05-14-001-…-plan.md (F4, Phase 4).
 *
 * @param {string} lead — the canonical synthesis lead
 * @param {string} headline — the rendered first card's headline
 * @returns {boolean} true = coherent (or check-skipped); false = the
 *   lead names none of the headline's proper-noun anchors
 */
export function leadGroundsAgainstStory(lead, headline) {
  const anchors = new Set(extractAnchorTokens(typeof headline === 'string' ? headline : ''));
  // No proper-noun anchors in the headline → cannot judge coherence,
  // skip (same "degenerate corpus → accept" stance as checkLeadGrounding).
  if (anchors.size === 0) return true;
  const leadTokens = groundingTokenSet(typeof lead === 'string' ? lead : '');
  for (const tok of anchors) {
    if (leadTokens.has(tok)) return true;
  }
  return false;
}


/**
 * #4921: mechanical citation verification. Every bracket citation [n]
 * in LLM brief prose must map to a real entry in the grounding source
 * list (1..sourceCount). Out-of-range markers are invented — strip them
 * rather than rendering a dead reference. Returns the cleaned text and
 * the count of stripped markers (callers log/telemeter when > 0).
 *
 * Pure string operation; deliberately does NOT try to verify the CLAIM
 * against the source — that is the grounding gates' job. This closes
 * the cheaper hole: "[9]" shipped against a 6-source list.
 *
 * @param {string} text
 * @param {number} sourceCount
 * @returns {{ text: string; stripped: number }}
 */
export function verifyCitationIndexes(text, sourceCount) {
  if (typeof text !== 'string' || text.length === 0) {
    return { text: typeof text === 'string' ? text : '', stripped: 0 };
  }
  const max = Number.isFinite(sourceCount) && sourceCount > 0 ? Math.floor(sourceCount) : 0;
  let stripped = 0;
  // 1-3 digits: [123] must not sail through unverified (review finding);
  // 4+ digit brackets ([2026]) are treated as prose, not citations.
  const cleaned = text.replace(/\s*\[(\d{1,3})\]/g, (full, numStr) => {
    const n = Number.parseInt(numStr, 10);
    if (n >= 1 && n <= max) return full;
    stripped++;
    return '';
  });
  return { text: cleaned, stripped };
}