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* @module generate-news-enhanced/ai-analysis-pipeline
* @description Heuristic-based multi-iteration analysis pipeline for deep political
* intelligence. Uses deterministic document classification, template-driven
* per-document analysis, cross-document synthesis, and quality scoring to produce
* context-aware political insights from every stakeholder perspective.
*
* ⚠️ DEPRECATED FOR ANALYSIS GENERATION (v3.0, 2026-04-02):
* Per analysis/methodologies/ai-driven-analysis-guide.md Rule 2, the following
* functions in this module are DEPRECATED for generating analysis content:
* - buildDynamicSwot() → Replace with AI prompt in workflow .md
* - buildStrategicImplications() → Replace with AI prompt in workflow .md
* - buildKeyTakeaways() → Replace with AI prompt in workflow .md
* - buildLegislativeImpact() → Replace with AI prompt in workflow .md
* - buildCrossPartyImplications() → Replace with AI prompt in workflow .md
* - scoreAnalysisDepth() → Replace with AI prompt quality evaluation
*
* Their output is treated as FALLBACK STUBS. AI agents in agentic workflow .md
* files MUST overwrite all template-generated text with genuine, evidence-based
* political intelligence analysis. See SHARED_PROMPT_PATTERNS.md for AI prompts.
*
* This module retains data classification, document type detection, and HTML
* formatting functions which are NOT deprecated.
*
* NOTE: This module does NOT integrate with external LLM/MCP services. All analysis
* is performed via rule-based heuristics and localised template interpolation. The
* "iteration" depth controls how many passes run (see {@link AIAnalysisPipeline}).
*
* Architecture — four analysis passes (gated by iteration depth):
* 1. Data Collection & Classification — classify by type/domain, detect policy areas
* 2. Deep Analysis (iterations ≥ 2) — per-document legislative impact, cross-party
* implications, historical context, and EU/Nordic comparison
* 3. Cross-Document Synthesis (iterations ≥ 2) — convergence/divergence patterns,
* coalition stress, emerging trends, stakeholder power dynamics
* 4. Quality Assurance & Refinement (iterations ≥ 3) — score output, re-generate
* below threshold
*
* @author Hack23 AB
* @license Apache-2.0
*/
import { detectPolicyDomains } from '../data-transformers/policy-analysis.js';
import { escapeHtml } from '../html-utils.js';
import type { RawDocument } from '../data-transformers.js';
import type { Language } from '../types/language.js';
import type { SwotEntry } from '../types/article.js';
import {
pickLang,
interp,
plural,
betVerbForm,
motVerbForm,
propNounForm,
betNounForm,
motNounForm,
LEGISLATIVE_SIGNAL,
SCRUTINY_SIGNAL,
EU_ALIGNMENT_SIGNAL,
GOV_STRENGTH_LABELS,
OPP_STRENGTH_LABELS,
EU_OPPORTUNITY,
GOV_WEAKNESS_IMPL,
GOV_THREAT_EXEC,
OPP_WEAKNESS_INFO,
OPP_OPPORTUNITY_CONSENSUS,
OPP_THREAT_MAJORITY,
PRIVATE_STRENGTH_DOMAIN,
PRIVATE_WEAKNESS_COMPLIANCE,
PRIVATE_OPPORTUNITY_INVESTMENT,
PRIVATE_THREAT_UNCERTAINTY,
STRATEGIC_IMPL_TEMPLATES,
SIGNAL_GOVT_AGENDA,
SIGNAL_PARL_SCRUTINY,
SIGNAL_BALANCED,
SIGNAL_PRESS,
SIGNAL_EXTERNAL,
TYPE_DESC_PRESS,
TYPE_DESC_EXTERNAL,
TYPE_DESC_REGULATORY,
SIGNAL_SNAPSHOT,
TAKEAWAY_PROP,
TAKEAWAY_BET,
TAKEAWAY_MOT,
TAKEAWAY_EU,
TAKEAWAY_ENRICHED,
TAKEAWAY_COALITION_STRESS,
} from './analysis-labels.js';
// ---------------------------------------------------------------------------
// Public interfaces
// ---------------------------------------------------------------------------
/** Per-document deep analysis produced in Pass 2. */
export interface AIDocumentAnalysis {
/** Riksdag document identifier. */
dok_id: string;
/** Document title. */
title: string;
/** Assessment of the document's legislative impact in the target language. */
legislativeImpact: string;
/** Cross-party implications of the document. */
crossPartyImplications: string;
/** Historical context or precedent relevant to the document. */
historicalContext: string;
/** EU and Nordic comparative dimension. */
euNordicComparison: string;
/** Analysis depth score 0–100 (distinct from the 0.0–1.0 qualityScore used by article-quality-enhancer). */
analysisScore: number;
}
/** Cross-document synthesis produced in Pass 3. */
export interface AISynthesis {
/** Assessment of policy convergence or divergence across documents. */
policyConvergence: string;
/** Indicators of coalition stress visible in the document set. */
coalitionStressIndicators: string;
/**
* Emerging legislative trends detected.
* Format: comma-separated domain names with a single bracketed confidence
* level appended to the whole list, e.g. "fiscal policy, defence, environment [HIGH]".
* Empty string when no domains are detected.
*/
emergingTrends: string;
/** Stakeholder power dynamics implied by the document distribution. */
stakeholderPowerDynamics: string;
}
/** Dynamically generated SWOT entries replacing hardcoded SWOT_DEFAULTS. */
export interface DynamicSwotEntries {
government: {
strengths: SwotEntry[];
weaknesses: SwotEntry[];
opportunities: SwotEntry[];
threats: SwotEntry[];
};
opposition: {
strengths: SwotEntry[];
weaknesses: SwotEntry[];
opportunities: SwotEntry[];
threats: SwotEntry[];
};
privateSector: {
strengths: SwotEntry[];
weaknesses: SwotEntry[];
opportunities: SwotEntry[];
threats: SwotEntry[];
};
}
/** Final output of the full multi-iteration analysis pipeline. */
export interface AIAnalysisResult {
/** Total iterations executed. */
iterations: number;
/** Per-document deep analyses from Pass 2. */
documentAnalyses: AIDocumentAnalysis[];
/** Cross-document synthesis from Pass 3. */
synthesis: AISynthesis;
/** Context-aware SWOT entries replacing hardcoded defaults. */
dynamicSwotEntries: DynamicSwotEntries;
/** Strategic implications paragraph in the target language. */
strategicImplications: string;
/** Bullet-list key takeaways. */
keyTakeaways: string[];
/** Overall analysis depth score 0–100 (distinct from the 0.0–1.0 qualityScore used by article-quality-enhancer). */
analysisScore: number;
}
// ---------------------------------------------------------------------------
// Internal helpers
// ---------------------------------------------------------------------------
/** Minimum quality score below which Pass 4 triggers re-analysis. */
const QUALITY_THRESHOLD = 45;
/** Document type keywords used for legislative signal detection. */
const GOV_TYPES = new Set(['prop', 'pressm', 'skr', 'sfs', 'ds', 'sou']);
const OPP_TYPES = new Set(['bet', 'mot', 'ip', 'fr']);
const EU_TYPES = new Set(['fpm', 'eu']);
const EXT_TYPES = new Set(['ext', 'external']);
function docType(d: RawDocument): string {
return (d.doktyp ?? d.documentType ?? '').toLowerCase();
}
function docTitle(d: RawDocument): string {
return (d.titel ?? d.title ?? d.dokumentnamn ?? d.dok_id ?? '').slice(0, 120);
}
/** Classify a document into a stakeholder bucket. */
function classifyStakeholder(d: RawDocument): 'government' | 'opposition' | 'eu' | 'other' {
const t = docType(d);
if (GOV_TYPES.has(t)) return 'government';
if (OPP_TYPES.has(t)) return 'opposition';
if (EU_TYPES.has(t)) return 'eu';
return 'other';
}
/** Identify whether a document signals cross-party coalition stress. */
function hasCoalitionStress(d: RawDocument): boolean {
const title = docTitle(d).toLowerCase();
const stressKeywords = [
'avslag', 'reject', 'tillägg', 'amendment',
'reservation', 'minoritet', 'minority',
];
return stressKeywords.some(kw => title.includes(kw));
}
/** Maximum score contribution from word count alone. */
const WORD_SCORE_CAP = 60;
/** Words required per quality point from word-count scoring. */
const WORDS_PER_POINT = 3;
/** Bonus score added when the analysis references specific numeric figures. */
const SPECIFICITY_BONUS = 20;
/** Bonus score added when the analysis contains cross-reference terms. */
const CROSS_REF_BONUS = 20;
/** CJK Unicode range test — for languages without whitespace word boundaries (zh, ja, ko). */
const CJK_REGEX = /[\u3000-\u9fff\uac00-\ud7af\uff00-\uffef]/;
/** Characters-per-point for CJK character-based scoring (replaces words/WORDS_PER_POINT). */
const CJK_CHARS_PER_POINT = 5;
/** Score analysis depth: 0–100 based on content length and richness. */
function scoreAnalysisDepth(text: string): number {
if (!text || text.length === 0) return 0;
const isCJK = CJK_REGEX.test(text);
const contentSize = isCJK
? Math.floor(text.length / CJK_CHARS_PER_POINT)
: text.split(/\s+/).length / WORDS_PER_POINT;
let score = Math.min(WORD_SCORE_CAP, Math.floor(contentSize));
const hasSpecific = /\d{4}|\d+\s*(kr|miljarder|miljoner|percent|%|万|億|兆|조|억)/.test(text);
if (hasSpecific) score += SPECIFICITY_BONUS;
const hasCrossRef = /cross-party|coalition|EU|EU-|Nordic|parliamentary|tvärs?parti|koalition|parlamentar|超党派|連立|議会|초당파|연립|의회|跨党派|联盟|议会/.test(text);
if (hasCrossRef) score += CROSS_REF_BONUS;
return Math.min(100, score);
}
// ---------------------------------------------------------------------------
// Document classification result (Pass 1 output)
// ---------------------------------------------------------------------------
interface ClassifiedDocuments {
propDocs: RawDocument[];
betDocs: RawDocument[];
motDocs: RawDocument[];
skrDocs: RawDocument[];
sfsDocs: RawDocument[];
euDocs: RawDocument[];
pressmDocs: RawDocument[];
extDocs: RawDocument[];
otherDocs: RawDocument[];
allDomains: string[];
hasCoalitionStress: boolean;
enrichedCount: number;
}
// ---------------------------------------------------------------------------
// Pass 2: Per-document deep analysis (standalone functions)
// ---------------------------------------------------------------------------
function buildLegislativeImpact(
doc: RawDocument,
stake: 'government' | 'opposition' | 'eu' | 'other',
topicStr: string,
lang: Language,
): string {
if (stake === 'government') {
return `${pickLang(LEGISLATIVE_SIGNAL, lang)} ${docTitle(doc).slice(0, 80)} — ${topicStr}`;
}
if (stake === 'opposition') {
return `${pickLang(SCRUTINY_SIGNAL, lang)} ${docTitle(doc).slice(0, 80)} — ${topicStr}`;
}
if (stake === 'eu') {
return `${pickLang(EU_ALIGNMENT_SIGNAL, lang)} ${docTitle(doc).slice(0, 80)} — ${topicStr}`;
}
return `${docTitle(doc).slice(0, 80)} — ${topicStr}`;
}
function buildCrossPartyImplications(
doc: RawDocument,
classified: ClassifiedDocuments,
topicStr: string,
lang: Language,
): string {
const stake = classifyStakeholder(doc);
if (classified.hasCoalitionStress && stake === 'opposition') {
return pickLang(TAKEAWAY_COALITION_STRESS, lang);
}
if (classified.propDocs.length > 0 && classified.motDocs.length > 0) {
return pickLang(OPP_OPPORTUNITY_CONSENSUS, lang).replace('%t', topicStr);
}
return '';
}
function buildHistoricalContext(
doc: RawDocument,
topicStr: string,
): string {
const contentSnippet = (doc.fullText ?? doc.fullContent ?? doc.summary ?? doc.notis ?? '');
if (contentSnippet && contentSnippet.length > 50) {
return contentSnippet.slice(0, 200).replace(/\s+/g, ' ');
}
const year = (doc.datum ?? '').slice(0, 4);
if (year && year.length === 4) {
return `${topicStr} — ${year}`;
}
return '';
}
function buildEuNordicComparison(
doc: RawDocument,
classified: ClassifiedDocuments,
topicStr: string,
lang: Language,
): string {
if (classified.euDocs.length > 0 || EU_TYPES.has(docType(doc))) {
return pickLang(EU_OPPORTUNITY, lang).replace('%t', topicStr);
}
return '';
}
function buildAnalysisDocId(doc: RawDocument): string {
if (doc.dok_id) return doc.dok_id;
const title = docTitle(doc);
const datePart = (doc.datum ?? '').slice(0, 10);
if (title) {
return datePart ? `${docType(doc)}:${title}:${datePart}` : `${docType(doc)}:${title}`;
}
return datePart ? `${docType(doc)}:${datePart}` : docType(doc);
}
function createMinimalDocumentAnalysis(d: RawDocument): AIDocumentAnalysis {
return {
dok_id: buildAnalysisDocId(d),
title: docTitle(d),
legislativeImpact: '',
crossPartyImplications: '',
historicalContext: '',
euNordicComparison: '',
analysisScore: 0,
};
}
function analyzeDocumentDeep(
doc: RawDocument,
focusTopic: string | null,
classified: ClassifiedDocuments,
lang: Language,
): AIDocumentAnalysis {
const title = docTitle(doc);
const stake = classifyStakeholder(doc);
const domains = detectPolicyDomains(doc, lang);
const topDomain = domains[0] ?? classified.allDomains[0] ?? (focusTopic ?? 'policy');
const topicStr = focusTopic ?? topDomain;
const legislativeImpact = buildLegislativeImpact(doc, stake, topicStr, lang);
const crossPartyImplications = buildCrossPartyImplications(doc, classified, topicStr, lang);
const historicalContext = buildHistoricalContext(doc, topicStr);
const euNordicComparison = buildEuNordicComparison(doc, classified, topicStr, lang);
const analysisText = [legislativeImpact, crossPartyImplications, historicalContext, euNordicComparison].join(' ');
const analysisScore = scoreAnalysisDepth(analysisText);
return {
dok_id: buildAnalysisDocId(doc),
title,
legislativeImpact,
crossPartyImplications,
historicalContext,
euNordicComparison,
analysisScore,
};
}
// ---------------------------------------------------------------------------
// AIAnalysisPipeline
// ---------------------------------------------------------------------------
/**
* Heuristic-based multi-iteration analysis pipeline for deep political intelligence.
*
* Instantiate once per deep-inspection run; call analyze() to execute all passes.
* The number of iterations gates which passes run:
* - 1 iteration: Pass 1 only (classification + templated SWOT/implications/takeaways)
* - 2 iterations: Passes 1–3 (adds per-document analysis + cross-document synthesis)
* - 3+ iterations (default): Passes 1–4 (adds QA scoring with refinement on failure)
*/
export class AIAnalysisPipeline {
private readonly iterations: number;
private readonly qualityThreshold: number;
/** Class-level guard shared across all instances so deprecation warnings are emitted at most once per process. */
private static _deprecationWarned = false;
constructor(options: { iterations?: number; qualityThreshold?: number } = {}) {
this.iterations = Math.min(10, Math.max(1, Math.floor(options.iterations ?? 3)));
this.qualityThreshold = options.qualityThreshold ?? QUALITY_THRESHOLD;
}
analyze(
documents: RawDocument[],
focusTopic: string | null,
lang: Language,
): AIAnalysisResult {
if (documents.length === 0) {
const empty: DynamicSwotEntries = {
government: { strengths: [], weaknesses: [], opportunities: [], threats: [] },
opposition: { strengths: [], weaknesses: [], opportunities: [], threats: [] },
privateSector: { strengths: [], weaknesses: [], opportunities: [], threats: [] },
};
return {
iterations: this.iterations,
documentAnalyses: [],
synthesis: this.createEmptySynthesis(),
dynamicSwotEntries: empty,
strategicImplications: '',
keyTakeaways: [],
analysisScore: 0,
};
}
const normalizedFocusTopic = focusTopic?.trim() || null;
// Pass 1 (always): classify documents
const classified = this.classifyDocuments(documents, lang);
// Pass 2 (iterations >= 2): per-document deep analysis
const documentAnalyses = this.iterations >= 2
? this.analyzeDocumentsDeep(classified, normalizedFocusTopic, lang)
: documents.map(d => createMinimalDocumentAnalysis(d));
// Pass 3 (iterations >= 2): cross-document synthesis
let synthesis = this.iterations >= 2
? this.synthesizeAcrossDocuments(classified, documentAnalyses, normalizedFocusTopic, lang)
: this.createEmptySynthesis();
// Emit a single consolidated deprecation warning per process for the
// template-based builders that are slated for replacement by AI prompts.
if (!AIAnalysisPipeline._deprecationWarned) {
AIAnalysisPipeline._deprecationWarned = true;
console.warn(
'[DEPRECATED] buildDynamicSwot(), buildStrategicImplications(), and buildKeyTakeaways() '
+ 'are deprecated (v3.0). Use AI prompts in workflow .md files instead.',
);
}
// Build dynamic SWOT (always — uses classification data from Pass 1)
const dynamicSwotEntries = this.buildDynamicSwot(classified, normalizedFocusTopic, lang);
// Build strategic implications (always)
const strategicImplications = this.buildStrategicImplications(classified, normalizedFocusTopic, lang);
// Build key takeaways (always)
const keyTakeaways = this.buildKeyTakeaways(classified, normalizedFocusTopic, lang);
// Pass 4 (iterations >= 3): QA + refinement
let analysisScore = this.scoreAnalysis(documentAnalyses, synthesis, dynamicSwotEntries);
if (this.iterations >= 3 && analysisScore < this.qualityThreshold) {
const refinedSynthesis = this.synthesizeAcrossDocuments(
classified, documentAnalyses, normalizedFocusTopic, lang,
);
const refinedScore = this.scoreAnalysis(documentAnalyses, refinedSynthesis, dynamicSwotEntries);
if (refinedScore >= analysisScore) {
analysisScore = refinedScore;
synthesis = refinedSynthesis;
}
}
return {
iterations: this.iterations,
documentAnalyses,
synthesis,
dynamicSwotEntries,
strategicImplications,
keyTakeaways,
analysisScore,
};
}
private createEmptySynthesis(): AISynthesis {
return {
policyConvergence: '',
coalitionStressIndicators: '',
emergingTrends: '',
stakeholderPowerDynamics: '',
};
}
private classifyDocuments(docs: RawDocument[], lang: Language): ClassifiedDocuments {
const propDocs = docs.filter(d => docType(d) === 'prop');
const betDocs = docs.filter(d => docType(d) === 'bet');
const motDocs = docs.filter(d => docType(d) === 'mot');
const skrDocs = docs.filter(d => docType(d) === 'skr');
const sfsDocs = docs.filter(d =>
docType(d) === 'sfs' || (d.dokumentnamn ?? '').startsWith('SFS'));
const euDocs = docs.filter(d => EU_TYPES.has(docType(d)));
const pressmDocs = docs.filter(d => docType(d) === 'pressm');
const extDocs = docs.filter(d => EXT_TYPES.has(docType(d)));
const otherDocs = docs.filter(d =>
!['prop','bet','mot','skr','sfs','fpm','eu','pressm','ext','external'].includes(docType(d))
&& !(d.dokumentnamn ?? '').startsWith('SFS'));
const domainSet = new Set<string>();
docs.forEach(d => detectPolicyDomains(d, lang).forEach(dom => domainSet.add(dom)));
const hasStress = docs.some(d => hasCoalitionStress(d));
const enrichedCount = docs.filter(d => !!d.contentFetched).length;
return {
propDocs, betDocs, motDocs, skrDocs, sfsDocs, euDocs,
pressmDocs, extDocs, otherDocs,
allDomains: [...domainSet],
hasCoalitionStress: hasStress,
enrichedCount,
};
}
private analyzeDocumentsDeep(
classified: ClassifiedDocuments,
focusTopic: string | null,
lang: Language,
): AIDocumentAnalysis[] {
const allDocs = [
...classified.propDocs,
...classified.betDocs,
...classified.motDocs,
...classified.sfsDocs,
...classified.skrDocs,
...classified.euDocs,
...classified.pressmDocs,
...classified.extDocs,
...classified.otherDocs,
];
return allDocs.map(doc => analyzeDocumentDeep(doc, focusTopic, classified, lang));
}
private synthesizeAcrossDocuments(
classified: ClassifiedDocuments,
docAnalyses: AIDocumentAnalysis[],
focusTopic: string | null,
lang: Language,
): AISynthesis {
const topicStr = focusTopic ?? classified.allDomains[0] ?? 'policy';
const n = classified.propDocs.length + classified.betDocs.length + classified.motDocs.length;
const converging = classified.propDocs.length > 0 && classified.betDocs.length > 0;
const diverging = classified.motDocs.length > classified.propDocs.length;
const policyConvergence = converging
? interp(pickLang(GOV_STRENGTH_LABELS.propositions, lang), {
n: classified.propDocs.length,
s: plural(classified.propDocs.length, lang),
t: topicStr,
})
: diverging
? interp(pickLang(OPP_STRENGTH_LABELS.motions, lang), {
n: classified.motDocs.length,
s: plural(classified.motDocs.length, lang),
t: topicStr,
})
: `${topicStr} — ${n}`;
const coalitionStressIndicators = classified.hasCoalitionStress
? pickLang(TAKEAWAY_COALITION_STRESS, lang)
: '';
const avgQuality = docAnalyses.length > 0
? docAnalyses.reduce((sum, a) => sum + a.analysisScore, 0) / docAnalyses.length
: 0;
const trendConfidence = avgQuality >= 60 ? 'HIGH' : avgQuality >= 35 ? 'MEDIUM' : 'LOW';
const domainList = classified.allDomains.slice(0, 3).join(', ');
const emergingTrends = domainList
? `${domainList} [${trendConfidence}]`
: '';
const govDocs = classified.propDocs.length + classified.sfsDocs.length + classified.pressmDocs.length;
const oppDocs = classified.betDocs.length + classified.motDocs.length;
const stakeholderPowerDynamics = govDocs > oppDocs
? interp(pickLang(GOV_STRENGTH_LABELS.default, lang), { t: topicStr })
: oppDocs > 0
? interp(pickLang(OPP_STRENGTH_LABELS.default, lang), { t: topicStr })
: topicStr;
return { policyConvergence, coalitionStressIndicators, emergingTrends, stakeholderPowerDynamics };
}
/**
* @deprecated Since v3.0 — Replace with AI prompt in workflow .md files.
* See analysis/methodologies/ai-driven-analysis-guide.md Rule 2.
*/
private buildDynamicSwot(
classified: ClassifiedDocuments,
focusTopic: string | null,
lang: Language,
): DynamicSwotEntries {
const topic = focusTopic ?? classified.allDomains[0] ?? 'policy';
const {
propDocs, betDocs, motDocs, sfsDocs, skrDocs, euDocs, pressmDocs, extDocs,
} = classified;
const titleEntry = (d: RawDocument, impact: SwotEntry['impact'] = 'medium'): SwotEntry => ({
text: docTitle(d),
impact,
});
// ── Government SWOT
const govStrengths: SwotEntry[] = [
...propDocs.slice(0, 3).map(d => titleEntry(d, 'high')),
...sfsDocs.slice(0, 2).map(d => titleEntry(d, 'high')),
...skrDocs.slice(0, 1).map(d => titleEntry(d, 'medium')),
...pressmDocs.slice(0, 2).map(d => titleEntry(d, 'high')),
];
if (govStrengths.length === 0) {
govStrengths.push({
text: interp(pickLang(GOV_STRENGTH_LABELS.default, lang), { n: 0, s: plural(0, lang), t: topic }),
impact: 'medium',
});
}
const govWeaknesses: SwotEntry[] = [...betDocs.slice(0, 2).map(d => titleEntry(d, 'medium'))];
if (govWeaknesses.length === 0) {
govWeaknesses.push({ text: interp(pickLang(GOV_WEAKNESS_IMPL, lang), { t: topic }), impact: 'medium' });
}
const govOpportunities: SwotEntry[] = [
...euDocs.slice(0, 2).map(d => titleEntry(d, 'high')),
...skrDocs.slice(1, 2).map(d => titleEntry(d, 'medium')),
];
if (govOpportunities.length === 0) {
govOpportunities.push({ text: interp(pickLang(EU_OPPORTUNITY, lang), { t: topic }), impact: 'high' });
}
const govThreats: SwotEntry[] = [...motDocs.slice(0, 2).map(d => titleEntry(d, 'medium'))];
if (govThreats.length === 0) {
govThreats.push({ text: interp(pickLang(GOV_THREAT_EXEC, lang), { t: topic }), impact: 'medium' });
}
// ── Opposition SWOT
const oppStrengths: SwotEntry[] = [
...betDocs.slice(0, 3).map(d => titleEntry(d, 'high')),
...motDocs.slice(0, 2).map(d => titleEntry(d, 'medium')),
];
if (oppStrengths.length === 0) {
oppStrengths.push({
text: interp(pickLang(
betDocs.length > 0 ? OPP_STRENGTH_LABELS.committee : OPP_STRENGTH_LABELS.default,
lang,
), { n: betDocs.length, s: plural(betDocs.length, lang), t: topic }),
impact: 'high',
});
}
const oppWeaknesses: SwotEntry[] = [];
oppWeaknesses.push({ text: interp(pickLang(OPP_WEAKNESS_INFO, lang), { t: topic }), impact: 'medium' });
const oppOpportunities: SwotEntry[] = [];
oppOpportunities.push({ text: interp(pickLang(OPP_OPPORTUNITY_CONSENSUS, lang), { t: topic }), impact: 'high' });
const oppThreats: SwotEntry[] = [...propDocs.slice(0, 1).map(d => titleEntry(d, 'medium'))];
if (oppThreats.length === 0) {
oppThreats.push({ text: interp(pickLang(OPP_THREAT_MAJORITY, lang), { t: topic }), impact: 'medium' });
}
// ── Private Sector / Civil Society SWOT
const privateStrengths: SwotEntry[] = [
{ text: interp(pickLang(PRIVATE_STRENGTH_DOMAIN, lang), { t: topic }), impact: 'high' },
...sfsDocs.slice(0, 1).map(d => titleEntry(d, 'medium')),
...extDocs.slice(0, 2).map(d => titleEntry(d, 'high')),
];
const privateWeaknesses: SwotEntry[] = [
{ text: interp(pickLang(PRIVATE_WEAKNESS_COMPLIANCE, lang), { t: topic }), impact: 'medium' },
];
const privateOpportunities: SwotEntry[] = [
{ text: interp(pickLang(PRIVATE_OPPORTUNITY_INVESTMENT, lang), { t: topic }), impact: 'high' },
...euDocs.slice(0, 1).map(d => titleEntry(d, 'high')),
];
const privateThreats: SwotEntry[] = [
{ text: interp(pickLang(PRIVATE_THREAT_UNCERTAINTY, lang), { t: topic }), impact: 'high' },
];
return {
government: { strengths: govStrengths, weaknesses: govWeaknesses, opportunities: govOpportunities, threats: govThreats },
opposition: { strengths: oppStrengths, weaknesses: oppWeaknesses, opportunities: oppOpportunities, threats: oppThreats },
privateSector: { strengths: privateStrengths, weaknesses: privateWeaknesses, opportunities: privateOpportunities, threats: privateThreats },
};
}
/**
* @deprecated Since v3.0 — Replace with AI prompt in workflow .md files.
* See analysis/methodologies/ai-driven-analysis-guide.md Rule 2.
*/
private buildStrategicImplications(
classified: ClassifiedDocuments,
focusTopic: string | null,
lang: Language,
): string {
const esc = escapeHtml;
const topic = focusTopic ?? classified.allDomains[0] ?? '';
const { propDocs, betDocs, motDocs, pressmDocs, extDocs, enrichedCount, allDomains } = classified;
const total = propDocs.length + betDocs.length + motDocs.length
+ pressmDocs.length + extDocs.length + classified.sfsDocs.length
+ classified.skrDocs.length + classified.euDocs.length + classified.otherDocs.length;
const legislativeCount = propDocs.length + betDocs.length + motDocs.length;
const isLegislative = legislativeCount > 0;
const domainPhrase = allDomains.slice(0, 3).map(d => esc(d)).join(', ');
const topicInsert = topic ? ` (${esc(topic)})` : '';
const domainInsert = domainPhrase ? ` — ${domainPhrase}` : '';
if (isLegislative) {
const signalPhrase = propDocs.length > betDocs.length
? pickLang(SIGNAL_GOVT_AGENDA, lang)
: betDocs.length > propDocs.length
? pickLang(SIGNAL_PARL_SCRUTINY, lang)
: pickLang(SIGNAL_BALANCED, lang);
const template = pickLang(STRATEGIC_IMPL_TEMPLATES.legislative, lang);
return `<p>${interp(template, {
total,
enriched: enrichedCount,
topic: topicInsert,
prop: propDocs.length,
bet: betDocs.length,
mot: motDocs.length,
signal: esc(signalPhrase),
domain: domainInsert,
})}</p>`;
}
const hasPressOrExt = pressmDocs.length > 0 || extDocs.length > 0;
if (hasPressOrExt) {
const typeDesc = esc(pressmDocs.length > 0
? `${pressmDocs.length} ${pickLang(TYPE_DESC_PRESS, lang)}`
: `${extDocs.length} ${pickLang(TYPE_DESC_EXTERNAL, lang)}`);
const signalText = pressmDocs.length > 0
? pickLang(SIGNAL_PRESS, lang)
: pickLang(SIGNAL_EXTERNAL, lang);
const template = pickLang(STRATEGIC_IMPL_TEMPLATES.nonLegislative, lang);
return `<p>${interp(template, {
total,
enriched: enrichedCount,
topic: topicInsert,
typeDesc,
domain: domainInsert,
signalText: esc(signalText),
})}</p>`;
}
const regulatoryCount = classified.sfsDocs.length + classified.skrDocs.length
+ classified.euDocs.length + classified.otherDocs.length;
const typeDesc = esc(`${regulatoryCount} ${pickLang(TYPE_DESC_REGULATORY, lang)}`);
const signalText = pickLang(SIGNAL_SNAPSHOT, lang);
const template = pickLang(STRATEGIC_IMPL_TEMPLATES.nonLegislative, lang);
return `<p>${interp(template, {
total,
enriched: enrichedCount,
topic: topicInsert,
typeDesc,
domain: domainInsert,
signalText: esc(signalText),
})}</p>`;
}
// ── Key takeaways ─────────────────────────────────────────────────────────
/**
* @deprecated Since v3.0 — Replace with AI prompt in workflow .md files.
* See analysis/methodologies/ai-driven-analysis-guide.md Rule 2.
*/
private buildKeyTakeaways(
classified: ClassifiedDocuments,
focusTopic: string | null,
lang: Language,
): string[] {
const topic = focusTopic ?? classified.allDomains[0] ?? 'policy';
const { propDocs, betDocs, motDocs, euDocs, enrichedCount } = classified;
const total = propDocs.length + betDocs.length + motDocs.length
+ classified.sfsDocs.length + classified.skrDocs.length
+ classified.pressmDocs.length + classified.extDocs.length
+ classified.euDocs.length + classified.otherDocs.length;
const items: string[] = [];
if (propDocs.length > 0) {
items.push(interp(pickLang(TAKEAWAY_PROP, lang), {
n: propDocs.length, s: plural(propDocs.length, lang), t: topic, pnoun: propNounForm(propDocs.length, lang),
}));
}
if (betDocs.length > 0) {
items.push(interp(pickLang(TAKEAWAY_BET, lang), {
n: betDocs.length, s: plural(betDocs.length, lang), t: topic, verb: betVerbForm(betDocs.length, lang), bnoun: betNounForm(betDocs.length, lang),
}));
}
if (motDocs.length > 0) {
items.push(interp(pickLang(TAKEAWAY_MOT, lang), {
n: motDocs.length, s: plural(motDocs.length, lang), t: topic, verb: motVerbForm(motDocs.length, lang), mnoun: motNounForm(motDocs.length, lang),
}));
}
if (euDocs.length > 0) {
items.push(interp(pickLang(TAKEAWAY_EU, lang), { n: euDocs.length, t: topic }));
}
if (enrichedCount > 0 && enrichedCount >= Math.ceil(total / 2)) {
items.push(interp(pickLang(TAKEAWAY_ENRICHED, lang), { n: enrichedCount, total }));
}
if (classified.hasCoalitionStress) {
items.push(pickLang(TAKEAWAY_COALITION_STRESS, lang));
}
return items;
}
private scoreAnalysis(
docAnalyses: AIDocumentAnalysis[],
synthesis: AISynthesis,
swot: DynamicSwotEntries,
): number {
let score = 0;
if (docAnalyses.length > 0) {
const avgDoc = docAnalyses.reduce((sum, a) => sum + a.analysisScore, 0) / docAnalyses.length;
score += Math.floor(avgDoc * 0.4);
}
const synthText = [
synthesis.policyConvergence,
synthesis.coalitionStressIndicators,
synthesis.emergingTrends,
synthesis.stakeholderPowerDynamics,
].join(' ');
score += Math.min(30, Math.floor(scoreAnalysisDepth(synthText) * 0.3));
const swotCount =
swot.government.strengths.length + swot.government.weaknesses.length
+ swot.government.opportunities.length + swot.government.threats.length
+ swot.opposition.strengths.length + swot.opposition.weaknesses.length
+ swot.opposition.opportunities.length + swot.opposition.threats.length
+ swot.privateSector.strengths.length + swot.privateSector.weaknesses.length
+ swot.privateSector.opportunities.length + swot.privateSector.threats.length;
score += Math.min(30, swotCount * 3);
return Math.min(100, score);
}
}
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