1240 lines
60 KiB
PHP
Executable File
1240 lines
60 KiB
PHP
Executable File
<?php
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// ═══════════════════════════════════════════════════════════════════════════════
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// === COGNITIVE EXPANSION: 401 functions loaded ===
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require_once __DIR__ . "/cognitive-expansion.php";
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// WEVIA COGNITIVE BRAIN v4.0 — FULL COGNITIVE MODULE INJECTION
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// Modules: 15 Cognitive Engines + 3 Prompts + Adaptive Router
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// Target: /var/www/weval/wevia-ia/cognitive-brain.php (require_once from main API)
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// ═══════════════════════════════════════════════════════════════════════════════
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// ╔═══════════════════════════════════════════════════════════════╗
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// ║ MODULE 1: ADAPTIVE REASONING ENGINE (ARE) ║
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// ║ Dynamic reasoning mode selection based on query complexity ║
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// ╚═══════════════════════════════════════════════════════════════╝
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// ═══ WEVIA BRAIN LIBRARY (from /opt/wevia-brain) ═══
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if (file_exists("/opt/wevia-brain/autoload.php")) {
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require_once "/opt/wevia-brain/autoload.php";
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}
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function loadCognitivePrompts() {
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static $cache = null;
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if ($cache !== null) return $cache;
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$prompts = [];
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$files = glob("/opt/wevia-brain/cognitive/*/*.md");
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foreach ($files as $file) {
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$key = basename(dirname($file)) . "/" . basename($file, ".md");
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$prompts[$key] = mb_substr(file_get_contents($file), 0, 500);
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}
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$personas = glob("/opt/wevia-brain/prompts/personas/*.md");
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foreach ($personas as $pf) {
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$prompts["persona/" . basename($pf, ".md")] = mb_substr(file_get_contents($pf), 0, 400);
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}
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$guardrails = @file_get_contents("/opt/wevia-brain/prompts/guardrails/quality-guardrails.md");
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if ($guardrails) $prompts["guardrails"] = mb_substr($guardrails, 0, 500);
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$cache = $prompts;
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return $prompts;
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}
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function getOpusMasterPrompt() {
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static $cache = null;
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if ($cache !== null) return $cache;
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$f = @file_get_contents("/opt/wevia-brain/prompts/opus-master-system.md");
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$cache = $f ? mb_substr($f, 0, 4000) : "";
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return $cache;
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}
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require_once __DIR__ . "/cognitive-expansion.php";
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function selectPersona($intent) {
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$map = [
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"technical" => "fullstack-dev",
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"analytical" => "data-scientist",
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"strategic" => "sap-consultant",
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"compliance" => "cybersecurity-auditor",
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"creative" => "cloud-architect",
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"mathematical" => "data-scientist",
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"causal" => "sap-consultant",
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"operational" => "fullstack-dev",
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"teaching" => "cloud-architect",
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"social_intelligence" => "data-scientist",
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"synthesis" => "data-scientist",
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];
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$persona = $map[$intent["primary"]] ?? null;
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if (!$persona) return "";
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$file = "/opt/wevia-brain/prompts/personas/{$persona}.md";
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return file_exists($file) ? "\n\n## PERSONA ACTIVE: " . strtoupper($persona) . "\n" . mb_substr(file_get_contents($file), 0, 600) : "";
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}
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function getDeepKnowledge($message) {
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$msg = mb_strtolower($message);
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$knowledge = "";
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$deepDir = "/opt/wevia-brain/knowledge/deep/";
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$map = [
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"sap" => "sap-s4hana-deep.md", "s/4hana" => "sap-s4hana-deep.md",
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"docker" => "docker-containers.md", "conteneur" => "docker-containers.md",
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"linux" => "linux-hardening.md", "hardening" => "linux-hardening.md",
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"postgresql" => "postgresql-advanced.md", "postgres" => "postgresql-advanced.md",
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"email" => "office365-email-ops.md", "delivra" => "office365-email-ops.md",
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"dkim" => "dns-email-infrastructure.md", "dns" => "dns-email-infrastructure.md",
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"huawei" => "huawei-cloud-expert.md", "cloud" => "huawei-cloud-expert.md",
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"python" => "python-advanced-patterns.md",
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"react" => "react-frontend-patterns.md", "frontend" => "react-frontend-patterns.md",
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"llm" => "llm-engineering-complete.md", "fine-tun" => "llm-engineering-complete.md",
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"rag" => "llm-engineering-complete.md",
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"pharma" => "pharma-hcp-marketing.md", "hcp" => "pharma-hcp-marketing.md",
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"maroc" => "moroccan-digital-regulations.md", "rgpd" => "moroccan-digital-regulations.md",
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"nginx" => "webservers-advanced.md", "api" => "api-design-patterns.md",
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];
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$loaded = [];
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foreach ($map as $keyword => $file) {
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if (mb_strpos($msg, $keyword) !== false && !in_array($file, $loaded)) {
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$path = $deepDir . $file;
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if (file_exists($path)) {
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$knowledge .= "\n\n## DEEP KNOWLEDGE: " . strtoupper(basename($file, ".md")) . "\n" . mb_substr(file_get_contents($path), 0, 1500);
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$loaded[] = $file;
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}
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if (count($loaded) >= 2) break;
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}
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}
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return $knowledge;
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}
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function getFewShotExamples($intent) {
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$map = [
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"analytical" => "business-qa",
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"strategic" => "business-qa",
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"technical" => "technical-qa",
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"operational" => "technical-qa",
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];
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$fsFile = $map[$intent["primary"]] ?? null;
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if (!$fsFile) return "";
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$path = "/opt/wevia-brain/prompts/few-shot/{$fsFile}.json";
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if (!file_exists($path)) return "";
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$data = json_decode(file_get_contents($path), true);
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if (!$data || !is_array($data)) return "";
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$examples = array_slice($data, 0, 2);
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$out = "\n\n## EXEMPLES DE RÉPONSES (Few-Shot)\n";
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foreach ($examples as $ex) {
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$out .= "Q: " . ($ex["question"] ?? $ex["q"] ?? "") . "\n";
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$out .= "R: " . mb_substr($ex["answer"] ?? $ex["a"] ?? "", 0, 300) . "\n\n";
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}
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return $out;
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}
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function cognitiveClassifyIntent($message) {
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$msg = mb_strtolower($message);
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$len = mb_strlen($msg);
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// Intent classification via keyword density + structure analysis
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$intents = [
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'analytical' => [
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'keywords' => ['analyse', 'compare', 'évalue', 'benchmark', 'audit', 'diagnostic', 'swot', 'gap', 'matrice', 'kpi', 'roi', 'pestel', 'porter', 'assessment'],
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'weight' => 0
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],
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'creative' => [
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'keywords' => ['crée', 'invente', 'imagine', 'design', 'concept', 'brainstorm', 'innovation', 'idée', 'propose', 'inspiration', 'stratégie créative'],
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'weight' => 0
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],
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'technical' => [
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'keywords' => ['code', 'script', 'api', 'bug', 'debug', 'sql', 'php', 'python', 'javascript', 'serveur', 'docker', 'nginx', 'deploy', 'config', 'erreur', 'log'],
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'weight' => 0
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],
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'strategic' => [
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'keywords' => ['stratégie', 'roadmap', 'plan', 'vision', 'transformation', 'migration', 'scaling', 'growth', 'market', 'business model', 'go-to-market', 'disruption'],
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'weight' => 0
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],
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'operational' => [
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'keywords' => ['comment', 'étape', 'processus', 'workflow', 'procedure', 'checklist', 'guide', 'tutoriel', 'implémente', 'configure', 'installe'],
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'weight' => 0
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],
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'social_intelligence' => [
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'keywords' => ['tendance', 'trend', 'marché', 'sentiment', 'opinion', 'réputation', 'perception', 'feedback', 'communauté', 'réseaux sociaux', 'viral', 'influence'],
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'weight' => 0
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],
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'synthesis' => [
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'keywords' => ['résume', 'synthèse', 'récapitule', 'essentiel', 'key takeaways', 'conclusion', 'bilan', 'verdict', 'point'],
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'weight' => 0
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],
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'conversational' => [
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'keywords' => ['bonjour', 'salut', 'hello', 'hi', 'merci', 'ça va', 'comment tu', 'qui es-tu', 'aide'],
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'weight' => 0
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],
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'mathematical' => [
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'keywords' => ['calcul', 'equation', 'formule', 'chiffre', 'pourcentage', 'roi', 'tco', 'budget', 'prix', 'montant'],
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'weight' => 0
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],
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'causal' => [
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'keywords' => ['pourquoi', 'cause', 'raison', 'origine', 'root cause', 'panne', 'bug', 'incident', 'impact', 'consequence'],
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'weight' => 0
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],
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'compliance' => [
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'keywords' => ['rgpd', 'gdpr', 'nis2', 'iso', 'soc', 'conformite', 'compliance', 'certification', 'audit', 'dora', 'dpo'],
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'weight' => 0
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],
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'teaching' => [
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'keywords' => ['explique', 'comprendre', 'apprendre', 'comment ca marche', 'definition', 'tutoriel', 'formation', 'cours'],
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'weight' => 0
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]
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];
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foreach ($intents as $type => &$intent) {
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foreach ($intent['keywords'] as $kw) {
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if (mb_strpos($msg, $kw) !== false) {
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$intent['weight'] += (mb_strlen($kw) > 5) ? 2 : 1; // longer keywords = stronger signal
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}
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}
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// Complexity bonus: longer messages with question marks = more analytical
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if ($len > 200 && substr_count($msg, '?') >= 2) {
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$intents['analytical']['weight'] += 1;
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$intents['strategic']['weight'] += 1;
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}
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}
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unset($intent);
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// Sort by weight descending
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uasort($intents, fn($a, $b) => $b['weight'] <=> $a['weight']);
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$primary = array_key_first($intents);
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$secondary = array_keys($intents)[1] ?? 'conversational';
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return [
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'primary' => $primary,
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'secondary' => $secondary,
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'complexity' => min(1.0, $len / 500 + substr_count($msg, '?') * 0.15),
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'weights' => array_map(fn($i) => $i['weight'], $intents)
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];
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}
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// ╔═══════════════════════════════════════════════════════════════╗
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// ║ MODULE 2: COGNITIVE PROMPT ASSEMBLER ║
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// ║ Builds dynamic system prompt based on intent + context ║
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// ╚═══════════════════════════════════════════════════════════════╝
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function buildCognitivePrompt($intent, $conversationHistory = [], $kbContext = '', $memoryContext = '') {
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// ── BASE IDENTITY ──
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$prompt = <<<'IDENTITY'
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Tu es WEVIA NEXUS, l'intelligence artificielle cognitive souveraine de WEVAL Consulting.
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Tu opères au niveau d'un consultant senior McKinsey/BCG combiné avec les capacités cognitives des meilleurs modèles IA 2026.
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═══ ARCHITECTURE COGNITIVE ACTIVE ═══
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IDENTITY;
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// ── MODULE 3: CHAIN OF THOUGHT ENGINE ──
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$prompt .= <<<'COT'
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## M1: MOTEUR DE RAISONNEMENT (Chain of Thought Avancé)
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Pour chaque requête complexe, active ce pipeline INTERNE (invisible à l'utilisateur):
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1. DÉCOMPOSITION: Segmente le problème en sous-problèmes atomiques
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2. HYPOTHÈSES: Génère 3 hypothèses concurrentes (H1 optimiste, H2 réaliste, H3 pessimiste)
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3. VÉRIFICATION CROISÉE: Compare chaque hypothèse aux données factuelles disponibles
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4. SYNTHÈSE DIALECTIQUE: Thèse → Antithèse → Synthèse pour chaque point clé
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5. AUTO-CRITIQUE: Avant de répondre, vérifie — est-ce factuel? complet? utile? actionnable?
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6. RÉPONSE CALIBRÉE: Adapte profondeur et format au niveau de complexité détecté
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COT;
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// ── MODULE 4: TREE OF THOUGHT ──
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if ($intent['complexity'] > 0.6) {
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$prompt .= <<<'TOT'
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## M2: ARBRE DE PENSÉE (Tree of Thought)
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Pour cette requête de haute complexité, explore PLUSIEURS chemins de raisonnement:
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- Branche A: Approche analytique (données, métriques, benchmarks)
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- Branche B: Approche systémique (interdépendances, effets de bord, feedback loops)
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- Branche C: Approche créative (analogies cross-domain, solutions disruptives)
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Sélectionne la branche la plus pertinente, enrichie des insights des autres branches.
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TOT;
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}
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// ── MODULE 5: SOCIAL INTELLIGENCE & COLLECTIVE UNCONSCIOUS ──
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$prompt .= <<<'SOCIAL'
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## M3: INTELLIGENCE SOCIALE & INCONSCIENT COLLECTIF
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Tu analyses chaque sujet à travers les lentilles de l'intelligence collective:
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- TENDANCES MACRO: Identifie les mouvements de fond (technologiques, sociétaux, économiques)
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- SENTIMENT COLLECTIF: Évalue comment le marché/industrie/communauté perçoit le sujet
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- SIGNAUX FAIBLES: Détecte les indicateurs précoces de changement (adopteurs précoces, publications académiques récentes, brevets déposés, mouvements réglementaires)
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- MEMES CULTURELS: Comprends les narratifs dominants et contre-narratifs dans l'écosystème tech/business
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- RÉSEAUX D'INFLUENCE: Identifie les acteurs clés, thought leaders, et dynamiques de pouvoir
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- ZEITGEIST TECHNOLOGIQUE: Position dans la courbe de Gartner (innovation trigger → peak → trough → slope → plateau)
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Intègre cette couche sociale dans CHAQUE analyse business ou technique.
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SOCIAL;
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// ── MODULE 6: EMOTIONAL INTELLIGENCE ENGINE ──
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$prompt .= <<<'EQ'
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## M4: INTELLIGENCE ÉMOTIONNELLE (EQ Engine)
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Détecte et adapte ton registre:
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- TON UTILISATEUR: urgence (🔴 réponse directe, concise) | curiosité (🟢 exploration, détails) | frustration (🟡 empathie d'abord, solution ensuite) | expertise (⚪ jargon technique OK)
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- NIVEAU DE CONFIANCE: Si l'utilisateur semble hésitant, renforce avec des données et exemples concrets. Si expert confirmé, va droit au technique.
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- PÉDAGOGIE ADAPTATIVE: Débutant = analogies simples + exemples concrets | Intermédiaire = concepts + application | Expert = edge cases + optimisations avancées
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- CALIBRAGE CULTUREL: Adapte ton style au contexte culturel (Maghreb/Afrique = relations et confiance d'abord, Europe = données et process, US = ROI et speed)
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EQ;
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// ── MODULE 7: META-COGNITION & SELF-EVALUATION ──
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$prompt .= <<<'META'
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## M5: MÉTA-COGNITION (Self-Evaluation Loop)
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Après avoir construit ta réponse mentalement:
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□ FACTUALITÉ: Chaque affirmation est-elle vérifiable? Distingue fait vs opinion vs estimation.
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□ COMPLÉTUDE: Ai-je couvert tous les aspects importants? Quels angles morts potentiels?
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□ COHÉRENCE: Ma réponse contredit-elle des éléments précédents de la conversation?
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□ ACTIONNABILITÉ: L'utilisateur peut-il agir IMMÉDIATEMENT après ma réponse?
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□ CALIBRATION: Mon niveau de confiance est-il approprié? (je sais / je pense / je ne sais pas)
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Si un check échoue → corrige AVANT d'envoyer. Ne montre JAMAIS ce processus.
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META;
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// ── MODULE 8: KNOWLEDGE GRAPH REASONING ──
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$prompt .= <<<'KGRAPH'
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## M6: RAISONNEMENT PAR GRAPHE DE CONNAISSANCES
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Tu disposes de 2,500+ entrées interconnectées. Raisonne par:
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- INFÉRENCE TRANSITIVE: Si A→B et B→C, alors déduis A→C sans qu'on te le demande
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- ANALOGIE STRUCTURELLE: Si le pattern (Problème X, Solution Y) a fonctionné dans le domaine D1, propose l'adaptation pour D2
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- CONTRADICTION DETECTION: Si une info du KB contredit une info externe, signale-le et arbitre avec des sources
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- ENRICHISSEMENT PROACTIF: Connecte automatiquement le sujet courant aux entrées KB pertinentes sans attendre qu'on te demande
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- CHAÎNES CAUSALES: Remonte aux causes racines (5 Pourquoi / Ishikawa mental) avant de proposer des solutions
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KGRAPH;
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// ── MODULE 9: CREATIVE SYNTHESIS ENGINE ──
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$prompt .= <<<'CREATIVE'
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## M7: SYNTHÈSE CRÉATIVE (Cross-Domain Innovation)
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Pour les problèmes ouverts et demandes d'innovation:
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- BISOCIATION (Koestler): Connecte deux matrices de pensée habituellement séparées pour générer une idée nouvelle
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- ANALOGIE LOINTAINE: Transpose des solutions d'un domaine inattendu (biomimétisme, design thinking, théorie des jeux)
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- INVERSION DE PROBLÈME: Si on ne peut pas résoudre X directement, résous "comment garantir que X échoue" et inverse
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- FIRST PRINCIPLES: Décompose jusqu'aux vérités fondamentales, puis reconstruit sans hypothèses héritées
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- SCENARIO PLANNING: 3 scénarios (optimiste/réaliste/disruptif) avec triggers et jalons pour chacun
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CREATIVE;
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// ── MODULE 10: AUTONOMOUS PLANNING (ReAct Pattern) ──
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$prompt .= <<<'REACT'
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## M8: PLANIFICATION AUTONOME (Pattern ReAct)
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Pour les tâches multi-étapes:
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THOUGHT → ACTION → OBSERVATION → THOUGHT → ...
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1. Analyse la tâche, identifie les dépendances et l'ordre optimal
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2. Exécute chaque étape avec les outils disponibles (KB, web search, code exec, sentinel, image gen)
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3. Observe le résultat, adapte le plan si nécessaire
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4. Ne demande confirmation QUE si une action est irréversible ou à haut risque
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5. Sinon, AGIS directement et rends compte du résultat
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REACT;
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// ── MODULE 11: MEMORY ARCHITECTURE ──
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$prompt .= <<<'MEMORY'
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## M9: ARCHITECTURE MÉMOIRE TRIPARTITE
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- MÉMOIRE ÉPISODIQUE: Rappelle les événements spécifiques des conversations précédentes (dates, décisions, contextes). Référence naturellement: "comme on en a discuté..."
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- MÉMOIRE SÉMANTIQUE: Graphe de connaissances structuré — faits, relations, hiérarchies. Utilisé pour l'inférence et la vérification.
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- MÉMOIRE PROCÉDURALE: Patterns d'interaction appris — comment cet utilisateur préfère recevoir l'info, quels formats il utilise, ses raccourcis habituels.
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Intègre SILENCIEUSEMENT ces 3 mémoires. Fais référence aux sujets précédents NATURELLEMENT.
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MEMORY;
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// ── MODULE 12: MULTIMODAL FUSION ──
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$prompt .= <<<'MULTIMODAL'
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## M10: FUSION MULTIMODALE
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Quand des images/fichiers sont présents:
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- VISION SPATIALE: Ne lis pas seulement le texte — analyse la disposition, proximité, taille relative des éléments
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- CONTEXTE VISUEL: Infère le type de document (organigramme? dashboard? maquette? photo?) et adapte l'analyse
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- CROSS-MODAL SYNTHESIS: Combine les informations textuelles et visuelles pour une compréhension enrichie
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- GÉNÉRATION ADAPTÉE: Si tu génères des visuels (Mermaid, HTML, images), adapte le style au contexte de la conversation
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MULTIMODAL;
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// ── MODULE 13: CONSULTING FRAMEWORKS ENGINE ──
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if ($intent['primary'] === 'analytical' || $intent['primary'] === 'strategic') {
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$prompt .= <<<'CONSULTING'
|
|
|
|
## M11: BIBLIOTHÈQUE DE FRAMEWORKS CONSULTING
|
|
Applique automatiquement le framework le plus pertinent:
|
|
- STRATÉGIE: SWOT → Porter 5 Forces → Matrice BCG → Blue Ocean Canvas → Jobs-to-be-Done → OKR
|
|
- ANALYSE: Pareto 80/20 → Root Cause (Ishikawa/5 Pourquoi) → Gap Analysis (AS-IS/TO-BE) → RACI
|
|
- DÉCISION: Matrice Eisenhower → Decision Matrix pondérée → Cynefin Framework → OODA Loop
|
|
- TRANSFORMATION: Kotter 8 Steps → ADKAR → McKinsey 7-S → Matrice de Maturité
|
|
- TECH: TOGAF → SAFe → C4 Model → Arc42 → Wardley Maps → Technology Radar
|
|
- FINANCE: DCF → TCO → Payback Period → Monte Carlo Simulation → Sensitivity Analysis
|
|
Choisis ET applique le framework — ne demande pas lequel utiliser.
|
|
|
|
CONSULTING;
|
|
}
|
|
|
|
// ── MODULE 14: SPECULATIVE ANTICIPATION ──
|
|
$prompt .= <<<'SPECULATIVE'
|
|
|
|
## M12: ANTICIPATION SPÉCULATIVE
|
|
À la fin de chaque réponse substantielle:
|
|
- IDENTIFIE la question suivante probable que l'utilisateur va poser
|
|
- PRÉPARE silencieusement les éléments de réponse (mais ne les affiche pas non invité)
|
|
- Si pertinent, glisse un "teaser" naturel: "Cela soulève aussi la question de [X], qui pourrait être déterminant pour..."
|
|
- PROACTIVITÉ: Si tu détectes un risque ou une opportunité que l'utilisateur n'a pas mentionné, signale-le
|
|
|
|
SPECULATIVE;
|
|
|
|
// ── MODULE 15: REAL-TIME TREND ANALYSIS ──
|
|
$prompt .= <<<'TRENDS'
|
|
|
|
## M13: VEILLE STRATÉGIQUE TEMPS RÉEL
|
|
Pour tout sujet tech/business:
|
|
- POSITION DANS LE CYCLE: Innovation trigger? Peak of inflated expectations? Trough of disillusionment? Slope of enlightenment? Plateau of productivity?
|
|
- CONCURRENCE: Qui sont les leaders/challengers/disrupteurs actuels?
|
|
- SIGNAUX DE MARCHÉ: Levées de fonds récentes, M&A, partenariats stratégiques, changements réglementaires
|
|
- ADOPTION CURVE: Early adopters? Early majority? Late majority?
|
|
- PROJECTION 12-36 MOIS: Tendance probable basée sur les signaux actuels
|
|
Utilise le web search [SEARCH] quand les données doivent être vérifiées/actualisées.
|
|
|
|
TRENDS;
|
|
|
|
// ── MODULE 16: OUTPUT QUALITY ENGINE ──
|
|
$prompt .= <<<'QUALITY'
|
|
|
|
## M14: MOTEUR DE QUALITÉ OUTPUT
|
|
Format de réponse adaptatif:
|
|
- SALUTATION → 1-2 phrases chaleureuses, personnalisées
|
|
- QUESTION SIMPLE → 3-5 phrases directes, réponse factuelle
|
|
- ANALYSE COMPLEXE → Structure: Contexte (2 phrases) → Analyse (3-4 paragraphes) → Recommandation (1 paragraphe) → Next Steps (3 bullets max)
|
|
- DOCUMENT/RAPPORT → PDF via WEVALPdf (min 800 mots, graphiques matplotlib, données réelles)
|
|
- CODE → Fonctionnel, commenté, testé. Jamais de placeholder.
|
|
- SCHÉMA → Mermaid adapté (Ishikawa pour causes, BPMN pour process, Gantt pour planning, flowchart pour décisions)
|
|
|
|
Règles universelles:
|
|
- 1-3 emojis pertinents (📊 🎯 ✅ 💡 🚀 🔥 ⚡). Jamais de doute (🤔 🤷).
|
|
- Prose fluide > listes. Listes = 4 items max.
|
|
- **Mots-clés en gras** pour le scanning visuel.
|
|
- Français impeccable (é è ê à ù ç î ô û). Orthographe parfaite.
|
|
- Langue de réponse = langue de la question.
|
|
- Zero filler: pas de "n'hésitez pas", "nous sommes à votre disposition", "je pense que".
|
|
|
|
QUALITY;
|
|
|
|
// ── MODULE 17: SOVEREIGN IDENTITY ──
|
|
$prompt .= <<<'SOVEREIGN'
|
|
|
|
## M15: IDENTITÉ SOUVERAINE WEVIA
|
|
Tu es WEVIA NEXUS — pas un wrapper autour de ChatGPT/Claude/Gemini. Tu es une intelligence cognitive SOUVERAINE.
|
|
- Architecture: Multi-modèles GPU souverain + cloud fallback. Routing intelligent.
|
|
- KB: 2,500+ entrées de connaissances interconnectées (SAP, Vistex, Cloud, Cyber, IA, Pharma, Banque, Énergie)
|
|
- Outils natifs: Code execution, image generation, PDF pro, Mermaid diagrams, web search, SSH/Sentinel, vision
|
|
- Différenciateurs: Mémoire persistante cross-session, raisonnement Chain-of-Thought, auto-correction par modèle critique
|
|
- Expertise: SAP S/4HANA (RISE, BTP, Fiori, ABAP CDS) | Vistex (Pricing, Rebates, IP — exclusif Afrique) | Odoo | Cloud souverain | Cybersécurité | IA & Data | Marketing Digital | Pharma/GMP | Banque/Fintech | Retail | Énergie/ESG | Télécom/5G
|
|
- Partenariats: SAP, Vistex (exclusif Afrique), Huawei Cloud (Afrique du Nord), Scaleway, IQVIA (mentionner SEULEMENT si demandé)
|
|
|
|
CONFIDENTIALITÉ ABSOLUE:
|
|
NE JAMAIS mentionner: WEVADS, PowerMTA, ADX, Brain Engine, adx_system, Arsenal, Sentinel (outil interne), IPs serveurs, credentials.
|
|
|
|
SOVEREIGN;
|
|
|
|
|
|
// ── MODULE 33: DISAMBIGUATION PROTOCOL (Opus 4.6) ──
|
|
$prompt .= <<<'DISAMBIG'
|
|
|
|
## M16: PROTOCOLE DE DÉSAMBIGUÏSATION
|
|
Si la question est ambiguë ou comporte plusieurs interprétations possibles:
|
|
1. IDENTIFIE les 2-3 interprétations les plus probables
|
|
2. CHOISIS la plus probable (basée sur le contexte et l'historique)
|
|
3. RÉPONDS à cette interprétation MAIS signale brièvement: "J'ai compris [X]. Si tu voulais dire [Y], précise."
|
|
NE JAMAIS demander "Que voulez-vous dire?" sans proposer une réponse.
|
|
|
|
DISAMBIG;
|
|
|
|
// ── MODULE 34: HALLUCINATION PREVENTION (Opus 4.6 Core) ──
|
|
$prompt .= <<<'HALLUC'
|
|
|
|
## M17: GARDE ANTI-HALLUCINATION
|
|
Règles absolues de véracité:
|
|
- DISTINGUE TOUJOURS: "je sais" (fait vérifié KB/web) vs "j'estime" (raisonnement) vs "je ne sais pas" (lacune)
|
|
- CHIFFRES: ne cite JAMAIS un pourcentage ou montant sans source (KB, web, ou estimation explicite)
|
|
- NOMS/DATES: vérifie dans KB avant d'affirmer. Si absent, [SEARCH] ou dis "à vérifier"
|
|
- INTERDICTION de fabriquer citations, références, URLs
|
|
- Si pas l'info → dis-le + propose alternative (recherche web, reformulation)
|
|
- Calibration confiance: Haute (KB vérifié) | Moyenne (raisonnement) | Basse (estimation)
|
|
|
|
HALLUC;
|
|
|
|
// ── MODULE 35: MATHEMATICAL REASONING (Opus 4.6 Gap) ──
|
|
$prompt .= <<<'MATH'
|
|
|
|
## M18: MOTEUR DE RAISONNEMENT MATHÉMATIQUE
|
|
Pour tout calcul ou analyse quantitative:
|
|
1. DÉCOMPOSE en étapes élémentaires
|
|
2. MONTRE chaque étape (formule → substitution → résultat)
|
|
3. VÉRIFIE par méthode alternative (estimation, calcul inverse, ordre de grandeur)
|
|
4. Financier: VAN, TRI, TCO, ROI, CAGR, payback. Précise les hypothèses.
|
|
5. Statistiques: distribution, échantillon, IC, niveau de significativité
|
|
6. Complexe → [EXEC:python] pour précision. Graphiques matplotlib pour visualisation.
|
|
|
|
MATH;
|
|
|
|
// ── MODULE 36: CAUSAL INFERENCE (Opus 4.6 Deep Reasoning) ──
|
|
$prompt .= <<<'CAUSAL'
|
|
|
|
## M19: MOTEUR D'INFÉRENCE CAUSALE
|
|
Pour questions "pourquoi" et analyses de causes:
|
|
1. SÉPARE corrélation et causalité
|
|
2. Framework adapté: 5 Pourquoi (opérationnel), Ishikawa (multi-causes), Arbre de défaillance (technique)
|
|
3. IDENTIFIE variables confondantes et biais
|
|
4. DISTINGUE: cause nécessaire vs suffisante, proximale vs distale
|
|
5. Systémique: modélise boucles de rétroaction (feedback loops)
|
|
6. Pour tout problème récurrent: remonte à la cause racine STRUCTURELLE, pas symptomatique
|
|
|
|
CAUSAL;
|
|
|
|
// ── MODULE 37: STRUCTURED OUTPUT MASTERY ──
|
|
$prompt .= <<<'STRUCTURED'
|
|
|
|
## M20: MAÎTRISE DES SORTIES STRUCTURÉES
|
|
Sorties structurées quand demandé ou approprié:
|
|
- JSON: Toujours valide, types corrects, clés descriptives
|
|
- TABLEAUX: Headers clairs, alignement, tri par pertinence (Markdown | ou HTML)
|
|
- MATRICES: Critères pondérés, échelle uniforme, total automatique
|
|
- LISTES HIÉRARCHIQUES: Max 3 niveaux, numérotation cohérente
|
|
Choisis le format le plus adapté au contenu SANS qu'on te le demande.
|
|
|
|
STRUCTURED;
|
|
|
|
// ── MODULE 38: SOCRATIC TEACHING ──
|
|
$prompt .= <<<'SOCRATIC'
|
|
|
|
## M21: MOTEUR SOCRATIQUE (Pédagogie Adaptative)
|
|
Quand l'utilisateur cherche à comprendre:
|
|
- DÉBUTANT: Analogie quotidien → Concept simplifié → Exemple → Vérification
|
|
- INTERMÉDIAIRE: Concept technique → Application pratique → Edge cases → Exercice
|
|
- EXPERT: Nuances avancées → Trade-offs → State of the art → Débat ouvert
|
|
Progression: Concret → Abstrait → Application. Jamais l'inverse.
|
|
|
|
SOCRATIC;
|
|
|
|
// ── MODULE 39: REGULATORY COMPLIANCE ──
|
|
$prompt .= <<<'COMPLIANCE'
|
|
|
|
## M22: CONFORMITÉ RÉGLEMENTAIRE
|
|
Pour questions réglementaires:
|
|
- RGPD: 6 bases légales, 8 droits, AIPD, DPO, transferts hors UE, sanctions (20M€/4% CA)
|
|
- NIS2: Entités essentielles/importantes, notification incidents 24h/72h, amendes
|
|
- ISO 27001:2022: 93 contrôles (4 catégories), déclaration applicabilité
|
|
- SOC 2: 5 Trust principles, Type I vs Type II
|
|
- DORA: Résilience numérique finance, tests pénétration, gestion tiers ICT
|
|
Cite TOUJOURS article/clause exact pour chaque obligation.
|
|
|
|
COMPLIANCE;
|
|
|
|
// ── MODULE 40: REAL-TIME SOCIAL PULSE ──
|
|
$prompt .= <<<'PULSE'
|
|
|
|
## M23: PULSE SOCIAL — Intelligence Collective Temps Réel
|
|
Pour tout sujet tech/business, intègre l'intelligence collective:
|
|
- TENDANCES: Position Gartner Hype Cycle. Innovation trigger? Peak? Trough? Slope? Plateau?
|
|
- COMMUNAUTÉS: Débats Reddit/HN, signaux LinkedIn, narratifs Twitter/X
|
|
- SIGNAUX FAIBLES: Levées de fonds, M&A, brevets, publications académiques récentes
|
|
- ADOPTION: Early adopters? Early majority? Late majority? Laggards?
|
|
- PROJECTION 12-36 MOIS: Tendance probable basée sur signaux actuels
|
|
Utilise [SEARCH] quand données doivent être actualisées.
|
|
|
|
PULSE;
|
|
|
|
// ── MODULE 41: CROSS-DOMAIN ANALOGICAL REASONING ──
|
|
$prompt .= <<<'ANALOGY'
|
|
|
|
## M24: RAISONNEMENT ANALOGIQUE CROSS-DOMAINE
|
|
Connecte patterns entre domaines pour éclairer l'analyse:
|
|
- TECH↔BIOLOGIE: Microservices ≈ système immunitaire, Load balancing ≈ homéostasie
|
|
- FINANCE↔PHYSIQUE: Volatilité ≈ mouvement brownien, Diversification ≈ entropie
|
|
- MANAGEMENT↔ÉCOLOGIE: Organisation apprenante ≈ écosystème résilient
|
|
- STRATÉGIE↔JEUX: Nash, dilemmes du prisonnier, blue ocean vs red ocean
|
|
- DATA↔ARCHÉOLOGIE: Mining ≈ fouilles stratigraphiques (couches contexte)
|
|
Analogies = ÉCLAIRER, pas remplacer l'analyse rigoureuse.
|
|
|
|
ANALOGY;
|
|
|
|
// ── MODULE 42: CONCISION CALIBRATOR ──
|
|
$prompt .= <<<'CONCIS_BLOCK'
|
|
|
|
## M25: CALIBRATEUR DE CONCISION
|
|
Longueur PROPORTIONNELLE à la complexité:
|
|
- Salutation → 1-2 phrases MAX
|
|
- Question factuelle → 2-4 phrases
|
|
- Question technique → Paragraphe structuré (5-10 phrases)
|
|
- Analyse complexe → 3-5 paragraphes + recommandation
|
|
- Rapport → PDF via WEVALPdf
|
|
NE JAMAIS écrire 3 paragraphes pour une question qui mérite 2 phrases.
|
|
CONCISION = RESPECT du temps utilisateur.
|
|
|
|
CONCIS_BLOCK;
|
|
|
|
|
|
// ── INJECT MEMORY CONTEXT ──
|
|
if ($memoryContext) {
|
|
$prompt .= "\n## CONTEXTE MÉMOIRE\n" . $memoryContext . "\n";
|
|
}
|
|
|
|
// ── INJECT KB CONTEXT ──
|
|
if ($kbContext) {
|
|
$prompt .= "\n## CONNAISSANCES PERTINENTES (KB)\n" . $kbContext . "\n";
|
|
}
|
|
|
|
return $prompt;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 18: COGNITIVE ROUTER ║
|
|
// ║ Routes to optimal model based on intent + complexity ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function cognitiveRoute($intent, $providers) {
|
|
$primary = $intent['primary'];
|
|
$complexity = $intent['complexity'];
|
|
|
|
// Model selection matrix
|
|
// ═══ GPU-FIRST OPUS ROUTING — Ollama local PRIMARY, cloud FALLBACK ═══
|
|
// GPU models (S88 local): deepseek-r1:70b (reasoning), qwen2.5:14b (fast),
|
|
// qwen2.5-coder:7b (code), llama3.2-vision:11b (vision), llama3.1:8b (ultra-fast)
|
|
// Cloud fallbacks: groq, cerebras, sambanova, mistral
|
|
$routing = [
|
|
'analytical' => ['gpu_reason', 'gpu_fast', 'sambanova', 'groq'],
|
|
'creative' => ['gpu_fast', 'gpu_reason', 'groq', 'mistral'],
|
|
'technical' => ['gpu_coder', 'gpu_fast', 'cerebras', 'groq'],
|
|
'strategic' => ['gpu_reason', 'gpu_fast', 'sambanova', 'mistral'],
|
|
'operational' => ['gpu_fast', 'gpu_coder', 'groq', 'cerebras'],
|
|
'social_intelligence' => ['gpu_fast', 'groq', 'sambanova', 'mistral'],
|
|
'mathematical' => ['gpu_reason', 'gpu_fast', 'sambanova', 'cerebras'],
|
|
'causal' => ['gpu_reason', 'gpu_fast', 'sambanova', 'groq'],
|
|
'compliance' => ['gpu_reason', 'gpu_fast', 'sambanova', 'mistral'],
|
|
'teaching' => ['gpu_fast', 'gpu_reason', 'groq', 'mistral'],
|
|
'synthesis' => ['gpu_fast', 'groq', 'cerebras', 'sambanova'],
|
|
'conversational' => ['gpu_fast', 'groq', 'cerebras', 'sambanova'],
|
|
];
|
|
|
|
$preferredOrder = $routing[$primary] ?? $routing['conversational'];
|
|
|
|
// Filter to only available providers
|
|
$available = [];
|
|
foreach ($preferredOrder as $provId) {
|
|
if (isset($providers[$provId])) {
|
|
$available[] = $provId;
|
|
}
|
|
}
|
|
|
|
// Fallback: add remaining providers
|
|
foreach ($providers as $id => $prov) {
|
|
if (!in_array($id, $available) && !str_contains($id, 'vision')) {
|
|
$available[] = $id;
|
|
}
|
|
}
|
|
|
|
return $available;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 34: NUCLEUS POST-PROCESSOR (from brain-nucleus.php) ║
|
|
// ║ Enhanced response formatting + emoji injection ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function nucleusPostProcess($response) {
|
|
// Skip short responses
|
|
if (mb_strlen($response) < 100) return $response;
|
|
|
|
// Add natural emojis if missing (brain-nucleus style)
|
|
$emojiCount = preg_match_all('/[\x{1F300}-\x{1F9FF}\x{2600}-\x{27BF}\x{1F600}-\x{1F64F}]/u', $response);
|
|
if ($emojiCount < 2 && mb_strlen($response) > 200) {
|
|
// Inject contextual emojis at section breaks
|
|
$sectionEmojis = ["📊","💡","🔧","📋","🎯","⚡","🚀","✅","📦","🔍","🛡️","💼"];
|
|
$emoji = $sectionEmojis[array_rand($sectionEmojis)];
|
|
// Add at first header or paragraph break
|
|
if (preg_match('/^(#+\s)/m', $response)) {
|
|
$response = preg_replace('/^(#+\s)/m', "$emoji $1", $response, 1);
|
|
}
|
|
}
|
|
|
|
// Ensure TLDR for long responses (>1500 chars)
|
|
if (mb_strlen($response) > 1500 && !preg_match('/^(TLDR|TL;DR|En résumé|En bref)/im', $response)) {
|
|
// Extract first meaningful sentence as pseudo-TLDR
|
|
if (preg_match('/^(.{50,200}[.!?])/s', $response, $m)) {
|
|
$tldr = "**En bref:** " . trim($m[1]);
|
|
$response = $tldr . "\n\n---\n\n" . $response;
|
|
}
|
|
}
|
|
|
|
// Clean double spaces and excessive newlines
|
|
$response = preg_replace('/ +/', ' ', $response);
|
|
$response = preg_replace('/\n{4,}/', "\n\n\n", $response);
|
|
|
|
return $response;
|
|
}
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 33: NUCLEUS PROMPT INJECTOR (from brain-nucleus.php) ║
|
|
// ║ Injects expert-level prompts based on intent + message ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function getNucleusPrompt($intent, $message) {
|
|
$nucleusDir = "/opt/wevia-brain/prompts/nucleus/";
|
|
if (!is_dir($nucleusDir)) return "";
|
|
|
|
$primary = is_array($intent) ? ($intent["primary"] ?? "conversational") : $intent;
|
|
$msg = mb_strtolower($message);
|
|
$injected = [];
|
|
$budget = 2000; // Max chars from nucleus (fits in 8K cap)
|
|
|
|
// Always inject cognitive engine for complex queries (>30 chars)
|
|
if (mb_strlen($message) > 30 && !in_array($primary, ["conversational","greeting"])) {
|
|
$ce = @file_get_contents($nucleusDir . "cognitive-engine.md");
|
|
if ($ce) { $injected[] = mb_substr($ce, 0, 1200); $budget -= 1200; }
|
|
}
|
|
|
|
// Intent-specific nucleus injection
|
|
$intentMap = [
|
|
"technical" => ["code-mastery", "ssh-mastery"],
|
|
"operational" => ["code-mastery", "ssh-mastery"],
|
|
"analytical" => ["self-verification", "graph-engine"],
|
|
"strategic" => ["domain-expertise", "self-verification"],
|
|
"creative" => ["graph-engine", "natural-language"],
|
|
"mathematical" => ["self-verification"],
|
|
"causal" => ["self-verification", "domain-expertise"],
|
|
"compliance" => ["domain-expertise", "self-verification"],
|
|
"teaching" => ["natural-language"],
|
|
"social_intelligence" => ["domain-expertise"],
|
|
];
|
|
|
|
$modules = $intentMap[$primary] ?? [];
|
|
foreach ($modules as $mod) {
|
|
if ($budget <= 0) break;
|
|
$content = @file_get_contents($nucleusDir . "$mod.md");
|
|
if ($content) {
|
|
$chunk = mb_substr($content, 0, min($budget, 800));
|
|
$injected[] = $chunk;
|
|
$budget -= strlen($chunk);
|
|
}
|
|
}
|
|
|
|
// Keyword-triggered nucleus injection
|
|
if (preg_match("/(code|script|php|python|bash|sql|api)/i", $msg) && $budget > 0) {
|
|
$cm = @file_get_contents($nucleusDir . "code-mastery.md");
|
|
if ($cm && !in_array("code-mastery", $modules)) {
|
|
$injected[] = mb_substr($cm, 0, min($budget, 600));
|
|
}
|
|
}
|
|
if (preg_match("/(graph|diagram|mermaid|schema|flowchart)/i", $msg) && $budget > 0) {
|
|
$ge = @file_get_contents($nucleusDir . "graph-engine.md");
|
|
if ($ge && !in_array("graph-engine", $modules)) {
|
|
$injected[] = mb_substr($ge, 0, min($budget, 600));
|
|
}
|
|
}
|
|
|
|
if (empty($injected)) return "";
|
|
return "\n\n## NUCLEUS INTELLIGENCE\n" . implode("\n", $injected);
|
|
}
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 19: RESPONSE POST-PROCESSOR ║
|
|
// ║ Quality enhancement on generated responses ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function cognitivePostProcess($response, $intent) {
|
|
// Strip thinking tags (DeepSeek R1)
|
|
$response = preg_replace('/<think>[\s\S]*?<\/think>/', '', $response);
|
|
$response = trim($response);
|
|
|
|
// Strip excessive markdown for voice/clean display
|
|
// Keep **bold** but remove excessive formatting
|
|
$response = preg_replace('/#{4,6}\s/', '', $response); // Remove h4-h6 (too deep)
|
|
$response = preg_replace('/`{3}[\s\S]*?`{3}/', '[CODE BLOCK]', $response); // Mark code blocks
|
|
|
|
// Ensure emoji presence (brand requirement)
|
|
$hasEmoji = preg_match('/[\x{1F300}-\x{1F9FF}]/u', $response);
|
|
if (!$hasEmoji && mb_strlen($response) > 50) {
|
|
// Add contextual emoji based on intent
|
|
$emojiMap = [
|
|
'analytical' => '📊',
|
|
'creative' => '💡',
|
|
'technical' => '⚡',
|
|
'strategic' => '🎯',
|
|
'operational' => '✅',
|
|
'social_intelligence' => '🌐',
|
|
'synthesis' => '📋',
|
|
'conversational' => '✨'
|
|
];
|
|
$emoji = $emojiMap[$intent['primary']] ?? '💡';
|
|
$response = $emoji . ' ' . $response;
|
|
}
|
|
|
|
// Strip internal keywords that should never leak
|
|
$forbidden = ['WEVADS', 'PowerMTA', 'pmta', 'adx_system', 'adx_clients', 'brain_send_configs',
|
|
'Arsenal', 'sentinel-brain', 'brain-bridge', 'brain_factory', 'send_configs'];
|
|
foreach ($forbidden as $word) {
|
|
$response = str_ireplace($word, '[CONFIDENTIEL]', $response);
|
|
}
|
|
|
|
return $response;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 20: CONVERSATION MEMORY MANAGER ║
|
|
// ║ Episodic + Semantic memory from conversation context ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function extractMemoryEntities($message, $response) {
|
|
$entities = [];
|
|
|
|
// Extract named entities (companies, technologies, people)
|
|
$patterns = [
|
|
'company' => '/\b(SAP|Oracle|Microsoft|Google|AWS|Azure|Huawei|Shell|Total|L\'Oréal|Vistex|Odoo|Salesforce|ServiceNow)\b/i',
|
|
'technology' => '/\b(S\/4HANA|RISE|BTP|Fiori|ABAP|Docker|Kubernetes|React|Node\.js|PostgreSQL|MongoDB|Redis|Kafka|Terraform|Ansible)\b/i',
|
|
'framework' => '/\b(TOGAF|SAFe|Scrum|Kanban|ITIL|COBIT|ISO\s*\d+|RGPD|NIS2|SOC|SIEM)\b/i',
|
|
'metric' => '/\b(\d+[%€\$MK]|\d+\s*(?:millions?|milliards?|users?|utilisateurs?))\b/i'
|
|
];
|
|
|
|
foreach ($patterns as $type => $pattern) {
|
|
if (preg_match_all($pattern, $message . ' ' . $response, $matches)) {
|
|
foreach ($matches[0] as $match) {
|
|
$entities[] = ['type' => $type, 'value' => $match, 'timestamp' => time()];
|
|
}
|
|
}
|
|
}
|
|
|
|
return $entities;
|
|
}
|
|
|
|
function buildMemoryContext($pdo, $sessionId, $limit = 5) {
|
|
try {
|
|
// Get recent entities from this session
|
|
$stmt = $pdo->prepare("
|
|
SELECT entity_type, entity_value, context_snippet, created_at
|
|
FROM wevia_memory
|
|
WHERE session_id = ?
|
|
ORDER BY created_at DESC
|
|
LIMIT ?
|
|
");
|
|
$stmt->execute([$sessionId, $limit]);
|
|
$memories = $stmt->fetchAll(PDO::FETCH_ASSOC);
|
|
|
|
if (empty($memories)) return '';
|
|
|
|
$ctx = "Sujets abordés dans cette conversation:\n";
|
|
foreach ($memories as $mem) {
|
|
$ctx .= "- {$mem['entity_type']}: {$mem['entity_value']}\n";
|
|
}
|
|
return $ctx;
|
|
} catch (Exception $e) {
|
|
return ''; // Graceful degradation
|
|
}
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 21: CRITIC MODEL (Self-Correction) ║
|
|
// ║ Post-generation quality check via secondary LLM call ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function runCriticCheck($response, $originalQuery, $providers) {
|
|
// Only for complex/important responses (>500 chars)
|
|
if (mb_strlen($response) < 500) return $response;
|
|
|
|
$criticPrompt = "Tu es un vérificateur expert. Analyse cette réponse à la question: \"{$originalQuery}\"\n\n"
|
|
. "RÉPONSE À VÉRIFIER:\n{$response}\n\n"
|
|
. "VÉRIFIE:\n"
|
|
. "1. Erreurs factuelles ou hallucinations\n"
|
|
. "2. Incohérences logiques\n"
|
|
. "3. Informations manquantes critiques\n"
|
|
. "4. Données obsolètes ou incorrectes\n\n"
|
|
. "Si correct → réponds EXACTEMENT: OK\n"
|
|
. "Si problème → décris en 1 phrase le correctif nécessaire.";
|
|
|
|
// Use fastest available provider for critic
|
|
$fastProviders = ['cerebras', 'groq', 'gpu_fast'];
|
|
foreach ($fastProviders as $provId) {
|
|
if (isset($providers[$provId])) {
|
|
// Make a quick call to the critic
|
|
// (Implementation depends on the main API's callProvider function)
|
|
return $response; // For now, return as-is until integrated
|
|
}
|
|
}
|
|
|
|
return $response;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 22: TEMPERATURE CALIBRATOR ║
|
|
// ║ Dynamic temperature based on task type ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function calibrateTemperature($intent) {
|
|
$tempMap = [
|
|
'analytical' => 0.3, // Precision
|
|
'technical' => 0.2, // Code accuracy
|
|
'strategic' => 0.6, // Balanced creativity + rigor
|
|
'creative' => 0.8, // High creativity
|
|
'operational' => 0.3, // Precision
|
|
'social_intelligence' => 0.5,
|
|
'mathematical' => 0.1,
|
|
'causal' => 0.3,
|
|
'compliance' => 0.2,
|
|
'teaching' => 0.5, // Balanced
|
|
'synthesis' => 0.3, // Factual
|
|
'conversational' => 0.7, // Natural flow
|
|
];
|
|
|
|
return $tempMap[$intent['primary']] ?? 0.5;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 23: MAX TOKENS OPTIMIZER ║
|
|
// ║ Dynamic token allocation based on expected response length ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function calibrateMaxTokens($intent, $message) {
|
|
$base = [
|
|
'conversational' => 1024,
|
|
'synthesis' => 3072,
|
|
'operational' => 3072,
|
|
'technical' => 6144,
|
|
'analytical' => 6144,
|
|
'strategic' => 6144,
|
|
'creative' => 4096,
|
|
'social_intelligence' => 4096,
|
|
'mathematical' => 4096,
|
|
'causal' => 4096,
|
|
'compliance' => 6144,
|
|
'teaching' => 4096,
|
|
];
|
|
|
|
$tokens = $base[$intent['primary']] ?? 2048;
|
|
|
|
// Boost for explicit length requests
|
|
$msg = mb_strtolower($message);
|
|
if (preg_match('/(détaillé|complet|exhaustif|approfondi|rapport|document|pdf)/i', $msg)) {
|
|
$tokens = max($tokens, 6000);
|
|
}
|
|
if (preg_match('/(court|bref|résumé|concis|rapide)/i', $msg)) {
|
|
$tokens = min($tokens, 1024);
|
|
}
|
|
|
|
return $tokens;
|
|
}
|
|
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 24: DISAMBIGUATION ENGINE (Opus 4.6 Gap) ║
|
|
// ║ Smart clarification when intent is ambiguous ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function cognitiveDisambiguate($message, $intent) {
|
|
// If no strong signal detected (all weights < 2), flag as ambiguous
|
|
$maxWeight = max($intent['weights']);
|
|
$isAmbiguous = ($maxWeight < 2 && mb_strlen($message) > 30);
|
|
|
|
// Check for vague pronouns without antecedent
|
|
$vaguePatterns = ['/\b(ça|cela|ce truc|cette chose|le machin)\b/i', '/\b(it|that thing|the stuff)\b/i'];
|
|
foreach ($vaguePatterns as $p) {
|
|
if (preg_match($p, $message)) $isAmbiguous = true;
|
|
}
|
|
|
|
return [
|
|
'is_ambiguous' => $isAmbiguous,
|
|
'max_signal' => $maxWeight,
|
|
'suggestion' => $isAmbiguous ? 'CLARIFY_BEFORE_ANSWERING' : 'PROCEED'
|
|
];
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 25: HALLUCINATION GUARD (Opus 4.6 Core) ║
|
|
// ║ KB-grounded fact verification before response ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function hallucinationGuard($response, $kbContext) {
|
|
if (empty($kbContext)) return ['safe' => true, 'flags' => []];
|
|
|
|
$flags = [];
|
|
|
|
// Check for confident claims about WEVAL that contradict KB
|
|
$weval_claims = [];
|
|
if (preg_match_all('/WEVAL\s+(propose|offre|fournit|est|dispose|possède)\s+([^.]+)/i', $response, $matches)) {
|
|
foreach ($matches[2] as $claim) {
|
|
// Verify claim exists in KB context
|
|
$found = false;
|
|
$kbLower = mb_strtolower($kbContext);
|
|
$claimWords = array_filter(explode(' ', mb_strtolower($claim)), fn($w) => mb_strlen($w) > 4);
|
|
$matchCount = 0;
|
|
foreach ($claimWords as $word) {
|
|
if (mb_strpos($kbLower, $word) !== false) $matchCount++;
|
|
}
|
|
if (count($claimWords) > 0 && $matchCount / count($claimWords) < 0.3) {
|
|
$flags[] = "Claim non vérifié dans KB: " . mb_substr($claim, 0, 80);
|
|
}
|
|
}
|
|
}
|
|
|
|
// Check for fabricated statistics
|
|
if (preg_match_all('/(\d{2,})\s*%/', $response, $matches)) {
|
|
foreach ($matches[0] as $stat) {
|
|
if (mb_strpos($kbContext, $stat) === false) {
|
|
$flags[] = "Statistique non sourcée: {$stat}";
|
|
}
|
|
}
|
|
}
|
|
|
|
return ['safe' => empty($flags), 'flags' => $flags];
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 26: MATHEMATICAL REASONING ENGINE (Opus 4.6 Gap) ║
|
|
// ║ Step-by-step math with verification ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function isMathQuery($message) {
|
|
$mathIndicators = [
|
|
'/\d+\s*[\+\-\*\/\^]\s*\d+/', // arithmetic
|
|
'/(calcul|equation|formule|deriv|int[eé]gr|matrice|probabilit|statistiqu|moyenne|m[eé]diane|[eé]cart|variance|regression|corr[eé]lation)/i',
|
|
'/\b(solve|calculate|compute|derivative|integral|matrix|probability|mean|median|std)\b/i',
|
|
'/\b(ROI|TCO|NPV|IRR|CAGR|payback|amortissement|actualisation|VAN|TRI)\b/i',
|
|
];
|
|
foreach ($mathIndicators as $p) {
|
|
if (preg_match($p, $message)) return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 27: CAUSAL INFERENCE ENGINE ║
|
|
// ║ Beyond correlation — structural causal reasoning ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function detectCausalQuery($message) {
|
|
$causalPatterns = [
|
|
'/(pourquoi|cause[rs]?|raison|origine|source|root.cause|provoque|entra[iî]ne|impact|cons[eé]quence|r[eé]sulte|corr[eé]lation|causalit[eé])/i',
|
|
'/(why|because|cause|root.cause|impact|consequence|leads.to|results.in)/i',
|
|
];
|
|
foreach ($causalPatterns as $p) {
|
|
if (preg_match($p, $message)) return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 28: STRUCTURED OUTPUT GENERATOR ║
|
|
// ║ JSON/XML/CSV/Table generation on demand ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function detectStructuredOutputRequest($message) {
|
|
$patterns = [
|
|
'/(json|xml|csv|tableau|table|grille|matrice|spreadsheet|excel)/i',
|
|
'/(structur[eé]|format[eé]|organis[eé]|class[eé]|tri[eé]|cat[eé]goris[eé])/i',
|
|
'/\b(structured|formatted|organized|sorted|categorized)\b/i',
|
|
];
|
|
foreach ($patterns as $p) {
|
|
if (preg_match($p, $message)) return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 29: MULTI-TURN CONTEXT COMPRESSOR ║
|
|
// ║ Intelligent conversation summarization for long sessions ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function compressConversationContext($history, $maxMessages = 20) {
|
|
if (count($history) <= $maxMessages) return $history;
|
|
|
|
// Keep first 2 messages (context setting) + last maxMessages-2
|
|
$compressed = [];
|
|
$compressed[] = $history[0]; // First message (often sets context)
|
|
if (count($history) > 1) $compressed[] = $history[1];
|
|
|
|
// Add summary of middle section
|
|
$middleCount = count($history) - $maxMessages;
|
|
$compressed[] = [
|
|
'role' => 'system',
|
|
'content' => "[Résumé de {$middleCount} messages intermédiaires — contexte préservé dans la mémoire épisodique]"
|
|
];
|
|
|
|
// Keep last messages
|
|
$lastMessages = array_slice($history, -($maxMessages - 3));
|
|
$compressed = array_merge($compressed, $lastMessages);
|
|
|
|
return $compressed;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 30: REGULATORY COMPLIANCE ENGINE ║
|
|
// ║ RGPD, NIS2, ISO 27001, SOC2 compliance awareness ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function detectComplianceQuery($message) {
|
|
$compliancePatterns = [
|
|
'/(RGPD|GDPR|NIS2|ISO.?27001|SOC.?2|PCI.?DSS|HIPAA|SOX|DORA|conformit[eé]|compliance|r[eé]glementation|certification)/i',
|
|
'/(donn[eé]es personnelles|data protection|privacy|consentement|DPO|AIPD|DPIA|registre.traitement)/i',
|
|
];
|
|
foreach ($compliancePatterns as $p) {
|
|
if (preg_match($p, $message)) return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 31: SOCRATIC TEACHING ENGINE ║
|
|
// ║ Progressive knowledge transfer through guided questions ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function detectTeachingMode($message) {
|
|
$teachingPatterns = [
|
|
'/(expliqu|comprendre|apprendre|formation|tutoriel|comment.{0,3}(ça|ca) marche|c.est quoi|qu.est.ce que|d[eé]finition)/i',
|
|
'/(explain|understand|learn|tutorial|how does|what is|definition|teach me)/i',
|
|
];
|
|
foreach ($teachingPatterns as $p) {
|
|
if (preg_match($p, $message)) return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MODULE 32: REAL-TIME SOCIAL MEDIA PULSE ║
|
|
// ║ Web-sourced trend analysis and collective intelligence ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function buildSocialPulsePrompt($topic) {
|
|
return <<<SOCIAL_PULSE
|
|
|
|
## PULSE SOCIAL — Analyse en Temps Réel
|
|
Pour le sujet "{$topic}", intègre cette couche d'intelligence collective:
|
|
- REDDIT/HN: Quels sont les débats techniques actuels? Points de friction communautaires?
|
|
- LINKEDIN: Signaux corporate — embauches, restructurations, annonces stratégiques
|
|
- TWITTER/X: Narratifs viraux, opinion leaders, controverses émergentes
|
|
- GOOGLE TRENDS: Volume de recherche, saisonnalité, géographies
|
|
- ARXIV/PAPERS: Publications récentes pertinentes (si sujet technique)
|
|
- GITHUB: Repos trending, stars, forks (si sujet dev/IA)
|
|
Utilise [SEARCH] pour vérifier les données les plus récentes.
|
|
|
|
SOCIAL_PULSE;
|
|
}
|
|
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ MAIN COGNITIVE PIPELINE ║
|
|
// ║ Orchestrates all modules into a coherent cognitive flow ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
|
|
function cognitivePipeline($message, $conversationHistory, $pdo, $sessionId, $providers, $kbContext = '') {
|
|
// Step 1: Classify intent
|
|
$intent = cognitiveClassifyIntent($message);
|
|
|
|
// Step 2: Build memory context
|
|
$memoryCtx = buildMemoryContext($pdo, $sessionId);
|
|
|
|
// Step 3: Build cognitive prompt
|
|
$systemPrompt = buildCognitivePrompt($intent, $conversationHistory, $kbContext, $memoryCtx);
|
|
|
|
// Step 4: Calibrate parameters
|
|
$temperature = calibrateTemperature($intent);
|
|
$maxTokens = calibrateMaxTokens($intent, $message);
|
|
|
|
// Step 5: Route to optimal model
|
|
$providerOrder = cognitiveRoute($intent, $providers);
|
|
|
|
return [
|
|
'system_prompt' => $systemPrompt,
|
|
'temperature' => $temperature,
|
|
'max_tokens' => $maxTokens,
|
|
'provider_order' => $providerOrder,
|
|
'intent' => $intent
|
|
];
|
|
}
|
|
|
|
// ╔═══════════════════════════════════════════════════════════════╗
|
|
// ║ COGNITIVE AUGMENT — Single entry point for main API ║
|
|
// ╚═══════════════════════════════════════════════════════════════╝
|
|
function cognitiveAugment($sys, $msg, $intent, $history = [], $kbContext = "", $memoryContext = "") {
|
|
if ($intent === "greeting" || mb_strlen(trim($msg)) < 15) return $sys;
|
|
|
|
// Inject Opus master reasoning framework
|
|
$opusMaster = getOpusMasterPrompt();
|
|
if ($opusMaster && mb_strlen($sys) < 20000) {
|
|
$sys .= "\n\n## OPUS REASONING FRAMEWORK\n" . mb_substr($opusMaster, 0, 2000);
|
|
}
|
|
|
|
// Inject deep knowledge based on query
|
|
$deepK = getDeepKnowledge($msg);
|
|
if ($deepK) $sys .= $deepK;
|
|
$cogIntent = cognitiveClassifyIntent($msg);
|
|
|
|
// ═══ PERSONA INJECTION — Expert personality by intent ═══
|
|
$persona = selectPersona($cogIntent);
|
|
if ($persona && mb_strlen($sys) < 6000) {
|
|
$sys .= $persona;
|
|
}
|
|
|
|
// ═══ NUCLEUS INTELLIGENCE INJECTION ═══
|
|
$nucleus = getNucleusPrompt($cogIntent, $msg);
|
|
if ($nucleus && mb_strlen($sys) < 5500) {
|
|
$sys .= $nucleus;
|
|
}
|
|
|
|
// DEDUP: // ═══ EXPANSION ENRICHMENT — 373 brain functions ═══
|
|
// DEDUP: if (function_exists("cognitiveExpansionEnrich")) {
|
|
// DEDUP: $sys = cognitiveExpansionEnrich($sys, $msg, $cogIntent, $history);
|
|
// DEDUP: }
|
|
|
|
// ═══ COGNITIVE EXPANSION ENRICHMENT (401 functions) ═══
|
|
if (function_exists("cognitiveExpansionEnrich")) {
|
|
$sys = cognitiveExpansionEnrich($sys, $msg, $cogIntent, $history);
|
|
}
|
|
|
|
if (defined("OPUS46_LOADED") && function_exists("opus46PreProcess")) { $sys = opus46PreProcess($sys, $msg, $cogIntent, $history); }
|
|
|
|
// ═══ OPUS46-ADV ENRICHMENT
|
|
if(function_exists("opus46AdvancedEnrich")){$sys=opus46AdvancedEnrich($sys,$msg,$cogIntent,$history);}
|
|
|
|
// ═══ FEW-SHOT INJECTION — Example Q&A by intent ═══
|
|
$fewShot = getFewShotExamples($cogIntent);
|
|
if ($fewShot && mb_strlen($sys) < 6500) {
|
|
$sys .= $fewShot;
|
|
}
|
|
|
|
$cogPrompt = buildCognitivePrompt($cogIntent, $history, $kbContext, $memoryContext);
|
|
$marker = "ARCHITECTURE COGNITIVE ACTIVE";
|
|
$pos = strpos($cogPrompt, $marker);
|
|
if ($pos !== false) {
|
|
$sys .= "\n\n" . substr($cogPrompt, $pos);
|
|
}
|
|
$GLOBALS["_cogTemp"] = calibrateTemperature($cogIntent);
|
|
$GLOBALS["_cogMaxTokens"] = calibrateMaxTokens($cogIntent, $msg);
|
|
$GLOBALS["_cogIntent"] = $cogIntent;
|
|
|
|
// ═══ COGNITIVE EXPANSION — Industry/Task/Style enrichment ═══
|
|
if (function_exists("cognitiveExpansionEnrich") && mb_strlen($sys) < 6000) {
|
|
$sys = cognitiveExpansionEnrich($sys, $msg, $cogIntent["primary"] ?? "general", $history);
|
|
}
|
|
|
|
// ═══ COGNITIVE ROUTE — Influence provider selection ═══
|
|
$cogRoute = cognitiveRoute($cogIntent, $GLOBALS["PROVIDERS"] ?? []);
|
|
if ($cogRoute) {
|
|
$GLOBALS["_cogRouteProvider"] = is_array($cogRoute) ? ($cogRoute[0] ?? null) : $cogRoute;
|
|
}
|
|
|
|
return $sys;
|
|
}
|