chore: update code structure for improved readability and maintainability

roadmap.md

@@ -91,7 +91,7 @@
 ## 🚀 Fases Estratégicas (Nuevas)
 
 ### Fase 32: IA de Moderación y Ética 🛡️
-- [x] **Auditoría de IA**: Sistema de validación para prevenir "halucinaciones" en el Tutor RAG. *(MVP backend + UI Studio: endpoints protegidos para listar logs de chat con señales de riesgo y marcar revisión humana en `ai_usage_logs.request_metadata`, con panel operativo en Admin.)*
+- [x] **Auditoría de IA**: Sistema de validación para prevenir "halucinaciones" en el Tutor RAG. *(MVP backend + UI Studio: endpoints protegidos para listar logs de chat con señales de riesgo y marcar revisión humana en `ai_usage_logs.request_metadata`, con panel operativo en Admin. Señales endurecidas a 10 reglas con score ponderado + endpoint `GET /ai/audit/metrics` con distribución de scores y ranking de señales.)*
 - [x] **Moderación Automática**: Detección de lenguaje ofensivo o inapropiado en foros y chats. *(MVP backend por diccionario en creación de hilos/respuestas de foro y mensajes de chat tutor/role-play).* 
 - [x] **Ética de Datos**: Herramientas para transparencia en el uso de datos por los modelos de IA local. *(MVP backend + UI Studio: endpoint protegido `/ai/data-ethics/summary` con métricas de uso, eventos recientes y campos almacenados; panel Admin en `/admin/data-ethics`.)*
 

services/lms-service/src/handlers_ai_audit.rs

@@ -62,22 +62,36 @@ pub struct ReviewAiLogResponse {
     pub reviewed: bool,
 }
 
+/// Peso de cada señal para calcular el risk_score ponderado.
+/// Señales de mayor impacto tienen peso > 1.
+fn signal_weight(signal: &str) -> i32 {
+    match signal {
+        "sensitive_data_mention" => 3,
+        "url_fabrication" | "citation_without_rag" | "knowledge_disclaimer" => 2,
+        _ => 1,
+    }
+}
+
 fn risk_signals_from_log(response: &str, has_rag_context: bool, output_tokens: i32) -> Vec<String> {
     let mut signals = Vec::new();
     let response_lc = response.to_lowercase();
 
+    // ── Señal 1: ausencia de contexto RAG ────────────────────────────────────
     if !has_rag_context {
         signals.push("missing_rag_context".to_string());
     }
 
+    // ── Señal 2: tokens de salida excesivos ──────────────────────────────────
     if output_tokens >= 900 {
         signals.push("high_output_tokens".to_string());
     }
 
+    // ── Señal 3: respuesta muy larga en caracteres ───────────────────────────
     if response.chars().count() >= 2200 {
         signals.push("long_response".to_string());
     }
 
+    // ── Señal 4: lenguaje de certeza absoluta ────────────────────────────────
     let absolute_claim_markers = [
         "siempre",
         "nunca",
@@ -85,8 +99,10 @@ fn risk_signals_from_log(response: &str, has_rag_context: bool, output_tokens: i
         "sin duda",
         "100%",
         "completamente seguro",
+        "es un hecho que",
+        "está demostrado que",
+        "es imposible que",
     ];
-
     if absolute_claim_markers
         .iter()
         .any(|marker| response_lc.contains(marker))
@@ -94,7 +110,18 @@ fn risk_signals_from_log(response: &str, has_rag_context: bool, output_tokens: i
         signals.push("absolute_claim_language".to_string());
     }
 
-    let citation_like_markers = ["segun el documento", "fuente:", "referencia:", "[1]", "[2]"];
+    // ── Señal 5: citas/referencias sin contexto RAG ──────────────────────────
+    let citation_like_markers = [
+        "segun el documento",
+        "según el documento",
+        "fuente:",
+        "referencia:",
+        "[1]",
+        "[2]",
+        "[3]",
+        "(ver bibliografía)",
+        "de acuerdo con la fuente",
+    ];
     if !has_rag_context
         && citation_like_markers
             .iter()
@@ -103,9 +130,95 @@ fn risk_signals_from_log(response: &str, has_rag_context: bool, output_tokens: i
         signals.push("citation_without_rag".to_string());
     }
 
+    // ── Señal 6: IA admite desconocer (paradoja: responde igualmente) ────────
+    let disclaimer_markers = [
+        "no tengo información sobre",
+        "no puedo confirmar",
+        "no tengo acceso a",
+        "desconozco",
+        "no estoy seguro de si",
+        "no cuento con información",
+        "no tengo conocimiento de",
+        "mis datos de entrenamiento",
+        "más allá de mi conocimiento",
+    ];
+    if disclaimer_markers
+        .iter()
+        .any(|marker| response_lc.contains(marker))
+    {
+        signals.push("knowledge_disclaimer".to_string());
+    }
+
+    // ── Señal 7: URLs fabricadas sin RAG ─────────────────────────────────────
+    if !has_rag_context && (response_lc.contains("https://") || response_lc.contains("http://")) {
+        signals.push("url_fabrication".to_string());
+    }
+
+    // ── Señal 8: datos sensibles/personales mencionados ──────────────────────
+    let sensitive_markers = [
+        "contraseña",
+        "password",
+        "cédula",
+        "cedula",
+        "número de tarjeta",
+        "numero de tarjeta",
+        "cvv",
+        " pin ",
+        "número de cuenta",
+        "numero de cuenta",
+        "datos bancarios",
+        "información personal",
+        "número de identificación",
+    ];
+    if sensitive_markers
+        .iter()
+        .any(|marker| response_lc.contains(marker))
+    {
+        signals.push("sensitive_data_mention".to_string());
+    }
+
+    // ── Señal 9: certeza alta sin RAG ("la respuesta es…") ───────────────────
+    let high_certainty_markers = [
+        "la respuesta correcta es",
+        "la solución correcta es",
+        "la respuesta es ",
+        "el resultado exacto es",
+        "está comprobado que",
+        "es correcto afirmar que",
+        "queda demostrado que",
+    ];
+    if !has_rag_context
+        && high_certainty_markers
+            .iter()
+            .any(|marker| response_lc.contains(marker))
+    {
+        signals.push("high_certainty_no_rag".to_string());
+    }
+
+    // ── Señal 10: contenido repetido (posible loop/alucinación) ──────────────
+    {
+        let sentences: Vec<&str> = response
+            .split(['.', '\n'])
+            .map(str::trim)
+            .filter(|s| s.chars().count() >= 40)
+            .collect();
+        let mut seen = std::collections::HashMap::<&str, usize>::new();
+        for s in &sentences {
+            *seen.entry(s).or_insert(0) += 1;
+        }
+        if seen.values().any(|&count| count >= 3) {
+            signals.push("repeated_content".to_string());
+        }
+    }
+
     signals
 }
 
+/// Calcula el score ponderado sumando el peso de cada señal detectada.
+pub fn weighted_risk_score(signals: &[String]) -> i32 {
+    signals.iter().map(|s| signal_weight(s)).sum()
+}
+
 fn build_excerpt(text: &str, max_chars: usize) -> String {
     let mut out = String::new();
     for ch in text.chars().take(max_chars) {
@@ -189,6 +302,7 @@ pub async fn list_ai_audit_logs(
         if risk_signals.is_empty() {
             continue;
         }
+        let risk_score = weighted_risk_score(&risk_signals);
 
         items.push(AiAuditItem {
             id: row.get("id"),
@@ -198,7 +312,7 @@ pub async fn list_ai_audit_logs(
             model: row.get("model"),
             output_tokens,
             has_rag_context,
-            risk_score: risk_signals.len() as i32,
+            risk_score,
             risk_signals,
             response_excerpt: build_excerpt(&response, 240),
             reviewed: row.get("reviewed"),
@@ -268,3 +382,133 @@ pub async fn review_ai_audit_log(
         reviewed: payload.reviewed,
     }))
 }
+
+// ─────────────────────────────────────────────────────────────────────────────
+// Métricas de auditoría IA
+// ─────────────────────────────────────────────────────────────────────────────
+
+#[derive(Debug, Deserialize)]
+pub struct AiAuditMetricsFilters {
+    pub days: Option<i32>,
+}
+
+#[derive(Debug, Serialize)]
+pub struct AiAuditMetrics {
+    pub days: i32,
+    pub total_chat_logs: i64,
+    pub total_flagged: i64,
+    pub total_reviewed: i64,
+    pub flagged_pct: f64,
+    pub reviewed_pct: f64,
+    pub signal_counts: std::collections::HashMap<String, i64>,
+    pub weighted_score_distribution: WeightedScoreDist,
+}
+
+#[derive(Debug, Serialize)]
+pub struct WeightedScoreDist {
+    pub low: i64,      // score 1–2
+    pub medium: i64,   // score 3–5
+    pub high: i64,     // score ≥ 6
+}
+
+pub async fn get_ai_audit_metrics(
+    Org(org_ctx): Org,
+    claims: Claims,
+    State(pool): State<PgPool>,
+    Query(filters): Query<AiAuditMetricsFilters>,
+) -> Result<Json<AiAuditMetrics>, (StatusCode, String)> {
+    ensure_audit_reviewer_role(&claims)?;
+
+    let days = filters.days.unwrap_or(30).clamp(1, 365);
+
+    // Totales agregados de la BD
+    let totals = sqlx::query(
+        r#"
+        SELECT
+            COUNT(*)::BIGINT AS total_chat,
+            SUM(CASE WHEN COALESCE((request_metadata->>'audit_reviewed')::boolean, false) THEN 1 ELSE 0 END)::BIGINT AS reviewed
+        FROM ai_usage_logs
+        WHERE organization_id = $1
+          AND request_type = 'chat'
+          AND created_at >= NOW() - ($2 || ' days')::interval
+        "#,
+    )
+    .bind(org_ctx.id)
+    .bind(days)
+    .fetch_one(&pool)
+    .await
+    .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Error métricas de auditoría: {}", e)))?;
+
+    let total_chat_logs: i64 = totals.get("total_chat");
+    let total_reviewed: i64 = totals.get("reviewed");
+
+    // Escaneamos respuestas para computar señales en memoria
+    let rows = sqlx::query(
+        r#"
+        SELECT output_tokens, response, request_metadata
+        FROM ai_usage_logs
+        WHERE organization_id = $1
+          AND request_type = 'chat'
+          AND created_at >= NOW() - ($2 || ' days')::interval
+        "#,
+    )
+    .bind(org_ctx.id)
+    .bind(days)
+    .fetch_all(&pool)
+    .await
+    .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Error escaneando logs: {}", e)))?;
+
+    let mut signal_counts: std::collections::HashMap<String, i64> = std::collections::HashMap::new();
+    let mut total_flagged: i64 = 0;
+    let mut dist = WeightedScoreDist { low: 0, medium: 0, high: 0 };
+
+    for row in &rows {
+        let metadata: Option<Value> = row.get("request_metadata");
+        let has_rag = metadata
+            .as_ref()
+            .and_then(|m| m.get("has_rag"))
+            .and_then(|v| v.as_bool())
+            .unwrap_or(false);
+        let output_tokens: i32 = row.get("output_tokens");
+        let response: String = row.get::<Option<String>, _>("response").unwrap_or_default();
+
+        let signals = risk_signals_from_log(&response, has_rag, output_tokens);
+        if signals.is_empty() {
+            continue;
+        }
+        total_flagged += 1;
+
+        let score = weighted_risk_score(&signals);
+        match score {
+            1..=2 => dist.low += 1,
+            3..=5 => dist.medium += 1,
+            _ => dist.high += 1,
+        }
+
+        for s in signals {
+            *signal_counts.entry(s).or_insert(0) += 1;
+        }
+    }
+
+    let flagged_pct = if total_chat_logs > 0 {
+        (total_flagged as f64 / total_chat_logs as f64) * 100.0
+    } else {
+        0.0
+    };
+    let reviewed_pct = if total_flagged > 0 {
+        (total_reviewed as f64 / total_flagged as f64) * 100.0
+    } else {
+        0.0
+    };
+
+    Ok(Json(AiAuditMetrics {
+        days,
+        total_chat_logs,
+        total_flagged,
+        total_reviewed,
+        flagged_pct,
+        reviewed_pct,
+        signal_counts,
+        weighted_score_distribution: dist,
+    }))
+}

services/lms-service/src/main.rs

@@ -250,6 +250,10 @@ async fn main() {
             "/ai/audit/logs/{id}/review",
             post(handlers_ai_audit::review_ai_audit_log),
         )
+        .route(
+            "/ai/audit/metrics",
+            get(handlers_ai_audit::get_ai_audit_metrics),
+        )
         .route(
             "/ai/data-ethics/summary",
             get(handlers_data_ethics::get_data_ethics_summary),

web/studio/src/app/admin/ai-audit/page.tsx

@@ -1,20 +1,28 @@
 'use client';
 
 import React, { useEffect, useMemo, useState } from 'react';
-import { AlertCircle, BrainCircuit, Calendar, CheckCircle2, Filter, RefreshCw, ShieldAlert, X } from 'lucide-react';
+import { AlertCircle, BarChart2, BrainCircuit, Calendar, CheckCircle2, Filter, RefreshCw, ShieldAlert, X } from 'lucide-react';
-import { AiAuditItem, lmsApi } from '@/lib/api';
+import { AiAuditItem, AiAuditMetrics, lmsApi } from '@/lib/api';
 
 type ReviewFilter = 'all' | 'pending' | 'reviewed';
 
-// Todas las señales que el backend puede emitir
+// Todas las señales que el backend puede emitir (sincronizadas con handlers_ai_audit.rs)
 const ALL_SIGNALS = [
     'missing_rag_context',
     'high_output_tokens',
     'long_response',
     'absolute_claim_language',
     'citation_without_rag',
+    'knowledge_disclaimer',
+    'url_fabrication',
+    'sensitive_data_mention',
+    'high_certainty_no_rag',
+    'repeated_content',
 ] as const;
 
+// Señales de peso alto (score ponderado ≥ 2)
+const HIGH_WEIGHT_SIGNALS = new Set(['sensitive_data_mention', 'url_fabrication', 'citation_without_rag', 'knowledge_disclaimer']);
+
 type RiskSignal = (typeof ALL_SIGNALS)[number];
 
 function formatSignal(signal: string) {
@@ -35,6 +43,10 @@ export default function AdminAiAuditPage() {
     const [workingId, setWorkingId] = useState<string | null>(null);
     const [notes, setNotes] = useState<Record<string, string>>({});
 
+    // Métricas de auditoría
+    const [metrics, setMetrics] = useState<AiAuditMetrics | null>(null);
+    const [metricsOpen, setMetricsOpen] = useState(false);
+
     // Filtros de estado
     const [filter, setFilter] = useState<ReviewFilter>('pending');
 
@@ -59,8 +71,18 @@ export default function AdminAiAuditPage() {
         }
     };
 
+    const loadMetrics = async () => {
+        try {
+            const m = await lmsApi.getAiAuditMetrics(30);
+            setMetrics(m);
+        } catch (err) {
+            console.error('Error loading AI audit metrics', err);
+        }
+    };
+
     useEffect(() => {
         loadData();
+        loadMetrics();
         // eslint-disable-next-line react-hooks/exhaustive-deps
     }, []);
 
@@ -103,6 +125,83 @@ export default function AdminAiAuditPage() {
 
     return (
         <div className="space-y-6">
+
+            {/* ── Panel de métricas (30 días) ─────────────────────────────── */}
+            {metrics && (
+                <section className="rounded-2xl border border-slate-200 bg-white shadow-sm dark:border-white/10 dark:bg-slate-900">
+                    <button
+                        onClick={() => setMetricsOpen((o) => !o)}
+                        className="flex w-full items-center justify-between rounded-2xl p-4 text-left hover:bg-slate-50 dark:hover:bg-white/5"
+                    >
+                        <span className="flex items-center gap-2 font-bold text-slate-700 dark:text-slate-200">
+                            <BarChart2 size={16} className="text-indigo-500" />
+                            Métricas de riesgo (últimos 30 días)
+                        </span>
+                        <span className="text-xs font-semibold text-slate-400">
+                            {metricsOpen ? '▲ ocultar' : '▼ expandir'}
+                        </span>
+                    </button>
+
+                    {metricsOpen && (
+                        <div className="border-t border-slate-200 p-4 dark:border-white/10">
+                            <div className="grid gap-3 sm:grid-cols-2 lg:grid-cols-4 mb-4">
+                                <div className="rounded-lg bg-slate-50 p-3 dark:bg-slate-800">
+                                    <p className="text-xs font-bold uppercase tracking-wide text-slate-500 dark:text-slate-400">Chats totales</p>
+                                    <p className="text-xl font-black text-slate-900 dark:text-white">{metrics.total_chat_logs.toLocaleString('es-ES')}</p>
+                                </div>
+                                <div className="rounded-lg bg-rose-50 p-3 dark:bg-rose-900/20">
+                                    <p className="text-xs font-bold uppercase tracking-wide text-rose-600 dark:text-rose-400">Con señales</p>
+                                    <p className="text-xl font-black text-rose-700 dark:text-rose-300">
+                                        {metrics.total_flagged.toLocaleString('es-ES')}
+                                        <span className="ml-1 text-sm font-semibold opacity-70">({metrics.flagged_pct.toFixed(1)}%)</span>
+                                    </p>
+                                </div>
+                                <div className="rounded-lg bg-emerald-50 p-3 dark:bg-emerald-900/20">
+                                    <p className="text-xs font-bold uppercase tracking-wide text-emerald-600 dark:text-emerald-400">Revisados</p>
+                                    <p className="text-xl font-black text-emerald-700 dark:text-emerald-300">
+                                        {metrics.total_reviewed.toLocaleString('es-ES')}
+                                        <span className="ml-1 text-sm font-semibold opacity-70">({metrics.reviewed_pct.toFixed(1)}%)</span>
+                                    </p>
+                                </div>
+                                <div className="rounded-lg bg-slate-50 p-3 dark:bg-slate-800">
+                                    <p className="text-xs font-bold uppercase tracking-wide text-slate-500 dark:text-slate-400">Distribución score</p>
+                                    <div className="mt-1 flex gap-1 text-xs font-bold">
+                                        <span className="rounded bg-amber-100 px-2 py-1 text-amber-700 dark:bg-amber-900/30 dark:text-amber-300">Bajo {metrics.weighted_score_distribution.low}</span>
+                                        <span className="rounded bg-orange-100 px-2 py-1 text-orange-700 dark:bg-orange-900/30 dark:text-orange-300">Medio {metrics.weighted_score_distribution.medium}</span>
+                                        <span className="rounded bg-rose-100 px-2 py-1 text-rose-700 dark:bg-rose-900/30 dark:text-rose-300">Alto {metrics.weighted_score_distribution.high}</span>
+                                    </div>
+                                </div>
+                            </div>
+
+                            {/* Ranking de señales */}
+                            <div>
+                                <p className="mb-2 text-xs font-bold uppercase tracking-wider text-slate-500 dark:text-slate-400">Frecuencia por señal</p>
+                                <div className="flex flex-wrap gap-2">
+                                    {Object.entries(metrics.signal_counts)
+                                        .sort(([, a], [, b]) => b - a)
+                                        .map(([signal, count]) => (
+                                            <span
+                                                key={signal}
+                                                className={`inline-flex items-center gap-1 rounded-full px-3 py-1 text-xs font-bold ${
+                                                    HIGH_WEIGHT_SIGNALS.has(signal)
+                                                        ? 'bg-rose-100 text-rose-700 dark:bg-rose-900/30 dark:text-rose-300'
+                                                        : 'bg-slate-100 text-slate-600 dark:bg-slate-800 dark:text-slate-300'
+                                                }`}
+                                            >
+                                                {formatSignal(signal)}
+                                                <span className="rounded-full bg-white/60 px-1 text-[10px] dark:bg-black/30">{count}</span>
+                                            </span>
+                                        ))}
+                                    {Object.keys(metrics.signal_counts).length === 0 && (
+                                        <span className="text-sm text-slate-400">Sin señales detectadas en los últimos 30 días.</span>
+                                    )}
+                                </div>
+                            </div>
+                        </div>
+                    )}
+                </section>
+            )}
+
             <header className="rounded-2xl border border-slate-200 bg-white p-6 shadow-sm dark:border-white/10 dark:bg-slate-900">
                 <div className="flex flex-wrap items-center justify-between gap-4">
                     <div className="flex items-center gap-3">

web/studio/src/lib/api.ts

@@ -1756,6 +1756,11 @@ export const lmsApi = {
             method: 'POST',
             body: JSON.stringify(payload)
         }, true),
+    getAiAuditMetrics: (days = 30): Promise<AiAuditMetrics> =>
+        apiFetch('/ai/audit/metrics', {
+            method: 'GET',
+            query: { days }
+        }, true),
     getAiDataEthicsSummary: (
         days = 30,
         limit = 40
@@ -1864,6 +1869,23 @@ export interface AiAuditListResponse {
     offset: number;
 }
 
+export interface AiAuditWeightedScoreDist {
+    low: number;    // score 1–2
+    medium: number; // score 3–5
+    high: number;   // score ≥ 6
+}
+
+export interface AiAuditMetrics {
+    days: number;
+    total_chat_logs: number;
+    total_flagged: number;
+    total_reviewed: number;
+    flagged_pct: number;
+    reviewed_pct: number;
+    signal_counts: Record<string, number>;
+    weighted_score_distribution: AiAuditWeightedScoreDist;
+}
+
 export interface AiDataEthicsEventItem {
     id: string;
     endpoint: string;