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188
src/app/api/audits/ai/findings/route.ts
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188
src/app/api/audits/ai/findings/route.ts
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import { z } from "zod";
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import { NextResponse } from "next/server";
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import { requireAdminApiUser } from "@/lib/auth/admin";
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import { callOpenAiJsonSchema } from "@/lib/ai/openai";
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import { storeAiSuggestionFromEnvelope } from "@/lib/ai/suggestions";
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import { buildInstitutionalDossier, getAuditSimulationDetail, getLatestInstitutionalDossierSnapshot, listAuditSimulationsForUser } from "@/lib/audits/server";
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const M10_PROMPT_VERSION = "m10_findings_v1";
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const M10FindingsSchema = z.object({
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auditorLikelyFindings: z
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.array(
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z.object({
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area: z.string().min(4).max(120),
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finding: z.string().min(8).max(500),
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severity: z.enum(["alto", "medio", "bajo"]),
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}),
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)
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.max(20),
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missingEvidence: z.array(z.string().min(6).max(280)).max(20),
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topRisks: z.array(z.string().min(6).max(280)).max(12),
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remediationPlan: z
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.array(
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z.object({
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step: z.string().min(8).max(420),
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priority: z.enum(["alta", "media", "baja"]),
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ownerSuggestion: z.string().min(3).max(120),
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targetDate: z.string().min(4).max(40),
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}),
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)
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.max(20),
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confidence: z.enum(["low", "medium", "high"]),
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});
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const M10FindingsJsonSchema = {
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type: "object",
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additionalProperties: false,
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required: ["auditorLikelyFindings", "missingEvidence", "topRisks", "remediationPlan", "confidence"],
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properties: {
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auditorLikelyFindings: {
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type: "array",
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items: {
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type: "object",
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additionalProperties: false,
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required: ["area", "finding", "severity"],
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properties: {
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area: { type: "string" },
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finding: { type: "string" },
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severity: { type: "string", enum: ["alto", "medio", "bajo"] },
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},
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},
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},
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missingEvidence: {
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type: "array",
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items: { type: "string" },
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},
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topRisks: {
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type: "array",
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items: { type: "string" },
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},
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remediationPlan: {
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type: "array",
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items: {
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type: "object",
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additionalProperties: false,
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required: ["step", "priority", "ownerSuggestion", "targetDate"],
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properties: {
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step: { type: "string" },
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priority: { type: "string", enum: ["alta", "media", "baja"] },
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ownerSuggestion: { type: "string" },
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targetDate: { type: "string" },
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},
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},
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},
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confidence: { type: "string", enum: ["low", "medium", "high"] },
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},
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} as const;
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function parseString(value: unknown) {
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return typeof value === "string" ? value.trim() : "";
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}
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async function resolveSimulation(userId: string, simulationId: string) {
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if (simulationId) {
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return getAuditSimulationDetail(userId, simulationId);
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}
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const simulations = await listAuditSimulationsForUser(userId);
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return simulations.find((simulation) => simulation.status === "COMPLETED") ?? simulations[0] ?? null;
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}
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export async function POST(request: Request) {
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const user = await requireAdminApiUser();
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if (!user) {
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return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
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}
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const body = (await request.json().catch(() => ({}))) as Record<string, unknown>;
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const simulationId = parseString(body.simulationId);
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const simulation = await resolveSimulation(user.id, simulationId);
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if (!simulation) {
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return NextResponse.json({ error: "No hay simulaciones disponibles para generar dictamen IA." }, { status: 400 });
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}
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const latestDossier = await getLatestInstitutionalDossierSnapshot(user.id);
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const dossier = latestDossier?.payload ?? (await buildInstitutionalDossier(user.id));
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const condensedContext = {
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simulation: {
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id: simulation.id,
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name: simulation.name,
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auditType: simulation.auditType,
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status: simulation.status,
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overallScore: simulation.overallScore,
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completedAt: simulation.completedAt,
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sections: simulation.sections,
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},
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dossier: {
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generatedAt: dossier.generatedAt,
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summary: dossier.summary,
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components: dossier.components,
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},
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};
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const systemPrompt = [
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"Eres auditor preventivo especializado en contratacion publica y cumplimiento documental en Mexico.",
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"Simula hallazgos probables de una revision formal sin modificar resultados deterministas existentes.",
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"Debes responder solo JSON valido en espanol.",
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].join(" ");
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const userPrompt = [
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"Contexto actual de simulacion y expediente institucional:",
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JSON.stringify(condensedContext),
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"",
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"Genera:",
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"- auditorLikelyFindings: hallazgos probables por area y severidad.",
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"- missingEvidence: evidencia concreta faltante.",
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"- topRisks: riesgos principales priorizados.",
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"- remediationPlan: plan de remediacion con prioridad, responsable sugerido y fecha objetivo.",
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].join("\n");
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const envelope = await callOpenAiJsonSchema({
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promptVersion: M10_PROMPT_VERSION,
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systemPrompt,
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userPrompt,
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outputSchema: M10FindingsSchema,
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schemaName: "m10_audit_findings",
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jsonSchema: M10FindingsJsonSchema as unknown as Record<string, unknown>,
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model: process.env.OPENAI_M10_MODEL?.trim() || undefined,
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});
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const payload =
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envelope.data ??
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({
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auditorLikelyFindings: [],
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missingEvidence: [],
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topRisks: [],
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remediationPlan: [],
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confidence: envelope.confidence ?? "low",
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} satisfies z.infer<typeof M10FindingsSchema>);
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const persisted = await storeAiSuggestionFromEnvelope({
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userId: user.id,
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moduleKey: "M10",
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featureKey: "audit_findings",
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subjectType: "audit_simulation",
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subjectId: simulation.id,
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inputForHash: condensedContext,
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envelope,
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responsePayload: payload,
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});
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return NextResponse.json({
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ok: true,
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simulationId: simulation.id,
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...payload,
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suggestionId: persisted.suggestionId,
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meta: {
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engine: envelope.engine,
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model: envelope.model,
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usage: envelope.usage,
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warnings: envelope.warnings,
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confidence: envelope.confidence,
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},
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});
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}
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