Reemplaza el NLU rígido (intent+entities) por un agente LLM con tool-calling
que decide y muta estado en cada turno. Opt-in vía AGENT_TURN_ENGINE=1.
DeepSeek V4 (deepseek-chat) configurado como modelo (OpenAI-compatible).
Arquitectura nueva en src/modules/3-turn-engine/agent/:
- workingMemory.js: arma el JSON contextual que recibe el LLM cada turno
(cart, pending, last_shown_options, store, customer_profile, history,
preparsed quantity).
- systemPrompt.js: prompt estático ~70 líneas. Define rol + reglas duras +
cómo procesar mensajes + cómo escribir el say. Sin enumeración de intents.
- runTurn.js: loop de tool-calling con tool_choice="required". Cap 10 tool
calls / 20s timeout. Métricas in-memory.
- customerProfile.js: lookup de frequent_items en woo_orders_cache por
teléfono (chat_id → phone), top 5 últimos 6 meses. Cache 10 min.
- tools/schemas.js: 11 tools (search_catalog, add_to_cart, set_quantity,
select_candidate, remove_from_cart, set_shipping, set_address,
confirm_order, pause, escalate_to_human, say).
- tools/executor.js: validación Ajv + dispatch + observación al LLM.
woo_id se valida contra snapshot — si no existe el agente vuelve a
search_catalog (anti-halucinación).
- tools/searchCatalog.js: wrappea retrieveCandidates + fallback por
categoría usando jsonb_array_elements_text del snapshot. Persiste
last_shown_options automáticamente.
- tools/{addToCart, setQuantity, selectCandidate, removeFromCart,
setShipping, setAddress, confirmOrder, pause, escalateToHuman}.js:
side effects atómicos sobre el order.
- quantityParser.js (D1): determinístico, parsea fracciones, frases
compuestas (media docena, cuarto kilo), numéricos. 46 tests.
FSM extendida (fsm.js): nuevo estado PAUSED (TTL 7d, cart preservado,
"después te digo" → pause tool).
pipeline.js: TTL stale ahora 24h general, 7d si PAUSED, infinito si
AWAITING_HUMAN.
turnEngineV3.js: nuevas flags AGENT_TURN_ENGINE y AGENT_TURN_ENGINE_SHADOW.
Branch a runTurnAgent cuando full o corre en paralelo escribiendo diffs
estructurales en audit_log (entity_type='agent_shadow') para validar
paridad antes de flippar.
Endpoint nuevo: GET /api/metrics/agent → turns, avg_tool_calls, fallback
rate, escalations, pauses, orders_confirmed.
Smoke test E2E con DeepSeek real:
- "hola" → say (2.3s, 1 tool)
- "2kg de vacio" → search → add_to_cart → say (8.8s, 3 tools)
- "media docena de chorizos" → search → say con clarificación (10.3s, 4 tools)
- "listo" → say (3.3s, 1 tool)
- "retiro" → set_shipping → confirm → say (5.1s, 3 tools)
Cart final correcto: 2kg de Vacío. Estado: CART → SHIPPING.
Tests: 238/238 pasando.
D9 (cleanup legacy ~1200 LOC NLU/handlers/replyRewriter) DEFERRED:
se hace después de paridad shadow validada con tráfico real. Hoy
agente coexiste con legacy; default sigue siendo el motor V3.
Plan completo en ~/.claude/plans/ok-creo-que-tiene-humming-sutton.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
320 lines
10 KiB
JavaScript
320 lines
10 KiB
JavaScript
/**
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* runTurn — Punto de entrada del agente tool-calling.
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*
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* Reemplaza turnEngineV3 cuando AGENT_TURN_ENGINE=1.
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* Mantiene la firma compatible con pipeline.js:
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* runTurnAgent({ tenantId, chat_id, text, prev_state, prev_context, conversation_history })
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* → { plan, decision }
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*/
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import OpenAI from "openai";
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import { migrateOldContext, createEmptyOrder } from "../orderModel.js";
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import { getStoreConfig } from "../../0-ui/db/settingsRepo.js";
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import { ConversationState, safeNextState } from "../fsm.js";
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import { buildWorkingMemory } from "./workingMemory.js";
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import { buildSystemPrompt } from "./systemPrompt.js";
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import { TOOL_SCHEMAS } from "./tools/schemas.js";
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import { executeToolCall } from "./tools/executor.js";
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import { getCustomerProfile } from "./customerProfile.js";
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import { debug as dbg } from "../../shared/debug.js";
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const MAX_TOOL_CALLS = parseInt(process.env.AGENT_MAX_TOOL_CALLS || "10", 10);
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const TURN_TIMEOUT_MS = parseInt(process.env.AGENT_TURN_TIMEOUT_MS || "20000", 10);
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// Métricas in-memory: turns/calls, fallback rate, escalation rate, avg duration.
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const _metrics = {
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turns: 0,
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total_tool_calls: 0,
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total_llm_calls: 0,
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total_duration_ms: 0,
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fallback_used: 0,
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llm_errors: 0,
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escalations: 0,
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pauses: 0,
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orders_confirmed: 0,
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};
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export function getAgentMetrics() {
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const t = _metrics.turns;
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return {
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turns: t,
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avg_tool_calls_per_turn: t ? +(_metrics.total_tool_calls / t).toFixed(2) : 0,
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avg_llm_calls_per_turn: t ? +(_metrics.total_llm_calls / t).toFixed(2) : 0,
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avg_duration_ms: t ? Math.round(_metrics.total_duration_ms / t) : 0,
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fallback_rate: t ? +(_metrics.fallback_used / t).toFixed(3) : 0,
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error_rate: t ? +(_metrics.llm_errors / t).toFixed(3) : 0,
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escalations: _metrics.escalations,
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pauses: _metrics.pauses,
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orders_confirmed: _metrics.orders_confirmed,
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};
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}
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export function resetAgentMetrics() {
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for (const k of Object.keys(_metrics)) _metrics[k] = 0;
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}
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let _client = null;
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function getClient() {
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if (_client) return _client;
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const apiKey = process.env.OPENAI_API_KEY;
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if (!apiKey) throw new Error("OPENAI_API_KEY not set");
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const baseURL = process.env.OPENAI_BASE_URL || undefined;
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_client = new OpenAI({ apiKey, ...(baseURL ? { baseURL } : {}) });
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return _client;
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}
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function getModel() {
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return process.env.OPENAI_MODEL || "deepseek-chat";
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}
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function withTimeout(promise, ms, label) {
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return Promise.race([
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promise,
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new Promise((_, reject) =>
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setTimeout(() => reject(new Error(`${label}_timeout_${ms}ms`)), ms)
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),
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]);
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}
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/**
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* Punto de entrada principal. Mismo signature que runTurnV3.
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*/
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export async function runTurnAgent({
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tenantId,
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chat_id,
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text,
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prev_state,
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prev_context,
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conversation_history,
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}) {
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const t0 = Date.now();
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const audit = {
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trace: { tenantId, chat_id, text_preview: String(text || "").slice(0, 50), prev_state, engine: "agent" },
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tool_calls: [],
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llm_calls: 0,
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};
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// Cargar order, store, last_shown_options, customer_profile
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const order = migrateOldContext(prev_context);
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const storeConfig = await getStoreConfig({ tenantId });
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const lastShownOptions = Array.isArray(prev_context?.last_shown_options)
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? prev_context.last_shown_options
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: [];
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const customerProfile = await getCustomerProfile({ tenantId, chat_id }).catch((err) => {
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audit.customer_profile_error = String(err?.message || err);
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return null;
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});
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// Construir working memory
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const wm = buildWorkingMemory({
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text,
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order,
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prev_state: prev_state || "IDLE",
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conversation_history,
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storeConfig,
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customerProfile,
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lastShownOptions,
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});
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// Estado mutable que los tools mutan
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const ctx = {
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tenantId,
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chat_id,
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order: { ...order, last_shown_options: lastShownOptions },
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pending_actions: [],
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last_shown_options: [...lastShownOptions],
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storeConfig,
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say_text: null,
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paused: false,
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paused_until: order.paused_until ?? null,
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awaiting_human: false,
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awaiting_human_reason: null,
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fsm_state: prev_state || "IDLE",
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};
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// Mensajes para el LLM
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const systemPrompt = buildSystemPrompt({ storeName: storeConfig?.name });
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const messages = [
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{ role: "system", content: systemPrompt },
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{ role: "user", content: JSON.stringify({ working_memory: wm }) },
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];
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// Loop tool-calling
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const client = getClient();
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const model = getModel();
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let turnDone = false;
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let llmError = null;
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try {
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for (let i = 0; i < MAX_TOOL_CALLS && !turnDone; i++) {
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audit.llm_calls++;
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const elapsed = Date.now() - t0;
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const remaining = Math.max(2000, TURN_TIMEOUT_MS - elapsed);
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if (dbg.llm) console.log("[agent] llm.request", { model, iteration: i, remaining_ms: remaining });
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const resp = await withTimeout(
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client.chat.completions.create({
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model,
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temperature: 0.4,
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max_tokens: 600,
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tools: TOOL_SCHEMAS,
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tool_choice: "required",
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messages,
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}),
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remaining,
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"agent_llm"
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);
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const msg = resp?.choices?.[0]?.message || {};
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messages.push({ role: "assistant", content: msg.content || "", tool_calls: msg.tool_calls || [] });
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const calls = msg.tool_calls || [];
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if (!calls.length) {
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// Sin tool calls → forzar say con fallback y salir
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audit.no_tool_calls = true;
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ctx.say_text = ctx.say_text || msg.content || "Disculpame, no te entendí. ¿Me lo decís de otra forma?";
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turnDone = true;
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break;
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}
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for (const call of calls) {
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const obs = await executeToolCall(call, ctx);
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audit.tool_calls.push({
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name: call.function?.name,
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ok: obs.ok !== false,
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error: obs.error || null,
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duration_ms: obs.duration_ms || null,
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});
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messages.push({
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role: "tool",
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tool_call_id: call.id,
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content: JSON.stringify(obs),
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});
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if (obs.terminal) {
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turnDone = true;
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}
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}
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}
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} catch (err) {
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llmError = String(err?.message || err);
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audit.llm_error = llmError;
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if (dbg.llm) console.error("[agent] error", llmError);
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}
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// Si no hay say, fallback determinista
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if (!ctx.say_text) {
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ctx.say_text = pickFallbackReply(ctx, llmError);
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audit.fallback_used = true;
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}
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audit.duration_ms = Date.now() - t0;
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// Actualizar métricas
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_metrics.turns++;
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_metrics.total_tool_calls += audit.tool_calls.length;
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_metrics.total_llm_calls += audit.llm_calls;
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_metrics.total_duration_ms += audit.duration_ms;
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if (audit.fallback_used) _metrics.fallback_used++;
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if (audit.llm_error) _metrics.llm_errors++;
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if (ctx.awaiting_human) _metrics.escalations++;
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if (ctx.paused) _metrics.pauses++;
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if (ctx.pending_actions.some((a) => a.type === "create_order")) _metrics.orders_confirmed++;
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// Derivar nextState desde el order resultante
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const signals = {
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confirm_order: ctx.pending_actions.some((a) => a.type === "create_order"),
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shipping_completed: ctx.pending_actions.some((a) => a.type === "create_order"),
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return_to_cart: false,
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};
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// Si el agente pausó la conversación, mantenemos el order pero el next_state
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// queda guardado en ctx.fsm_state ("PAUSED") para que pipeline lo persista.
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let nextState;
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if (ctx.awaiting_human) {
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nextState = ConversationState.AWAITING_HUMAN;
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} else if (ctx.paused) {
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nextState = "PAUSED"; // estado nuevo, fsm.js lo va a permitir tras D7
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} else {
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nextState = safeNextState(prev_state, ctx.order, signals).next_state;
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}
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return {
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plan: {
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reply: ctx.say_text,
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next_state: nextState,
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intent: detectIntent(audit.tool_calls),
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missing_fields: [],
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order_action: ctx.pending_actions[0]?.type || "none",
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basket_resolved: { items: (ctx.order.cart || []).map(toBasketItem) },
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},
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decision: {
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actions: ctx.pending_actions,
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context_patch: buildContextPatch(ctx),
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audit,
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},
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};
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}
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function pickFallbackReply(ctx, err) {
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if (ctx.awaiting_human) return "Te paso con un humano que pueda ayudarte.";
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if (ctx.paused) return "Dale, cuando quieras seguimos.";
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if (err) return "Disculpame, tuve un problema. ¿Lo intentás de nuevo?";
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return "No te seguí, ¿me lo decís de otra forma?";
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}
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function detectIntent(toolCalls = []) {
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const names = toolCalls.map((c) => c.name);
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if (names.includes("confirm_order")) return "confirm_order";
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if (names.includes("set_address") || names.includes("set_shipping")) return "select_shipping";
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if (names.includes("escalate_to_human")) return "escalate";
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if (names.includes("pause")) return "pause";
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if (names.includes("add_to_cart") || names.includes("set_quantity") || names.includes("select_candidate")) return "add_to_cart";
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if (names.includes("remove_from_cart")) return "remove_from_cart";
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if (names.includes("search_catalog")) return "browse";
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return "other";
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}
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function toBasketItem(item) {
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return {
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product_id: item.woo_id,
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woo_product_id: item.woo_id,
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quantity: item.qty,
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unit: item.unit,
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label: item.name,
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name: item.name,
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price: item.price,
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};
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}
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function buildContextPatch(ctx) {
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const order = ctx.order || createEmptyOrder();
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return {
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order,
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order_basket: { items: (order.cart || []).map(toBasketItem) },
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pending_items: (order.pending || []).map((p) => ({
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id: p.id,
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query: p.query,
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candidates: p.candidates,
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resolved_product: p.selected_woo_id
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? {
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woo_product_id: p.selected_woo_id,
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name: p.selected_name,
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price: p.selected_price,
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display_unit: p.selected_unit,
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}
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: null,
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quantity: p.qty,
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unit: p.unit,
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status: p.status?.toLowerCase() || "needs_type",
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})),
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shipping_method:
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order.is_delivery === true ? "delivery" : order.is_delivery === false ? "pickup" : null,
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delivery_address: order.shipping_address ? { text: order.shipping_address } : null,
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woo_order_id: order.woo_order_id,
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last_shown_options: ctx.last_shown_options || [],
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paused_until: ctx.paused_until || null,
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awaiting_human: ctx.awaiting_human || false,
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awaiting_human_reason: ctx.awaiting_human_reason || null,
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};
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}
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