botino/src/services/openai.js

import OpenAI from "openai";
import { z } from "zod";

let _client = null;
let _clientKey = null;

function getApiKey() {
  return process.env.OPENAI_API_KEY || process.env.OPENAI_APIKEY || null;
}

function getClient() {
  const apiKey = getApiKey();
  if (!apiKey) {
    const err = new Error("OPENAI_API_KEY is not set");
    err.code = "OPENAI_NO_KEY";
    throw err;
  }
  if (_client && _clientKey === apiKey) return _client;
  _clientKey = apiKey;
  _client = new OpenAI({ apiKey });
  return _client;
}

const NextStateSchema = z.enum([
  "IDLE",
  "BROWSING",
  "BUILDING_ORDER",
  "WAITING_ADDRESS",
  "WAITING_PAYMENT",
  "COMPLETED",
]);

const IntentSchema = z.enum([
  "ask_recommendation",
  "ask_price",
  "browse_products",
  "create_order",
  "add_item",
  "remove_item",
  "checkout",
  "provide_address",
  "confirm_payment",
  "track_order",
  "other",
]);

const OrderActionSchema = z.enum(["none", "create", "update", "cancel", "checkout"]);

const BasketItemSchema = z.object({
  product_id: z.number().int().nonnegative(),
  variation_id: z.number().int().nonnegative().nullable(),
  quantity: z.number().positive(),
  unit: z.enum(["kg", "g", "unit"]),
  label: z.string().min(1),
});

const PlanSchema = z
  .object({
    reply: z.string().min(1).max(350).catch(z.string().min(1)), // respetar guideline, sin romper si excede
    next_state: NextStateSchema,
    intent: IntentSchema,
    missing_fields: z.array(z.string()).default([]),
    order_action: OrderActionSchema.default("none"),
    basket_resolved: z
      .object({
        items: z.array(BasketItemSchema).default([]),
      })
      .default({ items: [] }),
  })
  .strict();

const ExtractItemSchema = z.object({
  label: z.string().min(1),
  quantity: z.number().positive(),
  unit: z.enum(["kg", "g", "unit"]),
});

const ExtractSchema = z
  .object({
    intent: IntentSchema,
    items: z.array(ExtractItemSchema).default([]),
  })
  .strict();

function extractJsonObject(text) {
  const s = String(text || "");
  const i = s.indexOf("{");
  const j = s.lastIndexOf("}");
  if (i >= 0 && j > i) return s.slice(i, j + 1);
  return null;
}

async function jsonCompletion({ system, user, model }) {
  const openai = getClient();
  const chosenModel = model || process.env.OPENAI_MODEL || "gpt-4o-mini";
  const debug = String(process.env.LLM_DEBUG || "") === "1";
  if (debug) console.log("[llm] openai.request", { model: chosenModel });

  const resp = await openai.chat.completions.create({
    model: chosenModel,
    temperature: 0.2,
    response_format: { type: "json_object" },
    messages: [
      { role: "system", content: system },
      { role: "user", content: user },
    ],
  });

  if (debug)
    console.log("[llm] openai.response", {
      id: resp?.id || null,
      model: resp?.model || null,
      usage: resp?.usage || null,
    });

  const text = resp?.choices?.[0]?.message?.content || "";
  let parsed;
  try {
    parsed = JSON.parse(text);
  } catch {
    const extracted = extractJsonObject(text);
    if (!extracted) throw new Error("openai_invalid_json");
    parsed = JSON.parse(extracted);
  }
  return { parsed, raw_text: text, model: chosenModel, usage: resp?.usage || null };
}

/**
 * Genera un "plan" de conversación (salida estructurada) usando OpenAI.
 *
 * - `promptSystem`: instrucciones del bot
 * - `input`: { last_user_message, conversation_history, current_conversation_state, context }
 */
export async function llmPlan({ promptSystem, input, model } = {}) {
  const system =
    `${promptSystem}\n\n` +
    "Respondé SOLO con un JSON válido (sin markdown). Respetá estrictamente el formato requerido.";
  const { parsed, raw_text, model: chosenModel, usage } = await jsonCompletion({
    system,
    user: JSON.stringify(input ?? {}),
    model,
  });

  const plan = PlanSchema.parse(parsed);
  return {
    plan,
    raw_text,
    model: chosenModel,
    usage,
  };
}

/**
 * Paso 1: extracción de intención + items mencionados (sin resolver IDs).
 * Devuelve SOLO: intent + items[{label, quantity, unit}]
 */
export async function llmExtract({ input, model } = {}) {
  const system =
    "Extraé intención e items del mensaje del usuario.\n" +
    "Respondé SOLO JSON válido (sin markdown) con keys EXACTAS:\n" +
    `intent (one of: ${IntentSchema.options.join("|")}), items (array of {label, quantity, unit(kg|g|unit)}).\n` +
    "Si no hay items claros, devolvé items: [].";

  const { parsed, raw_text, model: chosenModel, usage } = await jsonCompletion({
    system,
    user: JSON.stringify(input ?? {}),
    model,
  });

  const extracted = ExtractSchema.parse(parsed);
  return { extracted, raw_text, model: chosenModel, usage };
}