Contexto para IA

async function buildContextForContact(contactPhone: string) {
  const url = new URL("https://crm.oficinamartech.com/api/contacts/message-search");
  url.searchParams.set("organization_id", ORG_ID);
  url.searchParams.set("q", "."); // qualquer caractere — força lookup pelo identificador
  url.searchParams.set("direction", "all");
  url.searchParams.set("messages_limit", "20");

  // Filtra do lado do cliente após o retorno
  const res = await fetch(url, {
    headers: { Authorization: `Bearer ${OFM_API_KEY}` },
  });
  const { data } = await res.json();

  const match = data.find((c) => c.contact.phone === contactPhone);
  if (!match) return null;

  return {
    contact: {
      name: match.contact.name,
      phone: match.contact.phone,
      email: match.contact.email,
    },
    recentMessages: match.messages.map((m) => ({
      role: m.direction === "inbound" ? "user" : "assistant",
      content: m.content,
      at: m.sent_at,
    })),
  };
}

const ctx = await buildContextForContact("+5511999999999");

const systemPrompt = `Você é um assistente comercial. Contexto do contato:

Nome: ${ctx.contact.name}
Últimas mensagens:
${ctx.recentMessages
  .map((m) => `[${m.at}] ${m.role}: ${m.content}`)
  .join("\n")}

Responda de forma contextual e personalizada.`;

const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "system", content: systemPrompt },
    { role: "user", content: "Resuma o histórico em 2 frases." },
  ],
});