import { env } from "@/lib/env";

type ChatMessage = {
  role: "system" | "user" | "assistant";
  content: string;
};

type ChatCompletion = {
  choices?: Array<{
    message?: {
      content?: string;
    };
  }>;
  usage?: {
    total_tokens?: number;
  };
};

export type LlmResult = {
  content: string;
  model: string;
  tokenCount?: number;
  raw?: unknown;
};

export async function runLlmChat(messages: ChatMessage[], model = env.DEFAULT_LLM_MODEL): Promise<LlmResult> {
  if (!env.LITELLM_BASE_URL || !env.LITELLM_API_KEY) {
    return {
      content: localFallback(messages),
      model: `${model}:fallback`,
    };
  }

  const response = await fetch(`${env.LITELLM_BASE_URL.replace(/\/$/, "")}/chat/completions`, {
    method: "POST",
    headers: {
      "content-type": "application/json",
      authorization: `Bearer ${env.LITELLM_API_KEY}`,
    },
    body: JSON.stringify({
      model,
      messages,
      temperature: 0.2,
    }),
  });

  if (!response.ok) {
    const errorBody = await response.text();
    throw new Error(`LiteLLM request failed: ${response.status} ${errorBody}`);
  }

  const data = (await response.json()) as ChatCompletion;
  return {
    content: data.choices?.[0]?.message?.content?.trim() ?? "",
    model,
    tokenCount: data.usage?.total_tokens,
    raw: data,
  };
}

function localFallback(messages: ChatMessage[]) {
  const latestUserMessage = [...messages].reverse().find((message) => message.role === "user")?.content ?? "";
  return [
    "Lokaler Fallback: Es ist kein LiteLLM-Endpunkt konfiguriert.",
    "Die Anfrage wurde strukturiert angenommen und muss nach Konfiguration eines Modells erneut ausgeführt werden.",
    `Auszug der Anfrage: ${latestUserMessage.slice(0, 420)}`,
  ].join("\n\n");
}