/**
 * Ollama LLM client for blog generation and content enhancement.
 *
 * Uses qwen2.5:14b on Mac Studio (.213) for text generation.
 * Supports streaming and non-streaming modes.
 * Includes 429 retry with exponential backoff + server-side concurrency guard.
 */

const OLLAMA_URL = process.env.OLLAMA_URL || "http://localhost:11434";
const LLM_MODEL = process.env.OLLAMA_LLM_MODEL || "qwen2.5:14b";

interface LlmResponse {
  text: string;
  model: string;
  totalDuration: number;
  evalCount: number;
}

/** Sleep helper */
function sleep(ms: number): Promise<void> {
  return new Promise((resolve) => setTimeout(resolve, ms));
}

/**
 * Server-side concurrency guard — Ollama processes one generation at a time.
 * Queue ensures sequential execution even with multiple concurrent API requests.
 */
let ollamaQueue: Promise<unknown> = Promise.resolve();

function enqueueOllama<T>(fn: () => Promise<T>): Promise<T> {
  const result = ollamaQueue.then(fn);
  // Keep queue alive even if fn throws (attach no-op error handler on chain)
  ollamaQueue = result.catch(() => {});
  return result;
}

/** Generate text from a system prompt + user prompt — with 429 retry/backoff + queue */
export async function generate(
  systemPrompt: string,
  userPrompt: string,
  options?: { temperature?: number; maxTokens?: number; timeoutMs?: number },
): Promise<LlmResponse> {
  return enqueueOllama(async () => {
    const RETRY_DELAYS = [15000, 30000, 60000]; // 15s, 30s, 60s

    for (let attempt = 0; attempt <= RETRY_DELAYS.length; attempt++) {
      if (attempt > 0) {
        const delay = RETRY_DELAYS[attempt - 1];
        console.log(`Blog LLM: 429 rate-limit — retrying in ${delay / 1000}s (attempt ${attempt}/${RETRY_DELAYS.length})`);
        await sleep(delay);
      }

      const resp = await fetch(`${OLLAMA_URL}/api/generate`, {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify({
          model: LLM_MODEL,
          prompt: userPrompt,
          system: systemPrompt,
          stream: false,
          options: {
            temperature: options?.temperature ?? 0.7,
            num_predict: options?.maxTokens ?? 4096,
          },
        }),
        signal: AbortSignal.timeout(options?.timeoutMs ?? 300000),
      });

      if (resp.status === 429) {
        if (attempt < RETRY_DELAYS.length) continue;
        throw new Error(`Ollama generate failed: 429 Too Many Requests (all retries exhausted)`);
      }

      if (!resp.ok) {
        const errText = await resp.text();
        throw new Error(`Ollama generate failed: ${resp.status} ${errText}`);
      }

      const data = await resp.json() as {
        response: string;
        model: string;
        total_duration: number;
        eval_count: number;
      };

      return {
        text: data.response,
        model: data.model,
        totalDuration: data.total_duration,
        evalCount: data.eval_count,
      };
    }

    throw new Error("Ollama generate: unreachable");
  });
}

/** Chat-style generation with message history */
export async function chat(
  messages: ReadonlyArray<{ role: "system" | "user" | "assistant"; content: string }>,
  options?: { temperature?: number; maxTokens?: number },
): Promise<LlmResponse> {
  return enqueueOllama(async () => {
    const resp = await fetch(`${OLLAMA_URL}/api/chat`, {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify({
        model: LLM_MODEL,
        messages,
        stream: false,
        options: {
          temperature: options?.temperature ?? 0.7,
          num_predict: options?.maxTokens ?? 4096,
        },
      }),
      signal: AbortSignal.timeout(300000),
    });

    if (!resp.ok) {
      const errText = await resp.text();
      throw new Error(`Ollama chat failed: ${resp.status} ${errText}`);
    }

    const data = await resp.json() as {
      message: { content: string };
      model: string;
      total_duration: number;
      eval_count: number;
    };

    return {
      text: data.message.content,
      model: data.model,
      totalDuration: data.total_duration,
      evalCount: data.eval_count,
    };
  });
}

/** Check if Ollama is available and model is loaded */
export async function checkHealth(): Promise<{ ok: boolean; model: string; error?: string }> {
  try {
    const resp = await fetch(`${OLLAMA_URL}/api/tags`, { signal: AbortSignal.timeout(5000) });
    if (!resp.ok) return { ok: false, model: LLM_MODEL, error: `HTTP ${resp.status}` };

    const data = await resp.json() as { models: Array<{ name: string }> };
    const hasModel = data.models.some((m) => m.name.includes(LLM_MODEL.split(":")[0]));

    return { ok: hasModel, model: LLM_MODEL, error: hasModel ? undefined : `Model ${LLM_MODEL} not found` };
  } catch (err) {
    return { ok: false, model: LLM_MODEL, error: (err as Error).message };
  }
}