/**
 * LLM client for blog generation — supports Ollama (local) and Anthropic Claude (API).
 *
 * Provider selection:
 *   BLOG_LLM_PROVIDER=anthropic  → Claude Sonnet/Haiku via Anthropic API
 *   BLOG_LLM_PROVIDER=ollama     → qwen2.5 on local Ollama (default)
 *
 * Claude is strongly recommended for blog generation — qwen2.5:14b cannot
 * follow complex multi-constraint prompts (mode collapse).
 */

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

const ANTHROPIC_API_KEY = process.env.ANTHROPIC_API_KEY || "";
const ANTHROPIC_MODEL = process.env.ANTHROPIC_MODEL || "claude-sonnet-4-20250514";
const BLOG_LLM_PROVIDER = process.env.BLOG_LLM_PROVIDER || "ollama";

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

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

// ═══════════════════════════════════════════════════════
// ANTHROPIC CLAUDE PROVIDER
// ═══════════════════════════════════════════════════════

// Serialize Claude API calls to stay within TPM limits
// Tier-1 has 40,000 TPM — with ~20K tokens/step, only 1 concurrent call safe
let claudeQueue: Promise<unknown> = Promise.resolve();
let claudeQueueEnqueueTime = 0;

export function resetClaudeQueue(): void {
  claudeQueue = Promise.resolve();
  claudeQueueEnqueueTime = 0;
  console.log("[LLM] Claude queue reset — previous stuck requests cleared");
}

function enqueueClaude<T>(fn: () => Promise<T>): Promise<T> {
  claudeQueueEnqueueTime = Date.now();
  const result = claudeQueue.then(() => {
    // Auto-reset if queue has been stalled > 15 minutes (prevents deadlock on stuck requests)
    if (Date.now() - claudeQueueEnqueueTime > 900000) {
      console.warn("[LLM] Claude queue auto-reset after 15min stall");
      return Promise.reject(new Error("Claude queue auto-reset: previous request timed out"));
    }
    return fn();
  });
  claudeQueue = result.catch(() => {});
  return result;
}

// Direct API call without going through the serialization queue — used for 429 retries
// to avoid the circular-promise deadlock that recursive enqueueClaude creates
async function claudeApiCall(
  systemPrompt: string,
  userPrompt: string,
  options?: { temperature?: number; maxTokens?: number; timeoutMs?: number },
  retryCount = 0,
): Promise<LlmResponse> {
  const startTime = Date.now();
  const MAX_RETRIES = 3;
  const RETRY_DELAYS = [10000, 30000, 60000];

  const resp = await fetch("https://api.anthropic.com/v1/messages", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "x-api-key": ANTHROPIC_API_KEY,
      "anthropic-version": "2023-06-01",
    },
    body: JSON.stringify({
      model: ANTHROPIC_MODEL,
      max_tokens: options?.maxTokens ?? 4096,
      temperature: options?.temperature ?? 0.7,
      system: systemPrompt,
      messages: [{ role: "user", content: userPrompt }],
    }),
    signal: AbortSignal.timeout(options?.timeoutMs ?? 300000),
  });

  if (!resp.ok) {
    const errText = await resp.text();
    if (resp.status === 429 && retryCount < MAX_RETRIES) {
      const delay = RETRY_DELAYS[retryCount] ?? 60000;
      console.log(`[LLM] Claude 429 — retrying in ${delay / 1000}s (attempt ${retryCount + 1}/${MAX_RETRIES})...`);
      await sleep(delay);
      return claudeApiCall(systemPrompt, userPrompt, options, retryCount + 1);
    }
    throw new Error(`Claude API failed: ${resp.status} ${errText.slice(0, 200)}`);
  }

  const data = await resp.json() as {
    content: Array<{ type: string; text: string }>;
    model: string;
    usage: { input_tokens: number; output_tokens: number };
  };

  const text = data.content
    .filter((c) => c.type === "text")
    .map((c) => c.text)
    .join("");

  const duration = Date.now() - startTime;
  console.log(`[LLM] Claude ${data.model}: ${data.usage.input_tokens}+${data.usage.output_tokens} tokens, ${duration}ms`);

  return {
    text,
    model: data.model,
    totalDuration: duration * 1_000_000, // ns for compat
    evalCount: data.usage.output_tokens,
  };
}

async function generateClaude(
  systemPrompt: string,
  userPrompt: string,
  options?: { temperature?: number; maxTokens?: number; timeoutMs?: number },
): Promise<LlmResponse> {
  if (!ANTHROPIC_API_KEY) {
    throw new Error("ANTHROPIC_API_KEY not set — cannot use Claude provider");
  }
  // Use enqueueClaude for serialization, but call claudeApiCall (not generateClaude)
  // for retries to avoid circular-promise deadlock
  return enqueueClaude(() => claudeApiCall(systemPrompt, userPrompt, options));
}

// ═══════════════════════════════════════════════════════
// OLLAMA PROVIDER (existing)
// ═══════════════════════════════════════════════════════

let ollamaQueue: Promise<unknown> = Promise.resolve();
let queueDepth = 0;
let lastQueueEnqueueTime = 0;

export function resetOllamaQueue(): void {
  ollamaQueue = Promise.resolve();
  queueDepth = 0;
  console.log("[LLM] Queue reset — previous stuck requests cleared");
}

export function getQueueDepth(): number { return queueDepth; }

function enqueueOllama<T>(fn: () => Promise<T>): Promise<T> {
  queueDepth++;
  lastQueueEnqueueTime = Date.now();
  const result = ollamaQueue.then(() => {
    if (Date.now() - lastQueueEnqueueTime > 900000) {
      console.warn("[LLM] Queue auto-reset after 15min stall");
      queueDepth = Math.max(0, queueDepth - 1);
      return Promise.reject(new Error("Queue auto-reset: previous request timed out"));
    }
    return fn();
  });
  ollamaQueue = result.catch(() => {}).then(() => { queueDepth = Math.max(0, queueDepth - 1); });
  return result;
}

async function generateOllama(
  systemPrompt: string,
  userPrompt: string,
  options?: { temperature?: number; maxTokens?: number; timeoutMs?: number },
): Promise<LlmResponse> {
  return enqueueOllama(async () => {
    const RETRY_DELAYS = [15000, 30000, 60000];

    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");
  });
}

// ═══════════════════════════════════════════════════════
// PUBLIC API — auto-routes to configured provider
// ═══════════════════════════════════════════════════════

export async function generate(
  systemPrompt: string,
  userPrompt: string,
  options?: { temperature?: number; maxTokens?: number; timeoutMs?: number },
): Promise<LlmResponse> {
  if (BLOG_LLM_PROVIDER === "anthropic" && ANTHROPIC_API_KEY) {
    return generateClaude(systemPrompt, userPrompt, options);
  }
  return generateOllama(systemPrompt, userPrompt, options);
}

/** Chat-style generation with message history (Ollama only for now) */
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 configured LLM provider is available */
export async function checkHealth(): Promise<{ ok: boolean; model: string; provider: string; error?: string }> {
  if (BLOG_LLM_PROVIDER === "anthropic" && ANTHROPIC_API_KEY) {
    // Key presence check only — live API call causes 429 when pipeline is running
    return { ok: true, model: ANTHROPIC_MODEL, provider: "anthropic" };
  }

  try {
    const resp = await fetch(`${OLLAMA_URL}/api/tags`, { signal: AbortSignal.timeout(5000) });
    if (!resp.ok) return { ok: false, model: LLM_MODEL, provider: "ollama", 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, provider: "ollama", error: hasModel ? undefined : `Model ${LLM_MODEL} not found` };
  } catch (err) {
    return { ok: false, model: LLM_MODEL, provider: "ollama", error: (err as Error).message };
  }
}