import { QdrantClient } from '@qdrant/js-client-rest';
import redis from './redis';

const isDockerContainer =
  process.env.IS_DOCKER === 'true' || process.env.HOSTNAME?.includes('klz-app');
const qdrantUrl =
  process.env.QDRANT_URL ||
  (isDockerContainer ? 'http://klz-qdrant:6333' : 'http://localhost:6333');
const qdrantApiKey = process.env.QDRANT_API_KEY || '';

export const qdrant = new QdrantClient({
  url: qdrantUrl,
  apiKey: qdrantApiKey || undefined,
  // Disable qdrant client's own version check to avoid the warning spam
  checkCompatibility: false,
});

export const COLLECTION_NAME = 'klz_products';
export const VECTOR_SIZE = 1536; // OpenAI text-embedding-3-small

// Cache TTLs
const EMBEDDING_CACHE_TTL = 60 * 60 * 24; // 24h — embeddings are deterministic
const SEARCH_CACHE_TTL = 60 * 30; // 30 min — product data could change

// Track collection existence in-memory (don't re-check every request)
let collectionVerified = false;

/**
 * Ensure the collection exists in Qdrant (only checks once per process lifetime).
 */
export async function ensureCollection() {
  if (collectionVerified) return;

  try {
    const collections = await qdrant.getCollections();
    const exists = collections.collections.some((c) => c.name === COLLECTION_NAME);
    if (!exists) {
      await qdrant.createCollection(COLLECTION_NAME, {
        vectors: {
          size: VECTOR_SIZE,
          distance: 'Cosine',
        },
      });
      console.log(`Successfully created Qdrant collection: ${COLLECTION_NAME}`);
    }
    collectionVerified = true;
  } catch (error) {
    console.error('Error ensuring Qdrant collection:', error);
  }
}

/**
 * Simple hash for cache keys
 */
function hashKey(text: string): string {
  let hash = 0;
  for (let i = 0; i < text.length; i++) {
    const chr = text.charCodeAt(i);
    hash = (hash << 5) - hash + chr;
    hash |= 0;
  }
  return hash.toString(36);
}

/**
 * Generate an embedding for a given text using OpenRouter (OpenAI embedding proxy).
 * Results are cached in Redis for 24h since embeddings are deterministic.
 *
 * NOTE: We keep OpenRouter for embeddings because the Qdrant collection uses 1536-dim
 * vectors (OpenAI text-embedding-3-small). Switching to Mistral embed (1024-dim) would
 * require re-indexing the entire product catalog.
 * User-facing chat uses Mistral AI directly for DSGVO compliance.
 */
export async function generateEmbedding(text: string): Promise<number[]> {
  const cacheKey = `emb:${hashKey(text.toLowerCase().trim())}`;

  // Try Redis cache first
  try {
    const cached = await redis.get(cacheKey);
    if (cached) {
      return JSON.parse(cached);
    }
  } catch {
    // Redis down — proceed without cache
  }

  const openRouterKey = process.env.OPENROUTER_API_KEY;
  if (!openRouterKey) {
    throw new Error('OPENROUTER_API_KEY is not set');
  }

  const response = await fetch('https://openrouter.ai/api/v1/embeddings', {
    method: 'POST',
    headers: {
      Authorization: `Bearer ${openRouterKey}`,
      'Content-Type': 'application/json',
      'HTTP-Referer': process.env.NEXT_PUBLIC_BASE_URL || 'https://klz-cables.com',
      'X-Title': 'KLZ Cables Search AI',
    },
    body: JSON.stringify({
      model: 'openai/text-embedding-3-small',
      input: text,
    }),
  });

  if (!response.ok) {
    const errorBody = await response.text();
    throw new Error(
      `Failed to generate embedding: ${response.status} ${response.statusText} ${errorBody}`,
    );
  }

  const data = await response.json();
  const embedding = data.data[0].embedding;

  // Cache the embedding in Redis
  try {
    await redis.set(cacheKey, JSON.stringify(embedding), 'EX', EMBEDDING_CACHE_TTL);
  } catch {
    // Redis down — proceed without caching
  }

  return embedding;
}

/**
 * Upsert a product into Qdrant
 */
export async function upsertProductVector(
  id: string | number,
  text: string,
  payload: Record<string, any>,
) {
  try {
    await ensureCollection();
    const vector = await generateEmbedding(text);

    await qdrant.upsert(COLLECTION_NAME, {
      wait: true,
      points: [
        {
          id: id,
          vector,
          payload,
        },
      ],
    });
  } catch (error) {
    console.error('Error writing to Qdrant:', error);
  }
}

/**
 * Delete a product from Qdrant
 */
export async function deleteProductVector(id: string | number) {
  try {
    await ensureCollection();
    await qdrant.delete(COLLECTION_NAME, {
      wait: true,
      points: [id] as [string | number],
    });
  } catch (error) {
    console.error('Error deleting from Qdrant:', error);
  }
}

/**
 * Search products in Qdrant.
 * Results are cached in Redis for 30 minutes keyed by query text.
 */
export async function searchProducts(query: string, limit = 5) {
  const cacheKey = `search:${hashKey(query.toLowerCase().trim())}:${limit}`;

  // Try Redis cache first
  try {
    const cached = await redis.get(cacheKey);
    if (cached) {
      console.log(`[Qdrant] Cache HIT for query: "${query.substring(0, 50)}"`);
      return JSON.parse(cached);
    }
  } catch {
    // Redis down — proceed without cache
  }

  try {
    await ensureCollection();
    const vector = await generateEmbedding(query);

    const results = await qdrant.search(COLLECTION_NAME, {
      vector,
      limit,
      with_payload: true,
    });

    // Cache results in Redis
    try {
      await redis.set(cacheKey, JSON.stringify(results), 'EX', SEARCH_CACHE_TTL);
    } catch {
      // Redis down — proceed without caching
    }

    return results;
  } catch (error) {
    console.error('Error searching in Qdrant:', error);
    return [];
  }
}