/**
 * Vector similarity utilities
 */

/**
 * Compute cosine similarity between two vectors
 * @param {number[]} a
 * @param {number[]} b
 * @returns {number} Similarity score between -1 and 1
 */
function cosineSimilarity(a, b) {
  if (!a || !b || a.length !== b.length) {
    return 0;
  }

  let dotProduct = 0;
  let normA = 0;
  let normB = 0;

  for (let i = 0; i < a.length; i++) {
    dotProduct += a[i] * b[i];
    normA += a[i] * a[i];
    normB += b[i] * b[i];
  }

  const denominator = Math.sqrt(normA) * Math.sqrt(normB);
  if (denominator === 0) return 0;

  return dotProduct / denominator;
}

/**
 * Find top K most similar items from a collection
 * @param {number[]} queryEmbedding - The embedding to search for
 * @param {Array<{id: any, embedding: number[]}>} items - Items with embeddings
 * @param {number} topK - Number of results to return
 * @returns {Array<{id: any, similarity: number}>}
 */
function findTopMatches(queryEmbedding, items, topK = 10) {
  if (!queryEmbedding || !items || items.length === 0) {
    return [];
  }

  const scored = items.map(item => ({
    id: item.id,
    similarity: cosineSimilarity(queryEmbedding, item.embedding)
  }));

  scored.sort((a, b) => b.similarity - a.similarity);

  return scored.slice(0, topK);
}

/**
 * Find matches above a similarity threshold
 * @param {number[]} queryEmbedding
 * @param {Array<{id: any, embedding: number[]}>} items
 * @param {number} threshold - Minimum similarity (0-1)
 * @returns {Array<{id: any, similarity: number}>}
 */
function findMatchesAboveThreshold(queryEmbedding, items, threshold = 0.5) {
  if (!queryEmbedding || !items || items.length === 0) {
    return [];
  }

  const scored = items
    .map(item => ({
      id: item.id,
      similarity: cosineSimilarity(queryEmbedding, item.embedding)
    }))
    .filter(item => item.similarity >= threshold);

  scored.sort((a, b) => b.similarity - a.similarity);

  return scored;
}

module.exports = {
  cosineSimilarity,
  findTopMatches,
  findMatchesAboveThreshold
};