/**
 * Distance functions for vector similarity calculations
 * Optimized pure JavaScript implementations using enhanced array methods
 * Faster than GPU for small vectors (384 dims) due to no transfer overhead
 */
/**
 * Calculates the Euclidean distance between two vectors
 * Lower values indicate higher similarity
 * Optimized using array methods for Node.js 23.11+
 */
export const euclideanDistance = (a, b) => {
    if (a.length !== b.length) {
        throw new Error('Vectors must have the same dimensions');
    }
    // Use array.reduce for better performance in Node.js 23.11+
    const sum = a.reduce((acc, val, i) => {
        const diff = val - b[i];
        return acc + diff * diff;
    }, 0);
    return Math.sqrt(sum);
};
/**
 * Calculates the cosine distance between two vectors
 * Lower values indicate higher similarity
 * Range: 0 (identical) to 2 (opposite)
 * Optimized using array methods for Node.js 23.11+
 */
export const cosineDistance = (a, b) => {
    if (a.length !== b.length) {
        throw new Error('Vectors must have the same dimensions');
    }
    // Use array.reduce to calculate all values in a single pass
    const { dotProduct, normA, normB } = a.reduce((acc, val, i) => {
        return {
            dotProduct: acc.dotProduct + val * b[i],
            normA: acc.normA + val * val,
            normB: acc.normB + b[i] * b[i]
        };
    }, { dotProduct: 0, normA: 0, normB: 0 });
    if (normA === 0 || normB === 0) {
        return 2; // Maximum distance for zero vectors
    }
    const similarity = dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
    // Convert cosine similarity (-1 to 1) to distance (0 to 2)
    return 1 - similarity;
};
/**
 * Calculates the Manhattan (L1) distance between two vectors
 * Lower values indicate higher similarity
 * Optimized using array methods for Node.js 23.11+
 */
export const manhattanDistance = (a, b) => {
    if (a.length !== b.length) {
        throw new Error('Vectors must have the same dimensions');
    }
    // Use array.reduce for better performance in Node.js 23.11+
    return a.reduce((sum, val, i) => sum + Math.abs(val - b[i]), 0);
};
/**
 * Calculates the dot product similarity between two vectors
 * Higher values indicate higher similarity
 * Converted to a distance metric (lower is better)
 * Optimized using array methods for Node.js 23.11+
 */
export const dotProductDistance = (a, b) => {
    if (a.length !== b.length) {
        throw new Error('Vectors must have the same dimensions');
    }
    // Use array.reduce for better performance in Node.js 23.11+
    const dotProduct = a.reduce((sum, val, i) => sum + val * b[i], 0);
    // Convert to a distance metric (lower is better)
    return -dotProduct;
};
/**
 * Batch distance calculation using optimized JavaScript
 * More efficient than GPU for small vectors due to no memory transfer overhead
 *
 * @param queryVector The query vector to compare against all vectors
 * @param vectors Array of vectors to compare against
 * @param distanceFunction The distance function to use
 * @returns Promise resolving to array of distances
 */
export async function calculateDistancesBatch(queryVector, vectors, distanceFunction = euclideanDistance) {
    // For small batches, use the standard distance function
    if (vectors.length < 10) {
        return vectors.map((vector) => distanceFunction(queryVector, vector));
    }
    try {
        // Function for optimized batch distance calculation
        const distanceCalculator = (args) => {
            const { queryVector, vectors, distanceFnString } = args;
            // Optimized JavaScript implementations for different distance functions
            let distances;
            if (distanceFnString.includes('euclideanDistance')) {
                // Euclidean distance: sqrt(sum((a - b)^2))
                distances = vectors.map((vector) => {
                    let sum = 0;
                    for (let i = 0; i < queryVector.length; i++) {
                        const diff = queryVector[i] - vector[i];
                        sum += diff * diff;
                    }
                    return Math.sqrt(sum);
                });
            }
            else if (distanceFnString.includes('cosineDistance')) {
                // Cosine distance: 1 - (a·b / (||a|| * ||b||))
                distances = vectors.map((vector) => {
                    let dotProduct = 0;
                    let queryNorm = 0;
                    let vectorNorm = 0;
                    for (let i = 0; i < queryVector.length; i++) {
                        dotProduct += queryVector[i] * vector[i];
                        queryNorm += queryVector[i] * queryVector[i];
                        vectorNorm += vector[i] * vector[i];
                    }
                    queryNorm = Math.sqrt(queryNorm);
                    vectorNorm = Math.sqrt(vectorNorm);
                    if (queryNorm === 0 || vectorNorm === 0) {
                        return 1; // Maximum distance for zero vectors
                    }
                    const cosineSimilarity = dotProduct / (queryNorm * vectorNorm);
                    return 1 - cosineSimilarity;
                });
            }
            else if (distanceFnString.includes('manhattanDistance')) {
                // Manhattan distance: sum(|a - b|)
                distances = vectors.map((vector) => {
                    let sum = 0;
                    for (let i = 0; i < queryVector.length; i++) {
                        sum += Math.abs(queryVector[i] - vector[i]);
                    }
                    return sum;
                });
            }
            else if (distanceFnString.includes('dotProductDistance')) {
                // Dot product distance: -sum(a * b)
                distances = vectors.map((vector) => {
                    let dotProduct = 0;
                    for (let i = 0; i < queryVector.length; i++) {
                        dotProduct += queryVector[i] * vector[i];
                    }
                    return -dotProduct;
                });
            }
            else {
                // For unknown distance functions, use the provided function
                const distanceFunction = new Function('return ' + distanceFnString)();
                distances = vectors.map((vector) => distanceFunction(queryVector, vector));
            }
            return { distances };
        };
        // Use the optimized distance calculator
        const result = distanceCalculator({
            queryVector,
            vectors,
            distanceFnString: distanceFunction.toString()
        });
        return result.distances;
    }
    catch (error) {
        // If anything fails, fall back to the standard distance function
        console.error('Batch distance calculation failed:', error);
        return vectors.map((vector) => distanceFunction(queryVector, vector));
    }
}
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