ruve-db 0.1.0

A hybrid vector and full-text search database with HNSW approximate nearest-neighbour indexing and BM25
Documentation 
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// cosine similarity is a measure of similarity between two non-zero vectors in an inner product space. 
// the result ranges from -1 to 1. A value of 1 indicates that the vectors are identical, while a value of -1 indicates that they are opposite. 
// A value of 0 indicates that the vectors are orthogonal (i.e., they are at a right angle to each other).
pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
    // calculate the dot product of the two vectors
    // (a1 * b1) + (a2 * b2) + ... + (an * bn)
    let dot = a.
        iter().
        zip(b). // create the pairs of corresponding elements from both vectors
        map(|(x, y)| x * y). // multiply each pair of elements
        sum::<f32>(); // sum up all the products to get the dot product
    
    // calculate the magnitude (norm) of each vector
    // we need this to normalize the dot product and get the cosine similarity
    // because two vectors can be marked as similar if they point in the same direction, even if they have different magnitudes
    let norm_a = a.iter().map(|x| x * x).sum::<f32>().sqrt();
    let norm_b = b.iter().map(|x| x * x).sum::<f32>().sqrt();

    if norm_a == 0.0 || norm_b == 0.0 {
        return 0.0;
    }

    dot / (norm_a * norm_b)
}