Hierarchical Navigable Small Worlds

Kinda wish they would use some more letters when writing the sample code. This terse style in general is fine but when the purpose of the code is to try to explain how something works, it's adding an extra layer of difficulty decoding what the heck 'nns, 'cv', 'd', 'e' and 'ef' is supposed to mean.

Like this ChatGPT-looking function

    nns = [best]
    visit = set(best)  # set of visited nodes
    candid = [best]  # candidate nodes to insert into nearest neighbors

    # find top-k nearest neighbors
    while candid:
        cv = heappop(candid)

        if nns[-1][0] < cv[0]:
            break

        # loop through all nearest neighbors to the candidate vector
        for e in graph[cv[1]][1]:
            d = np.linalg.norm(graph[e][0] - query)
            if (d, e) not in visit:
                visit.add((d, e))

                # push only "better" vectors into candidate heap
                if d < nns[-1][0] or len(nns) < ef:
                    heappush(candid, (d, e))
                    insort(nns, (d, e))
                    if len(nns) > ef:
                        nns.pop()

code could have been trivially cleaned up and it would have been so much easier to follow. I'm guessing what some of these variables are supposed to mean.

    nearest_neighbors = [best]
    visited = set(best)
    candidates = [best]

    # find top-k nearest neighbors
    while candidates:
        candidate_vector = heappop(candidates)

        if nearest_neighbors[-1][0] < candidate_vector[0]:
            break

        # loop through all nearest neighbors to the candidate vector
        for neighbor in graph[candidate_vector[1]][1]:
            distance = np.linalg.norm(graph[e][0] - query)

            if (distance, neighbor) in visit:
                continue

            visited.add((distance, neighbor))

            # push only "better" vectors into candidate heap
            if distance < nearest_neighbors[-1][0] or len(nearest_neighbors) < epsilon:
               heappush(candidates, (distance, neighbor))
               insort(nns, (distance, neighbor))
               if len(nearest_neighbors) > epsilon:
                   nearest_neighbors.pop()

https://github.com/instant-labs/instant-distance is a compact, fairly readable, pretty fast implementation of the paper in Rust (that I built).

We use it for searching aftermarket domains on Instant Domain Search.

Why have a cookie dialog if my only option is to "accept"? Not much of a dialog there.