example_hnsw.py

#  Copyright 2024-present the vsag project
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.

import pyvsag
import numpy as np
import pickle
import sys
import json



def cal_recall(index, ids, data, k, search_params):
    correct = 0
    for _id, vector in zip(ids, data):
        _ids, dists = index.knn_search(vector=vector, k=k, parameters=search_params)
        if _id in _ids:
            correct += 1
    return correct / len(ids)



def float32_hnsw_test():
    dim = 128
    num_elements = 10000

    # Generating sample data
    ids = range(num_elements)
    data = np.float32(np.random.random((num_elements, dim)))

    # Declaring index
    index_params = json.dumps({
        "dtype": "float32",
        "metric_type": "l2",
        "dim": dim,
        "hnsw": {
            "max_degree": 16,
            "ef_construction": 100
        }
    })
    index = pyvsag.Index("hnsw", index_params)

    index.build(vectors=data,
                ids=ids,
                num_elements=num_elements,
                dim=dim)
    
    search_params = json.dumps({"hnsw": {"ef_search": 100}})
    
    print("[build] float32 recall:", cal_recall(index, ids, data, 11, search_params))
    filename = "./example_hnsw.index"
    file_sizes = index.save(filename)
    
    index = pyvsag.Index("hnsw", index_params)
    index.load(filename)
    print("float32 recall:", cal_recall(index, ids, data, 11, search_params))


if __name__ == '__main__':
    float32_hnsw_test()