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opensearch-pyd/samples/knn/knn_basics.py
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#!/usr/bin/env python
# SPDX-License-Identifier: Apache-2.0
#
# The OpenSearch Contributors require contributions made to
# this file be licensed under the Apache-2.0 license or a
# compatible open source license.
#
# Modifications Copyright OpenSearch Contributors. See
# GitHub history for details.
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import os
import random
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from opensearchpy import OpenSearch, helpers
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def main() -> None:
"""
create, bulk index, and query kNN. then delete the index
"""
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# connect to an instance of OpenSearch
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host = os.getenv("HOST", default="localhost")
port = int(os.getenv("PORT", 9200))
auth = (os.getenv("USERNAME", "admin"), os.getenv("PASSWORD", "admin"))
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client = OpenSearch(
hosts=[{"host": host, "port": port}],
http_auth=auth,
use_ssl=True,
verify_certs=False,
ssl_show_warn=False,
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)
# check whether an index exists
index_name = "my-index"
dimensions = 5
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if not client.indices.exists(index_name):
client.indices.create(
index_name,
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body={
"settings": {"index.knn": True},
"mappings": {
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"properties": {
"values": {"type": "knn_vector", "dimension": dimensions},
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}
},
},
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)
# index data
vectors = []
for i in range(10):
vec = []
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for _ in range(dimensions):
vec.append(round(random.uniform(0, 1), 2))
vectors.append(
{
"_index": index_name,
"_id": i,
"values": vec,
}
)
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# bulk index
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helpers.bulk(client, vectors)
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client.indices.refresh(index=index_name)
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# search
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vec = [round(random.uniform(0, 1), 2) for _ in range(dimensions)]
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print(f"Searching for {vec} ...")
search_query = {"query": {"knn": {"values": {"vector": vec, "k": 3}}}}
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results = client.search(index=index_name, body=search_query)
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(print(hit) for hit in results["hits"]["hits"])
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# delete index
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client.indices.delete(index=index_name)
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if __name__ == "__main__":
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main()