Files
opensearch-pyd/samples/knn/knn-basics.py
T
f54973e583 Added k-nn user guide and samples. (#449)
* Added k-nn user guide and samples.

Signed-off-by: dblock <[email protected]>

* Added async samples.

Signed-off-by: dblock <[email protected]>

* Renamed Lucene Filters with Efficient Filters.

Signed-off-by: dblock <[email protected]>

* Fixing TOC from Lucene filters to Efficient filters

Signed-off-by: Vacha Shah <[email protected]>

---------

Signed-off-by: dblock <[email protected]>
Signed-off-by: Vacha Shah <[email protected]>
Co-authored-by: Vacha Shah <[email protected]>
2023-07-25 19:04:13 -07:00

84 lines
1.8 KiB
Python
Executable File

#!/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.
import os
import random
from opensearchpy import OpenSearch, helpers
# connect to an instance of OpenSearch
host = os.getenv('HOST', default='localhost')
port = int(os.getenv('PORT', 9200))
auth = (
os.getenv('USERNAME', 'admin'),
os.getenv('PASSWORD', 'admin')
)
client = OpenSearch(
hosts = [{'host': host, 'port': port}],
http_auth = auth,
use_ssl = True,
verify_certs = False,
ssl_show_warn = False
)
# check whether an index exists
index_name = "my-index"
dimensions = 5
if not client.indices.exists(index_name):
client.indices.create(index_name,
body={
"settings":{
"index.knn": True
},
"mappings":{
"properties": {
"values": {
"type": "knn_vector",
"dimension": dimensions
},
}
}
}
)
# index data
vectors = []
for i in range(10):
vec = []
for j in range(dimensions):
vec.append(round(random.uniform(0, 1), 2))
vectors.append({
"_index": index_name,
"_id": i,
"values": vec,
})
# bulk index
helpers.bulk(client, vectors)
client.indices.refresh(index=index_name)
# search
vec = []
for j in range(dimensions):
vec.append(round(random.uniform(0, 1), 2))
print(f"Searching for {vec} ...")
search_query = {"query": {"knn": {"values": {"vector": vec, "k": 3}}}}
results = client.search(index=index_name, body=search_query)
for hit in results["hits"]["hits"]:
print(hit)
# delete index
client.indices.delete(index=index_name)