Daniel (dB.) DoubrovkineandGitHub
bcfef113c4
* Added samples for nox format. Signed-off-by: dblock <[email protected]> * Added space after #!/usr/bin/env python. Signed-off-by: dblock <[email protected]> * Added benchmarks and docs. Signed-off-by: dblock <[email protected]> --------- Signed-off-by: dblock <[email protected]>
93 lines
2.1 KiB
Python
Executable File
93 lines
2.1 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# SPDX-License-Identifier: Apache-2.0
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#
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# The OpenSearch Contributors require contributions made to
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# this file be licensed under the Apache-2.0 license or a
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# compatible open source license.
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#
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# Modifications Copyright OpenSearch Contributors. See
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# GitHub history for details.
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import os
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import random
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from opensearchpy import OpenSearch, helpers
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# connect to an instance of OpenSearch
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host = os.getenv("HOST", default="localhost")
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port = int(os.getenv("PORT", 9200))
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auth = (os.getenv("USERNAME", "admin"), os.getenv("PASSWORD", "admin"))
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client = OpenSearch(
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hosts=[{"host": host, "port": port}],
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http_auth=auth,
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use_ssl=True,
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verify_certs=False,
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ssl_show_warn=False,
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)
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# check whether an index exists
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index_name = "my-index"
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dimensions = 5
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if not client.indices.exists(index_name):
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client.indices.create(
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index_name,
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body={
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"settings": {"index.knn": True},
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"mappings": {
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"properties": {
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"values": {"type": "knn_vector", "dimension": dimensions},
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}
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},
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},
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)
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# index data
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vectors = []
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genres = ["fiction", "drama", "romance"]
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for i in range(3000):
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vec = []
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for j in range(dimensions):
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vec.append(round(random.uniform(0, 1), 2))
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vectors.append(
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{
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"_index": index_name,
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"_id": i,
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"values": vec,
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"metadata": {"genre": random.choice(genres)},
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}
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)
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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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genre = random.choice(genres)
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vec = []
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for j in range(dimensions):
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vec.append(round(random.uniform(0, 1), 2))
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print(f"Searching for {vec} with the '{genre}' genre ...")
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search_query = {
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"query": {
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"bool": {
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"filter": {"bool": {"must": [{"term": {"metadata.genre": genre}}]}},
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"must": {"knn": {"values": {"vector": vec, "k": 5}}},
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}
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}
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}
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results = client.search(index=index_name, body=search_query)
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for hit in results["hits"]["hits"]:
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print(hit)
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# delete index
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client.indices.delete(index=index_name)
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