f54973e583
* Added k-nn user guide and samples. Signed-off-by: dblock <dblock@amazon.com> * Added async samples. Signed-off-by: dblock <dblock@amazon.com> * Renamed Lucene Filters with Efficient Filters. Signed-off-by: dblock <dblock@amazon.com> * Fixing TOC from Lucene filters to Efficient filters Signed-off-by: Vacha Shah <vachshah@amazon.com> --------- Signed-off-by: dblock <dblock@amazon.com> Signed-off-by: Vacha Shah <vachshah@amazon.com> Co-authored-by: Vacha Shah <vachshah@amazon.com>
110 lines
2.4 KiB
Python
Executable File
110 lines
2.4 KiB
Python
Executable File
#!/usr/bin/env python
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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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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 = (
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os.getenv('USERNAME', 'admin'),
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os.getenv('PASSWORD', 'admin')
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)
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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(index_name,
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body={
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"settings":{
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"index.knn": True
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},
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"mappings":{
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"properties": {
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"values": {
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"type": "knn_vector",
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"dimension": dimensions
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},
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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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"_index": index_name,
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"_id": i,
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"values": vec,
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"metadata": {
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"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": {
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"bool": {
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"must": [{
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"term": {
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"metadata.genre": genre
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}
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}]
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}
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},
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"must": {
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"knn": {
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"values": {
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"vector": vec,
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"k": 5
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}
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}
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}
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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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