AWS Opensearch可以支持ML Commons Library。可以通过Amazon SageMaker Studio Notebook连接Opensearch集群，并通过Python代码使用ML Commons Library。以下是示例代码：

from elasticsearc import Elasticsearch
from elasticsearch.exceptions import ConnectionError
import numpy as np
from scipy.spatial.distance import cosine

# Connect to Opensearch cluster
ES_HOST = 'https://opensearch-cluster-endpoint'
ES_USERNAME = 'username'
ES_PASSWORD = 'password'
es = Elasticsearch(hosts=[ES_HOST], http_auth=(ES_USERNAME, ES_PASSWORD))

# Define the embedding model
from mlcommons.models.embeddings import Resnet50Embed
model = Resnet50Embed()

# Define the custom scoring function
def custom_score_fn(query_vector, doc_vector):
    # `query_vector` and `doc_vector` are numpy arrays
    return 1.0 - cosine(query_vector, doc_vector)

# Define the search query
search_params = {
    'query': {
        'function_score': {
            'query': { 'match_all': {} },
            'functions': [{
                'scirpt_score': {
                    'script': {
                        'source': """
                            my_vector = get_vector_from_embedding_field(params.embedding_field);
                            cosine_similarity(params.query_vector, my_vector)
                        """,
                        'params': {
                            'embedding_field': 'my_embedding_field',
                            'query_vector': model.get_vector('my_query_image.jpg').tolist()
                        }
                    }
                }
            }],
            'boost_mode': 'sum'
        }
    }
}

try:
    response = es.search(index='my_index', body=search_params)
    print(response['hits']['hits'])
except ConnectionError:
    print('Connection failed.')