OpenSearch:データのクエリ

1. パブリックアクセスホワイトリストを設定する

OpenSearch Vector Search Editionインスタンスが配置されている仮想プライベートクラウド（VPC）から、同じvSwitchを使用してインスタンスにアクセスする場合は、この手順をスキップしてください。

オンプレミス環境またはインターネットからOpenSearch Vector Search Editionインスタンスにアクセスする方法の詳細については、パブリックアクセスホワイトリストを設定するを参照してください。

2. テーブル内のデータをクエリする

SDKを使用する

次のサンプルコードは、Python用SDKを使用してテーブル内のデータをクエリする方法の例を示しています。

依存関係を追加する:

pip install alibabacloud-ha3engine-vector

データクエリ用のデモコード:

from alibabacloud_ha3engine_vector.client import Client
from alibabacloud_ha3engine_vector.models import Config
from alibabacloud_ha3engine_vector.models import QueryRequest

config = Config(
        # インスタンスのAPIエンドポイント。
        endpoint="<API endpoint>",
        # インスタンスID。インスタンスIDは、[インスタンスの詳細]ページの左上隅に表示されます。例：ha-cn-i7*****605。
        instance_id="<Instance ID>",
        # ユーザー名。ユーザー名は、[インスタンスの詳細]ページの[APIエンドポイント]セクションに表示されます。
        access_user_name="<Username>",
        # パスワード。パスワードは、[インスタンスの詳細]ページの[APIエンドポイント]セクションで表示および変更できます。
        access_pass_word="<Password>")
client = Client(config)

request = QueryRequest(table_name="<Table name>",
                    	vector=[0.1, 0.2, 0.3],
                        include_vector=True,
                        top_k=10)
result = client.query(request)
print(result.body)

他のプログラミング言語用SDKの詳細については、データのクエリを参照してください。

サンプルレスポンス:

{
    "totalCount": 1,
    "result": [
        {
            "id": "1",
            "vector": [
                0.0019676426891237499,
               ...
            ],
            "score": 0.0
        }
    ],
    "totalTime": 2.688
}

OpenSearchコンソールを使用する

1. OpenSearch Vector Search Editionコンソールにログインします。左側のナビゲーションペインで、[インスタンス]をクリックします。[インスタンス]ページで、クエリテストを実行するインスタンスを見つけ、インスタンスIDをクリックします。インスタンスの詳細ページで、左側のナビゲーションペインの[クエリテスト]をクリックします。

2. クエリテストページで、インデックステーブルを選択し、[検索]をクリックします。

例:

{
  "vector": [0.0019676427,0.005902928,0.021644069,0.21644068,0.12199384,0.043288138,0.007870571,0.0,0.08460863,0.041320495,0.043288138,0.035417568,0.011805856,0.055093993,0.12592913,0.017708784,0.021644069,0.0019676427,0.0,0.0,0.0019676427,0.078705706,0.1987319,0.041320495,0.039352853,0.0039352854,0.007870571,0.0039352854,0.0039352854,0.017708784,0.035417568,0.06886749,0.0019676427,0.0019676427,0.013773498,0.049191065,0.2125054,0.22824654,0.123961486,0.0039352854,0.0,0.0,0.021644069,0.14560555,0.078705706,0.1987319,0.22824654,0.005902928,0.064932205,0.0019676427,0.0019676427,0.021644069,0.027546996,0.035417568,0.22824654,0.22824654,0.1337997,0.023611711,0.009838213,0.007870571,0.0039352854,0.0039352854,0.017708784,0.20069954,0.033449925,0.005902928,0.019676426,0.035417568,0.015741142,0.029514639,0.13183205,0.123961486,0.029514639,0.0,0.027546996,0.22824654,0.15741141,0.0,0.0039352854,0.043288138,0.18889369,0.072802775,0.055093993,0.17315255,0.08460863,0.0019676427,0.007870571,0.035417568,0.22824654,0.10034977,0.009838213,0.021644069,0.062964566,0.027546996,0.015741142,0.04525578,0.086576276,0.033449925,0.023611711,0.017708784,0.0,0.0,0.03738521,0.072802775,0.16724962,0.035417568,0.031482283,0.20463483,0.043288138,0.011805856,0.0039352854,0.051158708,0.023611711,0.11412327,0.13183205,0.16134669,0.049191065,0.023611711,0.0039352854,0.0039352854,0.049191065,0.035417568,0.015741142,0.0039352854,0.03738521,0.08264099,0.094446845,0.021644069],
  "topK": 10,
  "includeVector": true
}

サンプルレスポンス:

[
  {
    "vector": [
      0.0019676426891237497,
      0.0059029278345406055,
   	...
    ],
    "id": "0",
    "score": 0
  }
]

関連するクエリ構文:

• ベクトルによるデータクエリ

• プライマリキーによるデータクエリ

• フィルター式