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の設定

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) 内から、同じ vSwitch を使用して OpenSearch Vector Search Edition インスタンスにアクセスする場合、この手順は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の設定」をご参照ください。

2. テーブル内のデータのクエリ

SDKの使用

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例です。

依存関係の追加：

pip install alibabacloud-ha3engine-vector

データクエリのサンプルコード：

from alibabacloud_ha3engine_vector.client import Client
from alibabacloud_ha3engine_vector.models import Config
from alibabacloud_ha3engine_vector.models import QueryRequest
config = Config(
        # プライベートネットワーク経由で呼び出す場合は、プライベートエンドポイントを使用します。
        endpoint="<your_api_endpoint>",
        # インスタンス ID。インスタンス詳細ページの左上で確認できます。 例：ha-cn-i7*****605
        instance_id="<your_instance_id>",
        # ユーザー名。インスタンス詳細ページの「ネットワーク情報」セクションで確認できます。
        access_user_name="<your_username>",
        # パスワード。インスタンス詳細ページの「ネットワーク情報」セクションで変更できます。
        access_pass_word="<your_password>")
client = Client(config)
request = QueryRequest(table_name="<your_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の SDK の詳細については、「データのクエリ」をご参照ください。

サンプルレスポンス：

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

OpenSearch Vector Search Editionコンソールの使用

1. OpenSearch Vector Search Edition コンソールで、ナビゲーションペインの[インスタンス] をクリックします。対象のインスタンスを見つけ、[操作] 列の[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、対象のインデックステーブルを選択します。

[ベクタークエリ] タブを選択します。[テーブル名] ドロップダウンリストから、[fengxue] などの対象のインデックステーブルを選択します。左側の JSON 入力エリアにクエリを入力し、[検索] をクリックします。クエリ結果と経過時間が右側に表示されます。

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：

• ベクター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