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クイック スタート: ベクター検索

このクイック スタートでは、Jupyter Notebook を使用してベクターを作成、読み込み、クエリします。 コード例では、 Azure AI Search クライアント ライブラリを使用してこれらの操作を実行します。 このライブラリは、データ インジェスト、検索操作、インデックス管理操作などのインデックス操作にアクセスするための REST API に対する抽象化を提供します。

Azure AI 検索のベクトル ストアにあるインデックス スキーマでは、ベクトル フィールドと非ベクトル フィールド、埋め込み空間を作成するアルゴリズムのためのベクトル検索構成、クエリ時に評価されるベクトル フィールド定義の設定が定義されています。 Create Index REST API によってベクトル ストアが作成されます。

注

このクイック スタートでは、ベクター化の手順を省略し、インライン埋め込みを提供します。 独自のコンテンツ に組み込みのデータ チャンクとベクター化を 追加する場合は、データの インポートとベクター化ウィザード を試して、エンドツーエンドのチュートリアルを実行してください。

前提条件

リソース情報の取得

検索エンドポイントへの要求は、認証および承認する必要があります。 このタスクには API キーまたはロールを使用することもできますが、 Microsoft Entra ID 経由のキーレス接続を使用することをお勧めします。

このクイック スタートでは、 DefaultAzureCredentialを使用します。これにより、開発と運用の両方のシナリオで認証が簡略化されます。 ただし、運用環境のシナリオでは、別のアプローチを必要とするより高度な要件がある場合があります。 すべてのオプションを理解 するには、Azure SDK for Python を使用した Azure サービスへの Python アプリの認証に関 するページを参照してください。

コードとセットアップ環境を複製する

  1. このクイック スタートのコードを含むリポジトリを複製します。

    git clone https://github.com/Azure-Samples/azure-search-python-samples

    このリポジトリには、それぞれ別のサブフォルダーにある複数の記事の Python コード例があります。

  2. Visual Studio Code で、サブフォルダー Quickstart-Vector-Searchを開きます。

    このフォルダーには、次の 3 つのファイルがあります。

    • vector-search-quickstart.ipynb
    • requirements.txt
    • sample.env

  3. sample.env ファイルの名前を.envに変更し、.env ファイル内の値を変更します。

    AZURE_SEARCH_ENDPOINTとして検索サービスの URL を使用します。 この URL は 、Azure portal で確認できます。 Azure AI Search サービスに移動し、[ 概要 ] ページで [URL] フィールドを探します。 たとえば、エンドポイントは https://mydemo.search.windows.net のようになります。

    最後に、新しい AZURE_SEARCH_INDEX_NAME 名を選択するか、ファイルで指定されたものを使用します。

  4. Visual Studio Code で、環境内で作業します。 [表示] メニューの [ターミナル...] を選択するか、Ctrl キーを押+`。

  5. ターミナルで次のコマンドを実行します。

    python -m venv .venv
source .venv/scripts/activate
where python

    注

    これは、ターミナルで Git Bash を使用していて、Windows で実行していることを前提としています。 別のシェルや別のオペレーティング システムを使用している場合は、特定の環境に合わせてこれらの手順を調整します。

    メッセージが表示されたら、Visual Studio Code で新しい環境を使用できるようにします。

    where python コマンドは、python.exe フォルダー内のQuickstart-Vector-Search\.venv\と、コンピューターのディレクトリのその他の場所を一覧表示することで、仮想環境から作業していることを検証します。

  6. 次のコマンドを実行して、必要なライブラリをインストールします。

    pip install requirements.txt

  7. Visual Studio Code で、 vector-search-quickstart.ipynbを開きます。

    注

    Visual Studio Code で Jupyter Notebook (.ipynb) を初めて使用する場合は、Jupyter Notebook カーネルとその他のツールをインストールするように求められます。 推奨されるツールをインストールして、このクイック スタートを続行することを選択します。

  8. "パッケージのインストールと変数の設定" というタイトルの下のセルを見つけて、セルの左側にある [セルの実行] (Ctrl + Alt + Enter) ボタン (一般的な実行ボタンのように見えます) を選択します。 セルを実行すると、環境変数が読み込まれます。DefaultAzureCredential が作成され、値が出力に出力され、ノートブックの依存関係と .env が正しく設定されていることを確認できます。

    # Load environment variables from .env file
# Rename the samples.env to .env and fill in the values
from azure.identity import DefaultAzureCredential
from dotenv import load_dotenv
import os

load_dotenv(override=True) # take environment variables from .env.

search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
credential = DefaultAzureCredential()
index_name = os.getenv("AZURE_SEARCH_INDEX", "vector-search-quickstart")

print(f"Using Azure Search endpoint: {search_endpoint}")
print(f"Using Azure Search index: {index_name}")
!pip list

    このセルを実行すると、次の出力が生成されます。

    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Package                 Version
----------------------- -----------
aiohappyeyeballs        2.6.1
aiohttp                 3.12.13
aiosignal               1.3.2
asttokens               3.0.0
attrs                   25.3.0
azure-ai-agents         1.0.0
azure-ai-projects       1.0.0b11
azure-common            1.1.28
azure-core              1.34.0
azure-identity          1.23.0
azure-search-documents  11.6.0b12
azure-storage-blob      12.25.1
...

    スクロール可能な要素で表示できるパッケージは他にも多数あります (セルの結果の下のメッセージを参照してください)。

ベクター インデックスを作成する

vector-search-quickstart.ipynbのコードでは、azure.search.documents ライブラリのいくつかのメソッドを使用して、ベクター インデックスと検索可能なフィールドを作成します。

  1. "インデックスの作成" というタイトルの下のセルを見つけて、セルを実行してインデックスを作成します。

    from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents import SearchClient
from azure.search.documents.models import VectorizedQuery
from azure.search.documents.indexes.models import (
    SimpleField,
    ComplexField,
    SearchField,
    SearchFieldDataType,    
    SearchableField,
    SearchIndex,
    SemanticConfiguration,
    SemanticField,
    SemanticPrioritizedFields,
    SemanticSearch,
    VectorSearch, 
    VectorSearchProfile,
    HnswAlgorithmConfiguration,
    ExhaustiveKnnAlgorithmConfiguration    
)

# Create a search schema
index_client = SearchIndexClient(
    endpoint=search_endpoint, credential=credential)
fields = [
    SimpleField(name="HotelId", type=SearchFieldDataType.String, key=True, filterable=True),
    SearchableField(name="HotelName", type=SearchFieldDataType.String, sortable=True),
    SearchableField(name="Description", type=SearchFieldDataType.String),
    SearchField(
        name="DescriptionVector",
        type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
        searchable=True,
        vector_search_dimensions=1536,
        vector_search_profile_name="my-vector-profile"
    ),
    SearchableField(name="Category", type=SearchFieldDataType.String, sortable=True, filterable=True, facetable=True),
    SearchField(name="Tags", type=SearchFieldDataType.Collection(SearchFieldDataType.String), searchable=True, filterable=True, facetable=True),
    SimpleField(name="ParkingIncluded", type=SearchFieldDataType.Boolean, filterable=True, sortable=True, facetable=True),
    SimpleField(name="LastRenovationDate", type=SearchFieldDataType.DateTimeOffset, filterable=True, sortable=True, facetable=True),
    SimpleField(name="Rating", type=SearchFieldDataType.Double, filterable=True, sortable=True, facetable=True),
    ComplexField(name="Address", fields=[
        SearchableField(name="StreetAddress", type=SearchFieldDataType.String),
        SearchableField(name="City", type=SearchFieldDataType.String, filterable=True, sortable=True, facetable=True),
        SearchableField(name="StateProvince", type=SearchFieldDataType.String, filterable=True, sortable=True, facetable=True),
        SearchableField(name="PostalCode", type=SearchFieldDataType.String, filterable=True, sortable=True, facetable=True),
        SearchableField(name="Country", type=SearchFieldDataType.String, filterable=True, sortable=True, facetable=True),
    ]),
    SimpleField(name="Location", type=SearchFieldDataType.GeographyPoint, filterable=True, sortable=True),
]

vector_search = VectorSearch(
        algorithms=[
            HnswAlgorithmConfiguration(name="my-hnsw-vector-config-1", kind="hnsw"),
            ExhaustiveKnnAlgorithmConfiguration(name="my-eknn-vector-config", kind="exhaustiveKnn")
        ],
        profiles=[
            VectorSearchProfile(name="my-vector-profile", algorithm_configuration_name="my-hnsw-vector-config-1")
        ]
    )

semantic_config = SemanticConfiguration(
        name="my-semantic-config",
        prioritized_fields=SemanticPrioritizedFields(
           title_field=SemanticField(field_name="HotelName"), 
           content_fields=[SemanticField(field_name="Description")], 
           keywords_fields=[SemanticField(field_name="Category")]
        )
    )

# Create the semantic settings with the configuration
semantic_search = SemanticSearch(configurations=[semantic_config])

semantic_settings = SemanticSearch(configurations=[semantic_config])
scoring_profiles = []
suggester = [{'name': 'sg', 'source_fields': ['Tags', 'Address/City', 'Address/Country']}]

# Create the search index with the semantic settings
index = SearchIndex(name=index_name, fields=fields, vector_search=vector_search, semantic_search=semantic_search)
result = index_client.create_or_update_index(index)
print(f' {result.name} created')

    インデックスが正常に作成されると、セルの下に次の結果が表示されます。

    vector-search-quickstart created

    azure.search.documentsを使用してベクター インデックスを作成するときの重要なポイント:

    • インデックスを定義するには、フィールドのリストを作成します。 各フィールドは、フィールドの種類とその設定を定義するヘルパー メソッドを使用して作成されます。

    • この特定のインデックスでは、次のような複数の検索機能がサポートされています。

      • フルテキスト キーワード検索 (SearchableField(name="HotelName", ...)、 SearchableField(name="Description", ...))
      • ベクター検索 (ベクトル フィールドと非ベクトル フィールドを併置してハイブリッド検索を有効にする) フィールド (DescriptionVector)
      • セマンティック検索 (semantic_search=SemanticSearch(configurations=[semantic_config]))
      • ファセット検索 (facetable=True)
      • セマンティック検索 (semantic_search=SemanticSearch(configurations=[semantic_config]))
      • 地理空間検索 (Location フィールドが GeographyPoint)
      • フィルター処理、並べ替え (フィルター可能および並べ替え可能とマークされた多くのフィールド)

ドキュメントのアップロード

インデックスの作成と読み込みは別の手順です。 前の手順でインデックス スキーマを作成しました。 次に、ドキュメントをインデックスに読み込む必要があります。

Azure AI Search では、インデックスには検索可能なすべてのコンテンツが格納されますが、検索エンジンはそのインデックスに対してクエリを実行します。

  1. "Create documents payload" というタイトルの下のセルを見つけて、セルを実行します。 このセルには、次のコードが含まれています (簡潔にするために切り捨てられます)。

       # Create a documents payload
   documents = [
       {
           "@search.action": "mergeOrUpload",
           "HotelId": "1",
           "HotelName": "Stay-Kay City Hotel",
           "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
           "DescriptionVector": [-0.048865054,-0.020307425,
           # <truncated>
           -0.018120624,-0.012772904],
           "Category": "Boutique",
           "Tags": [
               "view",
               "air conditioning",
               "concierge"
           ],
           "ParkingIncluded": "false",
           "LastRenovationDate": "2022-01-18T00:00:00Z",
           "Rating": 3.60,
           "Address": {
               "StreetAddress": "677 5th Ave",
               "City": "New York",
               "StateProvince": "NY",
               "PostalCode": "10022",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
                   -73.975403,
                   40.760586
               ]
           }
       },
       # <truncated>
   ]

    このセルは、 documents という名前の変数と、各ドキュメントを記述する JSON オブジェクトと、ベクター化されたバージョンの記事の説明を読み込みます。 このベクトルを使用すると、正確なキーワードではなく意味に基づいて一致する類似性検索が可能になります。

    Von Bedeutung

    この例のコードは実行できません。 簡潔にするために、いくつかの文字や行が削除されています。 代わりに、Jupyter ノートブックでコードを実行します。

  2. 「ドキュメントをアップロードする」というタイトルの下のセルを見つけて、セルを実行します。 このセルには、次のコードが含まれています (簡潔にするために切り捨てられます)。

    # Upload documents to the index
search_client = SearchClient(endpoint=search_endpoint,
                      index_name=index_name,
                      credential=credential)
try:
    result = search_client.upload_documents(documents=documents)
    for r in result:
        print(f"Key: {r.key}, Succeeded: {r.succeeded}, ErrorMessage: {r.error_message}")
except Exception as ex:
    print("Failed to upload documents:", ex)

# Create the index client which will be used later in the query examples
index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)

    これにより、 SearchClient() コンストラクターを呼び出して検索クライアントのインスタンスが作成され、オブジェクトに対して upload_documents() メソッドが呼び出されます。

    セルを実行すると、その下に各ドキュメントの状態が出力されます。

    Key: 1, Succeeded: True, ErrorMessage: None
Key: 2, Succeeded: True, ErrorMessage: None
Key: 3, Succeeded: True, ErrorMessage: None
Key: 4, Succeeded: True, ErrorMessage: None
Key: 48, Succeeded: True, ErrorMessage: None
Key: 49, Succeeded: True, ErrorMessage: None
Key: 13, Succeeded: True, ErrorMessage: None

    upload_documents() メソッドと次の例に関する重要なポイント:

    • コードは、SearchClient パッケージによって提供されるメイン オブジェクトであるazure-search-documentsを介して、Azure AI Search サービスでホストされている特定の検索インデックスと対話します。 SearchClientは、次のようなインデックス操作へのアクセスを提供します。

      • データ インジェスト - upload_documents()、 merge_documents()、 delete_documents()など
      • 検索操作 - search()、 autocomplete()、 suggest()
      • インデックス管理操作

    • ベクトル フィールドには浮動小数点値が含まれます。 ディメンション属性の最小値は 2 で、それぞれ最大 4,096 個の浮動小数点値があります。 このクイックスタートでは、ディメンション属性を 1,536 に設定します。これは、Azure OpenAI の text-embedding-ada-002 モデルによって生成される埋め込みのサイズであるためです。

クエリを実行する

ドキュメントが読み込まれたので、 search_client.search() を呼び出して VectorizedQuery オブジェクト、返されるフィールド、結果の数などを渡すことで、それらに対してベクター クエリを発行できます。

次のセクションでは、hotels-vector-quickstart インデックスに対してクエリを実行します。 クエリには以下が含まれます。

ベクター クエリ文字列を作成する

例のベクトル クエリは、次の 2 つの文字列に基づいています。

  • 検索文字列: historic hotel walk to restaurants and shopping
  • ベクトル クエリ文字列 (数学的表現にベクトル化): quintessential lodging near running trails, eateries, retail

このベクトル クエリ文字列は、意味的には検索文字列に似ていますが、検索インデックスに存在しない用語を含んでいます。 quintessential lodging near running trails, eateries, retail のキーワード検索を実行すると、結果は 0 になります。 この例を使用して、一致する言葉がない場合でも関連する結果を取得する方法を示します。

  • "ベクター クエリ文字列の作成" というタイトルの下のセルを見つけて、セルを実行します。 これにより、次のセクションのすべての検索を実行するために必要なベクター化されたクエリ データを含む vector 変数が読み込まれます。

最初の例では、検索文字列と密接に一致するドキュメントの説明を検索する基本的なシナリオを示します。

  • "単一ベクトル検索" というタイトルの下のセルを見つけて、セルを実行します。 このブロックには、検索インデックスに対してクエリを実行する要求が含まれています。

    # IMPORTANT: Before you run this code, make sure the documents were successfully
# created in the previous step. Sometimes it may take a few seconds for the index to be ready.
# Check the "Document count" for the index in the Azure portal.

# Execute vector search
if vector:
     try:
         vector_query = VectorizedQuery(
             vector=vector,
             k_nearest_neighbors=5,
             fields="DescriptionVector",
             kind="vector",
             exhaustive=True
         )

         results = search_client.search(
             vector_queries=[vector_query],
             select=["HotelId", "HotelName", "Description", "Category", "Tags"],
             top=5,
             include_total_count=True
         )

         print(f"Total results: {results.get_count()}")
         for result in results:
             doc = result  # result is a dict-like object
             print(f"- HotelId: {doc['HotelId']}, HotelName: {doc['HotelName']}, Category: {doc.get('Category')}")
     except Exception as ex:
         print("Vector search failed:", ex)
else:
    print("No vector loaded, skipping search.")

    このベクトル クエリは、簡潔にするために短縮されています。 vectorQueries.vector はクエリ入力のベクトル化されたテキストを含み、fields は検索するベクトル フィールドを決定し、k は返される最近傍の数を指定します。

    ベクトル クエリ文字列は quintessential lodging near running trails, eateries, retail であり、このクエリでは 1,536 個の埋め込みにベクトル化されます。

    quintessential lodging near running trails, eateries, retailに相当するベクトルの応答は 7 つの結果で構成されますが、コードはtop=5を指定するため、最初の 5 つの結果のみが返されます。 さらに、 select で指定されたフィールドのみが返されます。

    search_client.search() は、dict に似たオブジェクトを返します。 各結果は、 score = result.get("@search.score", "N/A")を使用してアクセスできる検索スコアを提供します。 この例では表示されませんが、類似性検索では、応答には常に値の類似性スコアで並べ替えられた k 結果が含まれます。

    セルを実行すると、その下に各ドキュメントの状態が出力されます。

    Total results: 5
- HotelId: 48, HotelName: Nordick's Valley Motel, Category: Boutique
- HotelId: 13, HotelName: Luxury Lion Resort, Category: Luxury
- HotelId: 4, HotelName: Sublime Palace Hotel, Category: Boutique
- HotelId: 49, HotelName: Swirling Currents Hotel, Category: Suite
- HotelId: 2, HotelName: Old Century Hotel, Category: Boutique

フィルターを使用した単一ベクトル検索

フィルターを追加できます。ただし、フィルターはインデックス内の非ベクトル コンテンツに適用されます。 この例では、Tags フィールドにフィルターが適用され、無料 Wi-Fi を備えていないホテルをすべて除外します。

  1. "フィルターを使用した単一ベクトル検索" というタイトルの下のセルを見つけて、セルを実行します。 このセルには、検索インデックスに対してクエリを実行する要求が含まれています。

    if vector:
   try:
       vector_query = VectorizedQuery(
           vector=vector,
           k_nearest_neighbors=5,
           fields="DescriptionVector",
           kind="vector",
           exhaustive=True
       )

       results = search_client.search(
           vector_queries=[vector_query],
           filter="Tags/any(tag: tag eq 'free wifi')",  # <--- NOTICE THE FILTER IS ADDED HERE
           select=["HotelId", "HotelName", "Description", "Category", "Tags"],
           top=7,
           include_total_count=True
       )

       print(f"Total filtered results: {results.get_count()}")
       for result in results:
           doc = result
           print(f"- HotelId: {doc['HotelId']}, HotelName: {doc['HotelName']}, Tags: {doc.get('Tags')}")
   except Exception as ex:
       print("Vector search with filter failed:", ex)
else:
   print("No vector loaded, skipping search.")

    セルを実行すると、その下に各ドキュメントの状態が出力されます。

    Total filtered results: 2
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ['continental breakfast', 'air conditioning', 'free wifi']
- HotelId: 2, HotelName: Old Century Hotel, Tags: ['pool', 'free wifi', 'air conditioning', 'concierge']

    クエリは前の 単一ベクトル検索の例と同じでしたが、後処理除外フィルターが含まれており、無料の Wi-Fi を持つ 2 つのホテルのみが返されます。

  2. 次のフィルターの例では 、geo フィルターを使用します。 "geo フィルターを使用したベクター クエリ" というセクションのセルを実行します。 このブロックには、検索インデックスに対してクエリを実行する要求が含まれています。

    if vector:
   try:
      vector_query = VectorizedQuery(
      vector=vector,
      k_nearest_neighbors=5,
      fields="DescriptionVector",
      kind="vector",
      exhaustive=True
   )

   results = search_client.search(
      include_total_count=True,
      top=5,
      select=[
          "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
      ],
      facets=["Address/StateProvince"],
      filter="geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
      vector_filter_mode="postFilter",
      vector_queries=[vector_query]
   )

   print(f"Total semantic hybrid results: {results.get_count()}")
   for result in results:
      doc = result
      score = result.get("@search.score", "N/A")
      print(f"- HotelId: {doc['HotelId']}")
      print(f"  HotelName: {doc['HotelName']}")
      print(f"  Score: {score}")
      print(f"  City/State: {doc['Address']['City']}, {doc['Address']['StateProvince']}")
      print(f"  Description: {doc.get('Description')}\n")

   except Exception as ex:
      print("Semantic hybrid search failed:", ex)
else:
   print("No vector loaded, skipping search.")

    クエリは前の 単一ベクトル検索の例と同じでしたが、後処理除外フィルターが含まれており、300 キロメートル以内の 2 つのホテルのみが返されます。

    Total semantic hybrid results: 2
- HotelId: 48
  HotelName: Nordick's Valley Motel
  Score: 0.6605852246284485
  City/State: Washington D.C., null
  Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.

- HotelId: 49
  HotelName: Swirling Currents Hotel
  Score: 0.602634072303772
  City/State: Arlington, VA
  Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.

ハイブリッド検索は、単一の検索要求内のキーワード クエリとベクトル クエリで構成されます。 次の例では、ベクター クエリとフルテキスト検索を同時に実行します。

  • 検索文字列: historic hotel walk to restaurants and shopping

  • ベクトル クエリ文字列 (数学的表現にベクトル化): quintessential lodging near running trails, eateries, retail

  • "ハイブリッド検索" というタイトルの下のセルを見つけて、セルを実行します。 このブロックには、検索インデックスに対してクエリを実行する要求が含まれています。

    if vector:
     try:
         vector_query = VectorizedQuery(
             vector=vector,
             k_nearest_neighbors=5,
             fields="DescriptionVector",
             kind="vector",
             exhaustive=True
         )

         results = search_client.search(
             include_total_count=True,
             search_text="historic hotel walk to restaurants and shopping",  # keyword part
             select=["HotelId", "HotelName", "Description", "Category", "Tags"],
             top=5,
             vector_queries=[vector_query]
         )

         print(f"Total hybrid results: {results.get_count()}")
         for result in results:
             doc = result
             score = result.get("@search.score", "N/A")
             print(f"- Score: {score}")
             print(f"  HotelId: {doc['HotelId']}")            
             print(f"  HotelName: {doc['HotelName']}")
             print(f"  Description: {doc.get('Description')}")
             print(f"  Category: {doc.get('Category')}")
             print(f"  Tags: {doc.get('Tags', 'N/A')}\n")

    except Exception as ex:
       print("Hybrid search failed:", ex)
else:
    print("No vector loaded, skipping search.")

    応答を確認します。

    Total hybrid results: 7
- Score: 0.03279569745063782
   HotelId: 4
   HotelName: Sublime Palace Hotel
   Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
   Category: Boutique
   Tags: ['concierge', 'view', 'air conditioning']

- Score: 0.032522473484277725
   HotelId: 13
   HotelName: Luxury Lion Resort
   Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
   Category: Luxury
   Tags: ['bar', 'concierge', 'restaurant']

- Score: 0.03205128386616707
   HotelId: 48
   HotelName: Nordick's Valley Motel
   Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
   Category: Boutique
   Tags: ['continental breakfast', 'air conditioning', 'free wifi']

- Score: 0.0317460335791111
   HotelId: 49
   HotelName: Swirling Currents Hotel
   Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.
   Category: Suite
   Tags: ['air conditioning', 'laundry service', '24-hour front desk service']

- Score: 0.03125
   HotelId: 2
   HotelName: Old Century Hotel
   Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
   Category: Boutique
   Tags: ['pool', 'free wifi', 'air conditioning', 'concierge']

    逆ランク 融合 (RRF) は結果をマージするため、入力を確認するのに役立ちます。 次の結果は、フル テキスト クエリのみからのものです。 上位 2 つの結果は、Sublime Palace Hotel と History Lion Resort です。 Sublime Palace Hotel は、より強い BM25 関連性スコアを持っています。

    {
     "@search.score": 2.2626662,
     "HotelName": "Sublime Palace Hotel",
     "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace."
},
{
     "@search.score": 0.86421645,
     "HotelName": "Luxury Lion Resort",
     "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort"
},

    マッチ検索に HNSW を使用するベクトルのみのクエリでは、Sublime Palace Hotel の順位は 4 位まで落ちます。 Historic Lion の場合、フル テキスト検索では 2 位、ベクトル検索では 3 位でしたが、変動範囲が同じでないため、均質化された結果セットでは最上位の一致として表示されます。

    "value": [
     {
         "@search.score": 0.857736,
         "HotelId": "48",
         "HotelName": "Nordick's Valley Motel",
         "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
         "Category": "Boutique"
     },
     {
         "@search.score": 0.8399129,
         "HotelId": "49",
         "HotelName": "Swirling Currents Hotel",
         "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
         "Category": "Luxury"
     },
     {
         "@search.score": 0.8383954,
         "HotelId": "13",
         "HotelName": "Luxury Lion Resort",
         "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
         "Category": "Resort and Spa"
     },
     {
         "@search.score": 0.8254346,
         "HotelId": "4",
         "HotelName": "Sublime Palace Hotel",
         "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
         "Category": "Boutique"
     },
     {
         "@search.score": 0.82380056,
         "HotelId": "1",
         "HotelName": "Stay-Kay City Hotel",
         "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of New York.",
         "Category": "Boutique"
     },
     {
         "@search.score": 0.81514084,
         "HotelId": "2",
         "HotelName": "Old Century Hotel",
         "Description": "The hotel is situated in a  nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts.",
         "Category": "Boutique"
     },
     {
         "@search.score": 0.8133763,
         "HotelId": "3",
         "HotelName": "Gastronomic Landscape Hotel",
         "Description": "The Hotel stands out for its gastronomic excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
         "Category": "Resort and Spa"
     }
]

ここでは、コレクションでの最後のクエリを示します。 このハイブリッド クエリでは、セマンティック クエリの種類とセマンティック構成を指定します。セマンティック 再ランク付けを使用するハイブリッド クエリを構築できることを示します。

  • "セマンティック ハイブリッド検索" というタイトルの下のセルを見つけて、セルを実行します。 このコード ブロックには、検索インデックスに対してクエリを実行する要求が含まれています。

    if semantic_hybrid_query_vector:
    try:
       vector_query = VectorizedQuery(
          vector=vector,
          k_nearest_neighbors=5,
          fields="DescriptionVector",
          kind="vector",
          exhaustive=True
       )

       results = search_client.search(
          include_total_count=True,
          search_text="historic hotel walk to restaurants and shopping",
          select=[
             "HotelId", "HotelName", "Category", "Description"
          ],
          query_type="semantic",
          semantic_configuration_name="my-semantic-config",
          top=7,            
          vector_queries=[vector_query]
       )

       print(f"Total semantic hybrid results: {results.get_count()}")
       for result in results:
          doc = result
          score = result.get("@search.score", "N/A")
          reranker_score = result.get("@search.reranker_score", "N/A")
          print(f"- Score: {score}")
          print(f"  Re-ranker Score: {reranker_score}")
          print(f"  HotelId: {doc['HotelId']}")
          print(f"  HotelName: {doc['HotelName']}")
          print(f"  Description: {doc.get('Description')}")
          print(f"  Category: {doc.get('Category')}")

except Exception as ex:
    print("Semantic hybrid search failed:", ex)
else:
    print("No vector loaded, skipping search.")

    セルの下の出力を確認します。

    セマンティックランキングでは、ストゥリングカレントホテルがトップに移動します。 W

    Total semantic hybrid results: 7
- Score: 0.0317460335791111
   Re-ranker Score: 2.6550590991973877
   HotelId: 49
   HotelName: Swirling Currents Hotel
   Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.
   Category: Suite
- Score: 0.03279569745063782
   Re-ranker Score: 2.599761724472046
   HotelId: 4
   HotelName: Sublime Palace Hotel
   Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
   Category: Boutique
- Score: 0.03125
   Re-ranker Score: 2.3480887413024902
   HotelId: 2
   HotelName: Old Century Hotel
   Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
   Category: Boutique
- Score: 0.016393441706895828
   Re-ranker Score: 2.2718777656555176
   HotelId: 1
   HotelName: Stay-Kay City Hotel
   Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
   Category: Boutique
- Score: 0.01515151560306549
   Re-ranker Score: 2.0582215785980225
   HotelId: 3
   HotelName: Gastronomic Landscape Hotel
   Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.
   Category: Suite

セマンティック ランク付けは、クエリ内の単語の背後にある意味とドキュメントの内容を理解することで、検索結果の関連性を向上させる方法と考えることができます。 この場合、セマンティック ランク付けは、キーワードに関連するだけでなく、クエリの意図と一致するホテルを特定するのに役立ちます。

重要なポイント:

  • ベクトル検索は、vectors.value プロパティを使用して指定されます。 キーワード検索は、search プロパティを使用して指定されます。

  • ハイブリッド検索では、キーワードに対するフルテキスト検索とベクトル検索を統合できます。 フィルター、スペル チェック、セマンティック ランク付けは、ベクトルではなくテキスト コンテンツにのみ適用されます。 この最後のクエリでは、セマンティックな answer はありません。これは、システムが生成したものが十分に強力ではなかったためです。

  • 実際の結果には、セマンティック キャプションや強調表示など、より詳細な情報が含まれます。 結果は読みやすくするために変更されています。 応答の構造全体を取得するには、REST クライアントで要求を実行します。

クリーンアップ

独自のサブスクリプションを使用している場合は、プロジェクトの最後に、作成したリソースがまだ必要かどうかを確認してください。 リソースを実行したままにすると、お金がかかる場合があります。 リソースは個別に削除することも、リソース グループを削除してリソースのセット全体を削除することもできます。

左端のペイン内にある [すべてのリソース] または [リソース グループ] リンクを使用して、Azure portal 内でリソースを検索および管理できます。

検索サービスを保持し、インデックスとドキュメントを削除する場合は、 SearchIndexClient オブジェクトの delete_index() メソッドを使用できます。 "クリーンアップ" というタイトルのセクションの下にあるセルを見つけて、 hotels-vector-quickstart インデックスを削除する場合はセルを実行します。

index_client.delete_index(index_name)
print(f"Index '{index_name}' deleted successfully.")

次のステップ

このクイック スタートでは、JavaScript を使用してベクターを作成、読み込み、クエリします。 コード例では、 Azure AI Search クライアント ライブラリを使用してこれらの操作を実行します。 このライブラリは、データ インジェスト、検索操作、インデックス管理操作などのインデックス操作にアクセスするための REST API に対する抽象化を提供します。

Azure AI 検索のベクトル ストアにあるインデックス スキーマでは、ベクトル フィールドと非ベクトル フィールド、埋め込み空間を作成するアルゴリズムのためのベクトル検索構成、クエリ時に評価されるベクトル フィールド定義の設定が定義されています。 Create Index REST API によってベクトル ストアが作成されます。

注

このクイック スタートでは、ベクター化の手順を省略し、インライン埋め込みを提供します。 独自のコンテンツ に組み込みのデータ チャンクとベクター化を 追加する場合は、データの インポートとベクター化ウィザード を試して、エンドツーエンドのチュートリアルを実行してください。

前提条件

サービス エンドポイントを取得する

残りのセクションでは、Azure OpenAI と Azure AI 検索への API 呼び出しを設定します。 サービス エンドポイントを取得して、コード内で変数として提供できるようにします。

  1. Azure portal にサインインします。

  2. 使用する検索サービスを見つけます。

  3. [概要] ホーム ページで、URL をコピーします。 たとえば、エンドポイントは https://example.search.windows.net のようになります。

Node.JS プロジェクトを設定する

Visual Studio Code と JavaScript を使用してプロジェクトを設定します。

  1. 新しいディレクトリで Visual Studio Code を起動します。

    mkdir vector-quickstart && cd vector-quickstart
code .

  2. プロジェクト ディレクトリに ESM モジュール用の新しいパッケージを作成します。

    npm init -y
npm pkg set type=module

    これにより、既定値を持つ package.json ファイルが作成されます。

  3. 次の npm パッケージをインストールします。

    npm install @azure/identity @azure/search-documents dotenv

  4. プロジェクト ディレクトリに src ディレクトリを作成します。

    mkdir src

ローカル開発用に環境変数を設定する

  1. vector-quickstart プロジェクト ディレクトリに.env ファイルを作成します。

  2. .env ファイルに次の環境変数を追加し、値を独自のサービス エンドポイントとキーに置き換えます。

    AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart

Azure にサインインする

接続には Microsoft Entra ID とロールの割り当てを使用しています。 Azure AI 検索および Azure OpenAI と同じテナントおよびサブスクリプションにログインしていることを確認します。 コマンド ラインで Azure CLI を使用して、現在のプロパティの表示、プロパティの変更、サインインを行うことができます。 詳細については、「キーを使用せずに接続する」を参照してください。

次の各コマンドを順番に実行します。

az account show

az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>

az login --tenant <PUT YOUR TENANT ID HERE>

これで、ローカル デバイスから Azure にログインできるようになりました。

ベクター インデックスを作成する

このセクションでは、 SearchIndexClient を使用して Azure AI Search でベクター インデックスを作成します。createOrUpdateIndex。 インデックス スキーマは、ベクター フィールド DescriptionVectorを含むフィールドを定義します。

  1. src ディレクトリにcreateIndex.js ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const indexClient = new SearchIndexClient(searchEndpoint, credential);
console.log('Creating index...');

// Define fields
const fields = [
    {
        name: "HotelId",
        type: "Edm.String",
        key: true,
        filterable: true
    },
    {
        name: "HotelName",
        type: "Edm.String",
        sortable: true,
        searchable: true,
        hidden: false,
    },
    {
        name: "Description",
        type: "Edm.String",
        searchable: true,
        hidden: false
    },
    {
        name: "DescriptionVector",
        type: "Collection(Edm.Single)",
        searchable: true,
        vectorSearchDimensions: 1536,
        vectorSearchProfileName: "my-vector-profile",
        hidden: true
    },
    {
        name: "Category",
        type: "Edm.String",
        sortable: true,
        filterable: true,
        facetable: true,
        searchable: true,
        hidden: false
    },
    {
        name: "Tags",
        type: "Collection(Edm.String)",
        searchable: true,
        filterable: true,
        facetable: true,
        hidden: false
    },
    {
        name: "ParkingIncluded",
        type: "Edm.Boolean",
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "LastRenovationDate",
        type: "Edm.DateTimeOffset",
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Rating",
        type: "Edm.Double",
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Address",
        type: "Edm.ComplexType",
        fields: [
            {
                name: "StreetAddress",
                type: "Edm.String",
                searchable: true,
            },
            {
                name: "City",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "StateProvince",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "PostalCode",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true
            },
            {
                name: "Country",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true,
            }
        ]
    },
    {
        name: "Location",
        type: "Edm.GeographyPoint",
        filterable: true,
        sortable: true
    }
];

// Define vector search configuration
const vectorSearch = {
    algorithms: [
        {
            name: "hnsw-vector-config",
            kind: "hnsw"
        },
        {
            name: "eknn-vector-config",
            kind: "exhaustiveKnn"
        }
    ],
    profiles: [
        {
            name: "my-vector-profile",
            algorithmConfigurationName: "hnsw-vector-config"
        }
    ]
};

// Define semantic configuration
const semanticConfig = {
    name: "semantic-config",
    prioritizedFields: {
        titleField: {
            name: "HotelName"
        },
        contentFields: [
            {
                name: "Description"
            }
        ],
        keywordsFields: [
            {
                name: "Category"
            }
        ]
    }
};

// Create the semantic settings with the configuration
const semanticSearch = {
    configurations: [semanticConfig]
};

// Define suggesters
const suggesters = [];

// Create the search index with the semantic settings
const indexDefinition = {
    name: indexName,
    fields: fields,
    vectorSearch: vectorSearch,
    semanticSearch: semanticSearch,
    suggesters: suggesters
};

const result = await indexClient.createOrUpdateIndex(indexDefinition);
console.log(`${result.name} created`);

    コード ファイルは、 HotelDocument の依存関係、環境変数、JavaScript 型をファイルの先頭に追加します。 createIndex関数を追加してインデックスを作成します。 この関数は、ベクトル フィールド DescriptionVectorを含むインデックス スキーマを定義します。

  3. ファイルを実行します。

    node -r dotenv/config src/createIndex.js

  4. このコードの出力は、インデックスが正常に作成されたことを示しています。

    Using Azure Search endpoint: https://my-service.search.windows.net
Using index name: hotels-vector-quickstart
Creating index...
hotels-vector-quickstart created

    @azure/search-documents ライブラリを使用してベクター インデックスを作成するときの重要なポイント:

    • インデックスを定義するには、フィールドのリストを作成します。

    • この特定のインデックスでは、次のような複数の検索機能がサポートされています。

      • フルテキスト キーワード検索 (searchable)
      • ベクトル検索 (ベクトル フィールドと非ベクトル フィールドを併置することによってハイブリッド検索を可能にする) フィールド (DescriptionVectorとvectorSearchProfileNameを使う)
      • セマンティック検索
      • ファセット検索 (searchSuggester)
      • 地理空間検索 (geo.distanceを含むLocation フィールド)
      • フィルター処理、並べ替え (フィルター可能および並べ替え可能とマークされた多くのフィールド)

インデックスにドキュメントをアップロードする

インデックスの作成と読み込みは別の手順です。 前の手順でインデックス スキーマを作成しました。 次に、 SearchClient を使用してドキュメントをインデックスに読み込む必要があります。uploadDocuments。 ドキュメントには、記事の説明のベクター化されたバージョンが含まれています。これにより、正確なキーワードではなく意味に基づく類似性検索が可能になります。

Azure AI Search では、インデックスには検索可能なすべてのコンテンツが格納されますが、検索エンジンはそのインデックスに対してクエリを実行します。

  1. src ディレクトリにuploadDocuments.js ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const DOCUMENTS =  [
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "1",
        "HotelName": "Stay-Kay City Hotel",
        "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
        "DescriptionVector": 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        "Category": "Boutique",
        "Tags": [
            "view",
            "air conditioning",
            "concierge"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2022-01-18T00:00:00Z",
        "Rating": 3.60,
        "Address": {
            "StreetAddress": "677 5th Ave",
            "City": "New York",
            "StateProvince": "NY",
            "PostalCode": "10022",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                -73.975403,
                40.760586
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "2",
        "HotelName": "Old Century Hotel",
        "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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        "Category": "Boutique",
        "Tags": [
            "pool",
            "free wifi",
            "air conditioning",
            "concierge"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2019-02-18T00:00:00Z",
        "Rating": 3.60,
        "Address": {
            "StreetAddress": "140 University Town Center Dr",
            "City": "Sarasota",
            "StateProvince": "FL",
            "PostalCode": "34243",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
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            ]
        }
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        "HotelId": "3",
        "HotelName": "Gastronomic Landscape Hotel",
        "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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        "Category": "Suite",
        "Tags": [
            "restaurant",
            "bar",
            "continental breakfast"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2015-09-20T00:00:00Z",
        "Rating": 4.80,
        "Address": {
            "StreetAddress": "3393 Peachtree Rd",
            "City": "Atlanta",
            "StateProvince": "GA",
            "PostalCode": "30326",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                33.847848,
                -84.363602
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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        "Category": "Boutique",
        "Tags": [
            "concierge",
            "view",
            "air conditioning"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2020-02-06T00:00:00Z",
        "Rating": 4.60,
        "Address": {
            "StreetAddress": "7400 San Pedro Ave",
            "City": "San Antonio",
            "StateProvince": "TX",
            "PostalCode": "78216",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                -98.498923,
                29.518029
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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        "Category": "Boutique",
        "Tags": [
            "continental breakfast",
            "air conditioning",
            "free wifi"
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        "ParkingIncluded": "false",
        "LastRenovationDate": "2018-02-19T00:00:00Z",
        "Rating": 4.5,
        "Address": {
            "StreetAddress": "1401 I St NW",
            "City": "Washington D.C.",
            "StateProvince": "null",
            "PostalCode": "20005",
            "Country": "USA"
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            "properties": {
                "name": "EPSG:4326"
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        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.",
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        "Category": "Suite",
        "Tags": [
            "air conditioning",
            "laundry service",
            "24-hour front desk service"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2018-01-27T00:00:00Z",
        "Rating": 2.7,
        "Address": {
            "StreetAddress": "1100 S Hayes St",
            "City": "Arlington",
            "StateProvince": "VA",
            "PostalCode": "22202",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -77.060066,
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            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
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        }
    },
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        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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        "Category": "Luxury",
        "Tags": [
            "bar",
            "concierge",
            "restaurant"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2020-03-18T00:00:00Z",
        "Rating": 4.1,
        "Address": {
            "StreetAddress": "3 Cityplace Dr",
            "City": "St. Louis",
            "StateProvince": "MO",
            "PostalCode": "63141",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -90.44081,
            38.67219
            ],
            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
                }
            }
        }
    }
];

async function waitUntilIndexed() {
    try {
        const searchClient = new SearchClient(searchEndpoint, indexName, credential);
        do {
            const count = await searchClient.getDocumentsCount();
            if (count == DOCUMENTS.length) {
                console.log("All documents indexed successfully.");
                break;
            }
            console.log(`Waiting for indexing... Current count: ${count}`);
            await new Promise((resolve) => setTimeout(resolve, 10000)); // Wait for 10 seconds
        } while (true);
    } catch (ex) {
        console.error("Failed to wait until indexed:", ex);
    }
}

const searchClient = new SearchClient(searchEndpoint, indexName, credential);

try {
    console.log("Uploading documents...");
    const result = await searchClient.uploadDocuments(DOCUMENTS);
    for (const r of result.results) {
        console.log(`Key: ${r.key}, Succeeded: ${r.succeeded}, ErrorMessage: ${r.errorMessage || 'none'}`);
    }
    await waitUntilIndexed();
} catch (ex) {
    console.error("Failed to upload documents:", ex);
}

    このコードは、JSON オブジェクトの配列を読み込みます。 各ドキュメントには、記事の descriptionのベクター化されたバージョンが含まれています。 このベクトルを使用すると、正確なキーワードではなく意味に基づいて一致する類似性検索が可能になります。

    このクイック スタート記事ではインデックスをすぐに検索するため、 waitUntilIndexed 関数はインデックスが検索操作の準備ができるまで待機します。 検索を実行するには、インデックスにドキュメントを完全に設定する必要があるため、これは重要です。

  3. ファイルをビルドして実行します。

    node -r dotenv/config src/uploadDocuments.js

  4. このコードの出力は、ドキュメントのインデックスが作成され、検索の準備ができていることを示しています。

    Uploading documents...
Key: 1, Succeeded: true, ErrorMessage: none
Key: 2, Succeeded: true, ErrorMessage: none
Key: 3, Succeeded: true, ErrorMessage: none
Key: 4, Succeeded: true, ErrorMessage: none
Key: 48, Succeeded: true, ErrorMessage: none
Key: 49, Succeeded: true, ErrorMessage: none
Key: 13, Succeeded: true, ErrorMessage: none
Waiting for indexing... Current count: 0
All documents indexed successfully.

    upload_documents() メソッドと次の例に関する重要なポイント:

    • コードは、 @azure/search-documents パッケージによって提供されるメイン オブジェクトである SearchClient を介して、Azure AI Search サービスでホストされている特定の検索インデックスと対話します。 SearchClient は、次のようなインデックス操作にアクセスできます。
      • データ インジェスト - uploadDocuments()、mergeDocuments()、deleteDocuments()、など。
      • 検索操作 - search()、autocomplete()、suggest()
      • createIndex()、deleteIndex()、getIndex() などのインデックス管理操作。
    • ベクトル フィールドには浮動小数点値が含まれます。 ディメンション属性の最小値は 2 で、それぞれ最大 4,096 個の浮動小数点値があります。 このクイック スタートでは、ディメンション属性を 1,536 に設定します。これは、Azure OpenAI text-embedding-ada-002 モデルによって生成される埋め込みのサイズであるためです。

クエリをベクターとして作成する

例のベクトル クエリは、次の 2 つの文字列に基づいています。

  • 検索文字列: historic hotel walk to restaurants and shopping
  • ベクトル クエリ文字列 (数学的表現にベクトル化): quintessential lodging near running trails, eateries, retail

このベクトル クエリ文字列は、意味的には検索文字列に似ていますが、検索インデックスに存在しない用語を含んでいます。 quintessential lodging near running trails, eateries, retail のキーワード検索を実行すると、結果は 0 になります。 この例を使用して、一致する言葉がない場合でも関連する結果を取得する方法を示します。

  1. src ディレクトリにqueryVector.js ファイルを作成し、クエリ ベクターを作成するコードを追加します。

    // Query is 1536-dimensional vector of "quintessential lodging near running trails, eateries, retail"
// Note: this same vector can be used for vector search and semantic search
// Replace with your own vector

export const vector = 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  2. このコードは、次のセクションでベクター検索を実行するために使用します。 クエリ ベクターは、Azure OpenAI の埋め込みモデルを使用して作成されます。

最初の例では、 SearchClient を使用して検索文字列と密接に一致するドキュメントの説明を検索する基本的なシナリオを示します。検索 と VectorQuery と SearchOptions。

  1. src ディレクトリにsearchSingle.js ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] ,
        includeTotalCount: true,
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

    vectorQuery には、 vectorとしてのクエリ入力のベクター化されたテキストを含む、ベクター化されたクエリの構成が含まれます。 fields は、検索するベクター フィールドを決定し、 kNearestNeighborsCount 返す最も近い近隣の数を指定します。

    ベクトル クエリ文字列は quintessential lodging near running trails, eateries, retail であり、このクエリでは 1,536 個の埋め込みにベクトル化されます。

  3. ファイルをビルドして実行します。

    node -r dotenv/config src/searchSingle.js

  4. このコードの出力には、クエリ quintessential lodging near running trails, eateries, retailに関連するドキュメントが表示されます。

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


Single Vector search found 5
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
- HotelId: 13, HotelName: Luxury Lion Resort, Tags: ["bar","concierge","restaurant"], Score 0.6333684
- HotelId: 4, HotelName: Sublime Palace Hotel, Tags: ["concierge","view","air conditioning"], Score 0.605672
- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: ["air conditioning","laundry service","24-hour front desk service"], Score 0.6026341
- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366

    quintessential lodging near running trails, eateries, retailに相当するベクトルの応答は、返される select で指定されたフィールドのみを含む 5 つの結果で構成されます。

フィルターを使用して 1 つのベクター検索を作成する

フィルターを追加できます。ただし、フィルターはインデックス内の非ベクトル コンテンツに適用されます。 この例では、Tags フィールドにフィルターが適用され、無料 Wi-Fi を備えていないホテルをすべて除外します。 この検索では SearchClient を使用します。検索 と VectorQuery と SearchOptions。

  1. src ディレクトリにsearchSingleWithFilter.js ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] ,
        includeTotalCount: true,
        filter: "Tags/any(tag: tag eq 'free wifi')", // Adding filter for "free wifi" tag
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

    依存関係、環境変数、および前の検索と同じ検索機能を追加し、 free wifiのホテルに対して後処理除外フィルターを追加します。

  3. ファイルをビルドして実行します。

    node -r dotenv/config src/searchSingleWithFilter.js

  4. このコードの出力には、 free wifi のフィルターが適用されたクエリに関連するドキュメントが表示されます。

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


Single Vector search found 2
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366

地理空間フィルターを使用して検索を作成する

地理空間フィルターを指定して、結果を特定の地理的領域に制限できます。 この例では、フィルターによって、ワシントン D.C. から 300 キロメートル以内のホテル (座標: -77.03241 38.90166) に結果が制限されます。 地理空間距離は常にキロメートル単位です。 この検索では SearchClient を使用します。検索 と VectorQuery と SearchOptions。

  1. src ディレクトリにsearchSingleWithFilterGeo.js ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions = {
        top: 5,
        includeTotalCount: true,
        select: [
            "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
        ],
        facets: ["Address/StateProvince"],
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
        filter: "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
    };
    const results = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

  3. ファイルをビルドして実行します。

    node -r dotenv/config src/searchSingleWithFilterGeo.js

  4. このコードの出力には、地理空間後処理除外フィルターが適用されたクエリに関連するドキュメントが表示されます。

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


Single Vector search found 2
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: N/A, Score 0.6605852246284485
- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: N/A, Score 0.602634072303772

ハイブリッド検索は、単一の検索要求内のキーワード クエリとベクトル クエリで構成されます。 次の例では、ベクター クエリとフルテキスト検索を同時に実行します。

  • 検索文字列: historic hotel walk to restaurants and shopping
  • ベクトル クエリ文字列 (数学的表現にベクトル化): quintessential lodging near running trails, eateries, retail

この検索では SearchClient を使用します。検索 と VectorQuery と SearchOptions。

  1. src ディレクトリにsearchHybrid.js ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create hybrid search options with both vector query and search text
    const searchOptions = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"],
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };

    // Use search_text for keyword search (hybrid search = vector + keyword)
    const searchText = "historic hotel walk to restaurants and shopping";
    const results = await searchClient.search(searchText, searchOptions);            // Convert results to typed format and log for debugging

    console.log(`\n\nHybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;

        console.log(`- Score: ${result.score}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log(`  Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}\n`);
    }

} catch (ex) {
    console.error("Hybrid search failed:", ex);
    throw ex;
}

  3. ファイルをビルドして実行します。

    node -r dotenv/config src/searchHybrid.js

  4. このコードの出力には、ハイブリッド検索に関連するドキュメントが表示されます。

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


Hybrid search found 7 then limited to top 5
- Score: 0.03279569745063782
  HotelId: 4
  HotelName: Sublime Palace Hotel
  Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
  Category: Boutique
  Tags: ["concierge","view","air conditioning"]

- Score: 0.032522473484277725
  HotelId: 13
  HotelName: Luxury Lion Resort
  Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
  Category: Luxury
  Tags: ["bar","concierge","restaurant"]

- Score: 0.03205128386616707
  HotelId: 48
  HotelName: Nordick's Valley Motel
  Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
  Category: Boutique
  Tags: ["continental breakfast","air conditioning","free wifi"]

- Score: 0.0317460335791111
  HotelId: 49
  HotelName: Swirling Currents Hotel
  Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
  Category: Suite
  Tags: ["air conditioning","laundry service","24-hour front desk service"]

- Score: 0.03125
  HotelId: 2
  HotelName: Old Century Hotel
  Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
  Category: Boutique
  Tags: ["pool","free wifi","air conditioning","concierge"]

    逆ランク 融合 (RRF) は結果をマージするため、入力を確認するのに役立ちます。 次の結果は、フル テキスト クエリのみからのものです。 上位 2 つの結果は、Sublime Palace Hotel と History Lion Resort です。 Sublime Palace Hotel は、より強い BM25 関連性スコアを持っています。

       {
       "@search.score": 2.2626662,
       "HotelName": "Sublime Palace Hotel",
       "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace."
   },
   {
       "@search.score": 0.86421645,
       "HotelName": "Luxury Lion Resort",
       "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort"
   },

    マッチ検索に HNSW を使用するベクトルのみのクエリでは、Sublime Palace Hotel の順位は 4 位まで落ちます。 Historic Lion の場合、フル テキスト検索では 2 位、ベクトル検索では 3 位でしたが、変動範囲が同じでないため、均質化された結果セットでは最上位の一致として表示されます。

    "value": [
    {
        "@search.score": 0.857736,
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.8399129,
        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
        "Category": "Luxury"
    },
    {
        "@search.score": 0.8383954,
        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
        "Category": "Resort and Spa"
    },
    {
        "@search.score": 0.8254346,
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.82380056,
        "HotelId": "1",
        "HotelName": "Stay-Kay City Hotel",
        "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of New York.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.81514084,
        "HotelId": "2",
        "HotelName": "Old Century Hotel",
        "Description": "The hotel is situated in a  nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.8133763,
        "HotelId": "3",
        "HotelName": "Gastronomic Landscape Hotel",
        "Description": "The Hotel stands out for its gastronomic excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
        "Category": "Resort and Spa"
    }
]

コレクション内の最後のクエリを次に示します。セマンティック ハイブリッド検索を追加の検索テキスト historic hotel walk to restaurants and shoppingで拡張します。

この検索では SearchClient を使用します。検索 と VectorQuery と SearchOptions。

  1. src ディレクトリにsearchSemanticHybrid.js ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create semantic hybrid search options with vector query and semantic configuration
    const searchOptions = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Category", "Description"],
        queryType: "semantic",
        semanticSearchOptions: {
            configurationName: "semantic-config"
        },
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
    };

    // Use search_text for semantic search
    const searchText = "historic hotel walk to restaurants and shopping";
    const results = await searchClient.search(searchText, searchOptions);

    console.log(`\n\nSemantic hybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;
        const score = result.score;
        const rerankerScoreDisplay = result.rerankerScore;

        console.log(`- Score: ${score}`);
        console.log(`  Re-ranker Score: ${rerankerScoreDisplay}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log('');
    }

} catch (ex) {
    console.error("Semantic hybrid search failed:", ex);
    throw ex;
}

  3. ファイルをビルドして実行します。

    node -r dotenv/config src/searchSemanticHybrid.js

  4. このコードの出力には、セマンティック ハイブリッド検索に関連するドキュメントが表示されます。

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


Semantic hybrid search found 7 then limited to top 5
- Score: 0.0317460335791111
  Re-ranker Score: 2.6550590991973877
  HotelId: 49
  HotelName: Swirling Currents Hotel
  Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
  Category: Suite

- Score: 0.03279569745063782
  Re-ranker Score: 2.599761724472046
  HotelId: 4
  HotelName: Sublime Palace Hotel
  Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
  Category: Boutique

- Score: 0.03125
  Re-ranker Score: 2.3480887413024902
  HotelId: 2
  HotelName: Old Century Hotel
  Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
  Category: Boutique

- Score: 0.016393441706895828
  Re-ranker Score: 2.2718777656555176
  HotelId: 1
  HotelName: Stay-Kay City Hotel
  Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
  Category: Boutique

- Score: 0.01515151560306549
  Re-ranker Score: 2.0582215785980225
  HotelId: 3
  HotelName: Gastronomic Landscape Hotel
  Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.
  Category: Suite

    セマンティック ランク付けは、クエリ内の単語の背後にある意味とドキュメントの内容を理解することで、検索結果の関連性を向上させる方法と考えることができます。 この場合、セマンティック ランク付けは、キーワードに関連するだけでなく、クエリの意図と一致するホテルを特定するのに役立ちます。

    重要なポイント:

    • ベクトル検索は、vectorSearchOptions プロパティを使用して指定されます。 キーワード検索は、semanticSearchOptions プロパティを使用して指定されます。

    • ハイブリッド検索では、キーワードに対するフルテキスト検索とベクトル検索を統合できます。 フィルター、スペル チェック、セマンティック ランク付けは、ベクトルではなくテキスト コンテンツにのみ適用されます。 この最後のクエリでは、セマンティックな answer はありません。これは、システムが生成したものが十分に強力ではなかったためです。

    • 実際の結果には、セマンティック キャプションや強調表示など、より詳細な情報が含まれます。 結果は読みやすくするために変更されています。 応答の構造全体を取得するには、REST クライアントで要求を実行します。

クリーンアップ

独自のサブスクリプションを使用している場合は、プロジェクトの最後に、作成したリソースがまだ必要かどうかを確認してください。 リソースを実行したままにすると、お金がかかる場合があります。 リソースは個別に削除することも、リソース グループを削除してリソースのセット全体を削除することもできます。

左端のペイン内にある [すべてのリソース] または [リソース グループ] リンクを使用して、Azure portal 内でリソースを検索および管理できます。

検索サービスを保持し、インデックスとドキュメントを削除する場合は、プログラムでインデックスを削除できます。

  1. src ディレクトリにdeleteIndex.js ファイルを作成します。

  2. 依存関係、環境変数、およびインデックスを削除するコードを追加します。

    import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const searchIndexClient = new SearchIndexClient(searchEndpoint, credential);

try {
    console.log("Deleting index...");
    await searchIndexClient.deleteIndex(indexName);
    console.log(`Index ${indexName} deleted`);
} catch (ex) {
    console.error("Failed to delete index:", ex);
}

  3. ファイルをビルドして実行します。

    node -r dotenv/config src/deleteIndex.js

次のステップ

  • JavaScript 用 Azure AI Search のベクター検索機能のコード サンプルのリポジトリを確認する

このクイック スタートでは、TypeScript を使用してベクターを作成、読み込み、クエリします。 コード例では、 Azure AI Search クライアント ライブラリを使用してこれらの操作を実行します。 このライブラリは、データ インジェスト、検索操作、インデックス管理操作などのインデックス操作にアクセスするための REST API に対する抽象化を提供します。

Azure AI 検索のベクトル ストアにあるインデックス スキーマでは、ベクトル フィールドと非ベクトル フィールド、埋め込み空間を作成するアルゴリズムのためのベクトル検索構成、クエリ時に評価されるベクトル フィールド定義の設定が定義されています。 Create Index REST API によってベクトル ストアが作成されます。

注

このクイック スタートでは、ベクター化の手順を省略し、インライン埋め込みを提供します。 独自のコンテンツ に組み込みのデータ チャンクとベクター化を 追加する場合は、データの インポートとベクター化ウィザード を試して、エンドツーエンドのチュートリアルを実行してください。

前提条件

サービス エンドポイントを取得する

残りのセクションでは、Azure OpenAI と Azure AI 検索への API 呼び出しを設定します。 サービス エンドポイントを取得して、コード内で変数として提供できるようにします。

  1. Azure portal にサインインします。

  2. 使用する検索サービスを見つけます。

  3. [概要] ホーム ページで、URL をコピーします。 たとえば、エンドポイントは https://example.search.windows.net のようになります。

Node.JS プロジェクトを設定する

Visual Studio Code と TypeScript を使用してプロジェクトを設定します。

  1. 新しいディレクトリで Visual Studio Code を起動します。

    mkdir vector-quickstart && cd vector-quickstart
code .

  2. プロジェクト ディレクトリに ESM モジュール用の新しいパッケージを作成します。

    npm init -y
npm pkg set type=module

    これにより、既定値を持つ package.json ファイルが作成されます。

  3. package.jsonを編集してビルド スクリプトを含めます。 scripts セクションに次の行を追加します。

    "build": "tsc"

  4. 次の npm パッケージをインストールします。

    npm install @azure/identity @azure/search-documents dotenv @types/node

  5. プロジェクト ディレクトリに src ディレクトリを作成します。

    mkdir src

  6. 次の内容を含む ESM のプロジェクト ディレクトリに tsconfig.json ファイルを作成します。

    {
  "compilerOptions": {
    "target": "esnext",
    "module": "NodeNext",
    "moduleResolution": "nodenext",
    "rootDir": "./src",
    "outDir": "./dist/",
    "esModuleInterop": true,
    "forceConsistentCasingInFileNames": true,
    "strict": true,
    "skipLibCheck": true,
    "declaration": true,
    "sourceMap": true,
    "resolveJsonModule": true,
    "moduleDetection": "force", // Add this for ESM
    "allowSyntheticDefaultImports": true // Helpful for ESM interop
  },
  "include": [
    "src/**/*.ts"
  ]
}

ローカル開発用に環境変数を設定する

  1. vector-quickstart プロジェクト ディレクトリに.env ファイルを作成します。

  2. .env ファイルに次の環境変数を追加し、値を独自のサービス エンドポイントとキーに置き換えます。

    AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart

Azure にサインインする

接続には Microsoft Entra ID とロールの割り当てを使用しています。 Azure AI 検索および Azure OpenAI と同じテナントおよびサブスクリプションにログインしていることを確認します。 コマンド ラインで Azure CLI を使用して、現在のプロパティの表示、プロパティの変更、サインインを行うことができます。 詳細については、「キーを使用せずに接続する」を参照してください。

次の各コマンドを順番に実行します。

az account show

az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>

az login --tenant <PUT YOUR TENANT ID HERE>

これで、ローカル デバイスから Azure にログインできるようになりました。

ベクター インデックスを作成する

このセクションでは、 SearchIndexClient を使用して Azure AI Search でベクター インデックスを作成します。createOrUpdateIndex。 インデックス スキーマは、ベクター フィールド DescriptionVectorを含むフィールドを定義します。 インデックスが作成されたら、ドキュメントをインデックスにアップロードします。 ドキュメントには、記事の説明のベクター化されたバージョンが含まれています。これにより、正確なキーワードではなく意味に基づく類似性検索が可能になります。

  1. src ディレクトリにcreateIndex.ts ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient,
    SearchIndex,
    SearchField,
    VectorSearch,
    SemanticSearch,
    SearchSuggester
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}


const indexClient = new SearchIndexClient(searchEndpoint, credential);
console.log('Creating index...');

// Define fields
const fields: SearchField[] = [
    {
        name: "HotelId",
        type: "Edm.String" as const,
        key: true,
        filterable: true
    },
    {
        name: "HotelName",
        type: "Edm.String" as const,
        sortable: true,
        searchable: true,
        hidden: false,
    },
    {
        name: "Description",
        type: "Edm.String" as const,
        searchable: true,
        hidden: false
    },
    {
        name: "DescriptionVector",
        type: "Collection(Edm.Single)" as const,
        searchable: true,
        vectorSearchDimensions: 1536,
        vectorSearchProfileName: "my-vector-profile",
        hidden: true
    },
    {
        name: "Category",
        type: "Edm.String" as const,
        sortable: true,
        filterable: true,
        facetable: true,
        searchable: true,
        hidden: false
    },
    {
        name: "Tags",
        type: "Collection(Edm.String)" as const,
        searchable: true,
        filterable: true,
        facetable: true,
        hidden: false
    },
    {
        name: "ParkingIncluded",
        type: "Edm.Boolean" as const,
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "LastRenovationDate",
        type: "Edm.DateTimeOffset" as const,
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Rating",
        type: "Edm.Double" as const,
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Address",
        type: "Edm.ComplexType" as const,
        fields: [
            {
                name: "StreetAddress",
                type: "Edm.String" as const,
                searchable: true,
            },
            {
                name: "City",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "StateProvince",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "PostalCode",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true
            },
            {
                name: "Country",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true,
            }
        ]
    },
    {
        name: "Location",
        type: "Edm.GeographyPoint" as const,
        filterable: true,
        sortable: true
    }
];

// Define vector search configuration
const vectorSearch: VectorSearch = {
    algorithms: [
        {
            name: "hnsw-vector-config",
            kind: "hnsw" as const
        },
        {
            name: "eknn-vector-config",
            kind: "exhaustiveKnn" as const
        }
    ],
    profiles: [
        {
            name: "my-vector-profile",
            algorithmConfigurationName: "hnsw-vector-config"
        }
    ]
};

// Define semantic configuration
const semanticConfig = {
    name: "semantic-config",
    prioritizedFields: {
        titleField: {
            name: "HotelName"
        },
        contentFields: [
            {
                name: "Description"
            }
        ],
        keywordsFields: [
            {
                name: "Category"
            }
        ]
    }
};

// Create the semantic settings with the configuration
const semanticSearch: SemanticSearch = {
    configurations: [semanticConfig]
};

// Define suggesters
const suggesters: SearchSuggester[] = [];

// Create the search index with the semantic settings
const indexDefinition: SearchIndex = {
    name: indexName,
    fields: fields,
    vectorSearch: vectorSearch,
    semanticSearch: semanticSearch,
    suggesters: suggesters
};

const result = await indexClient.createOrUpdateIndex(indexDefinition);
console.log(`${result.name} created`);

    コード ファイルは、 HotelDocument の依存関係、環境変数、JavaScript 型をファイルの先頭に追加します。 createIndex関数を追加してインデックスを作成します。 この関数は、ベクトル フィールド DescriptionVectorを含むインデックス スキーマを定義します。

  3. ファイルをビルドして実行します。

    npm run build && node -r dotenv/config dist/createIndex.js

  4. このコードの出力は、インデックスが正常に作成されたことを示しています。

    Using Azure Search endpoint: https://my-service.search.windows.net
Using index name: hotels-vector-quickstart
Creating index...
hotels-vector-quickstart created

    @azure/search-documents ライブラリを使用してベクター インデックスを作成するときの重要なポイント:

    • インデックスを定義するには、フィールドのリストを作成します。

    • この特定のインデックスでは、次のような複数の検索機能がサポートされています。

      • フルテキスト キーワード検索 (searchable)
      • ベクトル検索 (ベクトル フィールドと非ベクトル フィールドを併置することによってハイブリッド検索を可能にする) フィールド (DescriptionVectorとvectorSearchProfileNameを使う)
      • セマンティック検索
      • ファセット検索 (searchSuggester)
      • 地理空間検索 (geo.distanceを含むLocation フィールド)
      • フィルター処理、並べ替え (フィルター可能および並べ替え可能とマークされた多くのフィールド)

インデックスにドキュメントをアップロードする

インデックスの作成と読み込みは別の手順です。 前の手順でインデックス スキーマを作成しました。 次に、 SearchClient を使用してドキュメントをインデックスに読み込む必要があります。uploadDocuments。

Azure AI Search では、インデックスには検索可能なすべてのコンテンツが格納されますが、検索エンジンはそのインデックスに対してクエリを実行します。

  1. src ディレクトリにuploadDocuments.ts ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const DOCUMENTS =  [
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "1",
        "HotelName": "Stay-Kay City Hotel",
        "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
        "DescriptionVector": 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        "Category": "Boutique",
        "Tags": [
            "view",
            "air conditioning",
            "concierge"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2022-01-18T00:00:00Z",
        "Rating": 3.60,
        "Address": {
            "StreetAddress": "677 5th Ave",
            "City": "New York",
            "StateProvince": "NY",
            "PostalCode": "10022",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                -73.975403,
                40.760586
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "2",
        "HotelName": "Old Century Hotel",
        "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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        "Category": "Boutique",
        "Tags": [
            "pool",
            "free wifi",
            "air conditioning",
            "concierge"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2019-02-18T00:00:00Z",
        "Rating": 3.60,
        "Address": {
            "StreetAddress": "140 University Town Center Dr",
            "City": "Sarasota",
            "StateProvince": "FL",
            "PostalCode": "34243",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
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            ]
        }
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        "HotelId": "3",
        "HotelName": "Gastronomic Landscape Hotel",
        "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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        "Category": "Suite",
        "Tags": [
            "restaurant",
            "bar",
            "continental breakfast"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2015-09-20T00:00:00Z",
        "Rating": 4.80,
        "Address": {
            "StreetAddress": "3393 Peachtree Rd",
            "City": "Atlanta",
            "StateProvince": "GA",
            "PostalCode": "30326",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                33.847848,
                -84.363602
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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        "Category": "Boutique",
        "Tags": [
            "concierge",
            "view",
            "air conditioning"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2020-02-06T00:00:00Z",
        "Rating": 4.60,
        "Address": {
            "StreetAddress": "7400 San Pedro Ave",
            "City": "San Antonio",
            "StateProvince": "TX",
            "PostalCode": "78216",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                -98.498923,
                29.518029
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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        "Category": "Boutique",
        "Tags": [
            "continental breakfast",
            "air conditioning",
            "free wifi"
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        "ParkingIncluded": "false",
        "LastRenovationDate": "2018-02-19T00:00:00Z",
        "Rating": 4.5,
        "Address": {
            "StreetAddress": "1401 I St NW",
            "City": "Washington D.C.",
            "StateProvince": "null",
            "PostalCode": "20005",
            "Country": "USA"
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            "properties": {
                "name": "EPSG:4326"
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        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.",
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        "Category": "Suite",
        "Tags": [
            "air conditioning",
            "laundry service",
            "24-hour front desk service"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2018-01-27T00:00:00Z",
        "Rating": 2.7,
        "Address": {
            "StreetAddress": "1100 S Hayes St",
            "City": "Arlington",
            "StateProvince": "VA",
            "PostalCode": "22202",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -77.060066,
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            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
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        }
    },
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        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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        "Category": "Luxury",
        "Tags": [
            "bar",
            "concierge",
            "restaurant"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2020-03-18T00:00:00Z",
        "Rating": 4.1,
        "Address": {
            "StreetAddress": "3 Cityplace Dr",
            "City": "St. Louis",
            "StateProvince": "MO",
            "PostalCode": "63141",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -90.44081,
            38.67219
            ],
            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
                }
            }
        }
    }
];

async function waitUntilIndexed(): Promise<void> {
    try {
        const searchClient = new SearchClient(searchEndpoint, indexName, credential);
        do {
            const count = await searchClient.getDocumentsCount();
            if (count == DOCUMENTS.length) {
                console.log("All documents indexed successfully.");
                break;
            }
            console.log(`Waiting for indexing... Current count: ${count}`);
            await new Promise((resolve) => setTimeout(resolve, 10000)); // Wait for 10 seconds
        } while (true);
    } catch (ex) {
        console.error("Failed to wait until indexed:", ex);
    }
}

const searchClient = new SearchClient(searchEndpoint, indexName, credential);

try {
    console.log("Uploading documents...");
    const result = await searchClient.uploadDocuments(DOCUMENTS);
    for (const r of result.results) {
        console.log(`Key: ${r.key}, Succeeded: ${r.succeeded}, ErrorMessage: ${r.errorMessage || 'none'}`);
    }
    await waitUntilIndexed();
} catch (ex) {
    console.error("Failed to upload documents:", ex);
}

    このコードは、各ドキュメントを記述する JSON オブジェクトと、ベクター化されたバージョンの記事の説明を含む変数ドキュメントを読み込みます。 このベクトルを使用すると、正確なキーワードではなく意味に基づいて一致する類似性検索が可能になります。

    このクイック スタート記事ではインデックスをすぐに検索するため、 waitUntilIndexed 関数はインデックスが検索操作の準備ができるまで待機します。 検索を実行するには、インデックスにドキュメントを完全に設定する必要があるため、これは重要です。

  3. ファイルをビルドして実行します。

    npm run build && node -r dotenv/config dist/uploadDocuments.js

  4. このコードの出力は、ドキュメントのインデックスが作成され、検索の準備ができていることを示しています。

    Uploading documents...
Key: 1, Succeeded: true, ErrorMessage: none
Key: 2, Succeeded: true, ErrorMessage: none
Key: 3, Succeeded: true, ErrorMessage: none
Key: 4, Succeeded: true, ErrorMessage: none
Key: 48, Succeeded: true, ErrorMessage: none
Key: 49, Succeeded: true, ErrorMessage: none
Key: 13, Succeeded: true, ErrorMessage: none
Waiting for indexing... Current count: 0
All documents indexed successfully.

    upload_documents() メソッドと次の例に関する重要なポイント:

    • コードは、 @azure/search-documents パッケージによって提供されるメイン オブジェクトである SearchClient を介して、Azure AI Search サービスでホストされている特定の検索インデックスと対話します。 SearchClient は、次のようなインデックス操作にアクセスできます。
      • データ インジェスト - uploadDocuments()、mergeDocuments()、deleteDocuments()、など。
      • 検索操作 - search()、autocomplete()、suggest()
      • createIndex()、deleteIndex()、getIndex() などのインデックス管理操作。
    • ベクトル フィールドには浮動小数点値が含まれます。 ディメンション属性の最小値は 2 で、それぞれ最大 4,096 個の浮動小数点値があります。 このクイック スタートでは、ディメンション属性を 1,536 に設定します。これは、Azure OpenAI text-embedding-ada-002 モデルによって生成される埋め込みのサイズであるためです。

クエリをベクターとして作成する

例のベクトル クエリは、次の 2 つの文字列に基づいています。

  • 検索文字列: historic hotel walk to restaurants and shopping
  • ベクトル クエリ文字列 (数学的表現にベクトル化): quintessential lodging near running trails, eateries, retail

このベクトル クエリ文字列は、意味的には検索文字列に似ていますが、検索インデックスに存在しない用語を含んでいます。 quintessential lodging near running trails, eateries, retail のキーワード検索を実行すると、結果は 0 になります。 この例を使用して、一致する言葉がない場合でも関連する結果を取得する方法を示します。

  1. src ディレクトリにqueryVector.ts ファイルを作成し、クエリ ベクターを作成するコードを追加します。

    // Query is 1536-dimensional vector of "quintessential lodging near running trails, eateries, retail"
// Note: this same vector can be used for vector search and semantic search
// Replace with your own vector

export const vector = 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  2. このコードは、次のセクションでベクター検索を実行するために使用します。 クエリ ベクターは、Azure OpenAI の埋め込みモデルを使用して作成されます。

最初の例では、 SearchClient を使用して検索文字列と密接に一致するドキュメントの説明を検索する基本的なシナリオを示します。検索 と VectorQuery と SearchOptions。

  1. src ディレクトリにsearchSingle.ts ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] as const,
        includeTotalCount: true,
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

    vectorQuery には、 vectorとしてのクエリ入力のベクター化されたテキストを含む、ベクター化されたクエリの構成が含まれます。 fields は、検索するベクター フィールドを決定し、 kNearestNeighborsCount 返す最も近い近隣の数を指定します。

    ベクトル クエリ文字列は quintessential lodging near running trails, eateries, retail であり、このクエリでは 1,536 個の埋め込みにベクトル化されます。

  3. ファイルをビルドして実行します。

    npm run build && node -r dotenv/config dist/searchSingle.js

  4. このコードの出力には、クエリ quintessential lodging near running trails, eateries, retailに関連するドキュメントが表示されます。

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


Single Vector search found 5
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
- HotelId: 13, HotelName: Luxury Lion Resort, Tags: ["bar","concierge","restaurant"], Score 0.6333684
- HotelId: 4, HotelName: Sublime Palace Hotel, Tags: ["concierge","view","air conditioning"], Score 0.605672
- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: ["air conditioning","laundry service","24-hour front desk service"], Score 0.6026341
- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366

    quintessential lodging near running trails, eateries, retailに相当するベクトルの応答は、返される select で指定されたフィールドのみを含む 5 つの結果で構成されます。

フィルターを使用して 1 つのベクター検索を作成する

フィルターを追加できます。ただし、フィルターはインデックス内の非ベクトル コンテンツに適用されます。 この例では、Tags フィールドにフィルターが適用され、無料 Wi-Fi を備えていないホテルをすべて除外します。 この検索では SearchClient を使用します。検索 と VectorQuery と SearchOptions。

  1. src ディレクトリにsearchSingleWithFilter.ts ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] as const,
        includeTotalCount: true,
        filter: "Tags/any(tag: tag eq 'free wifi')", // Adding filter for "free wifi" tag
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search with filter found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search with filter failed:", ex);
    throw ex;
}

    依存関係、環境変数、および前の検索と同じ検索機能を追加し、 free wifiのホテルに対して後処理除外フィルターを追加します。

  3. ファイルをビルドして実行します。

    npm run build && node -r dotenv/config dist/searchSingleWithFilter.js

  4. このコードの出力には、 free wifi のフィルターが適用されたクエリに関連するドキュメントが表示されます。

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


Single Vector search found 2
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366

地理空間フィルターを使用して検索を作成する

地理空間フィルターを指定して、結果を特定の地理的領域に制限できます。 この例では、フィルターによって、ワシントン D.C. から 300 キロメートル以内のホテル (座標: -77.03241 38.90166) に結果が制限されます。 地理空間距離は常にキロメートル単位です。 この検索では SearchClient を使用します。検索 と VectorQuery と SearchOptions。

  1. src ディレクトリにsearchSingleWithFilterGeo.ts ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);


try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    const searchOptions: SearchOptions<HotelDocument> = {
        top: 5,
        includeTotalCount: true,
        select: [
            "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
        ] as const,
        facets: ["Address/StateProvince"],
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
        filter: "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
    };
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);

    console.log(`\n\nVector search with geo filter found ${results.count}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;

        console.log(`- HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Score: ${result.score}`);

        if (doc.Address) {
            console.log(`  City/State: ${doc.Address.City}, ${doc.Address.StateProvince}`);
        }

        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Score: ${result.score}\n`);
    }


} catch (ex) {
    console.error("Vector search with geo filter failed:", ex);
    throw ex;
}

  3. ファイルをビルドして実行します。

    npm run build && node -r dotenv/config dist/searchSingleWithFilterGeo.js

  4. このコードの出力には、地理空間後処理除外フィルターが適用されたクエリに関連するドキュメントが表示されます。

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


Single Vector search found 2
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: N/A, Score 0.6605852246284485
- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: N/A, Score 0.602634072303772

ハイブリッド検索は、単一の検索要求内のキーワード クエリとベクトル クエリで構成されます。 次の例では、ベクター クエリとフルテキスト検索を同時に実行します。

  • 検索文字列: historic hotel walk to restaurants and shopping
  • ベクトル クエリ文字列 (数学的表現にベクトル化): quintessential lodging near running trails, eateries, retail

この検索では SearchClient を使用します。検索 と VectorQuery と SearchOptions。

  1. src ディレクトリにsearchHybrid.ts ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create hybrid search options with both vector query and search text
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] as const,
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };

    // Use search_text for keyword search (hybrid search = vector + keyword)
    const searchText = "historic hotel walk to restaurants and shopping";
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search(searchText, searchOptions);            // Convert results to typed format and log for debugging

    console.log(`\n\nHybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;

        console.log(`- Score: ${result.score}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log(`  Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}\n`);
    }

} catch (ex) {
    console.error("Hybrid search failed:", ex);
    throw ex;
}

  3. ファイルをビルドして実行します。

    npm run build && node -r dotenv/config dist/searchHybrid.js

  4. このコードの出力には、ハイブリッド検索に関連するドキュメントが表示されます。

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


Hybrid search found 7 then limited to top 5
- Score: 0.03279569745063782
  HotelId: 4
  HotelName: Sublime Palace Hotel
  Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
  Category: Boutique
  Tags: ["concierge","view","air conditioning"]

- Score: 0.032522473484277725
  HotelId: 13
  HotelName: Luxury Lion Resort
  Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
  Category: Luxury
  Tags: ["bar","concierge","restaurant"]

- Score: 0.03205128386616707
  HotelId: 48
  HotelName: Nordick's Valley Motel
  Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
  Category: Boutique
  Tags: ["continental breakfast","air conditioning","free wifi"]

- Score: 0.0317460335791111
  HotelId: 49
  HotelName: Swirling Currents Hotel
  Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
  Category: Suite
  Tags: ["air conditioning","laundry service","24-hour front desk service"]

- Score: 0.03125
  HotelId: 2
  HotelName: Old Century Hotel
  Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
  Category: Boutique
  Tags: ["pool","free wifi","air conditioning","concierge"]

    逆ランク 融合 (RRF) は結果をマージするため、入力を確認するのに役立ちます。 次の結果は、フル テキスト クエリのみからのものです。 上位 2 つの結果は、Sublime Palace Hotel と History Lion Resort です。 Sublime Palace Hotel は、より強い BM25 関連性スコアを持っています。

       {
       "@search.score": 2.2626662,
       "HotelName": "Sublime Palace Hotel",
       "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace."
   },
   {
       "@search.score": 0.86421645,
       "HotelName": "Luxury Lion Resort",
       "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort"
   },

    マッチ検索に HNSW を使用するベクトルのみのクエリでは、Sublime Palace Hotel の順位は 4 位まで落ちます。 Historic Lion の場合、フル テキスト検索では 2 位、ベクトル検索では 3 位でしたが、変動範囲が同じでないため、均質化された結果セットでは最上位の一致として表示されます。

    "value": [
    {
        "@search.score": 0.857736,
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.8399129,
        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
        "Category": "Luxury"
    },
    {
        "@search.score": 0.8383954,
        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
        "Category": "Resort and Spa"
    },
    {
        "@search.score": 0.8254346,
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.82380056,
        "HotelId": "1",
        "HotelName": "Stay-Kay City Hotel",
        "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of New York.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.81514084,
        "HotelId": "2",
        "HotelName": "Old Century Hotel",
        "Description": "The hotel is situated in a  nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.8133763,
        "HotelId": "3",
        "HotelName": "Gastronomic Landscape Hotel",
        "Description": "The Hotel stands out for its gastronomic excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
        "Category": "Resort and Spa"
    }
]

コレクション内の最後のクエリを次に示します。セマンティック ハイブリッド検索を追加の検索テキスト historic hotel walk to restaurants and shoppingで拡張します。

この検索では SearchClient を使用します。検索 と VectorQuery と SearchOptions。

  1. src ディレクトリにsearchSemanticHybrid.ts ファイルを作成します。

  2. 次のコードをファイルにコピーします。

    import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create semantic hybrid search options with vector query and semantic configuration
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Category", "Description"] as const,
        queryType: "semantic" as const,
        semanticSearchOptions: {
            configurationName: "semantic-config"
        },
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
    };

    // Use search_text for semantic search
    const searchText = "historic hotel walk to restaurants and shopping";
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search(searchText, searchOptions);

    console.log(`\n\nSemantic hybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;
        const score = result.score;
        const rerankerScoreDisplay = result.rerankerScore;

        console.log(`- Score: ${score}`);
        console.log(`  Re-ranker Score: ${rerankerScoreDisplay}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log('');
    }

} catch (ex) {
    console.error("Semantic hybrid search failed:", ex);
    throw ex;
}

  3. ファイルをビルドして実行します。

    npm run build && node -r dotenv/config dist/searchSemanticHybrid.js

  4. このコードの出力には、セマンティック ハイブリッド検索に関連するドキュメントが表示されます。

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


Semantic hybrid search found 7 then limited to top 5
- Score: 0.0317460335791111
  Re-ranker Score: 2.6550590991973877
  HotelId: 49
  HotelName: Swirling Currents Hotel
  Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
  Category: Suite

- Score: 0.03279569745063782
  Re-ranker Score: 2.599761724472046
  HotelId: 4
  HotelName: Sublime Palace Hotel
  Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
  Category: Boutique

- Score: 0.03125
  Re-ranker Score: 2.3480887413024902
  HotelId: 2
  HotelName: Old Century Hotel
  Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
  Category: Boutique

- Score: 0.016393441706895828
  Re-ranker Score: 2.2718777656555176
  HotelId: 1
  HotelName: Stay-Kay City Hotel
  Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
  Category: Boutique

- Score: 0.01515151560306549
  Re-ranker Score: 2.0582215785980225
  HotelId: 3
  HotelName: Gastronomic Landscape Hotel
  Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.
  Category: Suite

    セマンティック ランク付けは、クエリ内の単語の背後にある意味とドキュメントの内容を理解することで、検索結果の関連性を向上させる方法と考えることができます。 この場合、セマンティック ランク付けは、キーワードに関連するだけでなく、クエリの意図と一致するホテルを特定するのに役立ちます。

    重要なポイント:

    • ベクトル検索は、vectorSearchOptions プロパティを使用して指定されます。 キーワード検索は、semanticSearchOptions プロパティを使用して指定されます。

    • ハイブリッド検索では、キーワードに対するフルテキスト検索とベクトル検索を統合できます。 フィルター、スペル チェック、セマンティック ランク付けは、ベクトルではなくテキスト コンテンツにのみ適用されます。 この最後のクエリでは、セマンティックな answer はありません。これは、システムが生成したものが十分に強力ではなかったためです。

    • 実際の結果には、セマンティック キャプションや強調表示など、より詳細な情報が含まれます。 結果は読みやすくするために変更されています。 応答の構造全体を取得するには、REST クライアントで要求を実行します。

クリーンアップ

独自のサブスクリプションを使用している場合は、プロジェクトの最後に、作成したリソースがまだ必要かどうかを確認してください。 リソースを実行したままにすると、お金がかかる場合があります。 リソースは個別に削除することも、リソース グループを削除してリソースのセット全体を削除することもできます。

左端のペイン内にある [すべてのリソース] または [リソース グループ] リンクを使用して、Azure portal 内でリソースを検索および管理できます。

検索サービスを保持し、インデックスとドキュメントを削除する場合は、プログラムでインデックスを削除できます。

  1. src ディレクトリにdeleteIndex.ts ファイルを作成します。

  2. 依存関係、環境変数、およびインデックスを削除するコードを追加します。

    import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const searchIndexClient = new SearchIndexClient(searchEndpoint, credential);

try {
    console.log("Deleting index...");
    await searchIndexClient.deleteIndex(indexName);
    console.log(`Index ${indexName} deleted`);
} catch (ex) {
    console.error("Failed to delete index:", ex);
}

  3. ファイルをビルドして実行します。

    npm run build && node -r dotenv/config dist/deleteIndex.js

次のステップ

  • JavaScript 用 Azure AI Search のベクター検索機能のコード サンプルのリポジトリを確認する

このクイック スタートでは、.NET アプリを使用してベクターの作成、設定、クエリを行います。 コード例では、 Azure AI Search クライアント ライブラリを使用してこれらの操作を実行します。 このライブラリは、データ インジェスト、検索操作、インデックス管理操作などのインデックス操作にアクセスするための REST API に対する抽象化を提供します。

Azure AI 検索のベクトル ストアにあるインデックス スキーマでは、ベクトル フィールドと非ベクトル フィールド、埋め込み空間を作成するアルゴリズムのためのベクトル検索構成、クエリ時に評価されるベクトル フィールド定義の設定が定義されています。 Create Index REST API によってベクトル ストアが作成されます。

注

このクイック スタートでは、ベクター化の手順を省略し、インライン埋め込みを提供します。 独自のコンテンツ に組み込みのデータ チャンクとベクター化を 追加する場合は、データの インポートとベクター化ウィザード を試して、エンドツーエンドのチュートリアルを実行してください。

前提条件

リソース情報の取得

検索エンドポイントへの要求は、認証および承認する必要があります。 このタスクには API キーまたはロールを使用することもできますが、 Microsoft Entra ID 経由のキーレス接続を使用することをお勧めします。

このクイック スタートでは、 DefaultAzureCredentialを使用します。これにより、開発と運用の両方のシナリオで認証が簡略化されます。 ただし、運用環境のシナリオでは、別のアプローチを必要とするより高度な要件がある場合があります。 すべてのオプションを理解 するには、Azure SDK for .NET を使用した Azure サービスへの .NET アプリの認証 に関するページを参照してください。

コードとセットアップ環境を複製する

  1. このクイック スタートのコードを含むリポジトリを複製します。

    git clone https://github.com/Azure-Samples/azure-search-dotnet-samples

    このリポジトリには、それぞれ別のサブフォルダーにある複数の記事の .NET コード例があります。

  2. Visual Studio Code でサブフォルダー quickstart-Vector-Search を開くか、 .sln ファイルをダブルクリックして Visual Studio でソリューションを開きます。

  3. VectorSearchExamplesフォルダーとVectorSearchCreatePopulateIndex フォルダー内のappsettings.json ファイルを開きます。 次の値を更新します。

    • AZURE_SEARCH_ENDPOINT: Azure portal で Azure AI Search サービスの URL を見つけます。 検索リソースの [概要 ] ページで、URL フィールドを探します。 たとえば、エンドポイントは https://mydemo.search.windows.net のようになります。
    • AZURE_SEARCH_INDEX_NAME: ファイルに指定された既定値のままにするか、独自のインデックス名を入力します。

ベクター インデックスを作成してドキュメントをアップロードする

Azure AI Search サービスに対して検索クエリを実行するには、まず検索インデックスを作成し、ドキュメントをサービスにアップロードする必要があります。

  1. VectorSearchCreatePopulateIndex フォルダーで新しいターミナルを開きます。

  2. dotnet run コマンドを使用してプロジェクトを実行します。

    dotnet run

次のコードを実行してインデックスを作成します。

static async Task CreateSearchIndex(string indexName, SearchIndexClient indexClient)
{
    var addressField = new ComplexField("Address");
    addressField.Fields.Add(new SearchableField("StreetAddress") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft });
    addressField.Fields.Add(new SearchableField("City") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
    addressField.Fields.Add(new SearchableField("StateProvince") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
    addressField.Fields.Add(new SearchableField("PostalCode") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
    addressField.Fields.Add(new SearchableField("Country") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });

    var allFields = new List<SearchField>()
{
    new SimpleField("HotelId", SearchFieldDataType.String) { IsKey = true, IsFacetable = true, IsFilterable = true },
    new SearchableField("HotelName") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft },
    new SearchableField("Description") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft },
    new VectorSearchField("DescriptionVector", 1536, "my-vector-profile"),
    new SearchableField("Category") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true },
    new SearchableField("Tags", collection: true) { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true },
    new SimpleField("ParkingIncluded", SearchFieldDataType.Boolean) { IsFacetable = true, IsFilterable = true },
    new SimpleField("LastRenovationDate", SearchFieldDataType.DateTimeOffset) { IsSortable = true },
    new SimpleField("Rating", SearchFieldDataType.Double) { IsFacetable = true, IsFilterable = true, IsSortable = true },
    addressField,
    new SimpleField("Location", SearchFieldDataType.GeographyPoint) { IsFilterable = true, IsSortable = true },
};

    // Create the suggester configuration
    var suggester = new SearchSuggester("sg", new[] { "Address/City", "Address/Country" });

    // Create the semantic search
    var semanticSearch = new SemanticSearch()
    {
        Configurations =
        {
            new SemanticConfiguration(
            name: "semantic-config",
            prioritizedFields: new SemanticPrioritizedFields
            {
                TitleField = new SemanticField("HotelName"),
                KeywordsFields = { new SemanticField("Category") },
                ContentFields = { new SemanticField("Description") }
            })
    }
    };

    // Add vector search configuration
    var vectorSearch = new VectorSearch();
    vectorSearch.Algorithms.Add(new HnswAlgorithmConfiguration(name: "my-hnsw-vector-config-1"));
    vectorSearch.Profiles.Add(new VectorSearchProfile(name: "my-vector-profile", algorithmConfigurationName: "my-hnsw-vector-config-1"));

    var definition = new SearchIndex(indexName)
    {
        Fields = allFields,
        Suggesters = { suggester },
        VectorSearch = vectorSearch,
        SemanticSearch = semanticSearch
    };

    // Create or update the index
    Console.WriteLine($"Creating or updating index '{indexName}'...");
    var result = await indexClient.CreateOrUpdateIndexAsync(definition);
    Console.WriteLine($"Index '{result.Value.Name}' updated.");
    Console.WriteLine();
}

次のコードは、 hotel-samples.json ファイル内の JSON 形式のドキュメントを Azure AI Search サービスにアップロードします。

static async Task UploadDocs(SearchClient searchClient)
{
    var jsonPath = Path.Combine(Directory.GetCurrentDirectory(), "HotelData.json");

    // Read and parse hotel data
    var json = await File.ReadAllTextAsync(jsonPath);
    List<Hotel> hotels = new List<Hotel>();
    try
    {
        using var doc = JsonDocument.Parse(json);
        if (doc.RootElement.ValueKind != JsonValueKind.Array)
        {
            Console.WriteLine("HotelData.json root is not a JSON array.");
        }
        // Deserialize all hotel objects
        hotels = doc.RootElement.EnumerateArray()
            .Select(e => JsonSerializer.Deserialize<Hotel>(e.GetRawText()))
            .Where(h => h != null)
            .ToList();
    }
    catch (JsonException ex)
    {
        Console.WriteLine($"Failed to parse HotelData.json: {ex.Message}");
    }

    try
    {
        // Upload hotel documents to Azure Search
        var result = await searchClient.UploadDocumentsAsync(hotels);
        foreach (var r in result.Value.Results)
        {
            Console.WriteLine($"Key: {r.Key}, Succeeded: {r.Succeeded}");
        }
    }
    catch (Exception ex)
    {
        Console.WriteLine("Failed to upload documents: " + ex);
    }
}

プロジェクトを実行すると、次の出力が出力されます。

Key: 1, Succeeded: True
Key: 2, Succeeded: True
Key: 3, Succeeded: True
Key: 4, Succeeded: True
Key: 48, Succeeded: True
Key: 49, Succeeded: True
Key: 13, Succeeded: True

重要なポイント:

  • コードは、SearchClient パッケージによって提供されるメイン オブジェクトであるazure-search-documentsを介して、Azure AI Search サービスでホストされている特定の検索インデックスと対話します。 SearchClientは、次のようなインデックス操作へのアクセスを提供します。

    • データ インジェスト: UploadDocuments()、 MergeDocuments()、 DeleteDocuments()
    • 検索操作: Search()、 Autocomplete()、 Suggest()
    • インデックス管理操作: CreateOrUpdateIndex()

  • ベクトル フィールドには浮動小数点値が含まれます。 ディメンション属性の最小値は 2 で、それぞれ最大 4,096 個の浮動小数点値があります。 このクイック スタートの Azure OpenAI text-embedding-3-small モデルによって生成される埋め込みの サイズは 1536 です。

検索クエリを実行する

インデックスが作成され、ドキュメントが読み込まれたら、さまざまなパラメーターで SearchAsync() を呼び出すことによって、それらに対してベクター クエリを発行できます。

  1. VectorSearchExamples フォルダーで、Program.cs ファイルを開きます。
  2. VectorSearchExamples フォルダーで新しいターミナルを開きます。

以降のセクションでは、 hotels-vector-quickstart インデックスに対してクエリを実行します。 クエリには以下が含まれます。

最初の例では、検索文字列と密接に一致するドキュメントの説明を検索する基本的なシナリオを示します。

  1. VectorSearchExamples フォルダーのProgram.cs ファイルで、メソッド呼び出しSearchExamples.SearchSingleVector(searchClient, vectorizedResult);のコメントを解除します。 このメソッドは、 SearchExamples.cs クラスで次の検索機能を実行します。

    public static async Task SearchSingleVector(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
        });

    Console.WriteLine($"Single Vector Search Results:");
    await foreach (SearchResult<Hotel> result in response.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}");
    }
    Console.WriteLine();
}

  2. dotnet run コマンドを使用してプロジェクトを実行します。

    dotnet run

    プロジェクトを実行すると、検索結果が出力ウィンドウに出力されます。

    Single Vector Search Results:
Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel
Score: 0.6333684, HotelId: 13, HotelName: Luxury Lion Resort
Score: 0.605672, HotelId: 4, HotelName: Sublime Palace Hotel
Score: 0.6026341, HotelId: 49, HotelName: Swirling Currents Hotel
Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel

フィルターを使用した単一ベクトル検索

フィルターを追加できます。ただし、フィルターはインデックス内の非ベクトル コンテンツに適用されます。 この例では、Tags フィールドにフィルターが適用され、無料 Wi-Fi を備えていないホテルをすべて除外します。

  1. VectorSearchExamples フォルダーのProgram.cs ファイルで、メソッド呼び出しSearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);のコメントを解除します。 このメソッドは、 SearchExamples.cs クラスで次の検索機能を実行します。

    public static async Task SearchSingleVectorWithFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Filter = "Tags/any(tag: tag eq 'free wifi')",
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" }
        });

    Console.WriteLine($"Single Vector Search With Filter Results:");
    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}, Tags: {string.Join(String.Empty, doc.Tags)}");
    }
    Console.WriteLine();
}

  2. プロジェクトをもう一度実行すると、各ドキュメントの状態が下に出力されます。

     Single Vector Search With Filter Results:
 Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel, Tags: continental breakfastair conditioningfree wifi
 Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel, Tags: poolfree wifiair conditioningconcierge

    クエリは前の 単一ベクトル検索の例と同じでしたが、後処理除外フィルターが含まれており、無料の Wi-Fi を持つ 2 つのホテルのみが返されます。

  3. 次のフィルターの例では 、geo フィルターを使用します。 VectorSearchExamples フォルダーのProgram.cs ファイルで、メソッド呼び出しSearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult);のコメントを解除します。 このメソッドは、 SearchExamples.cs クラスで次の検索機能を実行します。

    public static async Task SingleSearchWithGeoFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithGeoFilter = await searchClient.SearchAsync<Hotel>(
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Filter = "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
            Select = { "HotelId", "HotelName", "Description", "Address", "Category", "Tags" },
            Facets = { "Address/StateProvince" },
        });

    Console.WriteLine($"Vector query with a geo filter:");
    await foreach (SearchResult<Hotel> result in responseWithGeoFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"HotelId: {doc.HotelId}");
        Console.WriteLine($"HotelName: {doc.HotelName}");
        Console.WriteLine($"Score: {result.Score}");
        Console.WriteLine($"City/State: {doc.Address.City}/{doc.Address.StateProvince}");
        Console.WriteLine($"Description: {doc.Description}");
        Console.WriteLine();
    }
    Console.WriteLine();
}

    クエリは前の 単一ベクトル検索の例と同じでしたが、後処理除外フィルターが含まれており、300 キロメートル以内の 2 つのホテルのみが返されます。

  4. プロジェクトをもう一度実行すると、各ドキュメントの状態が下に出力されます。

    Vector query with a geo filter:
-HotelId: 48
HotelName: Nordick's Valley Motel
Score: 0.6605852246284485
City/State: Washington D.C./null
Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.

-HotelId: 49
HotelName: Swirling Currents Hotel
Score: 0.602634072303772
City/State: Arlington/VA
Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.

ハイブリッド検索は、単一の検索要求内のキーワード クエリとベクトル クエリで構成されます。 次の例では、ベクター クエリとフルテキスト検索を同時に実行します。

  • 検索文字列: historic hotel walk to restaurants and shopping
  • ベクトル クエリ文字列 (数学的表現にベクトル化): Quintessential lodging near running trails, eateries, retail

  1. VectorSearchExamples フォルダーのProgram.cs ファイルで、メソッド呼び出しSearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);のコメントを解除します。 このメソッドは、 SearchExamples.cs クラスで次の検索機能を実行します。

    public static async Task<SearchResults<Hotel>> SearchHybridVectorAndText(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
        "historic hotel walk to restaurants and shopping",
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
            Size = 5,
        });

    Console.WriteLine($"Hybrid search results:");
    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}");
        Console.WriteLine($"HotelId: {doc.HotelId}");
        Console.WriteLine($"HotelName: {doc.HotelName}");
        Console.WriteLine($"Description: {doc.Description}");
        Console.WriteLine($"Category: {doc.Category}");
        Console.WriteLine($"Tags: {string.Join(String.Empty, doc.Tags)}");
        Console.WriteLine();
    }
    Console.WriteLine();
    return responseWithFilter;
}

  2. プロジェクトをもう一度実行すると、各ドキュメントの状態が下に出力されます。

    Hybrid search results:
Score: 0.03279569745063782
HotelId: 4
HotelName: Sublime Palace Hotel
Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th   century resort, updated for every modern convenience.
Category: Boutique
Tags: conciergeviewair conditioning

Score: 0.032786883413791656
HotelId: 13
HotelName: Luxury Lion Resort
Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
Category: Luxury
Tags: barconciergerestaurant

Score: 0.03205128386616707
HotelId: 48
HotelName: Nordick's Valley Motel
Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
Category: Boutique
Tags: continental breakfastair conditioningfree wifi

Score: 0.0317460335791111
HotelId: 49
HotelName: Swirling Currents Hotel
Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
Category: Suite
Tags: air conditioninglaundry service24-hour front desk service

Score: 0.03077651560306549
HotelId: 2
HotelName: Old Century Hotel
Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer   dinners, and live music.
Category: Boutique
Tags: poolfree wifiair conditioningconcierge

逆ランク 融合 (RRF) は結果をマージするため、入力を確認するのに役立ちます。 次の結果は、フル テキスト クエリのみからのものです。 上位 2 つの結果は、Sublime Palace Hotel と History Lion Resort です。 Sublime Palace Hotel は、より強い BM25 関連性スコアを持っています。

{
    "@search.score": 2.2626662,
    "HotelName": "Sublime Palace Hotel",
    "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace."
},
{
    "@search.score": 0.86421645,
    "HotelName": "Luxury Lion Resort",
    "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort"
},

マッチ検索に HNSW を使用するベクトルのみのクエリでは、Sublime Palace Hotel の順位は 4 位まで落ちます。 Historic Lion の場合、フル テキスト検索では 2 位、ベクトル検索では 3 位でしたが、変動範囲が同じでないため、均質化された結果セットでは最上位の一致として表示されます。

"value": [
    {
        "@search.score": 0.857736,
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.8399129,
        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
        "Category": "Luxury"
    },
    {
        "@search.score": 0.8383954,
        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
        "Category": "Resort and Spa"
    },
    {
        "@search.score": 0.8254346,
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.82380056,
        "HotelId": "1",
        "HotelName": "Stay-Kay City Hotel",
        "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of New York.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.81514084,
        "HotelId": "2",
        "HotelName": "Old Century Hotel",
        "Description": "The hotel is situated in a  nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.8133763,
        "HotelId": "3",
        "HotelName": "Gastronomic Landscape Hotel",
        "Description": "The Hotel stands out for its gastronomic excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
        "Category": "Resort and Spa"
    }
]

フィルターを使用したセマンティック ハイブリッド検索

セマンティックランク付けされたハイブリッド クエリはフィルター処理され、ワシントン D.C. の半径 500 キロメートル以内のホテルのみが表示されます。

  1. VectorSearchExamples フォルダーのProgram.cs ファイルで、メソッド呼び出しSearchExamples.SearchHybridVectoryAndSemantic(searchClient, vectorizedResult);のコメントを解除します。 このメソッドは、 SearchExamples.cs クラスで次の検索機能を実行します。

    public static async Task SearchHybridVectorAndSemantic(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
        "historic hotel walk to restaurants and shopping",
        new SearchOptions
        {
            IncludeTotalCount = true,
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
            SemanticSearch = new SemanticSearchOptions
            {
                SemanticConfigurationName = "semantic-config"
            },
            QueryType = SearchQueryType.Semantic,
            Size = 5
        });

    Console.WriteLine($"Hybrid search results:");
    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}");
        Console.WriteLine($"HotelId: {doc.HotelId}");
        Console.WriteLine($"HotelName: {doc.HotelName}");
        Console.WriteLine($"Description: {doc.Description}");
        Console.WriteLine($"Category: {doc.Category}");
        Console.WriteLine();
    }
    Console.WriteLine();
}

  2. プロジェクトをもう一度実行し、セルの下の出力を確認します。 応答は 3 つのホテルとなり、場所でフィルター処理され、StateProvince によってファセットが適用され、意味的に再ランク付けされて、結果が昇格し、検索文字列クエリ (historic hotel walk to restaurants and shopping) に最も近くなります。

    今度は Swirling Currents Hotel が首位になります。 セマンティック ランク付けを行わない場合、Nordick's Valley Motel が 1 位となります。 セマンティック ランク付けを行う場合、機械読解モデルでは、historic が、"食事 (レストラン) やショッピングに徒歩圏内のホテル" に適用されると認識します。

    Total semantic hybrid results: 7
- Score: 0.0317460335791111
  Re-ranker Score: 2.6550590991973877
  HotelId: 49
  HotelName: Swirling Currents Hotel
  Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.
  Category: Suite
- Score: 0.03279569745063782
  Re-ranker Score: 2.599761724472046
  HotelId: 4
  HotelName: Sublime Palace Hotel
  Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
  Category: Boutique
- Score: 0.03125
  Re-ranker Score: 2.3480887413024902
  HotelId: 2
  HotelName: Old Century Hotel
  Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
  Category: Boutique
- Score: 0.016393441706895828
  Re-ranker Score: 2.2718777656555176
  HotelId: 1
  HotelName: Stay-Kay City Hotel
  Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
  Category: Boutique
- Score: 0.01515151560306549
  Re-ranker Score: 2.0582215785980225
  HotelId: 3
  HotelName: Gastronomic Landscape Hotel
  Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.
  Category: Suite

重要なポイント:

  • ハイブリッド検索では、キーワードに対するフルテキスト検索とベクトル検索を統合できます。 フィルター、スペル チェック、セマンティック ランク付けは、ベクトルではなくテキスト コンテンツにのみ適用されます。 この最後のクエリでは、セマンティックな answer はありません。これは、システムが生成したものが十分に強力ではなかったためです。

  • 実際の結果には、セマンティック キャプションや強調表示など、より詳細な情報が含まれます。 結果は読みやすくするために変更されています。 応答の構造全体を取得するには、REST クライアントで要求を実行します。

クリーンアップ

独自のサブスクリプションを使用している場合は、プロジェクトの最後に、作成したリソースがまだ必要かどうかを確認してください。 リソースを実行したままにすると、お金がかかる場合があります。 リソースは個別に削除することも、リソース グループを削除してリソースのセット全体を削除することもできます。

左端のペイン内にある [すべてのリソース] または [リソース グループ] リンクを使用して、Azure portal 内でリソースを検索および管理できます。

次のステップ

このクイック スタートでは、 Azure AI Search REST API を 使用して、ベクターの作成、読み込み、クエリを行います。

Azure AI 検索のベクトル ストアにあるインデックス スキーマでは、ベクトル フィールドと非ベクトル フィールド、埋め込み空間を作成するアルゴリズムのためのベクトル検索構成、クエリ時に評価されるベクトル フィールド定義の設定が定義されています。 Create Index REST API によってベクトル ストアが作成されます。

注

このクイック スタートでは、ベクター化の手順を省略し、インライン埋め込みを提供します。 独自のコンテンツ に組み込みのデータ チャンクとベクター化を 追加する場合は、データの インポートとベクター化ウィザード を試して、エンドツーエンドのチュートリアルを実行してください。

前提条件

リソース情報の取得

検索エンドポイントへの要求は、認証および承認する必要があります。 このタスクには API キーまたはロールを使用できます。 Microsoft Entra ID 経由でキーレス接続を使用することをお勧めします。

希望する認証方法に対応するタブを選択します。 このクイックスタートでは、すべての要求に同じ方法を使用します。

  1. Azure portal にサインインし、ご利用の検索サービスを探します。

  2. [概要] ホーム ページで、URL を見つけます。 たとえば、エンドポイントは https://mydemo.search.windows.net のようになります。

    [概要] ページ上の URL プロパティのスクリーンショット。

  3. キーレスのクイックスタートの手順に従って、Microsoft Entra トークンを取得します。

    前述のクイックスタートの手順 3 で az account get-access-token コマンドを実行すると、トークンが取得されます。

    az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv

コード ファイルを作成またはダウンロードする

このクイックスタートでは、1 つの .rest または .http ファイルを使用して、すべての要求を実行します。 このクイック スタートのコードが含まれている REST ファイルをダウンロードするか、Visual Studio Code で新しいファイルを作成してその中にコードをコピーすることができます。

  1. Visual Studio Code で、.rest または .http ファイル拡張子を持つ新しいファイルを作成します。 たとえば、「az-search-vector-quickstart.rest」のように入力します。 Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest ファイルの生の内容をコピーして、この新しいファイルに貼り付けます。

  2. ファイルの先頭にある @baseUrl のプレースホルダー値を検索サービスの URL に置き換えます。 検索サービスの URL を見つける方法については、「リソース情報の取得」セクションを参照してください。

    @baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HERE

  3. ファイルの先頭にある認証のプレースホルダー値を置き換えます。 Microsoft Entra トークンまたは API キーを取得する方法については、「リソース情報の取得」セクションを参照してください。

    Microsoft Entra ID による推奨されるキーレス認証の場合、@apiKey を @token 変数に置き換える必要があります。

    @token = PUT-YOUR-MICROSOFT-ENTRA-TOKEN-HERE

    API キーを使用する場合は、@apiKey を Azure portal からコピーしたキーに置き換えます。

    @apiKey = PUT-YOUR-ADMIN-KEY-HERE

  4. Microsoft Entra ID による推奨されるキーレス認証の場合、要求ヘッダーで api-key: {{apiKey}} を Authorization: Bearer {{token}} に置き換える必要があります。 ファイル内にある api-key: {{apiKey}} のすべてのインスタンスを置き換えます。

ベクトル インデックスを作成する

Create Index REST API を使用してベクトル インデックスを作成し、検索サービスに物理データ構造を設定します。

この例のインデックス スキーマは、ホテルのコンテンツを中心に構成されています。 サンプル データは、架空のホテルのベクトルと非ベクトルの説明で構成されます。 このスキーマには、ベクトル インデックス作成およびクエリとセマンティック ランク付けのための構成が含まれています。

  1. Visual Studio Code で、az-search-vector-quickstart.rest ファイルを開きます。

  2. ファイル内の ### Create a new index コード ブロックを見つけます。 このブロックには、検索サービスの hotels-vector-quickstart インデックスを作成するための要求が含まれています。

    ### Create a new index
POST  {{baseUrl}}/indexes?api-version=2024-07-01  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

{
    "name": "hotels-vector-quickstart",
    "fields": [
        {
            "name": "HotelId", 
            "type": "Edm.String",
            "searchable": false, 
            "filterable": true, 
            "retrievable": true, 
            "sortable": false, 
            "facetable": false,
            "key": true
        },
        {
            "name": "HotelName", 
            "type": "Edm.String",
            "searchable": true, 
            "filterable": false, 
            "retrievable": true, 
            "sortable": true, 
            "facetable": false
        },
        {
            "name": "Description", 
            "type": "Edm.String",
            "searchable": true, 
            "filterable": false, 
            "retrievable": true, 
            "sortable": false, 
            "facetable": false
        },
        {
            "name": "DescriptionVector",
            "type": "Collection(Edm.Single)",
            "searchable": true,
            "retrievable": true,
            "dimensions": 1536,
            "vectorSearchProfile": "my-vector-profile"
        },
        {
            "name": "Category", 
            "type": "Edm.String",
            "searchable": true, 
            "filterable": true, 
            "retrievable": true, 
            "sortable": true, 
            "facetable": true
        },
        {
            "name": "Tags",
            "type": "Collection(Edm.String)",
            "searchable": true,
            "filterable": true,
            "retrievable": true,
            "sortable": false,
            "facetable": true
        },
                {
            "name": "ParkingIncluded",
            "type": "Edm.Boolean",
            "searchable": false,
            "filterable": true,
            "retrievable": true,
            "sortable": true,
            "facetable": true
        },
        {
            "name": "LastRenovationDate",
            "type": "Edm.DateTimeOffset",
            "searchable": false,
            "filterable": true,
            "retrievable": true,
            "sortable": true,
            "facetable": true
        },
        {
            "name": "Rating",
            "type": "Edm.Double",
            "searchable": false,
            "filterable": true,
            "retrievable": true,
            "sortable": true,
            "facetable": true
        },
        {
            "name": "Address", 
            "type": "Edm.ComplexType",
            "fields": [
                {
                    "name": "StreetAddress", "type": "Edm.String",
                    "searchable": true, "filterable": false, "retrievable": true, "sortable": false, "facetable": false
                },
                {
                    "name": "City", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                },
                {
                    "name": "StateProvince", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                },
                {
                    "name": "PostalCode", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                },
                {
                    "name": "Country", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                }
            ]
        },
        {
            "name": "Location",
            "type": "Edm.GeographyPoint",
            "searchable": false, 
            "filterable": true, 
            "retrievable": true, 
            "sortable": true, 
            "facetable": false
        }
    ],
    "vectorSearch": {
        "algorithms": [
            {
                "name": "hnsw-vector-config",
                "kind": "hnsw",
                "hnswParameters": 
                {
                    "m": 4,
                    "efConstruction": 400,
                    "efSearch": 500,
                    "metric": "cosine"
                }
            },
            {
                "name": "eknn-vector-config",
                "kind": "exhaustiveKnn",
                "exhaustiveKnnParameters": 
                {
                    "metric": "cosine"
                }
            }
        ],
        "profiles": [      
            {
                "name": "my-vector-profile",
                "algorithm": "hnsw-vector-config"
            }
      ]
    },
    "semantic": {
        "configurations": [
            {
                "name": "semantic-config",
                "prioritizedFields": {
                    "titleField": {
                        "fieldName": "HotelName"
                    },
                    "prioritizedContentFields": [
                        { "fieldName": "Description" }
                    ],
                    "prioritizedKeywordsFields": [
                        { "fieldName": "Category" }
                    ]
                }
            }
        ]
    }
}

  3. [要求の送信] をクリックします。 HTTP/1.1 201 Created 応答が返されます。

    その応答本文にはインデックス スキーマの JSON 表現が含まれています。

    {
    "@odata.context": "https://my-demo-search.search.windows.net/$metadata#indexes/$entity",
    "@odata.etag": "\"0x8DD2E70E6C36D8E\"",
    "name": "hotels-vector-quickstart",
    "defaultScoringProfile": null,
    "fields": [
    {
        "name": "HotelId",
        "type": "Edm.String",
        "searchable": false,
        "filterable": true,
        "retrievable": true,
        "sortable": false,
        "facetable": false,
        "key": true,
        "indexAnalyzer": null,
        "searchAnalyzer": null,
        "analyzer": null,
        "dimensions": null,
        "vectorSearchProfile": null,
        "synonymMaps": []
    },
    [MORE FIELD DEFINITIONS OMITTED FOR BREVITY]
    ],
    "scoringProfiles": [],
    "corsOptions": null,
    "suggesters": [],
    "analyzers": [],
    "tokenizers": [],
    "tokenFilters": [],
    "charFilters": [],
    "encryptionKey": null,
    "similarity": {
    "@odata.type": "#Microsoft.Azure.Search.BM25Similarity",
    "k1": null,
    "b": null
    },
    "vectorSearch": {
    "algorithms": [
        {
        "name": "hnsw-vector-config",
        "kind": "hnsw",
        "hnswParameters": {
            "metric": "cosine",
            "m": 4,
            "efConstruction": 400,
            "efSearch": 500
        },
        "exhaustiveKnnParameters": null
        },
        "exhaustiveKnnParameters": null
        },
        {
        "name": "eknn-vector-config",
        "kind": "exhaustiveKnn",
        "hnswParameters": null,
        "exhaustiveKnnParameters": {
            "metric": "cosine"
        }
        }
    ],
    "profiles": [
        {
        "name": "my-vector-profile",
        "algorithm": "hnsw-vector-config-1"
        }
    ]
    },
    "semantic": {
    "defaultConfiguration": null,
    "configurations": [
        {
        "name": "semantic-config",
        "prioritizedFields": {
            "titleField": {
            "fieldName": "HotelName"
            },
            "prioritizedContentFields": [
            {
                "fieldName": "Description"
            }
            ],
            "prioritizedKeywordsFields": [
            {
                "fieldName": "Category"
            }
            ]
        }
        }
    ]
    }
}

Create Index REST API に関する重要なポイント:

  • fields コレクションには、テキストおよびベクトル検索のための必須のキー フィールドと、テキストおよびベクトル フィールド (Description や DescriptionVector など) が含まれています。 ベクトル フィールドと非ベクトル フィールドを同じインデックスに配置すると、ハイブリッド クエリが有効になります。 たとえば、フィルター、セマンティック ランク付けを使用したテキスト検索、およびベクトルを 1 つのクエリ操作に結合できます。

  • ベクター フィールドは、などのtype: Collection(Edm.Single)のいずれかである必要があります。 ベクター フィールドには、 dimensions プロパティと vectorSearchProfile プロパティもあります。

  • vectorSearch セクションは、近似最近隣 (ANN) アルゴリズムの構成とプロファイルの配列です。 サポートされるアルゴリズムには、階層ナビゲーション可能な Small World と、完全な K ニアレスト ネイバーが含まれます。 詳細については、「ベクトル検索の関連性スコアリング」を参照してください。

  • (オプションの) semantic 構成を使用すると、検索結果を再ランク付けできます。 結果の再ランク付けは、構成で指定された文字列フィールドに対する semantic 型のクエリで行うことができます。 詳細については、「セマンティック ランク付けの概要」を参照してください。

ドキュメントのアップロード

インデックスの作成と読み込みは別の手順です。 前の手順でインデックス スキーマを作成しました。 次に、ドキュメントをインデックスに読み込む必要があります。

Azure AI 検索では、インデックスには検索可能なすべてのデータと、検索サービスで実行されるクエリが含まれます。 REST 呼び出しでは、データは JSON ドキュメントとして提供されます。 このタスクでは Documents- Index REST API を使用します。 URI を拡張して、docs コレクションと index 操作を含めます。

  1. ドキュメントのアップロード要求を作成して、検索サービスの hotels-vector-quickstart インデックスにドキュメントをアップロードします。

    ### Upload documents
POST {{baseUrl}}/indexes/hotels-quickstart-vectors/docs/index?api-version=2024-07-01  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

{
    "value": [
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "1",
            "HotelName": "Stay-Kay City Hotel",
            "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
            "DescriptionVector": 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            "Category": "Boutique",
            "Tags": [
                "view",
                "air conditioning",
                "concierge"
            ],
            "ParkingIncluded": false,
            "LastRenovationDate": "2022-01-18T00:00:00Z",
            "Rating": 3.60,
            "Address": {
                "StreetAddress": "677 5th Ave",
                "City": "New York",
                "StateProvince": "NY",
                "PostalCode": "10022",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                    -73.975403,
                    40.760586
                ]
            }
        },
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "2",
            "HotelName": "Old Century Hotel",
            "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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            "Category": "Boutique",
            "Tags": [
                "pool",
                "free wifi",
                "air conditioning",
                "concierge"
            ],
            "ParkingIncluded": false,
            "LastRenovationDate": "2019-02-18T00:00:00Z",
            "Rating": 3.60,
            "Address": {
                "StreetAddress": "140 University Town Center Dr",
                "City": "Sarasota",
                "StateProvince": "FL",
                "PostalCode": "34243",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
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                ]
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            "HotelId": "3",
            "HotelName": "Gastronomic Landscape Hotel",
            "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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            "Category": "Suite",
            "Tags": [
                "restaurant",
                "bar",
                "continental breakfast"
            ],
            "ParkingIncluded": true,
            "LastRenovationDate": "2015-09-20T00:00:00Z",
            "Rating": 4.80,
            "Address": {
                "StreetAddress": "3393 Peachtree Rd",
                "City": "Atlanta",
                "StateProvince": "GA",
                "PostalCode": "30326",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                    33.847848,
                    -84.363602
                ]
            }
        },
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "4",
            "HotelName": "Sublime Palace Hotel",
            "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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            "Category": "Boutique",
            "Tags": [
                "concierge",
                "view",
                "air conditioning"
            ],
            "ParkingIncluded": true,
            "LastRenovationDate": "2020-02-06T00:00:00Z",
            "Rating": 4.60,
            "Address": {
                "StreetAddress": "7400 San Pedro Ave",
                "City": "San Antonio",
                "StateProvince": "TX",
                "PostalCode": "78216",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                    -98.498923,
                    29.518029
                ]
            }
        },
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "48",
            "HotelName": "Nordick's Valley Motel",
            "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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            "Category": "Boutique",
            "Tags": [
                "continental breakfast",
                "air conditioning",
                "free wifi"
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            "ParkingIncluded": false,
            "LastRenovationDate": "2018-02-19T00:00:00Z",
            "Rating": 4.5,
            "Address": {
                "StreetAddress": "1401 I St NW",
                "City": "Washington D.C.",
                "StateProvince": null,
                "PostalCode": "20005",
                "Country": "USA"
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                "properties": {
                    "name": "EPSG:4326"
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            "HotelId": "49",
            "HotelName": "Swirling Currents Hotel",
            "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
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           "Category": "Suite",
            "Tags": [
                "air conditioning",
                "laundry service",
                "24-hour front desk service"
            ],
            "ParkingIncluded": true,
            "LastRenovationDate": "2018-01-27T00:00:00Z",
            "Rating": 2.7,
            "Address": {
                "StreetAddress": "1100 S Hayes St",
                "City": "Arlington",
                "StateProvince": "VA",
                "PostalCode": "22202",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                -77.060066,
                38.863659
                ],
                "crs": {
                "type": "name",
                "properties": {
                    "name": "EPSG:4326"
                    }
                }
            }
        },
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "13",
            "HotelName": "Luxury Lion Resort",
            "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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            "Category": "Luxury",
            "Tags": [
                "bar",
                "concierge",
                "restaurant"
            ],
            "ParkingIncluded": false,
            "LastRenovationDate": "2020-03-18T00:00:00Z",
            "Rating": 4.1,
            "Address": {
                "StreetAddress": "3 Cityplace Dr",
                "City": "St. Louis",
                "StateProvince": "MO",
                "PostalCode": "63141",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                -90.44081,
                38.67219
                ],
                "crs": {
                "type": "name",
                "properties": {
                    "name": "EPSG:4326"
                    }
                }
            }
        }
    ]
}

  2. [要求の送信] をクリックします。 HTTP/1.1 200 OK 応答が返されます。 応答の本文に、検索ドキュメントの JSON 表現が含まれています。

Documents - Index REST API 要求に関する重要なポイント:

  • ペイロード内のドキュメントは、インデックス スキーマで定義されているフィールドで構成されます。

  • ベクトル フィールドには浮動小数点値が含まれます。 dimensions 属性の最小値は 2 で、それぞれ最大 4096 浮動小数点値です。 このクイック スタートでは、ディメンション属性を 1,536 に設定します。これは、Azure OpenAI text-embedding-3-small モデルによって生成される埋め込みの サイズであるためです。

クエリを実行する

ドキュメントが読み込まれたので、 Documents - Search Post (REST) を使用して、ドキュメントに対してベクター クエリを実行できます。

次のセクションでは、hotels-vector-quickstart インデックスに対してクエリを実行します。 クエリには以下が含まれます。

クエリの例は、次の 2 つの文字列に基づいています。

  • 検索文字列: historic hotel walk to restaurants and shopping
  • ベクトル クエリ文字列 (数学的表現にベクトル化): quintessential lodging near running trails, eateries, retail

このベクトル クエリ文字列は、意味的には検索文字列に似ていますが、検索インデックスに存在しない用語を含んでいます。 quintessential lodging near running trails, eateries, retailのキーワード検索を実行すると、セマンティックランク付けなしで純粋なキーワード検索で結果はゼロになります。 この例を使用して、一致する用語がない場合でも、ベクターを使用して関連する結果を取得する方法を示します。

  1. 要求を作成します。 クエリは、ランニングトレイル、飲食店、小売店の近くにある理想的な宿泊施設を1536フロート形式で表現したものです。 クエリは DescriptionVector を検索し、k-5 の結果を返します。 "完全な" オーバーライド パラメーターを使用して、ANN ではなくインデックスのフル スキャンを実行します。 完全な検索は、小さなインデックスに役立ちます。

    ### Run a single vector query
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "count": true,
        "select": "HotelId, HotelName, Description, Category, Tags",
        "vectorQueries": [
            {
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                "k": 5,
                "fields": "DescriptionVector",
                "kind": "vector",
                "exhaustive": true
            }
        ]
    }

    Documents - Search Post REST APIに関する重要なポイント:

    • vectorQueries.vectorはベクター クエリ文字列です。 これは、ランニングコース、飲食店、小売店の近くの典型的な宿泊施設のベクトル表現であり、このクエリ用に1,536個のベクトル数に変換されています。

    • fields は、検索するベクター フィールドを決定します。

    • kindvectorに設定すると、クエリ文字列がベクターであることを意味します。 kindが text に設定されている場合は、人間が判読できるテキスト文字列をクエリ時にベクターにエンコードする追加機能 (ベクター化) が必要になります。 このクイック スタートでは、演習を簡単にするためにベクターライザーを省略します。

    • k は、応答で返される一致の数を指定します。 count パラメーターは、インデックスで見つかった一致の数を指定します。 カウントを含めるのはクエリのベスト プラクティスですが、アルゴリズムがほぼすべてのドキュメントである程度の類似性を見つけることができる類似性検索にはあまり役に立ちません。

  2. [要求の送信] をクリックします。 HTTP/1.1 200 OK 応答が返されます。 応答の本文には、検索結果の JSON 表現が含まれています。

    quintessential lodging near running trails, eateries, retailに相当するベクトルの応答には k-5 の結果が含まれますが、検索エンジンでは 7 つの一致が見つかりました。 上位の結果は、クエリと最も意味的に似ていると見なされます。 各結果には、検索スコアと、select に一覧されているフィールドが表示されます。 類似性検索では、応答に常に値の類似性スコアで並べ替えられた k 個の結果が含まれます。

    {
  "@odata.context": "https://my-demo-search.search.windows.net/indexes('hotels-vector-quickstart')/$metadata#docs(*)",
  "@odata.count": 7,
  "value": [
    {
      "@search.score": 0.6605852,
      "HotelId": "48",
      "HotelName": "Nordick's Valley Motel",
      "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
      "Category": "Boutique",
      "Tags": [
        "continental breakfast",
        "air conditioning",
        "free wifi"
      ]
    },
    {
      "@search.score": 0.6333684,
      "HotelId": "13",
      "HotelName": "Luxury Lion Resort",
      "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
      "Category": "Luxury",
      "Tags": [
        "bar",
        "concierge",
        "restaurant"
      ]
    },
    {
      "@search.score": 0.605672,
      "HotelId": "4",
      "HotelName": "Sublime Palace Hotel",
      "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
      "Category": "Boutique",
      "Tags": [
        "concierge",
        "view",
        "air conditioning"
      ]
    },
    {
      "@search.score": 0.6026341,
      "HotelId": "49",
      "HotelName": "Swirling Currents Hotel",
      "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
      "Category": "Suite",
      "Tags": [
        "air conditioning",
        "laundry service",
        "24-hour front desk service"
      ]
    },
    {
      "@search.score": 0.57902366,
      "HotelId": "2",
      "HotelName": "Old Century Hotel",
      "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
      "Category": "Boutique",
      "Tags": [
        "pool",
        "free wifi",
        "air conditioning",
        "concierge"
      ]
    }
  ]
}

フィルターを使用した単一ベクトル検索

フィルターを追加できます。ただし、フィルターはインデックス内の非ベクトル コンテンツに適用されます。 この例では、Tags フィールドにフィルターが適用され、無料 Wi-Fi を備えていないホテルをすべて除外します。

  1. 要求を作成します。 これは前の要求と同じ要求で、追加のフィルターとフィルター モードのパラメーターがあります。

    ### Run a vector query with a filter
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "count": true,
        "select": "HotelId, HotelName, Category, Tags, Description",
        "filter": "Tags/any(tag: tag eq 'free wifi')",
        "vectorFilterMode": "postFilter",
        "vectorQueries": [
        {
            "vector": 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            "k": 5,
            "fields": "DescriptionVector",
            "kind": "vector",
            "exhaustive": true
        },
    ]
}

  2. [要求の送信] をクリックします。 HTTP/1.1 200 OK 応答が返されます。 応答の本文には、検索結果の JSON 表現が含まれています。

    クエリは前の 単一ベクトル検索の例と同じでしたが、後処理除外フィルターが含まれており、無料の Wi-Fi を持つ 2 つのホテルのみが返されます。

    {
  "@odata.context": "https://my-demo-search.search.windows.net/indexes('hotels-vector-quickstart')/$metadata#docs(*)",
  "@odata.count": 2,
  "value": [
    {
      "@search.score": 0.6605852,
      "HotelId": "48",
      "HotelName": "Nordick's Valley Motel",
      "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
      "Category": "Boutique",
      "Tags": [
        "continental breakfast",
        "air conditioning",
        "free wifi"
      ]
    },
    {
      "@search.score": 0.57902366,
      "HotelId": "2",
      "HotelName": "Old Century Hotel",
      "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
      "Category": "Boutique",
      "Tags": [
        "pool",
        "free wifi",
        "air conditioning",
        "concierge"
      ]
    }
  ]
}

  3. 次に示すのは、geo フィルターを使用する別のクエリで、ワシントン D.C. 周辺の 300 km 圏内のホテルに結果を制限します。

    ### Run a vector query with a geo filter
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "count": true,
        "select": "HotelId, HotelName, Description, Address/City, Address/StateProvince",
        "top": 5,
        "facets": [ "Address/StateProvince"],
        "filter": "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
        "vectorFilterMode": "postFilter",
        "vectorQueries": [
        {
            "vector": 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            "k": 5,
            "fields": "DescriptionVector",
            "kind": "vector",
            "exhaustive": true
        },
    ]
}

  4. 応答を確認します。 ワシントン D.C. 近郊の 2 つのホテルに限定され、ファセット構造を使用した集計が提供されています。

    "@odata.count": 2,
"@search.facets": {
  "Address/StateProvince": [
    {
      "value": "VA",
      "count": 1
    }
  ]
},
"value": [
  {
    "@search.score": 0.6605852246284485,
    "HotelId": "48",
    "HotelName": "Nordick's Valley Motel",
    "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
    "Address": {
      "City": "Washington D.C.",
      "StateProvince": null
    }
  },
  {
    "@search.score": 0.602634072303772,
    "HotelId": "49",
    "HotelName": "Swirling Currents Hotel",
    "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
    "Address": {
      "City": "Arlington",
      "StateProvince": "VA"
    }
  }
]

ハイブリッド検索は、単一の検索要求内のキーワード クエリとベクトル クエリで構成されます。 次の例では、ベクトル クエリとフル テキスト検索を同時に実行します。

  • 検索文字列: historic hotel walk to restaurants and shopping
  • ベクトル クエリ文字列 (数学的表現にベクトル化): quintessential lodging near running trails, eateries, retail

  1. ハイブリッド クエリを作成します。

    ### Run a hybrid query
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

{
    "count": true,
    "search": "historic hotel walk to restaurants and shopping",
    "select": "HotelName, Description",
    "top": 7,
    "vectorQueries": [
        {
            "vector": 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            "k": 5,
            "fields": "DescriptionVector",
            "kind": "vector",
            "exhaustive": true
        }
    ]
}

  2. [要求の送信] をクリックします。 HTTP/1.1 200 OK 応答が返されます。 応答の本文には、検索結果の JSON 表現が含まれています。

これはハイブリッド クエリであるため、結果は Reciprocal Rank Fusion (RRF) によってランク付けされます。 @search.score値の基準が異なり、値が一様に小さいことに注意してください。 RRF では、複数の検索結果の検索スコアを評価し、逆数を取り、結合した結果をマージして並べ替えます。 結果の top 数が返されます。

インデックス内の 7 つの一致するドキュメントのうち、上位の k-5 の一致で構成される応答を確認します。

{
  "@odata.count": 7,
  "value": [
    {
      "@search.score": 0.03279569745063782,
      "HotelName": "Sublime Palace Hotel",
      "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience."
    },
    {
      "@search.score": 0.032522473484277725,
      "HotelName": "Luxury Lion Resort",
      "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort."
    },
    {
      "@search.score": 0.03205128386616707,
      "HotelName": "Nordick's Valley Motel",
      "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley."
    },
    {
      "@search.score": 0.0317460335791111,
      "HotelName": "Swirling Currents Hotel",
      "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs."
    },
    {
      "@search.score": 0.03125,
      "HotelName": "Old Century Hotel",
      "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music."
    }
  ]
}

RRF は結果をマージするため、入力を個別に確認するのに役立ちます。

クエリのフルテキスト部分の結果を次に示します。 歴史的なホテルの徒歩でレストランやショッピングを楽しむことができます。 フルテキスト クエリの上位 3 つの結果は、サブライム パレス ホテル、Stay-Kay シティ ホテル、ラグジュアリー ライオン リゾートです。 Sublime Palace Hotel は、より強い BM25 関連性スコアを持っています。 融合クエリでは、これらの一致のうち 2 つだけが上位 3 に含まれており、2 番目の一致 (Stay-Kay City Hotel) はトップ 5 にまったく表示されません。

  "value": [
    {
      "@search.score": 1.4868739,
      "HotelName": "Sublime Palace Hotel",
      "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience."
    },
    {
      "@search.score": 1.2699215,
      "HotelName": "Stay-Kay City Hotel",
      "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities."
    },
    {
      "@search.score": 1.2456272,
      "HotelName": "Luxury Lion Resort",
      "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort."
    },
    ...
  ]

ベクター部分のクエリ (ランニングトレイル、飲食店、小売店の近くの理想的な宿泊施設) は別の文字列なので、異なった結果が期待されます。 このクエリは、ノルディッククのバレー モーテル、ラグジュアリー ライオン リゾート、サブライム パレス ホテルを上位 3 試合として返します。 融合クエリでは、これらの結果は上位 3 になりますが、逆の順序になります。

  "value": [
    {
      "@search.score": 0.6605852,
      "HotelName": "Nordick's Valley Motel",
      "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley."
    },
    {
      "@search.score": 0.6333684,
      "HotelName": "Luxury Lion Resort",
      "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort."
    },
    {
      "@search.score": 0.605672,
      "HotelName": "Sublime Palace Hotel",
      "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience."
    },
   ...
  ]

ここでは、コレクションでの最後のクエリを示します。 このハイブリッド クエリでは、L1 ランクの結果に対して機械読解を適用する L2 セマンティック ランク付けが追加され、関連性の高い一致が上位に昇格します。

  1. 要求を作成します。

    ### Run a hybrid query with semantic reranking
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

{
    "count": true,
    "search": "historic hotel walk to restaurants and shopping",
    "select": "HotelName, Description",
    "queryType": "semantic",
    "semanticConfiguration": "semantic-config",
    "top": 5,
    "vectorQueries": [
        {
            "vector": 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            "k": 5,
            "fields": "DescriptionVector",
            "kind": "vector",
            "exhaustive": true
        }
    ]
}

  2. [要求の送信] をクリックします。 HTTP/1.1 200 OK 応答が返されます。 応答の本文には、検索結果の JSON 表現が含まれています。

応答を確認します。これは、ハイブリッド クエリの RRF ランク付けされた結果のセマンティック再ランク付けで構成されます。 セマンティックランク付けは、テキスト入力から機能します。 テキストまたはハイブリッド クエリでは、この入力はクエリのテキスト部分です (歴史的なホテルからレストランやショッピングまで歩きます)。 最初の例など、純粋なベクター クエリでセマンティック ランク付けを使用する場合、またはセマンティック ランク付けに使用するテキストを明示的に制御するには、 semanticQuery 文字列を指定します。

セマンティックリランクの後、ショッピング、ダイニング、エンターテイメント、市内中心部へのウォーキングアクセスを誇るスワーリングカレントホテルがトップの位置に上がります。 機械理解モデルは、買い物や食事へ歩いてアクセスすることをレストランやショッピングに最も一致していますとして促進します。

セマンティック再ランキングの前に、サブライム宮殿は、街の名所やランドマークへの徒歩圏内という記述があり、教会、建物、ショップ、モニュメントの並外れた美しさに囲まれて、1位でした。

{
  "@odata.count": 7,
  "@search.answers": [],
  "value": [
    {
      "@search.score": 0.0317460335791111,
      "@search.rerankerScore": 2.6550590991973877,
      "HotelId": "49",
      "HotelName": "Swirling Currents Hotel",
      "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
      "Category": "Suite"
    },
    {
      "@search.score": 0.03279569745063782,
      "@search.rerankerScore": 2.599761724472046,
      "HotelId": "4",
      "HotelName": "Sublime Palace Hotel",
      "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
      "Category": "Boutique"
    },
    {
      "@search.score": 0.03125,
      "@search.rerankerScore": 2.3480887413024902,
      "HotelId": "2",
      "HotelName": "Old Century Hotel",
      "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
      "Category": "Boutique"
    },
    {
      "@search.score": 0.03154495730996132,
      "@search.rerankerScore": 2.2718777656555176,
      "HotelId": "1",
      "HotelName": "Stay-Kay City Hotel",
      "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
      "Category": "Boutique"
    },
    {
      "@search.score": 0.03053613007068634,
      "@search.rerankerScore": 2.0582215785980225,
      "HotelId": "3",
      "HotelName": "Gastronomic Landscape Hotel",
      "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel\u2019s restaurant services.",
      "Category": "Suite"
    }
  ]
}

ヒント

セマンティックにランク付けされた結果には、セマンティック回答、キャプション、強調表示など、より詳細な情報を含めることができます。 要求にパラメーターを追加すると、追加の詳細が生成されます。 詳細については、「 セマンティック クエリの設定」を参照してください。

クリーンアップ

独自のサブスクリプションを使用している場合は、プロジェクトの最後に、作成したリソースがまだ必要かどうかを確認してください。 リソースを実行したままにすると、お金がかかる場合があります。 リソースは個別に削除することも、リソース グループを削除してリソースのセット全体を削除することもできます。

左端のペイン内にある [すべてのリソース] または [リソース グループ] リンクを使用して、Azure portal 内でリソースを検索および管理できます。

検索サービスを維持しながらインデックスとドキュメントを削除する場合は、REST クライアントで DELETE コマンドを使用できます。 このコマンド (az-search-vector-quickstart.rest ファイルの末尾にある) は、hotels-vector-quickstart インデックスを削除します。

### Delete an index
DELETE  {{baseUrl}}/indexes/hotels-vector-quickstart?api-version=2024-07-01 HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

次のステップ

次のステップとして、API キーを使わずに REST API 呼び出しを行う方法を覚えることをお勧めします。

また、Python、C#、またはJavaScript のデモ コードを確認することもお勧めします。