このクイック スタートでは、Jupyter Notebook を使用して ベクター インデックスを作成、読み込み、クエリを実行します。 このコードでは、 Python 用の 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。これにより、インデックス操作にアクセスするための REST API に対する抽象化が提供されます。
Azure AI Search では、ベクター インデックスには、ベクター フィールドと非ベクトル フィールドを定義するインデックス スキーマ、埋め込み空間を作成するアルゴリズムのベクター検索構成、およびクエリ時に評価されるベクター フィールド定義の設定があります。 インデックス - 作成または更新 (REST API) によってベクター インデックスが作成されます。
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、エンド ツー エンドのチュートリアルで データのインポート (新しい) ウィザード を試してください。
前提条件
アクティブなサブスクリプションを持つ Azure アカウント。 無料でアカウントを作成できます。
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このクイック スタートのほとんどは Free レベルを使用できますが、より大きなデータ ファイルには Basic 以上をお勧めします。
Microsoft Entra ID による キーレス認証 の場合は、 検索インデックス データ共同作成者ロール をユーザー アカウントに割り当てます。
セマンティック ハイブリッド クエリを実行するには、 セマンティック ランカーを有効にする必要があります。
Git を使用してサンプル リポジトリを複製します。
サービス情報を取得する
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
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-samplesVisual Studio Code で、
Quickstart-Vector-Searchフォルダーを開きます。sample.envファイルの名前を.envに変更します。AZURE_SEARCH_ENDPOINT検索サービスの URL に設定します。これはhttps://mydemo.search.windows.netのようになります。AZURE_SEARCH_INDEX_NAMEをインデックスの一意の名前に設定します。 既定のvector-search-quickstart名を使用することもできます。[ 表示>新しいターミナル] を選択し、次のコマンドを実行して仮想環境を作成してアクティブ化します。
python -m venv .venv source .venv/scripts/activate where python注
この手順では、ターミナルで Git Bash を使用し、Windows で実行していることを前提としています。 別のシェルやオペレーティング システムを使用している場合は、これらの手順を特定の環境に合わせて調整します。
where pythonコマンドは、python.exeフォルダーやその他の場所にQuickstart-Vector-Search\.venv\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。
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。
必要なライブラリをインストールします。
pip install requirements.txtvector-search-quickstart.ipynbファイルを開きます。Install packages and set variablesコード セルを実行して環境変数を読み込みます。# 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出力には、インストールされているパッケージの一部が含まれています。
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 ...
ベクトル 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。
ベクター インデックスを作成するには:
Create an indexコード セルを実行します。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、コード セルの下に次の出力が表示されます。
vector-search-quickstart created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)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)
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、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。
ドキュメントをインデックスにアップロードするには:
Create documents payloadコード セルを実行します。# 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。
Upload the documentsコード セルを実行します。# 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、オブジェクトに対してupload_documents()メソッドを呼び出すことによって、検索クライアントのインスタンスが作成されます。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:
get_index_statistics()、get_document_count()
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、
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、 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。
このセクションのクエリ:
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 - ベクター クエリ文字列:
quintessential lodging near running trails, eateries, retail(数学的表現にベクター化)
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。
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Create the vector query stringコード 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。
単一ベクトル検索
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。
Single vector searchコード 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。
フィルターを使用して 1 つのベクター検索を作成するには:
Single vector search with filterコード 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。
次のフィルターの例では、geo フィルターを使用します。
Vector query with a geo filterコード 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
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 - ベクター クエリ文字列:
quintessential lodging near running trails, eateries, retail(数学的表現にベクター化)
ハイブリッド検索を作成するには:
Hybrid searchコード 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.")セルの下の出力を確認します。
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 クエリからのみ取得されます。 上位 2 つの結果は、サブライム パレス ホテルとラグジュアリー 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。
{ "@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" },HNSW を使用して一致を検索するベクターのみのクエリでは、Sublime Palace Hotel は 4 番目の位置にドロップします。 フルテキスト検索で 2 位、ベクター検索で 3 番目だった Luxury Lion では、同じ変動範囲が発生しないため、均質化された結果セットでは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" } ]
セマンティック ハイブリッド検索
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 ハイブリッド検索を作成するには:
Semantic hybrid searchコード 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。
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。
リソースをクリーンアップする
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。
または、 Clean up コード セルを実行して、このクイック スタートで作成したベクター インデックスを削除することもできます。
index_client.delete_index(index_name)
print(f"Index '{index_name}' deleted successfully.")
次のステップ
- Azure AI Search for Python のベクター検索機能については、コード サンプルのリポジトリを確認してください。
- azure-search-python-samples リポジトリの他の Python と Azure AI Search のコード サンプルを確認します。
このクイック スタートでは、Java を使用して ベクター インデックスの作成、読み込み、クエリを実行します。 このコードでは、 Java 用 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。これにより、インデックス操作にアクセスするための REST API に対する抽象化が提供されます。
Azure AI Search では、ベクター インデックスには、ベクター フィールドと非ベクトル フィールドを定義するインデックス スキーマ、埋め込み空間を作成するアルゴリズムのベクター検索構成、およびクエリ時に評価されるベクター フィールド定義の設定があります。 インデックス - 作成または更新 (REST API) によってベクター インデックスが作成されます。
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、エンド ツー エンドのチュートリアルで データのインポート (新しい) ウィザード を試してください。
前提条件
アクティブなサブスクリプションを持つ Azure アカウント。 無料でアカウントを作成できます。
-
このクイック スタートのほとんどは Free レベルを使用できますが、より大きなデータ ファイルには Basic 以上をお勧めします。
Microsoft Entra ID による キーレス認証 の場合は、 検索インデックス データ共同作成者ロール をユーザー アカウントに割り当てます。
セマンティック ハイブリッド クエリを実行するには、 セマンティック ランカーを有効にする必要があります。
サービス情報を取得する
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。
サービス エンドポイントを取得するには:
Azure portal にサインインします。
[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。
Java プロジェクトを設定する
新しい Maven プロジェクト ディレクトリを作成します。
mkdir vector-quickstart && cd vector-quickstart必要な依存関係を持つ
pom.xmlファイルを作成します。<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.example.search</groupId> <artifactId>vector-search-quickstart</artifactId> <version>1.0.0-SNAPSHOT</version> <properties> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <maven.compiler.source>21</maven.compiler.source> <maven.compiler.target>21</maven.compiler.target> </properties> <dependencies> <dependency> <groupId>com.azure</groupId> <artifactId>azure-identity</artifactId> <version>1.17.0</version> </dependency> <dependency> <groupId>com.azure</groupId> <artifactId>azure-search-documents</artifactId> <version>11.7.9</version> </dependency> <dependency> <groupId>org.slf4j</groupId> <artifactId>slf4j-nop</artifactId> <version>2.0.17</version> <scope>runtime</scope> </dependency> </dependencies> </project>プロジェクトをコンパイルして依存関係を解決します。
mvn compileソース ディレクトリ構造を作成します。
mkdir -p src/main/java/com/example/search mkdir -p src/main/resourcesapplication.propertiesディレクトリにsrc/main/resourcesファイルを作成し、検索サービス エンドポイントを指定します。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。
ベクトル 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。
ベクター インデックスを作成するには:
CreateIndex.javaディレクトリにsrc/main/java/com/example/searchファイルを作成します。次のコードをファイルにコピーします。
package com.example.search; import com.azure.identity.DefaultAzureCredentialBuilder; import com.azure.search.documents.indexes.SearchIndexClient; import com.azure.search.documents.indexes.SearchIndexClientBuilder; import com.azure.search.documents.indexes.models.SearchIndex; import com.azure.search.documents.indexes.models.SearchField; import com.azure.search.documents.indexes.models.SearchFieldDataType; import com.azure.search.documents.indexes.models.SearchSuggester; import com.azure.search.documents.indexes.models.HnswAlgorithmConfiguration; import com.azure.search.documents.indexes.models.HnswParameters; import com.azure.search.documents.indexes.models.ExhaustiveKnnAlgorithmConfiguration; import com.azure.search.documents.indexes.models.ExhaustiveKnnParameters; import com.azure.search.documents.indexes.models.VectorSearchProfile; import com.azure.search.documents.indexes.models.VectorSearch; import com.azure.search.documents.indexes.models.SemanticPrioritizedFields; import com.azure.search.documents.indexes.models.SemanticField; import com.azure.search.documents.indexes.models.SemanticSearch; import com.azure.search.documents.indexes.models.SemanticConfiguration; import java.io.IOException; import java.util.Arrays; import java.util.List; import java.util.Properties; public class CreateIndex { private static final String searchEndpoint; private static final String indexName; static { var properties = new Properties(); try (var in = CreateIndex.class.getClassLoader() .getResourceAsStream("application.properties")) { if (in != null) properties.load(in); } catch (IOException e) { System.out.println("Warning: could not read application.properties."); } searchEndpoint = properties.getProperty("azure.search.endpoint"); indexName = properties.getProperty("azure.search.index-name"); } public static void main(String[] args) { System.out.println("Using Azure Search endpoint: " + searchEndpoint); System.out.println("Using index name: " + indexName); // Create a SearchIndexClient using DefaultAzureCredential. SearchIndexClient searchIndexClient = new SearchIndexClientBuilder() .credential(new DefaultAzureCredentialBuilder().build()) .endpoint(searchEndpoint) .buildClient(); System.out.println("Creating index..."); // Define fields List<SearchField> fields = Arrays.asList( new SearchField("HotelId", SearchFieldDataType.STRING) .setKey(true) .setFilterable(true), new SearchField("HotelName", SearchFieldDataType.STRING) .setSortable(true) .setSearchable(true), new SearchField("Description", SearchFieldDataType.STRING) .setSearchable(true), new SearchField("DescriptionVector", SearchFieldDataType.collection(SearchFieldDataType.SINGLE)) .setSearchable(true) .setVectorSearchDimensions(1536) .setVectorSearchProfileName("my-vector-profile"), new SearchField("Category", SearchFieldDataType.STRING) .setSortable(true) .setFilterable(true) .setFacetable(true) .setSearchable(true), new SearchField("Tags", SearchFieldDataType.collection( SearchFieldDataType.STRING)) .setSearchable(true) .setFilterable(true) .setFacetable(true), new SearchField("ParkingIncluded", SearchFieldDataType.BOOLEAN) .setFilterable(true) .setSortable(true) .setFacetable(true), new SearchField("LastRenovationDate", SearchFieldDataType.DATE_TIME_OFFSET) .setFilterable(true) .setSortable(true) .setFacetable(true), new SearchField("Rating", SearchFieldDataType.DOUBLE) .setFilterable(true) .setSortable(true) .setFacetable(true), new SearchField("Address", SearchFieldDataType.COMPLEX) .setFields(Arrays.asList( new SearchField("StreetAddress", SearchFieldDataType.STRING), new SearchField("City", SearchFieldDataType.STRING) .setFilterable(true) .setSortable(true) .setFacetable(true), new SearchField("StateProvince", SearchFieldDataType.STRING) .setFilterable(true) .setFacetable(true), new SearchField("PostalCode", SearchFieldDataType.STRING), new SearchField("Country", SearchFieldDataType.STRING) .setFilterable(true) .setFacetable(true) )), new SearchField("Location", SearchFieldDataType.GEOGRAPHY_POINT) .setFilterable(true) .setSortable(true) ); var searchIndex = new SearchIndex(indexName, fields); // Define vector search configuration var hnswParams = new HnswParameters() .setM(16) .setEfConstruction(200) .setEfSearch(128); var hnsw = new HnswAlgorithmConfiguration("hnsw-vector-config"); hnsw.setParameters(hnswParams); var eknnParams = new ExhaustiveKnnParameters(); var eknn = new ExhaustiveKnnAlgorithmConfiguration("eknn-vector-config"); eknn.setParameters(eknnParams); var vectorProfile = new VectorSearchProfile( "my-vector-profile", "hnsw-vector-config"); var vectorSearch = new VectorSearch() .setAlgorithms(Arrays.asList(hnsw, eknn)) .setProfiles(Arrays.asList(vectorProfile)); searchIndex.setVectorSearch(vectorSearch); // Define semantic configuration var prioritizedFields = new SemanticPrioritizedFields() .setTitleField(new SemanticField("HotelName")) .setContentFields(Arrays.asList(new SemanticField("Description"))) .setKeywordsFields(Arrays.asList(new SemanticField("Category"))); var semanticConfig = new SemanticConfiguration( "semantic-config", prioritizedFields); var semanticSearch = new SemanticSearch() .setConfigurations(Arrays.asList(semanticConfig)); searchIndex.setSemanticSearch(semanticSearch); // Define suggesters var suggester = new SearchSuggester("sg", Arrays.asList("HotelName")); searchIndex.setSuggesters(Arrays.asList(suggester)); // Create the search index with the semantic settings. SearchIndex result = searchIndexClient.createOrUpdateIndex(searchIndex); System.out.println("%s created".formatted(result.getName())); System.exit(0); } }コード ファイルはインデックスを作成し、
DescriptionVectorベクター フィールドを含むインデックス スキーマを定義します。ファイルを実行します。
mvn compile exec:java -Dexec.mainClass="com.example.search.CreateIndex"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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> Creating index... hotels-vector-quickstart created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 (
setSearchable)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setVectorSearchProfileName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 (
Locationを含むSearchFieldDataType.GEOGRAPHY_POINT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。 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。
ドキュメントをインデックスにアップロードするには:
UploadDocuments.javaディレクトリにsrc/main/java/com/example/searchファイルを作成します。次のコードをファイルにコピーします。
package com.example.search; import com.azure.identity.DefaultAzureCredentialBuilder; import com.azure.search.documents.SearchClient; import com.azure.search.documents.SearchClientBuilder; import com.azure.search.documents.models.IndexDocumentsResult; import com.azure.search.documents.models.IndexingResult; import java.io.IOException; import java.util.Arrays; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.Properties; public class UploadDocuments { private static final String searchEndpoint; private static final String indexName; static { var properties = new Properties(); try (var in = UploadDocuments.class.getClassLoader() .getResourceAsStream("application.properties")) { if (in != null) properties.load(in); } catch (IOException e) { System.out.println("Warning: could not read application.properties."); } searchEndpoint = properties.getProperty("azure.search.endpoint"); indexName = properties.getProperty("azure.search.index-name"); } private static final List<Map<String, Object>> DOCUMENTS = Arrays.asList( new HashMap<>() {{ put("@search.action", "mergeOrUpload"); put("HotelId", "1"); put("HotelName", "Stay-Kay City Hotel"); put("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."); put("DescriptionVector", 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put("Category", "Boutique"); put("Tags", Arrays.asList("view", "air conditioning", "concierge")); put("ParkingIncluded", false); put("LastRenovationDate", "2022-01-18T00:00:00Z"); put("Rating", 3.6); put("Address", new HashMap<String, Object>() {{ put("StreetAddress", "677 5th Ave"); put("City", "New York"); put("StateProvince", "NY"); put("PostalCode", "10022"); put("Country", "USA"); }}); put("Location", new HashMap<String, Object>() {{ put("type", "Point"); put("coordinates", Arrays.asList(-73.975403, 40.760586)); }}); }}, new HashMap<>() {{ put("@search.action", "mergeOrUpload"); put("HotelId", "2"); put("HotelName", "Old Century Hotel"); put("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."); put("DescriptionVector", 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put("Category", "Boutique"); put("Tags", Arrays.asList("pool", "free wifi", "air conditioning", "concierge")); put("ParkingIncluded", false); put("LastRenovationDate", "2019-02-18T00:00:00Z"); put("Rating", 3.6); put("Address", new HashMap<String, Object>() {{ put("StreetAddress", "140 University Town Center Dr"); put("City", "Sarasota"); put("StateProvince", "FL"); put("PostalCode", "34243"); put("Country", "USA"); }}); put("Location", new HashMap<String, Object>() {{ put("type", "Point"); put("coordinates", Arrays.asList(-82.452843, 27.384417)); }}); }}, new HashMap<>() {{ put("@search.action", "mergeOrUpload"); put("HotelId", "3"); put("HotelName", "Gastronomic Landscape Hotel"); put("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."); put("DescriptionVector", 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put("Category", "Suite"); put("Tags", Arrays.asList("restaurant", "bar", "continental breakfast")); put("ParkingIncluded", true); put("LastRenovationDate", "2015-09-20T00:00:00Z"); put("Rating", 4.8); put("Address", new HashMap<String, Object>() {{ put("StreetAddress", "3393 Peachtree Rd"); put("City", "Atlanta"); put("StateProvince", "GA"); put("PostalCode", "30326"); put("Country", "USA"); }}); put("Location", new HashMap<String, Object>() {{ put("type", "Point"); put("coordinates", Arrays.asList(33.847848, -84.363602)); }}); }}, new HashMap<>() {{ put("@search.action", "mergeOrUpload"); put("HotelId", "4"); put("HotelName", "Sublime Palace Hotel"); put("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."); put("DescriptionVector", 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put("Category", "Boutique"); put("Tags", Arrays.asList("view", "concierge", "air conditioning")); put("ParkingIncluded", true); put("Rating", 4.6); put("Address", new HashMap<String, Object>() {{ put("StreetAddress", "7400 San Pedro Ave"); put("City", "San Antonio"); put("StateProvince", "TX"); put("PostalCode", "78216"); put("Country", "USA"); }}); put("Location", new HashMap<String, Object>() {{ put("type", "Point"); put("coordinates", Arrays.asList(-98.498923, 29.518029)); }}); }}, new HashMap<>() {{ put("@search.action", "mergeOrUpload"); put("HotelId", "48"); put("HotelName", "Nordick's Valley Motel"); put("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."); put("DescriptionVector", 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put("Category", "Boutique"); put("Tags", Arrays.asList("continental breakfast", "air conditioning", "free wifi")); put("ParkingIncluded", false); put("LastRenovationDate", "2018-02-19T00:00:00Z"); put("Rating", 4.5); put("Address", new HashMap<String, Object>() {{ put("StreetAddress", "1401 I St NW"); put("City", "Washington D.C."); put("StateProvince", ""); put("PostalCode", "20005"); put("Country", "USA"); }}); put("Location", new HashMap<String, Object>() {{ put("type", "Point"); put("coordinates", Arrays.asList(-77.03241, 38.90166)); }}); }}, new HashMap<>() {{ put("@search.action", "mergeOrUpload"); put("HotelId", "49"); put("HotelName", "Swirling Currents Hotel"); put("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."); put("DescriptionVector", 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put("Category", "Suite"); put("Tags", Arrays.asList("air conditioning", "laundry service", "24-hour front desk service")); put("ParkingIncluded", true); put("LastRenovationDate", "2018-01-27T00:00:00Z"); put("Rating", 2.7); put("Address", new HashMap<String, Object>() {{ put("StreetAddress", "1100 S Hayes St"); put("City", "Arlington"); put("StateProvince", "VA"); put("PostalCode", "22202"); put("Country", "USA"); }}); put("Location", new HashMap<String, Object>() {{ put("type", "Point"); put("coordinates", Arrays.asList(-77.060066, 38.863659)); }}); }}, new HashMap<>() {{ put("@search.action", "mergeOrUpload"); put("HotelId", "13"); put("HotelName", "Luxury Lion Resort"); put("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."); put("DescriptionVector", 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put("Category", "Luxury"); put("Tags", Arrays.asList("bar", "concierge", "restaurant")); put("ParkingIncluded", false); put("LastRenovationDate", "2020-03-18T00:00:00Z"); put("Rating", 4.1); put("Address", new HashMap<String, Object>() {{ put("StreetAddress", "3 Cityplace Dr"); put("City", "St. Louis"); put("StateProvince", "MO"); put("PostalCode", "63141"); put("Country", "USA"); }}); put("Location", new HashMap<String, Object>() {{ put("type", "Point"); put("coordinates", Arrays.asList(-90.44081, 38.67219)); }}); }} ); private static void waitUntilIndexed(SearchClient searchClient, int expected) throws InterruptedException { do { try { var count = searchClient.getDocumentCount(); if (count == expected) { System.out.println("All documents indexed successfully."); break; } System.out.println("Waiting for indexing... Current count: %s".formatted(count)); } catch (RuntimeException ex) { System.out.println("Warning: failed to get document count: " + ex.getMessage()); } Thread.sleep(10_000); } while (true); } public static void main(String[] args) { System.out.println("Using Azure Search endpoint: " + searchEndpoint); System.out.println("Using index name: " + indexName); // Create a SearchClient using DefaultAzureCredential. var searchClient = new SearchClientBuilder() .credential(new DefaultAzureCredentialBuilder().build()) .endpoint(searchEndpoint) .indexName(indexName) .buildClient(); try { // Upload documents to the index. System.out.println("Uploading documents..."); IndexDocumentsResult result = searchClient.uploadDocuments(DOCUMENTS); for (IndexingResult r : result.getResults()) { String err = r.getErrorMessage() == null ? "none" : r.getErrorMessage(); System.out.println("Key: %s, Succeeded: %s, ErrorMessage: %s" .formatted(r.getKey(), r.isSucceeded(), err)); } // Wait until the documents appear in the index. This is a simple // polling loop suitable for samples. For production, consider a // more robust approach with retries and timeouts. waitUntilIndexed(searchClient, DOCUMENTS.size()); } catch (Exception ex) { System.err.println("Failed to upload documents: " + ex); } System.exit(0); } }このコードは、ドキュメントのコレクションを読み込みます。 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メソッド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。ファイルをビルドして実行します。
mvn compile exec:java -Dexec.mainClass="com.example.search.UploadDocuments"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。
Using Azure Search endpoint: https://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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.重要なポイント:
コードは、
SearchClientを介して Azure AI 検索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など。検索操作:
search、autocomplete、suggest
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、
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 - ベクター クエリ文字列:
quintessential lodging near running trails, eateries, retail(数学的表現にベクター化)
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。
QueryVector.java ディレクトリにsrc/main/java/com/example/search 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。
package com.example.search;
import java.util.ArrayList;
// 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.
public class QueryVector {
// The vector data is stored as a private static final double[].
// Use getVectorList() to obtain a List<Float> suitable for Azure SDK vector search APIs.
// This approach avoids exposing a mutable array and provides the format required by the SDK.
private static final double[] VECTOR = 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public static ArrayList<Float> getVectorList() {
var vectorList = new ArrayList<Float>(VECTOR.length);
for (double v : VECTOR) {
vectorList.add((float) v);
}
return vectorList;
}
}
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。
1 つのベクター検索を作成する
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 つのベクター検索を作成するには:
SearchSingle.javaディレクトリにsrc/main/java/com/example/searchファイルを作成します。次のコードをファイルにコピーします。
package com.example.search; import com.azure.identity.DefaultAzureCredentialBuilder; import com.azure.search.documents.SearchClientBuilder; import com.azure.search.documents.models.SearchOptions; import com.azure.search.documents.models.SearchResult; import com.azure.core.util.Context; import com.azure.search.documents.SearchDocument; import com.azure.search.documents.models.VectorizedQuery; import com.azure.search.documents.models.VectorSearchOptions; import com.azure.search.documents.models.VectorFilterMode; import java.io.IOException; import java.util.Properties; public class SearchSingle { private static final String searchEndpoint; private static final String indexName; static { var properties = new Properties(); try (var in = SearchSingle.class.getClassLoader() .getResourceAsStream("application.properties")) { if (in != null) properties.load(in); } catch (IOException e) { System.out.println("Warning: could not read application.properties."); } searchEndpoint = properties.getProperty("azure.search.endpoint"); indexName = properties.getProperty("azure.search.index-name"); } public static void main(String[] args) { System.out.println("Using Azure Search endpoint: " + searchEndpoint); System.out.println("Using index name: " + indexName); // Create a SearchClient using DefaultAzureCredential. var searchClient = new SearchClientBuilder() .credential(new DefaultAzureCredentialBuilder().build()) .endpoint(searchEndpoint) .indexName(indexName) .buildClient(); try { var vectorQuery = new VectorizedQuery(QueryVector.getVectorList()) .setKNearestNeighborsCount(5) .setFields("DescriptionVector") .setExhaustive(true); // Create vector search options with the vector query and filter. // Use POST_FILTER to apply filter after vector similarity is calculated. var vectorSearchOptions = new VectorSearchOptions() .setQueries(vectorQuery) .setFilterMode(VectorFilterMode.POST_FILTER); var searchOptions = new SearchOptions() .setTop(7) .setIncludeTotalCount(true) .setSelect("HotelId", "HotelName", "Description", "Category", "Tags") .setVectorSearchOptions(vectorSearchOptions); var results = searchClient.search("*", searchOptions, Context.NONE); System.out.println(); Long count = results.getTotalCount(); System.out.println("Single Vector search found %s" .formatted(count == null ? 0 : count)); for (SearchResult result : results) { // Log each result. SearchDocument document = result.getDocument(SearchDocument.class); Object tags = document.get("Tags"); var tagsString = (tags == null ? "N/A" : tags.toString()); System.out.println( "- HotelId: %s, HotelName: %s, Tags: %s, Score: %s".formatted( document.get("HotelId"), document.get("HotelName"), tagsString, result.getScore())); } } catch (Exception ex) { System.err.println("Search failed: " + ex); } System.exit(0); } }vectorQueryには、QueryVector.getVectorListを通じて取得されたクエリ入力のベクトル化されたテキストを含む、ベクトル化されたクエリの構成が含まれます。setFieldsは、検索するベクトル フィールドを決定し、setKNearestNeighborsCountは返す最も近い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。ファイルをビルドして実行します。
mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingle"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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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 0.57902366quintessential 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.SearchClient と SearchOptions が使用されます。
フィルターを使用して 1 つのベクター検索を作成するには:
SearchSingleWithFilter.javaディレクトリにsrc/main/java/com/example/searchファイルを作成します。次のコードをファイルにコピーします。
package com.example.search; import com.azure.identity.DefaultAzureCredentialBuilder; import com.azure.search.documents.SearchClientBuilder; import com.azure.search.documents.models.SearchOptions; import com.azure.search.documents.models.SearchResult; import com.azure.core.util.Context; import com.azure.search.documents.SearchDocument; import com.azure.search.documents.models.VectorizedQuery; import com.azure.search.documents.models.VectorSearchOptions; import com.azure.search.documents.models.VectorFilterMode; import java.io.IOException; import java.util.Properties; public class SearchSingleWithFilter { private static final String searchEndpoint; private static final String indexName; static { var properties = new Properties(); try (var in = SearchSingleWithFilter.class.getClassLoader() .getResourceAsStream("application.properties")) { if (in != null) properties.load(in); } catch (IOException e) { System.out.println("Warning: could not read application.properties."); } searchEndpoint = properties.getProperty("azure.search.endpoint"); indexName = properties.getProperty("azure.search.index-name"); } public static void main(String[] args) { System.out.println("Using Azure Search endpoint: " + searchEndpoint); System.out.println("Using index name: " + indexName); // Create a SearchClient using DefaultAzureCredential. var searchClient = new SearchClientBuilder() .credential(new DefaultAzureCredentialBuilder().build()) .endpoint(searchEndpoint) .indexName(indexName) .buildClient(); try { var vectorQuery = new VectorizedQuery(QueryVector.getVectorList()) .setKNearestNeighborsCount(5) .setFields("DescriptionVector") .setExhaustive(true); // Create vector search options with the vector query and filter. // Use POST_FILTER to apply filter after vector similarity is calculated. var vectorSearchOptions = new VectorSearchOptions() .setQueries(vectorQuery) .setFilterMode(VectorFilterMode.POST_FILTER); // Add filter for "free wifi" tag. var searchOptions = new SearchOptions() .setTop(7) .setIncludeTotalCount(true) .setSelect("HotelId", "HotelName", "Description", "Category", "Tags") .setFilter("Tags/any(tag: tag eq 'free wifi')") .setVectorSearchOptions(vectorSearchOptions); var results = searchClient.search("*", searchOptions, Context.NONE); System.out.println(); Long count = results.getTotalCount(); System.out.println("Single Vector search found %s" .formatted(count == null ? 0 : count)); for (SearchResult result : results) { // Log each result. SearchDocument document = result.getDocument(SearchDocument.class); Object tags = document.get("Tags"); var tagsString = (tags == null ? "N/A" : tags.toString()); System.out.println( "- HotelId: %s, HotelName: %s, Tags: %s, Score: %s".formatted( document.get("HotelId"), document.get("HotelName"), tagsString, result.getScore())); } } catch (Exception ex) { System.err.println("Vector search failed: " + ex); } System.exit(0); } }このコードには、前の検索と同じ検索機能がありますが、
free wifiを持つホテルの後処理除外フィルターが追加されます。ファイルをビルドして実行します。
mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilter"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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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.SearchClient と SearchOptions が使用されます。
地理空間フィルターを使用して 1 つのベクター検索を作成するには:
SearchSingleWithFilterGeo.javaディレクトリにsrc/main/java/com/example/searchファイルを作成します。次のコードをファイルにコピーします。
package com.example.search; import com.azure.identity.DefaultAzureCredentialBuilder; import com.azure.search.documents.SearchClientBuilder; import com.azure.search.documents.models.SearchOptions; import com.azure.search.documents.models.SearchResult; import com.azure.core.util.Context; import com.azure.search.documents.SearchDocument; import com.azure.search.documents.models.VectorizedQuery; import com.azure.search.documents.models.VectorSearchOptions; import com.azure.search.documents.models.VectorFilterMode; import java.io.IOException; import java.util.Properties; public class SearchSingleWithFilterGeo { private static final String searchEndpoint; private static final String indexName; static { var properties = new Properties(); try (var in = SearchSingleWithFilterGeo.class.getClassLoader() .getResourceAsStream("application.properties")) { if (in != null) properties.load(in); } catch (IOException e) { System.out.println("Warning: could not read application.properties."); } searchEndpoint = properties.getProperty("azure.search.endpoint"); indexName = properties.getProperty("azure.search.index-name"); } public static void main(String[] args) { System.out.println("Using Azure Search endpoint: " + searchEndpoint); System.out.println("Using index name: " + indexName); // Create a SearchClient using DefaultAzureCredential. var searchClient = new SearchClientBuilder() .credential(new DefaultAzureCredentialBuilder().build()) .endpoint(searchEndpoint) .indexName(indexName) .buildClient(); try { var vectorQuery = new VectorizedQuery(QueryVector.getVectorList()) .setKNearestNeighborsCount(5) .setFields("DescriptionVector") .setExhaustive(true); // Create vector search options with the vector query and filter. // Use POST_FILTER to apply filter after vector similarity is calculated. var vectorSearchOptions = new VectorSearchOptions() .setQueries(vectorQuery) .setFilterMode(VectorFilterMode.POST_FILTER); var searchOptions = new SearchOptions() .setTop(5) .setIncludeTotalCount(true) .setSelect("HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince") .setFacets("Address/StateProvince") .setFilter("geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300") .setVectorSearchOptions(vectorSearchOptions); var results = searchClient.search("*", searchOptions, Context.NONE); System.out.println(); Long count = results.getTotalCount(); System.out.println("Single Vector search found %s" .formatted(count == null ? 0 : count)); for (SearchResult result : results) { // Log each result. SearchDocument document = result.getDocument(SearchDocument.class); Object tags = document.get("Tags"); var tagsString = (tags == null ? "N/A" : tags.toString()); System.out.println( "- HotelId: %s, HotelName: %s, Tags: %s, Score: %s".formatted( document.get("HotelId"), document.get("HotelName"), tagsString, result.getScore())); } } catch (Exception ex) { System.err.println("Vector search failed: " + ex); } System.exit(0); } }ファイルをビルドして実行します。
mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilterGeo"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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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
ハイブリッド検索を作成する
ハイブリッド検索 では、キーワード クエリとベクター クエリが 1 つの要求に結合されます。 この例では、次のフルテキスト クエリ文字列とベクター クエリ文字列を同時に実行します。
- 検索文字列:
historic hotel walk to restaurants and shopping - ベクター クエリ文字列:
quintessential lodging near running trails, eateries, retail(数学的表現にベクター化)
この検索では、SearchClient.SearchClient と SearchOptions が使用されます。
Tp はハイブリッド検索を作成します。
SearchHybrid.javaディレクトリにsrc/main/java/com/example/searchファイルを作成します。次のコードをファイルにコピーします。
package com.example.search; import com.azure.identity.DefaultAzureCredentialBuilder; import com.azure.search.documents.SearchClientBuilder; import com.azure.search.documents.models.SearchOptions; import com.azure.search.documents.models.SearchResult; import com.azure.core.util.Context; import com.azure.search.documents.SearchDocument; import com.azure.search.documents.models.VectorizedQuery; import com.azure.search.documents.models.VectorSearchOptions; import com.azure.search.documents.models.VectorFilterMode; import java.io.IOException; import java.util.Properties; public class SearchHybrid { private static final String searchEndpoint; private static final String indexName; static { var properties = new Properties(); try (var in = SearchHybrid.class.getClassLoader() .getResourceAsStream("application.properties")) { if (in != null) properties.load(in); } catch (IOException e) { System.out.println("Warning: could not read application.properties."); } searchEndpoint = properties.getProperty("azure.search.endpoint"); indexName = properties.getProperty("azure.search.index-name"); } public static void main(String[] args) { System.out.println("Using Azure Search endpoint: " + searchEndpoint); System.out.println("Using index name: " + indexName); // Create a SearchClient using DefaultAzureCredential. var searchClient = new SearchClientBuilder() .credential(new DefaultAzureCredentialBuilder().build()) .endpoint(searchEndpoint) .indexName(indexName) .buildClient(); try { var vectorQuery = new VectorizedQuery(QueryVector.getVectorList()) .setKNearestNeighborsCount(5) .setFields("DescriptionVector") .setExhaustive(true); // Create vector search options with the vector query and filter. // Use POST_FILTER to apply filter after vector similarity is calculated. var vectorSearchOptions = new VectorSearchOptions() .setQueries(vectorQuery) .setFilterMode(VectorFilterMode.POST_FILTER); var searchOptions = new SearchOptions() .setTop(5) .setIncludeTotalCount(true) .setSelect("HotelId", "HotelName", "Description", "Category", "Tags") .setVectorSearchOptions(vectorSearchOptions); var results = searchClient.search( "historic hotel walk to restaurants and shopping", searchOptions, Context.NONE); System.out.println(); Long count = results.getTotalCount(); System.out.println("Hybrid search found %s then limited to top %s" .formatted(count == null ? 0 : count, searchOptions.getTop() == null ? 0 : searchOptions.getTop())); for (SearchResult result : results) { // Log each result. SearchDocument document = result.getDocument(SearchDocument.class); Object description = document.get("Description"); Object category = document.get("Category"); Object tags = document.get("Tags"); System.out.println( "- Score: %s\n".formatted(result.getScore()) + " HotelId: %s\n".formatted(document.get("HotelId")) + " HotelName: %s\n".formatted(document.get("HotelName")) + " Description: %s\n".formatted(description == null ? "N/A" : description) + " Category: %s\n".formatted(category == null ? "N/A" : category) + " Tags: %s\n".formatted(tags == null ? "N/A" : tags.toString()) ); } } catch (Exception ex) { System.err.println("Hybrid search failed: " + ex); } System.exit(0); } }ファイルをビルドして実行します。
mvn compile exec:java -Dexec.mainClass="com.example.search.SearchHybrid"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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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 クエリからのみ取得されます。 上位 2 つの結果は、サブライム パレス ホテルとラグジュアリー 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。
{ "@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" },HNSW を使用して一致を検索するベクターのみのクエリでは、Sublime Palace Hotel は 4 番目の位置にドロップします。 フルテキスト検索で 2 位、ベクター検索で 3 番目だった Luxury Lion では、同じ変動範囲が発生しないため、均質化された結果セットでは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" } ]
セマンティック ハイブリッド検索を作成する
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.SearchClient と SearchOptions が使用されます。
セマンティック ハイブリッド検索を作成するには:
SearchSemanticHybrid.javaディレクトリにsrc/main/java/com/example/searchファイルを作成します。次のコードをファイルにコピーします。
package com.example.search; import com.azure.identity.DefaultAzureCredentialBuilder; import com.azure.search.documents.SearchClientBuilder; import com.azure.search.documents.models.SearchOptions; import com.azure.search.documents.models.SearchResult; import com.azure.core.util.Context; import com.azure.search.documents.SearchDocument; import com.azure.search.documents.models.VectorizedQuery; import com.azure.search.documents.models.VectorSearchOptions; import com.azure.search.documents.models.VectorFilterMode; import com.azure.search.documents.models.QueryType; import com.azure.search.documents.models.SemanticSearchOptions; import java.io.IOException; import java.util.Properties; public class SearchSemanticHybrid { private static final String searchEndpoint; private static final String indexName; static { var properties = new Properties(); try (var in = SearchSemanticHybrid.class.getClassLoader() .getResourceAsStream("application.properties")) { if (in != null) properties.load(in); } catch (IOException e) { System.out.println("Warning: could not read application.properties."); } searchEndpoint = properties.getProperty("azure.search.endpoint"); indexName = properties.getProperty("azure.search.index-name"); } public static void main(String[] args) { System.out.println("Using Azure Search endpoint: " + searchEndpoint); System.out.println("Using index name: " + indexName); // Create a SearchClient using DefaultAzureCredential. var searchClient = new SearchClientBuilder() .credential(new DefaultAzureCredentialBuilder().build()) .endpoint(searchEndpoint) .indexName(indexName) .buildClient(); try { var vectorQuery = new VectorizedQuery(QueryVector.getVectorList()) .setKNearestNeighborsCount(5) .setFields("DescriptionVector") .setExhaustive(true); // Create vector search options with the vector query and filter. // Use POST_FILTER to apply filter after vector similarity is calculated. var vectorSearchOptions = new VectorSearchOptions() .setQueries(vectorQuery) .setFilterMode(VectorFilterMode.POST_FILTER); SemanticSearchOptions semanticSearchOptions = new SemanticSearchOptions() .setSemanticConfigurationName("semantic-config"); var searchOptions = new SearchOptions() .setTop(5) .setIncludeTotalCount(true) .setSelect("HotelId", "HotelName", "Category", "Description") .setQueryType(QueryType.SEMANTIC) .setSemanticSearchOptions(semanticSearchOptions) .setVectorSearchOptions(vectorSearchOptions); var results = searchClient.search( "historic hotel walk to restaurants and shopping", searchOptions, Context.NONE); System.out.println(); Long count = results.getTotalCount(); System.out.println("Semantic hybrid search found %s then limited to top %s" .formatted(count == null ? 0 : count, searchOptions.getTop() == null ? 0 : searchOptions.getTop())); for (SearchResult result : results) { // Log each result. SearchDocument document = result.getDocument(SearchDocument.class); var semanticResult = result.getSemanticSearch(); Double rerankerScore = semanticResult == null ? null : semanticResult.getRerankerScore(); Object desc = document.get("Description"); Object category = document.get("Category"); System.out.println( "- Score: %s\n".formatted(result.getScore()) + " Re-ranker Score: %s\n".formatted(rerankerScore == null ? "N/A" : rerankerScore) + " HotelId: %s\n".formatted(document.get("HotelId")) + " HotelName: %s\n".formatted(document.get("HotelName")) + " Description: %s\n".formatted(desc == null ? "N/A" : desc) + " Category: %s\n".formatted(category == null ? "N/A" : category) ); } } catch (Exception ex) { System.err.println("Semantic hybrid search failed: " + ex); } System.exit(0); } }ファイルをビルドして実行します。
mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSemanticHybrid"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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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クラスを使用して指定されます。 キーワード検索は、SemanticSearchOptionsクラス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。
リソースをクリーンアップする
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。
または、このクイック スタートで作成したベクター インデックスをプログラムで削除することもできます。
DeleteIndex.javaディレクトリにsrc/main/java/com/example/searchファイルを作成します。次のコードをファイルに追加します。
package com.example.search; import com.azure.identity.DefaultAzureCredentialBuilder; import com.azure.search.documents.indexes.SearchIndexClient; import com.azure.search.documents.indexes.SearchIndexClientBuilder; import java.io.IOException; import java.util.Properties; public class DeleteIndex { private static final String searchEndpoint; private static final String indexName; static { var properties = new Properties(); try (var in = DeleteIndex.class.getClassLoader() .getResourceAsStream("application.properties")) { if (in != null) properties.load(in); } catch (IOException e) { System.out.println("Warning: could not read application.properties."); } searchEndpoint = properties.getProperty("azure.search.endpoint"); indexName = properties.getProperty("azure.search.index-name"); } public static void main(String[] args) { System.out.println("Using Azure Search endpoint: " + searchEndpoint); System.out.println("Using index name: " + indexName); // Create a SearchIndexClient using DefaultAzureCredential. SearchIndexClient searchIndexClient = new SearchIndexClientBuilder() .credential(new DefaultAzureCredentialBuilder().build()) .endpoint(searchEndpoint) .buildClient(); try { System.out.println("Deleting index..."); searchIndexClient.deleteIndex(indexName); System.out.println("Index %s deleted".formatted(indexName)); } catch (Exception ex) { System.err.println("Failed to delete index: " + ex); } System.exit(0); } }ファイルをビルドして実行します。
mvn compile exec:java -Dexec.mainClass="com.example.search.DeleteIndex"
次のステップ
- Azure AI Search for Java のベクター検索機能については、コード サンプルのリポジトリを確認してください。
このクイック スタートでは、JavaScript を使用して ベクター インデックスを作成、読み込み、クエリを実行します。 このコードでは、 JavaScript 用の 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。これにより、インデックス操作にアクセスするための REST API に対する抽象化が提供されます。
Azure AI Search では、ベクター インデックスには、ベクター フィールドと非ベクトル フィールドを定義するインデックス スキーマ、埋め込み空間を作成するアルゴリズムのベクター検索構成、およびクエリ時に評価されるベクター フィールド定義の設定があります。 インデックス - 作成または更新 (REST API) によってベクター インデックスが作成されます。
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、エンド ツー エンドのチュートリアルで データのインポート (新しい) ウィザード を試してください。
前提条件
アクティブなサブスクリプションを持つ Azure アカウント。 無料でアカウントを作成できます。
-
このクイック スタートのほとんどは Free レベルを使用できますが、より大きなデータ ファイルには Basic 以上をお勧めします。
Microsoft Entra ID による キーレス認証 の場合は、 検索インデックス データ共同作成者ロール をユーザー アカウントに割り当てます。
セマンティック ハイブリッド クエリを実行するには、 セマンティック ランカーを有効にする必要があります。
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。
サービス エンドポイントを取得するには:
Azure portal にサインインします。
[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
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 .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。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
.envプロジェクト ディレクトリにvector-quickstartファイルを作成します。.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。
ベクトル 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。
ベクター インデックスを作成するには:
createIndex.jsディレクトリにsrcファイルを作成します。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。ファイルを実行します。
node -r dotenv/config src/createIndex.js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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> Creating index... hotels-vector-quickstart created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 (
Locationを含むgeo.distance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。 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。
ドキュメントをインデックスにアップロードするには:
uploadDocuments.jsディレクトリにsrcファイルを作成します。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.", "DescriptionVector": 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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": [ -82.452843, 27.384417 ] } }, { "@search.action": "mergeOrUpload", "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.", "DescriptionVector": 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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.", "DescriptionVector": 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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.", "DescriptionVector": 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"Category": "Boutique", "Tags": [ "continental breakfast", "air conditioning", "free wifi" ], "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" }, "Location": { "type": "Point", "coordinates": [ -77.03241, 38.90166 ], "crs": { "type": "name", "properties": { "name": "EPSG:4326" } } } }, { "@search.action": "mergeOrUpload", "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.", "DescriptionVector": 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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.", "DescriptionVector": 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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。ファイルをビルドして実行します。
node -r dotenv/config src/uploadDocuments.js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。
Using Azure Search endpoint: https://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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.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など。検索操作:
search、autocomplete、suggestインデックス管理操作:
createIndex、deleteIndex、getIndexなど。
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、
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 - ベクター クエリ文字列:
quintessential lodging near running trails, eateries, retail(数学的表現にベクター化)
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。
queryVector.js ディレクトリにsrc 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 ranking
// Replace with your own vector
export const vector = 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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。
1 つのベクター検索を作成する
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 つのベクター検索を作成するには:
searchSingle.jsディレクトリにsrcファイルを作成します。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。ファイルをビルドして実行します。
node -r dotenv/config src/searchSingle.js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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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 0.57902366quintessential 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。
フィルターを使用して 1 つのベクター検索を作成するには:
searchSingleWithFilter.jsディレクトリにsrcファイルを作成します。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。ファイルをビルドして実行します。
node -r dotenv/config src/searchSingleWithFilter.js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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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。
地理空間フィルターを使用して検索を作成するには:
searchSingleWithFilterGeo.jsディレクトリにsrcファイルを作成します。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; }ファイルをビルドして実行します。
node -r dotenv/config src/searchSingleWithFilterGeo.js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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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
ハイブリッド検索を作成する
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 - ベクター クエリ文字列:
quintessential lodging near running trails, eateries, retail(数学的表現にベクター化)
この検索では SearchClient を使用します。検索 と VectorQuery と SearchOptions。
ハイブリッド検索を作成するには:
searchHybrid.jsディレクトリにsrcファイルを作成します。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; }ファイルをビルドして実行します。
node -r dotenv/config src/searchHybrid.js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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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 クエリからのみ取得されます。 上位 2 つの結果は、サブライム パレス ホテルとラグジュアリー 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。
{ "@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" },HNSW を使用して一致を検索するベクターのみのクエリでは、Sublime Palace Hotel は 4 番目の位置にドロップします。 フルテキスト検索で 2 位、ベクター検索で 3 番目だった Luxury Lion では、同じ変動範囲が発生しないため、均質化された結果セットでは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" } ]
セマンティック ハイブリッド検索を作成する
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。
セマンティック ハイブリッド検索を作成するには:
searchSemanticHybrid.jsディレクトリにsrcファイルを作成します。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 ranking 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; }ファイルをビルドして実行します。
node -r dotenv/config src/searchSemanticHybrid.js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://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> 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。
リソースをクリーンアップする
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。
または、このクイック スタートで作成したベクター インデックスをプログラムで削除することもできます。
deleteIndex.jsディレクトリにsrcファイルを作成します。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); }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については、コード サンプルのリポジトリを確認 してください。
このクイック スタートでは、TypeScript を使用して ベクター インデックスを作成、読み込み、クエリを実行します。 このコードでは、 TypeScript 用の 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。これにより、インデックス操作にアクセスするための REST API に対する抽象化が提供されます。
Azure AI Search では、ベクター インデックスには、ベクター フィールドと非ベクトル フィールドを定義するインデックス スキーマ、埋め込み空間を作成するアルゴリズムのベクター検索構成、およびクエリ時に評価されるベクター フィールド定義の設定があります。 インデックス - 作成または更新 (REST API) によってベクター インデックスが作成されます。
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、エンド ツー エンドのチュートリアルで データのインポート (新しい) ウィザード を試してください。
前提条件
アクティブなサブスクリプションを持つ Azure アカウント。 無料でアカウントを作成できます。
-
このクイック スタートのほとんどは Free レベルを使用できますが、より大きなデータ ファイルには Basic 以上をお勧めします。
Microsoft Entra ID による キーレス認証 の場合は、 検索インデックス データ共同作成者ロール をユーザー アカウントに割り当てます。
セマンティック ハイブリッド クエリを実行するには、 セマンティック ランカーを有効にする必要があります。
TypeScript。 npm を使用して TypeScript をグローバルにインストールできます。
npm install -g typescript
サービス情報を取得する
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。
サービス エンドポイントを取得するには:
Azure portal にサインインします。
[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
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 .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。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"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
.envプロジェクト ディレクトリにvector-quickstartファイルを作成します。.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。
ベクトル 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。
ベクター インデックスを作成するには:
createIndex.tsディレクトリにsrcファイルを作成します。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。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 (
Locationを含むgeo.distance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。 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。
ドキュメントをインデックスにアップロードするには:
uploadDocuments.tsディレクトリにsrcファイルを作成します。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.", "DescriptionVector": 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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": [ -82.452843, 27.384417 ] } }, { "@search.action": "mergeOrUpload", "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.", "DescriptionVector": 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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.", "DescriptionVector": 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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.", "DescriptionVector": 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"Category": "Boutique", "Tags": [ "continental breakfast", "air conditioning", "free wifi" ], "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" }, "Location": { "type": "Point", "coordinates": [ -77.03241, 38.90166 ], "crs": { "type": "name", "properties": { "name": "EPSG:4326" } } } }, { "@search.action": "mergeOrUpload", "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.", "DescriptionVector": 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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.", "DescriptionVector": 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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。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.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など。検索操作:
search、autocomplete、suggestインデックス管理操作:
createIndex、deleteIndex、getIndexなど。
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、
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 - ベクター クエリ文字列:
quintessential lodging near running trails, eateries, retail(数学的表現にベクター化)
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。
queryVector.ts ディレクトリにsrc 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 ranking
// Replace with your own vector
export const vector = 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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。
1 つのベクター検索を作成する
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 つのベクター検索を作成するには:
searchSingle.tsディレクトリにsrcファイルを作成します。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。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 0.57902366quintessential 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。
フィルターを使用して 1 つのベクター検索を作成するには:
searchSingleWithFilter.tsディレクトリにsrcファイルを作成します。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。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。
地理空間フィルターを使用して検索を作成するには:
searchSingleWithFilterGeo.tsディレクトリにsrcファイルを作成します。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; }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
ハイブリッド検索を作成する
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 - ベクター クエリ文字列:
quintessential lodging near running trails, eateries, retail(数学的表現にベクター化)
この検索では SearchClient を使用します。検索 と VectorQuery と SearchOptions。
ハイブリッド検索を作成するには:
searchHybrid.tsディレクトリにsrcファイルを作成します。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; }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 クエリからのみ取得されます。 上位 2 つの結果は、サブライム パレス ホテルとラグジュアリー 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。
{ "@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" },HNSW を使用して一致を検索するベクターのみのクエリでは、Sublime Palace Hotel は 4 番目の位置にドロップします。 フルテキスト検索で 2 位、ベクター検索で 3 番目だった Luxury Lion では、同じ変動範囲が発生しないため、均質化された結果セットでは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" } ]
セマンティック ハイブリッド検索を作成する
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。
セマンティック ハイブリッド検索を作成するには:
searchSemanticHybrid.tsディレクトリにsrcファイルを作成します。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 ranking 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; }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。
リソースをクリーンアップする
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。
または、このクイック スタートで作成したベクター インデックスをプログラムで削除することもできます。
deleteIndex.tsディレクトリにsrcファイルを作成します。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); }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については、コード サンプルのリポジトリを確認 してください。
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 インデックスを作成、読み込み、クエリを実行 します。 このコードでは、 .NET 用 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。これにより、インデックス操作にアクセスするための REST API に対する抽象化が提供されます。
Azure AI Search では、ベクター インデックスには、ベクター フィールドと非ベクトル フィールドを定義するインデックス スキーマ、埋め込み空間を作成するアルゴリズムのベクター検索構成、およびクエリ時に評価されるベクター フィールド定義の設定があります。 インデックス - 作成または更新 (REST API) によってベクター インデックスが作成されます。
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、エンド ツー エンドのチュートリアルで データのインポート (新しい) ウィザード を試してください。
前提条件
アクティブなサブスクリプションを持つ Azure アカウント。 無料でアカウントを作成できます。
-
このクイック スタートのほとんどは Free レベルを使用できますが、より大きなデータ ファイルには Basic 以上をお勧めします。
Microsoft Entra ID による キーレス認証 の場合は、 検索インデックス データ共同作成者ロール をユーザー アカウントに割り当てます。
セマンティック ハイブリッド クエリを実行するには、 セマンティック ランカーを有効にする必要があります。
Git を使用してサンプル リポジトリを複製します。
サービス情報を取得する
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
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-samplesVisual Studio Code で
quickstart-vector-searchフォルダーを開くか、VectorSearchQuickstart.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。appsettings.jsonフォルダーとVectorSearchCreatePopulateIndexフォルダーの両方でVectorSearchExamplesを開きます。Endpoint検索サービスの URL に設定します。これはhttps://mydemo.search.windows.netのようになります。IndexNameをインデックスの一意の名前に設定します。 既定のhotels-vector-quickstart名を使用することもできます。
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、ドキュメントをインデックスにアップロードする必要があります。
インデックスを作成して設定するには:
VectorSearchCreatePopulateIndexフォルダー内のターミナルを開きます。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($"Index '{result.Value.Name}' updated."); Console.WriteLine(); }次のコードは、
hotel-samples.jsonファイル内の JSON 形式のドキュメントを検索サービスにアップロードします。static async Task UploadDocs(SearchClient searchClient) { var jsonPath = Path.Combine(Directory.GetCurrentDirectory(), "HotelData.json"); // Read and parse hotel data var json = await File.ReadAllTextAsync(jsonPath); List<Hotel> hotels = new List<Hotel>(); try { using var doc = JsonDocument.Parse(json); if (doc.RootElement.ValueKind != JsonValueKind.Array) { Console.WriteLine("HotelData.json root is not a JSON array."); } // Deserialize all hotel objects hotels = doc.RootElement.EnumerateArray() .Select(e => JsonSerializer.Deserialize<Hotel>(e.GetRawText())) .Where(h => h != null) .ToList(); } catch (JsonException ex) { Console.WriteLine($"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($"Key: {r.Key}, Succeeded: {r.Succeeded}"); } } catch (Exception ex) { Console.WriteLine("Failed to upload documents: " + ex); } }プロジェクトを実行すると、次の出力が出力されます。
Key: 1, Succeeded: True Key: 2, Succeeded: True Key: 3, Succeeded: True Key: 4, Succeeded: True Key: 48, Succeeded: True Key: 49, Succeeded: True Key: 13, Succeeded: True重要なポイント:
コードは、Azure-search-documents パッケージによって提供されるメイン オブジェクトである SearchClient を介して、Azure AI Search サービスでホストされている特定の検索インデックスと対話します。 SearchClient は、次のようなインデックス操作へのアクセスを提供します。
データ インジェスト:
UploadDocuments()、MergeDocuments()、DeleteDocuments()検索操作:
Search()、Autocomplete()、Suggest()インデックス管理操作:
CreateOrUpdateIndex()
ベクトル フィールドには浮動小数点値が含まれます。 ディメンション属性の最小値は 2 で、それぞれ最大 4,096 個の浮動小数点値があります。 このクイック スタートでは、ディメンション属性を 1,536 に設定します。これは、
text-embedding-ada-002モデルによって生成される埋め込みのサイズであるためです。
クエリを実行する
このセクションでベクター クエリを発行するには、Program.cs フォルダーでVectorSearchExamplesを開き、VectorSearchExamples フォルダー内のターミナルを開きます。
このセクションのクエリ:
単一ベクトル検索
最初のクエリは、検索文字列と密接に一致するドキュメントの説明を検索する基本的なシナリオを示しています。
1 つのベクター検索を作成するには:
Program.csフォルダーのVectorSearchExamplesファイルで、SearchExamples.SearchSingleVector(searchClient, vectorizedResult);メソッド呼び出しのコメントを解除します。このメソッドは、
SearchExamples.csクラスで次の検索機能を実行します。public static async Task SearchSingleVector(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector) { SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>( new SearchOptions { VectorSearch = new() { Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } } }, Select = { "HotelId", "HotelName", "Description", "Category", "Tags" }, }); Console.WriteLine($"Single Vector Search Results:"); await foreach (SearchResult<Hotel> result in response.GetResultsAsync()) { Hotel doc = result.Document; Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}"); } Console.WriteLine(); }dotnet run実行してクエリを実行すると、次の結果が返されます。Single Vector Search Results: Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel Score: 0.6333684, HotelId: 13, HotelName: Luxury Lion Resort Score: 0.605672, HotelId: 4, HotelName: Sublime Palace Hotel Score: 0.6026341, HotelId: 49, HotelName: Swirling Currents Hotel Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel
フィルターを使用した単一ベクトル検索
Azure AI Search では、 インデックス 内の非ベクトル フィールドにフィルターが適用されます。 次の使用例は、 Tags フィールドをフィルター処理して、無料の Wi-Fi を提供していないホテルを除外します。
フィルターを使用して 1 つのベクター検索を作成するには:
Program.csフォルダーのVectorSearchExamplesファイルで、SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);メソッド呼び出しのコメントを解除します。このメソッドは、
SearchExamples.csクラスで次の検索機能を実行します。public static async Task SearchSingleVectorWithFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector) { SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>( new SearchOptions { VectorSearch = new() { Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } } }, Filter = "Tags/any(tag: tag eq 'free wifi')", Select = { "HotelId", "HotelName", "Description", "Category", "Tags" } }); Console.WriteLine($"Single Vector Search With Filter Results:"); await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync()) { Hotel doc = result.Document; Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}, Tags: {string.Join(String.Empty, doc.Tags)}"); } Console.WriteLine(); }dotnet runを実行してクエリを実行します。このクエリは、無料の Wi-Fi を提供するホテルのみを返します。Single Vector Search With Filter Results: Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel, Tags: continental breakfastair conditioningfree wifi Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel, Tags: poolfree wifiair conditioningconciergegeo フィルターの場合は、
Program.csでSearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult);のコメントを解除します。このメソッドは、
SearchExamples.csクラスで次の検索機能を実行します。public static async Task SingleSearchWithGeoFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector) { SearchResults<Hotel> responseWithGeoFilter = await searchClient.SearchAsync<Hotel>( new SearchOptions { VectorSearch = new() { Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } } }, Filter = "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300", Select = { "HotelId", "HotelName", "Description", "Address", "Category", "Tags" }, Facets = { "Address/StateProvince" }, }); Console.WriteLine($"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($"Description: {doc.Description}"); Console.WriteLine(); } Console.WriteLine(); }dotnet runを実行してクエリを実行します。クエリは、300 キロメートル以内のホテルのみを返します。Vector query with a geo filter: -HotelId: 48 HotelName: Nordick's Valley Motel Score: 0.6605852246284485 City/State: Washington D.C./null Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley. -HotelId: 49 HotelName: Swirling Currents Hotel Score: 0.602634072303772 City/State: Arlington/VA Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.
ハイブリッド検索
ハイブリッド検索 では、キーワード クエリとベクター クエリが 1 つの要求に結合されます。 この例では、次のフルテキスト クエリ文字列とベクター クエリ文字列を同時に実行します。
- 検索文字列:
historic hotel walk to restaurants and shopping - ベクター クエリ文字列:
quintessential lodging near running trails, eateries, retail(数学的表現にベクター化)
ハイブリッド検索を作成するには:
Program.csファイルで、SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);メソッド呼び出しのコメントを解除します。このメソッドは、
SearchExamples.csクラスで次の検索機能を実行します。public static async Task<SearchResults<Hotel>> SearchHybridVectorAndText(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector) { SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>( "historic hotel walk to restaurants and shopping", new SearchOptions { VectorSearch = new() { Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } } }, Select = { "HotelId", "HotelName", "Description", "Category", "Tags" }, Size = 5, }); Console.WriteLine($"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; }dotnet run実行してクエリを実行すると、次の結果が返されます。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 クエリからのみ取得されます。 上位 2 つの結果は、サブライム パレス ホテルとラグジュアリー 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。
{ "@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" },HNSW を使用して一致を検索するベクターのみのクエリでは、Sublime Palace Hotel は 4 番目の位置にドロップします。 フルテキスト検索で 2 位、ベクター検索で 3 番目だった Luxury Lion では、同じ変動範囲が発生しないため、均質化された結果セットでは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 ランク付けを追加して、言語の理解に基づいて結果を再ランク付けします。
フィルターを使用してセマンティック ハイブリッド検索を作成するには:
Program.csファイルで、SearchExamples.SearchHybridVectorAndSemantic(searchClient, vectorizedResult);メソッド呼び出しのコメントを解除してください。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(); }dotnet run実行してクエリを実行すると、次のホテルが返されます。 ホテルは場所によってフィルター処理され、StateProvinceでファセットされ、意味的に再ランク付けされ、検索文字列クエリ (historic hotel walk to restaurants and shopping) に最も近い結果が昇格されます。現在、ザ・スワール・カレントズ・ホテルはトップに位置しています。 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 ランク付けでは、機械理解モデルは、
historicが "飲食店やショッピングに徒歩圏内のホテル" に適用されることを認識します。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。
リソースをクリーンアップする
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
- Azure AI Search for .NET のベクター検索機能について、コード サンプルのリポジトリを確認します。
- azure-search-dotnet-samples リポジトリの他の .NET と Azure AI Search のコード サンプルを確認します。
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 インデックスの作成、読み込み、クエリを実行します。
Azure AI Search では、ベクター インデックスには、ベクター フィールドと非ベクトル フィールドを定義するインデックス スキーマ、埋め込み空間を作成するアルゴリズムのベクター検索構成、およびクエリ時に評価されるベクター フィールド定義の設定があります。 インデックス - 作成または更新 (REST API) によってベクター インデックスが作成されます。
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、エンド ツー エンドのチュートリアルで データのインポート (新しい) ウィザード を試してください。
前提条件
アクティブなサブスクリプションを持つ Azure アカウント。 無料でアカウントを作成できます。
-
このクイック スタートのほとんどは Free レベルを使用できますが、より大きなデータ ファイルには Basic 以上をお勧めします。
セマンティック ハイブリッド クエリを実行するには、 セマンティック ランカーを有効にする必要があります。
サービス情報を取得する
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。
Azure portal にサインインし、ご利用の検索サービスを探します。
[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。](media/search-get-started-rest/get-endpoint.png)
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
![[概要] ページ上の URL プロパティのスクリーンショット。](media/search-get-started-rest/get-endpoint.png#lightbox)
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。
コード ファイルを作成するには:
Visual Studio Code で、
.restまたは.httpファイル拡張子 (az-search-vector-quickstart.restなど) を持つファイルを作成します。az-search-vector-quickstart.rest ファイルの生の内容をコピーして新しいファイルに貼り付けます。
@baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HERE@baseUrlを、「サービス情報の取得」で取得したエンドポイントに設定します。認証のキーまたはトークンを設定します。 この値は、[ サービス情報の取得] で取得しました。
Microsoft Entra ID で 推奨される キーレス認証の場合は、
@apiKeyを@token変数に置き換えます。@token = PUT-YOUR-MICROSOFT-ENTRA-TOKEN-HEREAPI 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
Microsoft Entra ID で 推奨される キーレス認証の場合は、
api-key: {{apiKey}}のすべてのインスタンスを要求ヘッダーのAuthorization: Bearer {{token}}に置き換えます。
ベクトル インデックスを作成する
インデックス - 作成 (REST API) を使用してベクター インデックスを作成し、検索サービスで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。
ベクター インデックスを作成するには:
Visual Studio Code で、
az-search-vector-quickstart.restファイルを開きます。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=2025-09-01 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" } ] } } ] } }[要求の送信] を選択します。
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" } ] } } ] } }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。
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、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。 このタスクに ドキュメント - インデックス (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。
ドキュメントをインデックスにアップロードするには:
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=2025-09-01 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.", "DescriptionVector": 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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": [ -82.452843, 27.384417 ] } }, { "@search.action": "mergeOrUpload", "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.", "DescriptionVector": 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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.", "DescriptionVector": 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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.", "DescriptionVector": 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"Category": "Boutique", "Tags": [ "continental breakfast", "air conditioning", "free wifi" ], "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" }, "Location": { "type": "Point", "coordinates": [ -77.03241, 38.90166 ], "crs": { "type": "name", "properties": { "name": "EPSG:4326" } } } }, { "@search.action": "mergeOrUpload", "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.", "DescriptionVector": 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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.", "DescriptionVector": 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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" } } } } ] }[要求の送信] を選択します。
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。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。 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、
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、 Documents - Search Post (REST API) 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。
このセクションのクエリ:
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 - ベクター クエリ文字列:
quintessential lodging near running trails, eateries, retail(数学的表現にベクター化)
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。
単一ベクトル検索
1 つのベクター検索を作成するには:
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=2025-09-01 HTTP/1.1 Content-Type: application/json Authorization: Bearer {{token}} { "count": true, "select": "HotelId, HotelName, Description, Category, Tags", "vectorQueries": [ { "vector"": 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"k": 5, "fields": "DescriptionVector", "kind": "vector", "exhaustive": true } ] }ドキュメントに関する重要なポイント - 検索投稿 (REST API):
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。
[要求の送信] を選択します。
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。
フィルターを使用して 1 つのベクター検索を作成するには:
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=2025-09-01 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 }, ] }[要求の送信] を選択します。
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" ] } ] }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=2025-09-01 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 }, ] }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
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 - ベクター クエリ文字列:
quintessential lodging near running trails, eateries, retail(数学的表現にベクター化)
ハイブリッド検索を作成するには:
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=2025-09-01 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 } ] }[要求の送信] を選択します。
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." }, ... ]
セマンティック ハイブリッド検索
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。
セマンティック ハイブリッド検索を作成するには:
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=2025-09-01 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 } ] }[要求の送信] を選択します。
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。
リソースをクリーンアップする
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。
または、次の要求を送信して、このクイックスタートで作成したベクター インデックスを削除することもできます。
### Delete an index
DELETE {{baseUrl}}/indexes/hotels-vector-quickstart?api-version=2025-09-01 HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}
次のステップ
- API キーを使用せずに REST API 呼び出しを呼び出す方法について説明します。