이 빠른 시작에서는 Jupyter Notebook을 사용하여 벡터를 만들고, 로드하고, 쿼리합니다. 코드 예제는 Azure AI Search 클라이언트 라이브러리를 사용하여 이러한 작업을 수행합니다. 라이브러리는 데이터 수집, 검색 작업 및 인덱스 관리 작업과 같은 인덱스 작업에 액세스하기 위해 REST API에 대한 추상화 기능을 제공합니다.
Azure AI 검색에서 벡터 저장소에는 벡터 및 벡터가 아닌 필드를 정의하는 인덱스 스키마, 포함 공간을 만드는 알고리즘에 대한 벡터 검색 구성, 쿼리 시간에 평가되는 벡터 필드 정의에 대한 설정이 있습니다. 인덱스 만들기 REST API는 벡터 저장소를 만듭니다.
참고
이 빠른 시작에서는 벡터화 단계를 생략하고 인라인 포함을 제공합니다. 사용자 고유의 콘텐츠에 기본 제공 데이터 청크 처리 및 벡터화를 추가하려면 종합적인 단계별 안내를 위해 데이터 가져오기(새) 마법사를 사용해 보세요.
필수 구성 요소
활성 구독이 있는 Azure 계정. 무료로 계정을 만듭니다.
Azure AI 검색 서비스입니다. 현재 구독에서 서비스를 만들거나기존 서비스를 찾습니다.
- 이 빠른 시작의 대부분에 대해 무료 검색 서비스를 사용할 수 있지만 더 큰 데이터 파일의 경우 기본 계층 이상을 사용하는 것이 좋습니다.
- 의미 재순위를 호출하는 쿼리 예제를 실행하려면 검색 서비스가 의미 순위 설정이 활성화된 기본 계층 이상에 있어야 합니다.
Git 을 사용하여 Jupyter Notebook 및 기타 관련 파일이 포함된 리포지토리를 복제합니다.
리소스 정보 검색
검색 엔드포인트에 대한 요청은 인증 및 권한 부여를 받아야 합니다. 이 작업에 API 키 또는 역할을 사용할 수 있지만 Microsoft Entra ID를 통해 키 없는 연결을 사용하는 것이 좋습니다.
이 빠른 시작 가이드에서는 개발 및 운영 환경 모두에서 인증을 간소화하는 DefaultAzureCredential를 사용합니다. 그러나 프로덕션 시나리오의 경우 다른 접근 방식이 필요한 고급 요구 사항이 있을 수 있습니다. 모든 옵션을 이해 하려면 Python용 Azure SDK를 사용하여 Azure 서비스에 Python 앱 인증 을 참조하세요.
코드 복제 및 설정 환경
이 빠른 시작의 코드가 포함된 리포지토리를 복제합니다.
git clone https://github.com/Azure-Samples/azure-search-python-samples이 리포지토리에는 각각 별도의 하위 폴더에 있는 여러 아티클에 대한 Python 코드 예제가 있습니다.
Visual Studio Code에서 하위 폴더
Quickstart-Vector-Search를 엽니다.이 폴더에는 다음 세 개의 파일이 있습니다.
vector-search-quickstart.ipynbrequirements.txtsample.env
sample.env파일의 이름을.env로 바꾸고,.env파일의 값을 수정하십시오.검색 서비스 URL을
AZURE_SEARCH_ENDPOINT로 사용합니다. Azure Portal에서 URL을 찾을 수 있습니다. Azure AI Search 서비스로 이동하여 개요 페이지에서 URL 필드를 찾습니다. 엔드포인트의 예는 다음과 같습니다.https://mydemo.search.windows.net마지막으로 새
AZURE_SEARCH_INDEX_NAME이름을 선택하거나 파일에 제공된 이름을 사용합니다.Visual Studio Code의 환경에서 작업합니다. 보기 메뉴에서 터미널을 선택하거나 Ctrl 키를+` 선택합니다.
터미널에서 다음 명령을 실행합니다.
python -m venv .venv source .venv/scripts/activate where python참고
터미널에서 Git Bash를 사용하고 있으며 Windows에서 실행 중이라고 가정합니다. 다른 셸 및/또는 다른 운영 체제를 사용하는 경우 특정 환경에 대해 이러한 지침을 조정합니다.
메시지가 표시되면 Visual Studio Code에서 새 환경을 사용하도록 허용합니다.
가상 환경에서 작업 중임을 확인하기 위해
where python명령은python.exe폴더에 있는Quickstart-Vector-Search\.venv\및 컴퓨터 디렉터리의 다른 위치를 나열합니다.다음 명령을 실행하여 필요한 라이브러리를 설치합니다.
pip install requirements.txtVisual Studio Code에서
vector-search-quickstart.ipynb을 엽니다.참고
Visual Studio Code에서 Jupyter Notebook(.ipynb)을 처음 사용한 경우 Jupyter Notebook 커널 및 기타 도구를 설치하라는 메시지가 표시됩니다. 이 빠른 시작을 계속하려면 제안된 도구를 설치하도록 선택합니다.
아래 섹션의 "패키지 설치 및 변수 설정"이라는 셀을 찾아 셀 왼쪽의 셀 실행(Ctrl + Alt + Enter) 단추(일반적인 실행 단추처럼 표시됨)를 선택합니다. 셀을 실행하면 환경 변수가 로드되고 DefaultAzureCredential이 생성되며, 출력된 값으로 Notebook의 종속성과
.env가 올바르게 설정되었는지 확인할 수 있습니다.# 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 list이 셀을 실행하면 다음 출력이 생성됩니다.
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 ...스크롤 가능한 요소에서 볼 수 있는 패키지가 더 많이 있습니다(셀 결과 아래 메시지 참조).
벡터 인덱스 만들기
vector-search-quickstart.ipynb의 코드는 azure.search.documents 라이브러리의 여러 메서드를 사용하여 벡터 인덱스와 검색 가능한 필드를 만듭니다.
아래 섹션의 "인덱스 만들기"를 찾아 셀을 실행하여 인덱스 만들기
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')인덱스가 성공적으로 만들어지면 셀 아래에 다음 결과가 표시됩니다.
vector-search-quickstart created벡터 인덱스를
azure.search.documents를 사용하여 만들 때의 주요 요점필드 목록을 만들어 인덱스 정의 각 필드는 필드 형식 및 해당 설정을 정의하는 도우미 메서드를 사용하여 만들어집니다.
이 특정 인덱스는 다음과 같은 여러 검색 기능을 지원합니다.
- 전체 텍스트 키워드 검색(
SearchableField(name="HotelName", ...),SearchableField(name="Description", ...)) - 벡터 검색(벡터 및 비벡터 필드를 배치하여 하이브리드 검색 사용) 필드(
DescriptionVector) - 의미 체계 검색(
semantic_search=SemanticSearch(configurations=[semantic_config])) - 패싯 검색(
facetable=True) - 의미 체계 검색(
semantic_search=SemanticSearch(configurations=[semantic_config])) - 지역 공간 검색(
Location필드는GeographyPoint입니다). - 필터링, 정렬(필터링 가능하고 정렬 가능한 것으로 표시된 많은 필드)
- 전체 텍스트 키워드 검색(
문서 업로드
인덱스 만들기 및 로드는 별도의 단계입니다. 이전 단계 에서 인덱스 스키마를 만들었습니다. 이제 인덱스에 문서를 로드해야 합니다.
Azure AI Search에서 인덱스는 검색 가능한 모든 콘텐츠를 저장하고 검색 엔진은 해당 인덱스에 대해 쿼리를 실행합니다.
아래 섹션의 "문서 페이로드 만들기"를 찾아 셀을 실행합니다. 이 셀에는 다음 코드가 포함되어 있습니다(간결성을 위해 잘림).
# 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> ]이 셀은 문서 설명의 벡터화된 버전과 함께 각 문서를 설명하는 JSON 개체로 명명
documents된 변수를 로드합니다. 이 벡터는 유사성 검색을 사용하도록 설정합니다. 여기서 일치는 정확한 키워드가 아닌 의미를 기반으로 합니다.중요합니다
이 예제의 코드는 실행할 수 없습니다. 간결성을 위해 여러 문자나 줄이 제거되었습니다. 대신 Jupyter Notebook에서 코드를 실행합니다.
아래 섹션의 "문서 업로드"를 찾아서 셀을 실행합니다. 이 셀에는 다음 코드가 포함되어 있습니다(간결성을 위해 잘림).
# 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)이렇게 하면 생성자를 호출하여 검색 클라이언트의 인스턴스를
SearchClient()만든 다음 개체에서 메서드를upload_documents()호출합니다.셀을 실행하면 각 문서의 상태가 그 아래에 인쇄됩니다.
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: Noneupload_documents()메서드와 이 예제에 대한 주요 내용:Azure AI Search 서비스에서 호스팅된
SearchClient를 통해 코드는 패키지에서 제공하는 주요 객체인azure-search-documents와 특정 검색 인덱스를 상호작용합니다. 다음과SearchClient같은 인덱스 작업에 대한 액세스를 제공합니다.-
데이터 수집 -
upload_documents(),merge_documents()delete_documents()등 -
검색 작업 -
search(),autocomplete()suggest() - 인덱스 관리 작업
-
데이터 수집 -
벡터 필드에는 부동 소수점 값이 포함되어 있습니다. 차원 특성에는 각각 최소 2개 및 최대 4096개의 부동 소수점 값이 있습니다. 이 빠른 시작에서는 차원 특성을 1,536으로 설정합니다. 이 값이 Azure OpenAI의 text-embedding-ada-002 모델에서 생성된 포함 크기이기 때문입니다.
쿼리 실행
이제 문서가 로드되었으므로 VectorizedQuery 개체, 반환하려는 필드, 결과 수 등을 호출 search_client.search() 하고 전달하여 벡터 쿼리를 실행할 수 있습니다.
다음 섹션에서는 hotels-vector-quickstart 인덱스에 대해 쿼리를 실행합니다. 쿼리에는 다음이 포함됩니다.
벡터 쿼리 문자열 만들기
벡터 쿼리 예는 두 개의 문자열을 기반으로 합니다.
-
검색 문자열:
historic hotel walk to restaurants and shopping -
벡터 쿼리 문자열(수학적 표현으로 벡터화):
quintessential lodging near running trails, eateries, retail
벡터 쿼리 문자열은 검색 문자열과 의미 체계상 유사하지만 검색 인덱스에 존재하지 않는 용어를 포함합니다.
quintessential lodging near running trails, eateries, retail에 대한 키워드 검색을 수행하면 결과는 0입니다. 이 예를 사용하여 일치하는 용어가 없더라도 관련 결과를 가져올 수 있는 방법을 보여 줍니다.
- 아래 섹션의 "벡터 쿼리 문자열 만들기"를 찾아서 셀을 실행합니다. 그러면 다음 섹션에서 모든 검색을 실행하는 데 필요한 벡터화된 쿼리 데이터를 사용하여 변수를 로드
vector합니다.
단일 벡터 검색
첫 번째 예제에서는 검색 문자열과 밀접하게 일치하는 문서 설명을 찾으려는 기본 시나리오를 보여 줍니다.
아래 섹션의 "단일 벡터 검색"을 찾아서 셀을 실행합니다. 이 블록에는 검색 인덱스를 쿼리하는 요청이 포함되어 있습니다.
# 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.")이 벡터 쿼리는 간결성을 위해 단축되었습니다.
vectorQueries.vector는 쿼리 입력의 벡터화된 텍스트를 포함하고,fields는 검색할 벡터 필드를 결정하고,k는 반환할 가장 인접한 항목의 수를 지정합니다.벡터 쿼리 문자열은
quintessential lodging near running trails, eateries, retail이며, 이 쿼리에서 1,536개의 포함으로 벡터화됩니다.해당하는 벡터에
quintessential lodging near running trails, eateries, retail대한 응답은 7개의 결과로 구성되지만 코드는 처음 5개 결과만 반환되도록 지정합니다top=5. 또한 지정된 필드select만 반환됩니다.search_client.search()는 dict와 유사한 개체를 반환합니다. 각 결과는 검색 점수를 제공하며 , 이 점수는 .를 사용하여score = result.get("@search.score", "N/A")액세스할 수 있습니다. 이 예제에는 표시되지 않지만 유사성 검색에서 응답에는 항상 값 유사성 점수에 따라 정렬된 결과가 포함됩니다k.셀을 실행하면 각 문서의 상태가 그 아래에 인쇄됩니다.
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
필터를 사용한 단일 벡터 검색
필터를 추가할 수 있지만 필터는 인덱스의 벡터가 아닌 콘텐츠에 적용됩니다. 이 예에서는 필터가 Tags 필드에 적용되어 무료 Wi-Fi를 제공하지 않는 호텔을 필터링합니다.
아래 섹션의 "필터를 사용한 단일 벡터 검색"이라는 셀을 찾아서 셀을 실행합니다. 이 셀에는 검색 인덱스를 쿼리하는 요청이 포함되어 있습니다.
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.")셀을 실행하면 각 문서의 상태가 그 아래에 인쇄됩니다.
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']쿼리는 이전 의 단일 벡터 검색 예제와 동일하지만 사후 처리 제외 필터를 포함하고 무료 Wi-Fi가 있는 두 개의 호텔만 반환합니다.
다음 필터 예제에서는 지역 필터를 사용합니다. "지역 필터를 사용하여 벡터 쿼리"라는 섹션의 셀을 실행합니다. 이 블록에는 검색 인덱스를 쿼리하는 요청이 포함되어 있습니다.
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.")이 쿼리는 이전 의 단일 벡터 검색 예제와 동일하지만 후처리 제외 필터를 포함하고 300km 이내의 두 호텔만 반환합니다.
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.
하이브리드 검색
하이브리드 검색은 단일 검색 요청의 키워드 쿼리와 벡터 쿼리로 구성됩니다. 이 예제에서는 벡터 쿼리와 전체 텍스트 검색을 동시에 실행합니다.
검색 문자열:
historic hotel walk to restaurants and shopping벡터 쿼리 문자열(수학적 표현으로 벡터화):
quintessential lodging near running trails, eateries, retail아래 섹션의 "하이브리드 검색"이라는 셀을 찾아서 실행합니다. 이 블록에는 검색 인덱스를 쿼리하는 요청이 포함되어 있습니다.
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.")응답 검토:
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']상호 순위 Fusion(RRF)은 결과를 병합하므로 입력을 검토하는 데 도움이 됩니다. 다음은 전체 텍스트 쿼리에서만 나온 결과입니다. 상위 2개 검색 결과는 Sublime Palace Hotel과 History Lion Resort입니다. Sublime Palace Hotel의 BM25 관련성 점수가 더 높습니다.
{ "@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" },HNSW를 사용하여 일치 항목을 찾는 벡터 전용 쿼리에서 Sublime Palace Hotel은 4위로 떨어집니다. 전체 텍스트 검색에서 2위, 벡터 검색에서 3위를 차지했던 Historic Lion은 동일한 변동 범위를 가지지 않으므로 균질화된 결과 집합에서 상위 일치 항목으로 표시됩니다.
"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" } ]
의미 체계 하이브리드 검색
컬렉션의 마지막 쿼리는 다음과 같습니다. 이 하이브리드 쿼리는 시맨틱 쿼리 유형 및 시맨틱 구성을 지정하여 시맨틱 재정렬을 사용하는 하이브리드 쿼리를 빌드할 수 있음을 보여줍니다.
아래 섹션의 "의미 체계 하이브리드 검색"을 찾아서 셀을 실행합니다. 이 코드 블록에는 검색 인덱스를 쿼리하는 요청이 포함되어 있습니다.
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.")셀 아래의 출력을 검토합니다.
의미 체계 순위에 따르면 Swirling Currents Hotel이 이제 1위를 차지했습니다. W
Total semantic hybrid results: 7 - Score: 0.0317460335791111 Re-ranker Score: 2.6550590991973877 HotelId: 49 HotelName: Swirling Currents Hotel Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs. Category: Suite - Score: 0.03279569745063782 Re-ranker Score: 2.599761724472046 HotelId: 4 HotelName: Sublime Palace Hotel Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience. Category: Boutique - Score: 0.03125 Re-ranker Score: 2.3480887413024902 HotelId: 2 HotelName: Old Century Hotel Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music. Category: Boutique - Score: 0.016393441706895828 Re-ranker Score: 2.2718777656555176 HotelId: 1 HotelName: Stay-Kay City Hotel Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities. Category: Boutique - Score: 0.01515151560306549 Re-ranker Score: 2.0582215785980225 HotelId: 3 HotelName: Gastronomic Landscape Hotel Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services. Category: Suite
의미 체계 순위를 쿼리의 단어 뒤에 있는 의미와 문서의 내용을 이해하여 검색 결과의 관련성을 개선하는 방법으로 생각할 수 있습니다. 이 경우 의미 체계 순위는 키워드와 관련이 있는 호텔뿐만 아니라 쿼리의 의도와도 일치하는 호텔을 식별하는 데 도움이 됩니다.
핵심 내용:
벡터 검색은
vectors.value속성을 통해 지정됩니다. 키워드 검색은search속성을 통해 지정됩니다.하이브리드 검색에서는 키워드에 대한 전체 텍스트 검색과 벡터 검색을 통합할 수 있습니다. 필터, 맞춤법 검사 및 의미 체계 순위 지정은 텍스트 콘텐츠에만 적용되며 벡터에는 적용되지 않습니다. 이 마지막 쿼리에는 시스템이 충분히 강력한 쿼리를 생성하지 않았기 때문에 의미 체계
answer가 없습니다.실제 결과에는 의미 체계 캡션 및 강조 표시를 포함한 더 자세한 내용이 포함됩니다. 가독성을 위해 결과가 수정되었습니다. 응답의 전체 구조를 확인하려면 REST 클라이언트에서 요청을 실행하세요.
정리
본인 소유의 구독으로 이 모듈을 진행하고 있는 경우에는 프로젝트가 끝날 때 여기서 만든 리소스가 계속 필요한지 확인하는 것이 좋습니다. 계속 실행되는 리소스에는 요금이 부과될 수 있습니다. 리소스를 개별적으로 삭제하거나 리소스 그룹을 삭제하여 전체 리소스 세트를 삭제할 수 있습니다.
가장 왼쪽 창에 있는 모든 리소스 또는 리소스 그룹 링크를 사용하여 Azure Portal에서 리소스를 찾고 관리할 수 있습니다.
검색 서비스를 유지하지만 인덱스와 문서를 삭제하려면 개체의 SearchIndexClient 메서드를 delete_index() 사용할 수 있습니다. 아래 "정리" 섹션 아래의 셀을 찾아서, hotels-vector-quickstart 인덱스를 삭제하려면 해당 셀을 실행하세요.
index_client.delete_index(index_name)
print(f"Index '{index_name}' deleted successfully.")
다음 단계
- Python용 Azure AI Search에서 벡터 검색 기능에 대한 코드 샘플 리포지토리 검토
- azure-search-python-samples 리포지토리에서 다른 Python 및 Azure AI Search 코드 샘플을 검토합니다.
이 빠른 시작에서는 Java를 사용하여 벡터를 만들고, 로드하고, 쿼리합니다. 코드 예제는 Azure AI Search 클라이언트 라이브러리를 사용하여 이러한 작업을 수행합니다. 라이브러리는 데이터 수집, 검색 작업 및 인덱스 관리 작업과 같은 인덱스 작업에 액세스하기 위해 REST API에 대한 추상화 기능을 제공합니다.
Azure AI 검색에서 벡터 저장소에는 벡터 및 벡터가 아닌 필드를 정의하는 인덱스 스키마, 포함 공간을 만드는 알고리즘에 대한 벡터 검색 구성, 쿼리 시간에 평가되는 벡터 필드 정의에 대한 설정이 있습니다. 인덱스 만들기 REST API는 벡터 저장소를 만듭니다.
참고
이 빠른 시작에서는 벡터화 단계를 생략하고 인라인 포함을 제공합니다. 사용자 고유의 콘텐츠에 기본 제공 데이터 청크 처리 및 벡터화를 추가하려면 종합적인 단계별 안내를 위해 데이터 가져오기(새) 마법사를 사용해 보세요.
필수 구성 요소
활성 구독이 있는 Azure 계정. 무료로 계정을 만듭니다.
Azure AI 검색 서비스입니다. 현재 구독에서 서비스를 만들거나기존 서비스를 찾습니다.
- 서비스 범위에서 할당된
Search Index Data Contributor역할입니다. - 이 빠른 시작의 대부분에 대해 무료 검색 서비스를 사용할 수 있지만 더 큰 데이터 파일의 경우 기본 계층 이상을 사용하는 것이 좋습니다.
- 의미 재순위를 호출하는 쿼리 예제를 실행하려면 검색 서비스가 의미 순위 설정이 활성화된 기본 계층 이상에 있어야 합니다.
- 서비스 범위에서 할당된
서비스 엔드포인트 가져오기
나머지 섹션에서는 Azure OpenAI 및 Azure AI 검색에 대한 API 호출을 설정합니다. 서비스 엔드포인트를 가져와서 코드에서 변수로 제공할 수 있습니다.
Azure Portal에 로그인합니다.
개요 홈페이지에서 URL을 복사합니다. 엔드포인트의 예는 다음과 같습니다.
https://example.search.windows.net
Java 프로젝트 설정
새 Maven 프로젝트 디렉터리를 만듭니다.
mkdir vector-quickstart && cd vector-quickstart필수 종속성이 있는
pom.xml파일을 만듭니다.<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>종속성을 해결하기 위해 프로젝트를 컴파일합니다.
mvn compile원본 디렉터리 구조를 만듭니다.
mkdir -p src/main/java/com/example/search mkdir -p src/main/resourcessrc/main/resources/application.properties을 만들고 검색 서비스 엔드포인트를 제공합니다.azure.search.endpoint=<YOUR AZURE AI SEARCH ENDPOINT> azure.search.index-name=hotels-vector-quickstart
Azure에 로그인
연결에 Microsoft Entra ID와 역할 할당을 사용하고 있습니다. Azure AI 검색 및 Azure OpenAI와 동일한 테넌트 및 구독에 로그인했는지 확인합니다. 명령줄에서 Azure CLI를 사용하여 현재 속성을 표시하고, 속성을 변경하고, 로그인할 수 있습니다. 자세한 내용은 키 없이 연결을 참조하세요.
다음 명령을 순서대로 실행합니다.
az account show
az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>
az login --tenant <PUT YOUR TENANT ID HERE>
이제 로컬 디바이스에서 Azure에 로그인되었을 것입니다.
벡터 인덱스 만들기
이 섹션에서는 SearchIndexClient를 사용하여 Azure AI Search에서 벡터 인덱스를 만듭니다. createOrUpdateIndex. 인덱스 스키마는 벡터 필드를 포함하여 필드를 DescriptionVector정의합니다.
CreateIndex.java디렉터리에src/main/java/com/example/search파일을 만듭니다.다음 코드를 파일에 복사합니다.
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); } }코드 파일은 인덱스를 만들고 벡터 필드
DescriptionVector를 포함하여 인덱스 스키마를 정의합니다.파일을 실행합니다.
mvn compile exec:java -Dexec.mainClass="com.example.search.CreateIndex"이 코드의 출력은 인덱스가 성공적으로 생성되었음을 보여줍니다.
Using Azure Search endpoint: https://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> Creating index... hotels-vector-quickstart created라이브러리를 사용하여 벡터 인덱스를 만들 때의 주요 사항:
azure-search-documents필드 목록을 만들어 인덱스 정의
이 특정 인덱스는 다음과 같은 여러 검색 기능을 지원합니다.
- 전체 텍스트 키워드 검색(
setSearchable) - 벡터 검색(벡터 및 비벡터 필드를 배치하여 하이브리드 검색 사용) 필드(
DescriptionVector포함setVectorSearchProfileName) - 의미 체계 검색
- 패싯 검색(
SearchSuggester) - 지리적 공간 검색(
Location필드 포함SearchFieldDataType.GEOGRAPHY_POINT) - 필터링, 정렬(필터링 가능하고 정렬 가능한 것으로 표시된 많은 필드)
- 전체 텍스트 키워드 검색(
인덱스로 문서 업로드
인덱스 만들기 및 로드는 별도의 단계입니다. 이전 단계 에서 인덱스 스키마를 만들었습니다. 이제 SearchClient를 사용하여 문서를 인덱스로 로드해야 합니다. uploadDocuments. 문서에는 정확한 키워드가 아닌 의미에 따라 유사성 검색을 가능하게 하는 문서 설명의 벡터화된 버전이 포함되어 있습니다.
Azure AI Search에서 인덱스는 검색 가능한 모든 콘텐츠를 저장하고 검색 엔진은 해당 인덱스에 대해 쿼리를 실행합니다.
UploadDocuments.java디렉터리에src/main/java/com/example/search파일을 만듭니다.다음 코드를 파일에 복사합니다.
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); } }이 코드는 문서 컬렉션을 로드합니다. 각 문서에는 문서의 벡터화된 버전이 포함되어 있습니다
Description. 이 벡터는 유사성 검색을 사용하도록 설정합니다. 여기서 일치는 정확한 키워드가 아닌 의미를 기반으로 합니다.이 빠른 시작 문서에서는 인덱스를 즉시 검색하므로
waitUntilIndexed메서드는 인덱스가 검색 작업에 대해 준비될 때까지 기다립니다. 검색을 수행하려면 인덱스를 문서로 완전히 채워야 하기 때문에 중요합니다.파일을 빌드하고 실행합니다.
mvn compile exec:java -Dexec.mainClass="com.example.search.UploadDocuments"이 코드의 출력은 문서가 인덱싱되고 검색할 준비가 되었음을 보여줍니다.
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.uploadDocuments메서드와 이 예제에 대한 주요 내용:- 코드는
SearchClient를 통해 Azure AI 검색 서비스에서 호스트되는 특정 검색 인덱스와 상호 작용합니다.SearchClient는 다음과 같은 작업에 대한 액세스를 제공합니다.- 데이터 수집 -
uploadDocuments,mergeDocuments,deleteDocuments등. - 검색 작업 -
search,autocomplete,suggest
- 데이터 수집 -
- 벡터 필드에는 부동 소수점 값이 포함되어 있습니다. 차원 특성에는 각각 최소 2개 및 최대 4096개의 부동 소수점 값이 있습니다. 이 빠른 시작에서는 차원 특성을 1,536으로 설정합니다. 이 값이 Azure OpenAI
text-embedding-ada-002모델에서 생성된 포함 크기이기 때문입니다.
- 코드는
쿼리를 벡터로 만들기
벡터 쿼리 예는 두 개의 문자열을 기반으로 합니다.
-
검색 문자열:
historic hotel walk to restaurants and shopping -
벡터 쿼리 문자열(수학적 표현으로 벡터화):
quintessential lodging near running trails, eateries, retail
벡터 쿼리 문자열은 검색 문자열과 의미 체계상 유사하지만 검색 인덱스에 존재하지 않는 용어를 포함합니다.
quintessential lodging near running trails, eateries, retail에 대한 키워드 검색을 수행하면 결과는 0입니다. 이 예를 사용하여 일치하는 용어가 없더라도 관련 결과를 가져올 수 있는 방법을 보여 줍니다.
QueryVector.java 디렉터리에 파일을 src/main/java/com/example/search 만들고 코드를 추가하여 쿼리 벡터를 만듭니다.
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;
}
}
이 코드는 다음 섹션에서 벡터 검색을 수행하는 데 사용됩니다. 쿼리 벡터는 Azure OpenAI의 포함 모델을 사용하여 만들어집니다.
단일 벡터 검색 만들기
첫 번째 예제에서는 SearchClient를 사용하여 검색 문자열과 밀접하게 일치하는 문서 설명을 찾으려는 기본 시나리오를 보여 줍니다. 검색 및 VectorQuery 및 SearchOptions
SearchSingle.java디렉터리에src/main/java/com/example/search파일을 만듭니다.다음 코드를 파일에 복사합니다.
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); } }vectorQuery는QueryVector.getVectorList를 통해 검색된 쿼리 입력의 벡터화된 텍스트를 포함하여 벡터화된 쿼리의 구성을 포함합니다.setFields는 검색할 벡터 필드를 결정하고,setKNearestNeighborsCount는 반환할 가장 인접한 항목의 수를 지정합니다.벡터 쿼리 문자열은
quintessential lodging near running trails, eateries, retail이며, 이 쿼리에서 1,536개의 포함으로 벡터화됩니다.파일을 빌드하고 실행합니다.
mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingle"이 코드의 출력은 쿼리
quintessential lodging near running trails, eateries, retail에 대한 관련 문서를 보여 줍니다.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 0.57902366해당하는 벡터에
quintessential lodging near running trails, eateries, retail대한 응답은 반환된 선택에서 지정한 필드만 있는 5개의 결과로 구성됩니다.
필터를 사용하여 단일 벡터 검색 만들기
필터를 추가할 수 있지만 필터는 인덱스의 벡터가 아닌 콘텐츠에 적용됩니다. 이 예에서는 필터가 Tags 필드에 적용되어 무료 Wi-Fi를 제공하지 않는 호텔을 필터링합니다. 이 검색에서는 SearchClient.SearchClient 및 SearchOptions를 사용합니다.
SearchSingleWithFilter.java디렉터리에src/main/java/com/example/search파일을 만듭니다.다음 코드를 파일에 복사합니다.
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); } }이 코드에는
free wifi가 있는 호텔에 후처리 제외 필터가 추가된 이전 검색과 동일한 검색 기능이 있습니다.파일을 빌드하고 실행합니다.
mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilter"이 코드의 출력은 적용된 필터를 사용하여 쿼리에 대한
free wifi관련 문서를 표시합니다.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
지리 공간적 필터를 사용하여 검색 만들기
지리 공간 필터를 지정하여 결과를 특정 지리적 영역으로 제한할 수 있습니다. 이 예제에서 필터는 워싱턴 D.C.에서 300km 이내의 호텔로 결과를 제한합니다(좌표: -77.03241 38.90166). 지리 공간적 거리는 항상 킬로미터입니다. 이 검색에서는 SearchClient.SearchClient 및 SearchOptions를 사용합니다.
SearchSingleWithFilterGeo.java디렉터리에src/main/java/com/example/search파일을 만듭니다.다음 코드를 파일에 복사합니다.
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); } }파일을 빌드하고 실행합니다.
mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilterGeo"이 코드의 출력에는 지리 공간적 후처리 제외 필터가 적용된 쿼리 관련 문서가 표시됩니다.
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
하이브리드 검색 만들기
하이브리드 검색은 단일 검색 요청의 키워드 쿼리와 벡터 쿼리로 구성됩니다. 이 예제에서는 벡터 쿼리와 전체 텍스트 검색을 동시에 실행합니다.
-
검색 문자열:
historic hotel walk to restaurants and shopping -
벡터 쿼리 문자열(수학적 표현으로 벡터화):
quintessential lodging near running trails, eateries, retail
이 검색에서는 SearchClient.SearchClient 및 SearchOptions를 사용합니다.
SearchHybrid.java디렉터리에src/main/java/com/example/search파일을 만듭니다.다음 코드를 파일에 복사합니다.
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); } }파일을 빌드하고 실행합니다.
mvn compile exec:java -Dexec.mainClass="com.example.search.SearchHybrid"이 코드의 출력은 하이브리드 검색에 대한 관련 문서를 보여줍니다.
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"]상호 순위 Fusion(RRF)은 결과를 병합하므로 입력을 검토하는 데 도움이 됩니다. 다음은 전체 텍스트 쿼리에서만 나온 결과입니다. 상위 2개 검색 결과는 Sublime Palace Hotel과 History Lion Resort입니다. Sublime Palace Hotel의 BM25 관련성 점수가 더 높습니다.
{ "@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" },HNSW를 사용하여 일치 항목을 찾는 벡터 전용 쿼리에서 Sublime Palace Hotel은 4위로 떨어집니다. 전체 텍스트 검색에서 2위, 벡터 검색에서 3위를 차지했던 Historic Lion은 동일한 변동 범위를 가지지 않으므로 균질화된 결과 집합에서 상위 일치 항목으로 표시됩니다.
"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" } ]
의미 체계 하이브리드 검색 만들기
추가 검색 텍스트를 historic hotel walk to restaurants and shopping사용하여 의미 체계 하이브리드 검색을 확장하는 컬렉션의 마지막 쿼리는 다음과 같습니다.
이 검색에서는 SearchClient.SearchClient 및 SearchOptions를 사용합니다.
SearchSemanticHybrid.java디렉터리에src/main/java/com/example/search파일을 만듭니다.다음 코드를 파일에 복사합니다.
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); } }파일을 빌드하고 실행합니다.
mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSemanticHybrid"이 코드의 출력은 의미 체계 하이브리드 검색에 대한 관련 문서를 보여줍니다.
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: Suite의미 체계 순위를 쿼리의 단어 뒤에 있는 의미와 문서의 내용을 이해하여 검색 결과의 관련성을 개선하는 방법으로 생각할 수 있습니다. 이 경우 의미 체계 순위는 키워드와 관련이 있는 호텔뿐만 아니라 쿼리의 의도와도 일치하는 호텔을 식별하는 데 도움이 됩니다.
핵심 내용:
벡터 검색은
VectorSearchOptions클래스를 통해 지정됩니다. 키워드 검색은SemanticSearchOptions클래스를 통해 지정됩니다.하이브리드 검색에서는 키워드에 대한 전체 텍스트 검색과 벡터 검색을 통합할 수 있습니다. 필터, 맞춤법 검사 및 의미 체계 순위 지정은 텍스트 콘텐츠에만 적용되며 벡터에는 적용되지 않습니다. 이 마지막 쿼리에는 시스템이 충분히 강력한 쿼리를 생성하지 않았기 때문에 의미 체계
answer가 없습니다.실제 결과에는 의미 체계 캡션 및 강조 표시를 포함한 더 자세한 내용이 포함됩니다. 가독성을 위해 결과가 수정되었습니다. 응답의 전체 구조를 확인하려면 REST 클라이언트에서 요청을 실행하세요.
정리
본인 소유의 구독으로 이 모듈을 진행하고 있는 경우에는 프로젝트가 끝날 때 여기서 만든 리소스가 계속 필요한지 확인하는 것이 좋습니다. 계속 실행되는 리소스에는 요금이 부과될 수 있습니다. 리소스를 개별적으로 삭제하거나 리소스 그룹을 삭제하여 전체 리소스 세트를 삭제할 수 있습니다.
가장 왼쪽 창에 있는 모든 리소스 또는 리소스 그룹 링크를 사용하여 Azure Portal에서 리소스를 찾고 관리할 수 있습니다.
검색 서비스를 유지하지만 인덱스와 문서를 삭제하려면 인덱스 및 문서를 프로그래밍 방식으로 삭제할 수 있습니다.
DeleteIndex.java디렉터리에src/main/java/com/example/search파일을 만듭니다.인덱스 삭제 코드를 추가합니다.
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); } }파일을 빌드하고 실행합니다.
mvn compile exec:java -Dexec.mainClass="com.example.search.DeleteIndex"
다음 단계
- Java용 Azure AI 검색에서 벡터 검색 기능에 대한 코드 샘플 리포지토리 검토
이 빠른 시작에서는 JavaScript를 사용하여 벡터를 만들고, 로드하고, 쿼리합니다. 코드 예제는 Azure AI Search 클라이언트 라이브러리를 사용하여 이러한 작업을 수행합니다. 라이브러리는 데이터 수집, 검색 작업 및 인덱스 관리 작업과 같은 인덱스 작업에 액세스하기 위해 REST API에 대한 추상화 기능을 제공합니다.
Azure AI 검색에서 벡터 저장소에는 벡터 및 벡터가 아닌 필드를 정의하는 인덱스 스키마, 포함 공간을 만드는 알고리즘에 대한 벡터 검색 구성, 쿼리 시간에 평가되는 벡터 필드 정의에 대한 설정이 있습니다. 인덱스 만들기 REST API는 벡터 저장소를 만듭니다.
참고
이 빠른 시작에서는 벡터화 단계를 생략하고 인라인 포함을 제공합니다. 사용자 고유의 콘텐츠에 기본 제공 데이터 청크 처리 및 벡터화를 추가하려면 종합적인 단계별 안내를 위해 데이터 가져오기(새) 마법사를 사용해 보세요.
필수 구성 요소
활성 구독이 있는 Azure 계정. 무료로 계정을 만듭니다.
Azure AI 검색 서비스입니다. 현재 구독에서 서비스를 만들거나기존 서비스를 찾습니다.
- 서비스 범위에서 할당된
Search Index Data Contributor역할입니다. - 이 빠른 시작의 대부분에 대해 무료 검색 서비스를 사용할 수 있지만 더 큰 데이터 파일의 경우 기본 계층 이상을 사용하는 것이 좋습니다.
- 의미 재순위를 호출하는 쿼리 예제를 실행하려면 검색 서비스가 의미 순위 설정이 활성화된 기본 계층 이상에 있어야 합니다.
- 서비스 범위에서 할당된
서비스 엔드포인트 가져오기
나머지 섹션에서는 Azure OpenAI 및 Azure AI 검색에 대한 API 호출을 설정합니다. 서비스 엔드포인트를 가져와서 코드에서 변수로 제공할 수 있습니다.
Azure Portal에 로그인합니다.
개요 홈페이지에서 URL을 복사합니다. 엔드포인트의 예는 다음과 같습니다.
https://example.search.windows.net
Node.JS 프로젝트 설정
Visual Studio Code 및 JavaScript를 사용하여 프로젝트를 설정합니다.
새 디렉터리에서 Visual Studio Code를 시작합니다.
mkdir vector-quickstart && cd vector-quickstart code .프로젝트 디렉터리에서 ESM 모듈에 대한 새 패키지를 만듭니다.
npm init -y npm pkg set type=module그러면 기본값이 있는
package.json파일이 만들어집니다.다음 npm 패키지를 설치합니다.
npm install @azure/identity @azure/search-documents dotenvsrc프로젝트 디렉터리에 디렉터리를 만듭니다.mkdir src
로컬 개발을 위한 환경 변수 설정
프로젝트 디렉터리
.env에 파일vector-quickstart을 만듭니다.다음 환경 변수를
.env파일에 추가하여 값을 사용자 고유의 서비스 엔드포인트 및 키로 바꿉니다.AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT> AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart
Azure에 로그인
연결에 Microsoft Entra ID와 역할 할당을 사용하고 있습니다. Azure AI 검색 및 Azure OpenAI와 동일한 테넌트 및 구독에 로그인했는지 확인합니다. 명령줄에서 Azure CLI를 사용하여 현재 속성을 표시하고, 속성을 변경하고, 로그인할 수 있습니다. 자세한 내용은 키 없이 연결을 참조하세요.
다음 명령을 순서대로 실행합니다.
az account show
az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>
az login --tenant <PUT YOUR TENANT ID HERE>
이제 로컬 디바이스에서 Azure에 로그인되었을 것입니다.
벡터 인덱스 만들기
이 섹션에서는 SearchIndexClient를 사용하여 Azure AI Search에서 벡터 인덱스를 만듭니다. createOrUpdateIndex. 인덱스 스키마는 벡터 필드를 포함하여 필드를 DescriptionVector정의합니다.
createIndex.js디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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`);코드 파일은 파일 맨 위에 종속성, 환경 변수 및 JavaScript 형식
HotelDocument을 추가합니다. 인덱스createIndex만들기 함수를 추가합니다. 이 함수는 벡터 필드를DescriptionVector포함하여 인덱스 스키마를 정의합니다.파일을 실행합니다.
node -r dotenv/config src/createIndex.js이 코드의 출력은 인덱스가 성공적으로 생성되었음을 보여줍니다.
Using Azure Search endpoint: https://<search-service-name>.search.windows.net Using Azure Search index: <vector-index-name> Creating index... hotels-vector-quickstart created라이브러리를 사용하여 벡터 인덱스를 만들 때의 주요 사항:
@azure/search-documents필드 목록을 만들어 인덱스 정의
이 특정 인덱스는 다음과 같은 여러 검색 기능을 지원합니다.
- 전체 텍스트 키워드 검색(
searchable) - 벡터 검색(벡터 및 비벡터 필드를 배치하여 하이브리드 검색 사용) 필드(
DescriptionVector포함vectorSearchProfileName) - 의미 체계 검색
- 패싯 검색(
searchSuggester) - 지리적 공간 검색(
Location필드 포함geo.distance) - 필터링, 정렬(필터링 가능하고 정렬 가능한 것으로 표시된 많은 필드)
- 전체 텍스트 키워드 검색(
인덱스로 문서 업로드
인덱스 만들기 및 로드는 별도의 단계입니다. 이전 단계 에서 인덱스 스키마를 만들었습니다. 이제 SearchClient를 사용하여 문서를 인덱스로 로드해야 합니다. uploadDocuments. 문서에는 정확한 키워드가 아닌 의미에 따라 유사성 검색을 가능하게 하는 문서 설명의 벡터화된 버전이 포함되어 있습니다.
Azure AI Search에서 인덱스는 검색 가능한 모든 콘텐츠를 저장하고 검색 엔진은 해당 인덱스에 대해 쿼리를 실행합니다.
uploadDocuments.js디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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); }이 코드는 JSON 개체의 배열을 로드합니다. 각 문서에는 문서의 벡터화된 버전이 포함되어 있습니다
description. 이 벡터는 유사성 검색을 사용하도록 설정합니다. 여기서 일치는 정확한 키워드가 아닌 의미를 기반으로 합니다.이 빠른 시작 문서에서는 인덱스를 즉시 검색하므로 함수는
waitUntilIndexed인덱스가 검색 작업에 대해 준비될 때까지 기다립니다. 검색을 수행하려면 인덱스를 문서로 완전히 채워야 하기 때문에 중요합니다.파일을 빌드하고 실행합니다.
node -r dotenv/config src/uploadDocuments.js이 코드의 출력은 문서가 인덱싱되고 검색할 준비가 되었음을 보여줍니다.
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.uploadDocuments() 메서드 및 이 예제에 대한 주요 내용:
- 코드는 패키지에서 제공하는 기본 개체인 SearchClient를 통해 Azure AI Search 서비스에서 호스트되는 @azure/search-documents 특정 검색 인덱스와 상호 작용합니다. SearchClient는 다음과 같은 인덱스 작업에 대한 액세스를 제공합니다.
- 데이터 수집 - uploadDocuments(), mergeDocuments(), deleteDocuments() 등
- 검색 작업 - 검색(), 자동완성(), 제안()
- createIndex(), deleteIndex(), getIndex() 등과 같은 인덱스 관리 작업
- 벡터 필드에는 부동 소수점 값이 포함되어 있습니다. 차원 특성에는 각각 최소 2개 및 최대 4096개의 부동 소수점 값이 있습니다. 퀵스타트에서는 Azure OpenAI text-embedding-ada-002 모델이 생성하는 임베딩의 크기가 1,536이므로 차원 속성을 1,536으로 설정합니다.
- 코드는 패키지에서 제공하는 기본 개체인 SearchClient를 통해 Azure AI Search 서비스에서 호스트되는 @azure/search-documents 특정 검색 인덱스와 상호 작용합니다. SearchClient는 다음과 같은 인덱스 작업에 대한 액세스를 제공합니다.
쿼리를 벡터로 만들기
벡터 쿼리 예는 두 개의 문자열을 기반으로 합니다.
-
검색 문자열:
historic hotel walk to restaurants and shopping -
벡터 쿼리 문자열(수학적 표현으로 벡터화):
quintessential lodging near running trails, eateries, retail
벡터 쿼리 문자열은 검색 문자열과 의미 체계상 유사하지만 검색 인덱스에 존재하지 않는 용어를 포함합니다.
quintessential lodging near running trails, eateries, retail에 대한 키워드 검색을 수행하면 결과는 0입니다. 이 예를 사용하여 일치하는 용어가 없더라도 관련 결과를 가져올 수 있는 방법을 보여 줍니다.
queryVector.js디렉터리에 파일을src만들고 코드를 추가하여 쿼리 벡터를 만듭니다.// Query is 1536-dimensional vector of "quintessential lodging near running trails, eateries, retail" // Note: this same vector can be used for vector search and semantic search // Replace with your own vector export const vector = 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코드는 다음 섹션에서 벡터 검색을 수행하는 데 사용됩니다. 쿼리 벡터는 Azure OpenAI의 포함 모델을 사용하여 만들어집니다.
단일 벡터 검색 만들기
첫 번째 예제에서는 SearchClient를 사용하여 검색 문자열과 밀접하게 일치하는 문서 설명을 찾으려는 기본 시나리오를 보여 줍니다. 검색 및 VectorQuery 및 SearchOptions
searchSingle.js디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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; }vectorQuery는 쿼리 입력
vector의 벡터화된 텍스트를 포함하여 벡터화된 쿼리의 구성을 포함하고 검색fields할kNearestNeighborsCount벡터 필드를 결정하고 반환할 가장 가까운 인접 항목의 수를 지정합니다.벡터 쿼리 문자열은
quintessential lodging near running trails, eateries, retail이며, 이 쿼리에서 1,536개의 포함으로 벡터화됩니다.파일을 빌드하고 실행합니다.
node -r dotenv/config src/searchSingle.js이 코드의 출력은 쿼리
quintessential lodging near running trails, eateries, retail에 대한 관련 문서를 보여 줍니다.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 0.57902366해당하는 벡터에
quintessential lodging near running trails, eateries, retail대한 응답은 반환된 선택에서 지정한 필드만 있는 5개의 결과로 구성됩니다.
필터를 사용하여 단일 벡터 검색 만들기
필터를 추가할 수 있지만 필터는 인덱스의 벡터가 아닌 콘텐츠에 적용됩니다. 이 예에서는 필터가 Tags 필드에 적용되어 무료 Wi-Fi를 제공하지 않는 호텔을 필터링합니다. 이 검색에서는 SearchClient를 사용합니다. 검색 및 VectorQuery 및 SearchOptions
searchSingleWithFilter.js디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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; }종속성, 환경 변수 및 이전 검색과 동일한 검색 기능을 추가합니다. 호텔과 관련된
free wifi을 위한 후처리 제외 필터를 추가하세요.파일을 빌드하고 실행합니다.
node -r dotenv/config src/searchSingleWithFilter.js이 코드의 출력은 적용된 필터를 사용하여 쿼리에 대한
free wifi관련 문서를 표시합니다.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
지리 공간적 필터를 사용하여 검색 만들기
지리 공간 필터를 지정하여 결과를 특정 지리적 영역으로 제한할 수 있습니다. 이 예제에서 필터는 워싱턴 D.C.에서 300km 이내의 호텔로 결과를 제한합니다(좌표: -77.03241 38.90166). 지리 공간적 거리는 항상 킬로미터입니다. 이 검색에서는 SearchClient를 사용합니다. 검색 및 VectorQuery 및 SearchOptions
searchSingleWithFilterGeo.js디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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; }파일을 빌드하고 실행합니다.
node -r dotenv/config src/searchSingleWithFilterGeo.js이 코드의 출력에는 지리 공간적 후처리 제외 필터가 적용된 쿼리 관련 문서가 표시됩니다.
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
하이브리드 검색 만들기
하이브리드 검색은 단일 검색 요청의 키워드 쿼리와 벡터 쿼리로 구성됩니다. 이 예제에서는 벡터 쿼리와 전체 텍스트 검색을 동시에 실행합니다.
-
검색 문자열:
historic hotel walk to restaurants and shopping -
벡터 쿼리 문자열(수학적 표현으로 벡터화):
quintessential lodging near running trails, eateries, retail
이 검색에서는 SearchClient를 사용합니다. 검색 및 VectorQuery 및 SearchOptions
searchHybrid.js디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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; }파일을 빌드하고 실행합니다.
node -r dotenv/config src/searchHybrid.js이 코드의 출력은 하이브리드 검색에 대한 관련 문서를 보여줍니다.
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"]상호 순위 Fusion(RRF)은 결과를 병합하므로 입력을 검토하는 데 도움이 됩니다. 다음은 전체 텍스트 쿼리에서만 나온 결과입니다. 상위 2개 검색 결과는 Sublime Palace Hotel과 History Lion Resort입니다. Sublime Palace Hotel의 BM25 관련성 점수가 더 높습니다.
{ "@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" },HNSW를 사용하여 일치 항목을 찾는 벡터 전용 쿼리에서 Sublime Palace Hotel은 4위로 떨어집니다. 전체 텍스트 검색에서 2위, 벡터 검색에서 3위를 차지했던 Historic Lion은 동일한 변동 범위를 가지지 않으므로 균질화된 결과 집합에서 상위 일치 항목으로 표시됩니다.
"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" } ]
의미 체계 하이브리드 검색 만들기
추가 검색 텍스트를 historic hotel walk to restaurants and shopping사용하여 의미 체계 하이브리드 검색을 확장하는 컬렉션의 마지막 쿼리는 다음과 같습니다.
이 검색에서는 SearchClient를 사용합니다. 검색 및 VectorQuery 및 SearchOptions
searchSemanticHybrid.js디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
import { SearchClient } from "@azure/search-documents"; import { vector } from "./queryVector.js"; import { DefaultAzureCredential } from "@azure/identity"; export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT; export const indexName = process.env.AZURE_SEARCH_INDEX_NAME; console.log(`Using Azure Search endpoint: ${searchEndpoint}`); console.log(`Using index name: ${indexName}`); // const key = process.env.AZURE_SEARCH_KEY || ""; // const searchClient = new SearchClient( // searchEndpoint!, // indexName, // new AzureKeyCredential(key) // ); const searchClient = new SearchClient( searchEndpoint, indexName, new DefaultAzureCredential() ); try { const vectorQuery = { vector: vector, kNearestNeighborsCount: 5, fields: ["DescriptionVector"], kind: "vector", exhaustive: true }; // Create semantic hybrid search options with vector query and semantic configuration const searchOptions = { top: 5, includeTotalCount: true, select: ["HotelId", "HotelName", "Category", "Description"], queryType: "semantic", semanticSearchOptions: { configurationName: "semantic-config" }, vectorSearchOptions: { queries: [vectorQuery], filterMode: "postFilter" // Apply filter after vector similarity is calculated }, }; // Use search_text for semantic search const searchText = "historic hotel walk to restaurants and shopping"; const results = await searchClient.search(searchText, searchOptions); console.log(`\n\nSemantic hybrid search found ${results.count} then limited to top ${searchOptions.top}`); for await (const result of results.results) { // Log each result const doc = result.document; const score = result.score; const rerankerScoreDisplay = result.rerankerScore; console.log(`- Score: ${score}`); console.log(` Re-ranker Score: ${rerankerScoreDisplay}`); console.log(` HotelId: ${doc.HotelId}`); console.log(` HotelName: ${doc.HotelName}`); console.log(` Description: ${doc.Description || 'N/A'}`); console.log(` Category: ${doc.Category || 'N/A'}`); console.log(''); } } catch (ex) { console.error("Semantic hybrid search failed:", ex); throw ex; }파일을 빌드하고 실행합니다.
node -r dotenv/config src/searchSemanticHybrid.js이 코드의 출력은 의미 체계 하이브리드 검색에 대한 관련 문서를 보여줍니다.
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: Suite의미 체계 순위를 쿼리의 단어 뒤에 있는 의미와 문서의 내용을 이해하여 검색 결과의 관련성을 개선하는 방법으로 생각할 수 있습니다. 이 경우 의미 체계 순위는 키워드와 관련이 있는 호텔뿐만 아니라 쿼리의 의도와도 일치하는 호텔을 식별하는 데 도움이 됩니다.
핵심 내용:
벡터 검색은
vectorSearchOptions속성을 통해 지정됩니다. 키워드 검색은semanticSearchOptions속성을 통해 지정됩니다.하이브리드 검색에서는 키워드에 대한 전체 텍스트 검색과 벡터 검색을 통합할 수 있습니다. 필터, 맞춤법 검사 및 의미 체계 순위 지정은 텍스트 콘텐츠에만 적용되며 벡터에는 적용되지 않습니다. 이 마지막 쿼리에는 시스템이 충분히 강력한 쿼리를 생성하지 않았기 때문에 의미 체계
answer가 없습니다.실제 결과에는 의미 체계 캡션 및 강조 표시를 포함한 더 자세한 내용이 포함됩니다. 가독성을 위해 결과가 수정되었습니다. 응답의 전체 구조를 확인하려면 REST 클라이언트에서 요청을 실행하세요.
정리
본인 소유의 구독으로 이 모듈을 진행하고 있는 경우에는 프로젝트가 끝날 때 여기서 만든 리소스가 계속 필요한지 확인하는 것이 좋습니다. 계속 실행되는 리소스에는 요금이 부과될 수 있습니다. 리소스를 개별적으로 삭제하거나 리소스 그룹을 삭제하여 전체 리소스 세트를 삭제할 수 있습니다.
가장 왼쪽 창에 있는 모든 리소스 또는 리소스 그룹 링크를 사용하여 Azure Portal에서 리소스를 찾고 관리할 수 있습니다.
검색 서비스를 유지하지만 인덱스와 문서를 삭제하려면 인덱스 및 문서를 프로그래밍 방식으로 삭제할 수 있습니다.
deleteIndex.js디렉터리에src파일을 만듭니다.종속성, 환경 변수 및 코드를 추가하여 인덱스를 삭제합니다.
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); }파일을 빌드하고 실행합니다.
node -r dotenv/config src/deleteIndex.js
다음 단계
- JavaScript용 Azure AI Search에서 벡터 검색 기능에 대한 코드 샘플 리포지토리 검토
이 빠른 시작에서는 TypeScript를 사용하여 벡터를 만들고, 로드하고, 쿼리합니다. 코드 예제는 Azure AI Search 클라이언트 라이브러리를 사용하여 이러한 작업을 수행합니다. 라이브러리는 데이터 수집, 검색 작업 및 인덱스 관리 작업과 같은 인덱스 작업에 액세스하기 위해 REST API에 대한 추상화 기능을 제공합니다.
Azure AI 검색에서 벡터 저장소에는 벡터 및 벡터가 아닌 필드를 정의하는 인덱스 스키마, 포함 공간을 만드는 알고리즘에 대한 벡터 검색 구성, 쿼리 시간에 평가되는 벡터 필드 정의에 대한 설정이 있습니다. 인덱스 만들기 REST API는 벡터 저장소를 만듭니다.
참고
이 빠른 시작에서는 벡터화 단계를 생략하고 인라인 포함을 제공합니다. 사용자 고유의 콘텐츠에 기본 제공 데이터 청크 처리 및 벡터화를 추가하려면 종합적인 단계별 안내를 위해 데이터 가져오기(새) 마법사를 사용해 보세요.
필수 구성 요소
활성 구독이 있는 Azure 계정. 무료로 계정을 만듭니다.
Azure AI 검색 서비스입니다. 현재 구독에서 서비스를 만들거나기존 서비스를 찾습니다.
- 서비스 범위에서 할당된
Search Index Data Contributor역할입니다. - 이 빠른 시작의 대부분에 대해 무료 검색 서비스를 사용할 수 있지만 더 큰 데이터 파일의 경우 기본 계층 이상을 사용하는 것이 좋습니다.
- 의미 재순위를 호출하는 쿼리 예제를 실행하려면 검색 서비스가 의미 순위 설정이 활성화된 기본 계층 이상에 있어야 합니다.
- 서비스 범위에서 할당된
TypeScript. npm을 사용하여 TypeScript를 전역적으로 설치할 수 있습니다.
npm install -g typescript
서비스 엔드포인트 가져오기
나머지 섹션에서는 Azure OpenAI 및 Azure AI 검색에 대한 API 호출을 설정합니다. 서비스 엔드포인트를 가져와서 코드에서 변수로 제공할 수 있습니다.
Azure Portal에 로그인합니다.
개요 홈페이지에서 URL을 복사합니다. 엔드포인트의 예는 다음과 같습니다.
https://example.search.windows.net
Node.JS 프로젝트 설정
Visual Studio Code 및 TypeScript를 사용하여 프로젝트를 설정합니다.
새 디렉터리에서 Visual Studio Code를 시작합니다.
mkdir vector-quickstart && cd vector-quickstart code .프로젝트 디렉터리에서 ESM 모듈에 대한 새 패키지를 만듭니다.
npm init -y npm pkg set type=module그러면 기본값이 있는
package.json파일이 만들어집니다.package.json을(를) 편집하여 빌드 스크립트를 포함하십시오.scripts섹션에 다음 줄을 추가합니다."build": "tsc"다음 npm 패키지를 설치합니다.
npm install @azure/identity @azure/search-documents dotenv @types/nodesrc프로젝트 디렉터리에 디렉터리를 만듭니다.mkdir src다음 콘텐츠를 사용하여
tsconfig.jsonESM용 프로젝트 디렉터리에 파일을 만듭니다.{ "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" ] }
로컬 개발을 위한 환경 변수 설정
프로젝트 디렉터리
.env에 파일vector-quickstart을 만듭니다.다음 환경 변수를
.env파일에 추가하여 값을 사용자 고유의 서비스 엔드포인트 및 키로 바꿉니다.AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT> AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart
Azure에 로그인
연결에 Microsoft Entra ID와 역할 할당을 사용하고 있습니다. Azure AI 검색 및 Azure OpenAI와 동일한 테넌트 및 구독에 로그인했는지 확인합니다. 명령줄에서 Azure CLI를 사용하여 현재 속성을 표시하고, 속성을 변경하고, 로그인할 수 있습니다. 자세한 내용은 키 없이 연결을 참조하세요.
다음 명령을 순서대로 실행합니다.
az account show
az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>
az login --tenant <PUT YOUR TENANT ID HERE>
이제 로컬 디바이스에서 Azure에 로그인되었을 것입니다.
벡터 인덱스 만들기
이 섹션에서는 SearchIndexClient를 사용하여 Azure AI Search에서 벡터 인덱스를 만듭니다. createOrUpdateIndex. 인덱스 스키마는 벡터 필드를 포함하여 필드를 DescriptionVector정의합니다. 인덱스가 만들어지면 인덱스로 문서를 업로드합니다. 문서에는 정확한 키워드가 아닌 의미에 따라 유사성 검색을 가능하게 하는 문서 설명의 벡터화된 버전이 포함되어 있습니다.
createIndex.ts디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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`);코드 파일은 파일 맨 위에 종속성, 환경 변수 및 JavaScript 형식
HotelDocument을 추가합니다. 인덱스createIndex만들기 함수를 추가합니다. 이 함수는 벡터 필드를DescriptionVector포함하여 인덱스 스키마를 정의합니다.파일을 빌드하고 실행합니다.
npm run build && node -r dotenv/config dist/createIndex.js이 코드의 출력은 인덱스가 성공적으로 생성되었음을 보여줍니다.
Using Azure Search endpoint: https://my-service.search.windows.net Using index name: hotels-vector-quickstart Creating index... hotels-vector-quickstart created라이브러리를 사용하여 벡터 인덱스를 만들 때의 주요 사항:
@azure/search-documents필드 목록을 만들어 인덱스 정의
이 특정 인덱스는 다음과 같은 여러 검색 기능을 지원합니다.
- 전체 텍스트 키워드 검색(
searchable) - 벡터 검색(벡터 및 비벡터 필드를 배치하여 하이브리드 검색 사용) 필드(
DescriptionVector포함vectorSearchProfileName) - 의미 체계 검색
- 패싯 검색(
searchSuggester) - 지리적 공간 검색(
Location필드 포함geo.distance) - 필터링, 정렬(필터링 가능하고 정렬 가능한 것으로 표시된 많은 필드)
- 전체 텍스트 키워드 검색(
인덱스로 문서 업로드
인덱스 만들기 및 로드는 별도의 단계입니다. 이전 단계 에서 인덱스 스키마를 만들었습니다. 이제 SearchClient를 사용하여 문서를 인덱스로 로드해야 합니다. uploadDocuments.
Azure AI Search에서 인덱스는 검색 가능한 모든 콘텐츠를 저장하고 검색 엔진은 해당 인덱스에 대해 쿼리를 실행합니다.
uploadDocuments.ts디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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); }이 코드는 문서 설명의 벡터화된 버전과 함께 각 문서를 설명하는 JSON 개체가 있는 변수 문서를 로드합니다. 이 벡터는 유사성 검색을 사용하도록 설정합니다. 여기서 일치는 정확한 키워드가 아닌 의미를 기반으로 합니다.
이 빠른 시작 문서에서는 인덱스를 즉시 검색하므로 함수는
waitUntilIndexed인덱스가 검색 작업에 대해 준비될 때까지 기다립니다. 검색을 수행하려면 인덱스를 문서로 완전히 채워야 하기 때문에 중요합니다.파일을 빌드하고 실행합니다.
npm run build && node -r dotenv/config dist/uploadDocuments.js이 코드의 출력은 문서가 인덱싱되고 검색할 준비가 되었음을 보여줍니다.
Uploading documents... Key: 1, Succeeded: true, ErrorMessage: none Key: 2, Succeeded: true, ErrorMessage: none Key: 3, Succeeded: true, ErrorMessage: none Key: 4, Succeeded: true, ErrorMessage: none Key: 48, Succeeded: true, ErrorMessage: none Key: 49, Succeeded: true, ErrorMessage: none Key: 13, Succeeded: true, ErrorMessage: none Waiting for indexing... Current count: 0 All documents indexed successfully.upload_documents() 메서드 및 이 예제에 대한 주요 내용:
- 코드는 패키지에서 제공하는 기본 개체인 SearchClient를 통해 Azure AI Search 서비스에서 호스트되는 @azure/search-documents 특정 검색 인덱스와 상호 작용합니다. SearchClient는 다음과 같은 인덱스 작업에 대한 액세스를 제공합니다.
- 데이터 수집 - uploadDocuments(), mergeDocuments(), deleteDocuments() 등
- 검색 작업 - 검색(), 자동완성(), 제안()
- createIndex(), deleteIndex(), getIndex() 등과 같은 인덱스 관리 작업
- 벡터 필드에는 부동 소수점 값이 포함되어 있습니다. 차원 특성에는 각각 최소 2개 및 최대 4096개의 부동 소수점 값이 있습니다. 퀵스타트에서는 Azure OpenAI text-embedding-ada-002 모델이 생성하는 임베딩의 크기가 1,536이므로 차원 속성을 1,536으로 설정합니다.
- 코드는 패키지에서 제공하는 기본 개체인 SearchClient를 통해 Azure AI Search 서비스에서 호스트되는 @azure/search-documents 특정 검색 인덱스와 상호 작용합니다. SearchClient는 다음과 같은 인덱스 작업에 대한 액세스를 제공합니다.
쿼리를 벡터로 만들기
벡터 쿼리 예는 두 개의 문자열을 기반으로 합니다.
-
검색 문자열:
historic hotel walk to restaurants and shopping -
벡터 쿼리 문자열(수학적 표현으로 벡터화):
quintessential lodging near running trails, eateries, retail
벡터 쿼리 문자열은 검색 문자열과 의미 체계상 유사하지만 검색 인덱스에 존재하지 않는 용어를 포함합니다.
quintessential lodging near running trails, eateries, retail에 대한 키워드 검색을 수행하면 결과는 0입니다. 이 예를 사용하여 일치하는 용어가 없더라도 관련 결과를 가져올 수 있는 방법을 보여 줍니다.
queryVector.ts디렉터리에 파일을src만들고 코드를 추가하여 쿼리 벡터를 만듭니다.// Query is 1536-dimensional vector of "quintessential lodging near running trails, eateries, retail" // Note: this same vector can be used for vector search and semantic search // Replace with your own vector export const vector = 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코드는 다음 섹션에서 벡터 검색을 수행하는 데 사용됩니다. 쿼리 벡터는 Azure OpenAI의 포함 모델을 사용하여 만들어집니다.
단일 벡터 검색 만들기
첫 번째 예제에서는 SearchClient를 사용하여 검색 문자열과 밀접하게 일치하는 문서 설명을 찾으려는 기본 시나리오를 보여 줍니다. 검색 및 VectorQuery 및 SearchOptions
searchSingle.ts디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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; }vectorQuery는 쿼리 입력
vector의 벡터화된 텍스트를 포함하여 벡터화된 쿼리의 구성을 포함하고 검색fields할kNearestNeighborsCount벡터 필드를 결정하고 반환할 가장 가까운 인접 항목의 수를 지정합니다.벡터 쿼리 문자열은
quintessential lodging near running trails, eateries, retail이며, 이 쿼리에서 1,536개의 포함으로 벡터화됩니다.파일을 빌드하고 실행합니다.
npm run build && node -r dotenv/config dist/searchSingle.js이 코드의 출력은 쿼리
quintessential lodging near running trails, eateries, retail에 대한 관련 문서를 보여 줍니다.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 0.57902366해당하는 벡터에
quintessential lodging near running trails, eateries, retail대한 응답은 반환된 선택에서 지정한 필드만 있는 5개의 결과로 구성됩니다.
필터를 사용하여 단일 벡터 검색 만들기
필터를 추가할 수 있지만 필터는 인덱스의 벡터가 아닌 콘텐츠에 적용됩니다. 이 예에서는 필터가 Tags 필드에 적용되어 무료 Wi-Fi를 제공하지 않는 호텔을 필터링합니다. 이 검색에서는 SearchClient를 사용합니다. 검색 및 VectorQuery 및 SearchOptions
searchSingleWithFilter.ts디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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; }종속성, 환경 변수 및 이전 검색과 동일한 검색 기능을 추가합니다. 호텔과 관련된
free wifi을 위한 후처리 제외 필터를 추가하세요.파일을 빌드하고 실행합니다.
npm run build && node -r dotenv/config dist/searchSingleWithFilter.js이 코드의 출력은 적용된 필터를 사용하여 쿼리에 대한
free wifi관련 문서를 표시합니다.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
지리 공간적 필터를 사용하여 검색 만들기
지리 공간 필터를 지정하여 결과를 특정 지리적 영역으로 제한할 수 있습니다. 이 예제에서 필터는 워싱턴 D.C.에서 300km 이내의 호텔로 결과를 제한합니다(좌표: -77.03241 38.90166). 지리 공간적 거리는 항상 킬로미터입니다. 이 검색에서는 SearchClient를 사용합니다. 검색 및 VectorQuery 및 SearchOptions
searchSingleWithFilterGeo.ts디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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; }파일을 빌드하고 실행합니다.
npm run build && node -r dotenv/config dist/searchSingleWithFilterGeo.js이 코드의 출력에는 지리 공간적 후처리 제외 필터가 적용된 쿼리 관련 문서가 표시됩니다.
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
하이브리드 검색 만들기
하이브리드 검색은 단일 검색 요청의 키워드 쿼리와 벡터 쿼리로 구성됩니다. 이 예제에서는 벡터 쿼리와 전체 텍스트 검색을 동시에 실행합니다.
-
검색 문자열:
historic hotel walk to restaurants and shopping -
벡터 쿼리 문자열(수학적 표현으로 벡터화):
quintessential lodging near running trails, eateries, retail
이 검색에서는 SearchClient를 사용합니다. 검색 및 VectorQuery 및 SearchOptions
searchHybrid.ts디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
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; }파일을 빌드하고 실행합니다.
npm run build && node -r dotenv/config dist/searchHybrid.js이 코드의 출력은 하이브리드 검색에 대한 관련 문서를 보여줍니다.
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"]상호 순위 Fusion(RRF)은 결과를 병합하므로 입력을 검토하는 데 도움이 됩니다. 다음은 전체 텍스트 쿼리에서만 나온 결과입니다. 상위 2개 검색 결과는 Sublime Palace Hotel과 History Lion Resort입니다. Sublime Palace Hotel의 BM25 관련성 점수가 더 높습니다.
{ "@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" },HNSW를 사용하여 일치 항목을 찾는 벡터 전용 쿼리에서 Sublime Palace Hotel은 4위로 떨어집니다. 전체 텍스트 검색에서 2위, 벡터 검색에서 3위를 차지했던 Historic Lion은 동일한 변동 범위를 가지지 않으므로 균질화된 결과 집합에서 상위 일치 항목으로 표시됩니다.
"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" } ]
의미 체계 하이브리드 검색 만들기
추가 검색 텍스트를 historic hotel walk to restaurants and shopping사용하여 의미 체계 하이브리드 검색을 확장하는 컬렉션의 마지막 쿼리는 다음과 같습니다.
이 검색에서는 SearchClient를 사용합니다. 검색 및 VectorQuery 및 SearchOptions
searchSemanticHybrid.ts디렉터리에src파일을 만듭니다.다음 코드를 파일에 복사합니다.
import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents"; import { vector } from "./queryVector.js"; import { DefaultAzureCredential } from "@azure/identity"; export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!; export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!; console.log(`Using Azure Search endpoint: ${searchEndpoint}`); console.log(`Using index name: ${indexName}`); // Define an interface for the hotel document export interface HotelDocument { HotelId: string; HotelName: string; Description: string; DescriptionVector?: number[]; Category?: string; Tags?: string[] | string; Address?: { City: string; StateProvince: string; }; Location?: { type: string; coordinates: [number, number]; // [longitude, latitude] }; "@search.score"?: number; "@search.reranker_score"?: number; } // const key = process.env.AZURE_SEARCH_KEY || ""; // const searchClient = new SearchClient<HotelDocument>( // searchEndpoint!, // indexName, // new AzureKeyCredential(key) // ); const searchClient = new SearchClient<HotelDocument>( searchEndpoint, indexName, new DefaultAzureCredential() ); try { const vectorQuery: VectorQuery<HotelDocument> = { vector: vector, kNearestNeighborsCount: 5, fields: ["DescriptionVector"], kind: "vector", exhaustive: true }; // Create semantic hybrid search options with vector query and semantic configuration const searchOptions: SearchOptions<HotelDocument> = { top: 5, includeTotalCount: true, select: ["HotelId", "HotelName", "Category", "Description"] as const, queryType: "semantic" as const, semanticSearchOptions: { configurationName: "semantic-config" }, vectorSearchOptions: { queries: [vectorQuery], filterMode: "postFilter" // Apply filter after vector similarity is calculated }, }; // Use search_text for semantic search const searchText = "historic hotel walk to restaurants and shopping"; const results: SearchDocumentsResult<HotelDocument> = await searchClient.search(searchText, searchOptions); console.log(`\n\nSemantic hybrid search found ${results.count} then limited to top ${searchOptions.top}`); for await (const result of results.results) { // Log each result const doc = result.document; const score = result.score; const rerankerScoreDisplay = result.rerankerScore; console.log(`- Score: ${score}`); console.log(` Re-ranker Score: ${rerankerScoreDisplay}`); console.log(` HotelId: ${doc.HotelId}`); console.log(` HotelName: ${doc.HotelName}`); console.log(` Description: ${doc.Description || 'N/A'}`); console.log(` Category: ${doc.Category || 'N/A'}`); console.log(''); } } catch (ex) { console.error("Semantic hybrid search failed:", ex); throw ex; }파일을 빌드하고 실행합니다.
npm run build && node -r dotenv/config dist/searchSemanticHybrid.js이 코드의 출력은 의미 체계 하이브리드 검색에 대한 관련 문서를 보여줍니다.
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: Suite의미 체계 순위를 쿼리의 단어 뒤에 있는 의미와 문서의 내용을 이해하여 검색 결과의 관련성을 개선하는 방법으로 생각할 수 있습니다. 이 경우 의미 체계 순위는 키워드와 관련이 있는 호텔뿐만 아니라 쿼리의 의도와도 일치하는 호텔을 식별하는 데 도움이 됩니다.
핵심 내용:
벡터 검색은
vectorSearchOptions속성을 통해 지정됩니다. 키워드 검색은semanticSearchOptions속성을 통해 지정됩니다.하이브리드 검색에서는 키워드에 대한 전체 텍스트 검색과 벡터 검색을 통합할 수 있습니다. 필터, 맞춤법 검사 및 의미 체계 순위 지정은 텍스트 콘텐츠에만 적용되며 벡터에는 적용되지 않습니다. 이 마지막 쿼리에는 시스템이 충분히 강력한 쿼리를 생성하지 않았기 때문에 의미 체계
answer가 없습니다.실제 결과에는 의미 체계 캡션 및 강조 표시를 포함한 더 자세한 내용이 포함됩니다. 가독성을 위해 결과가 수정되었습니다. 응답의 전체 구조를 확인하려면 REST 클라이언트에서 요청을 실행하세요.
정리
본인 소유의 구독으로 이 모듈을 진행하고 있는 경우에는 프로젝트가 끝날 때 여기서 만든 리소스가 계속 필요한지 확인하는 것이 좋습니다. 계속 실행되는 리소스에는 요금이 부과될 수 있습니다. 리소스를 개별적으로 삭제하거나 리소스 그룹을 삭제하여 전체 리소스 세트를 삭제할 수 있습니다.
가장 왼쪽 창에 있는 모든 리소스 또는 리소스 그룹 링크를 사용하여 Azure Portal에서 리소스를 찾고 관리할 수 있습니다.
검색 서비스를 유지하지만 인덱스와 문서를 삭제하려면 인덱스 및 문서를 프로그래밍 방식으로 삭제할 수 있습니다.
deleteIndex.ts디렉터리에src파일을 만듭니다.종속성, 환경 변수 및 코드를 추가하여 인덱스를 삭제합니다.
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); }파일을 빌드하고 실행합니다.
npm run build && node -r dotenv/config dist/deleteIndex.js
다음 단계
- JavaScript용 Azure AI Search에서 벡터 검색 기능에 대한 코드 샘플 리포지토리 검토
이 빠른 시작에서는 .NET 앱을 사용하여 벡터를 만들고, 채우고, 쿼리합니다. 코드 예제는 Azure AI Search 클라이언트 라이브러리를 사용하여 이러한 작업을 수행합니다. 라이브러리는 데이터 수집, 검색 작업 및 인덱스 관리 작업과 같은 인덱스 작업에 액세스하기 위해 REST API에 대한 추상화 기능을 제공합니다.
Azure AI 검색에서 벡터 저장소에는 벡터 및 벡터가 아닌 필드를 정의하는 인덱스 스키마, 포함 공간을 만드는 알고리즘에 대한 벡터 검색 구성, 쿼리 시간에 평가되는 벡터 필드 정의에 대한 설정이 있습니다. 인덱스 만들기 REST API는 벡터 저장소를 만듭니다.
참고
이 빠른 시작에서는 벡터화 단계를 생략하고 인라인 포함을 제공합니다. 사용자 고유의 콘텐츠에 기본 제공 데이터 청크 처리 및 벡터화를 추가하려면 종합적인 단계별 안내를 위해 데이터 가져오기(새) 마법사를 사용해 보세요.
필수 구성 요소
활성 구독이 있는 Azure 계정. 무료로 계정을 만듭니다.
Azure AI 검색 서비스입니다. 현재 구독에서 서비스를 만들거나기존 서비스를 찾습니다.
- 이 빠른 시작의 대부분에 대해 무료 검색 서비스를 사용할 수 있지만 더 큰 데이터 파일의 경우 기본 계층 이상을 사용하는 것이 좋습니다.
- 의미 재순위를 호출하는 쿼리 예제를 실행하려면 검색 서비스가 의미 순위 설정이 활성화된 기본 계층 이상에 있어야 합니다.
샘플 리포지토리를 복제하려면 Git을 사용하세요.
리소스 정보 검색
검색 엔드포인트에 대한 요청은 인증 및 권한 부여를 받아야 합니다. 이 작업에 API 키 또는 역할을 사용할 수 있지만 Microsoft Entra ID를 통해 키 없는 연결을 사용하는 것이 좋습니다.
이 빠른 시작 가이드에서는 개발 및 운영 환경 모두에서 인증을 간소화하는 DefaultAzureCredential를 사용합니다. 그러나 프로덕션 시나리오의 경우 다른 접근 방식이 필요한 고급 요구 사항이 있을 수 있습니다. 모든 옵션을 이해 하려면 .NET용 Azure SDK를 사용하여 Azure 서비스에 .NET 앱 인증 을 참조하세요.
코드 복제 및 설정 환경
이 빠른 시작의 코드가 포함된 리포지토리를 복제합니다.
git clone https://github.com/Azure-Samples/azure-search-dotnet-samples이 리포지토리에는 각각 별도의 하위 폴더에 있는 여러 아티클에 대한 .NET 코드 예제가 있습니다.
Visual Studio Code에서 하위 폴더를
quickstart-Vector-Search열거나 파일을 두 번 클릭하여.slnVisual Studio에서 솔루션을 엽니다.appsettings.json파일을VectorSearchExamples및VectorSearchCreatePopulateIndex폴더에서 여십시오. 다음 값을 업데이트합니다.-
AZURE_SEARCH_ENDPOINT: Azure Portal에서 Azure AI Search 서비스의 URL을 찾습니다. 검색 리소스의 개요 페이지에서 URL 필드를 찾습니다. 엔드포인트의 예는 다음과 같습니다.https://mydemo.search.windows.net -
AZURE_SEARCH_INDEX_NAME: 파일에 제공된 기본값을 그대로 두거나 고유한 인덱스 이름을 입력합니다.
-
벡터 인덱스 만들기 및 문서 업로드
Azure AI Search 서비스에 대해 검색 쿼리를 실행하려면 먼저 검색 인덱스를 만들고 서비스에 문서를 업로드해야 합니다.
VectorSearchCreatePopulateIndex폴더에서 새 터미널을 엽니다.다음 명령을 사용하여 프로젝트를 실행합니다.
dotnet rundotnet run
다음 코드는 인덱스를 만들기 위해 실행됩니다.
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($"Index '{result.Value.Name}' updated.");
Console.WriteLine();
}
다음 코드는 파일의 JSON 형식 문서를 hotel-samples.json Azure AI Search 서비스에 업로드합니다.
static async Task UploadDocs(SearchClient searchClient)
{
var jsonPath = Path.Combine(Directory.GetCurrentDirectory(), "HotelData.json");
// Read and parse hotel data
var json = await File.ReadAllTextAsync(jsonPath);
List<Hotel> hotels = new List<Hotel>();
try
{
using var doc = JsonDocument.Parse(json);
if (doc.RootElement.ValueKind != JsonValueKind.Array)
{
Console.WriteLine("HotelData.json root is not a JSON array.");
}
// Deserialize all hotel objects
hotels = doc.RootElement.EnumerateArray()
.Select(e => JsonSerializer.Deserialize<Hotel>(e.GetRawText()))
.Where(h => h != null)
.ToList();
}
catch (JsonException ex)
{
Console.WriteLine($"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($"Key: {r.Key}, Succeeded: {r.Succeeded}");
}
}
catch (Exception ex)
{
Console.WriteLine("Failed to upload documents: " + ex);
}
}
프로젝트를 실행한 후 다음 출력이 출력됩니다.
Key: 1, Succeeded: True
Key: 2, Succeeded: True
Key: 3, Succeeded: True
Key: 4, Succeeded: True
Key: 48, Succeeded: True
Key: 49, Succeeded: True
Key: 13, Succeeded: True
핵심 내용:
Azure AI Search 서비스에서 호스팅된
SearchClient를 통해 코드는 패키지에서 제공하는 주요 객체인azure-search-documents와 특정 검색 인덱스를 상호작용합니다. 다음과SearchClient같은 인덱스 작업에 대한 액세스를 제공합니다.-
데이터 수집:
UploadDocuments(), ,MergeDocuments()DeleteDocuments() -
검색 작업:
Search(),Autocomplete()Suggest() -
인덱스 관리 작업:
CreateOrUpdateIndex()
-
데이터 수집:
벡터 필드에는 부동 소수점 값이 포함되어 있습니다. 차원 특성에는 각각 최소 2개 및 최대 4096개의 부동 소수점 값이 있습니다. 이 빠른 시작을 위해 Azure OpenAI text-embedding-3-small 모델에서 생성된 포함 크기는 1536입니다.
검색 쿼리 실행
인덱스를 만들고 문서를 로드한 후에는 다양한 매개 변수를 사용하여 호출 SearchAsync() 하여 벡터 쿼리를 실행할 수 있습니다.
- 폴더에서
VectorSearchExamples파일을 엽니다Program.cs. -
VectorSearchExamples폴더에서 새 터미널을 엽니다.
다음 섹션에서는 hotels-vector-quickstart 인덱스에 대해 쿼리를 실행합니다. 쿼리에는 다음이 포함됩니다.
단일 벡터 검색
첫 번째 예제에서는 검색 문자열과 밀접하게 일치하는 문서 설명을 찾으려는 기본 시나리오를 보여 줍니다.
Program.cs폴더의VectorSearchExamples파일에서 메서드 호출SearchExamples.SearchSingleVector(searchClient, vectorizedResult);의 주석을 제거하십시오. 이 메서드는 클래스에서 다음 검색 함수를 실행합니다SearchExamples.cs.public static async Task SearchSingleVector(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector) { SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>( new SearchOptions { VectorSearch = new() { Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } } }, Select = { "HotelId", "HotelName", "Description", "Category", "Tags" }, }); Console.WriteLine($"Single Vector Search Results:"); await foreach (SearchResult<Hotel> result in response.GetResultsAsync()) { Hotel doc = result.Document; Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}"); } Console.WriteLine(); }다음 명령을 사용하여 프로젝트를 실행합니다.
dotnet rundotnet run프로젝트를 실행하면 검색 결과가 출력 창에 인쇄됩니다.
Single Vector Search Results: Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel Score: 0.6333684, HotelId: 13, HotelName: Luxury Lion Resort Score: 0.605672, HotelId: 4, HotelName: Sublime Palace Hotel Score: 0.6026341, HotelId: 49, HotelName: Swirling Currents Hotel Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel
필터를 사용한 단일 벡터 검색
필터를 추가할 수 있지만 필터는 인덱스의 벡터가 아닌 콘텐츠에 적용됩니다. 이 예에서는 필터가 Tags 필드에 적용되어 무료 Wi-Fi를 제공하지 않는 호텔을 필터링합니다.
Program.cs폴더의VectorSearchExamples파일에서 메서드 호출SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);의 주석을 제거하십시오. 이 메서드는 클래스에서 다음 검색 함수를 실행합니다SearchExamples.cs.public static async Task SearchSingleVectorWithFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector) { SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>( new SearchOptions { VectorSearch = new() { Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } } }, Filter = "Tags/any(tag: tag eq 'free wifi')", Select = { "HotelId", "HotelName", "Description", "Category", "Tags" } }); Console.WriteLine($"Single Vector Search With Filter Results:"); await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync()) { Hotel doc = result.Document; Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}, Tags: {string.Join(String.Empty, doc.Tags)}"); } Console.WriteLine(); }프로젝트를 다시 실행하면 각 문서의 상태가 그 아래에 인쇄됩니다.
Single Vector Search With Filter Results: Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel, Tags: continental breakfastair conditioningfree wifi Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel, Tags: poolfree wifiair conditioningconcierge쿼리는 이전 의 단일 벡터 검색 예제와 동일하지만 사후 처리 제외 필터를 포함하고 무료 Wi-Fi가 있는 두 개의 호텔만 반환합니다.
다음 필터 예제에서는 지역 필터를 사용합니다.
Program.cs폴더의VectorSearchExamples파일에서 메서드 호출SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult);의 주석을 제거하십시오. 이 메서드는 클래스에서 다음 검색 함수를 실행합니다SearchExamples.cs.public static async Task SingleSearchWithGeoFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector) { SearchResults<Hotel> responseWithGeoFilter = await searchClient.SearchAsync<Hotel>( new SearchOptions { VectorSearch = new() { Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } } }, Filter = "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300", Select = { "HotelId", "HotelName", "Description", "Address", "Category", "Tags" }, Facets = { "Address/StateProvince" }, }); Console.WriteLine($"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($"Description: {doc.Description}"); Console.WriteLine(); } Console.WriteLine(); }이 쿼리는 이전 의 단일 벡터 검색 예제와 동일하지만 후처리 제외 필터를 포함하고 300km 이내의 두 호텔만 반환합니다.
프로젝트를 다시 실행하면 각 문서의 상태가 그 아래에 인쇄됩니다.
Vector query with a geo filter: -HotelId: 48 HotelName: Nordick's Valley Motel Score: 0.6605852246284485 City/State: Washington D.C./null Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley. -HotelId: 49 HotelName: Swirling Currents Hotel Score: 0.602634072303772 City/State: Arlington/VA Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.
하이브리드 검색
하이브리드 검색은 단일 검색 요청의 키워드 쿼리와 벡터 쿼리로 구성됩니다. 이 예제에서는 벡터 쿼리와 전체 텍스트 검색을 동시에 실행합니다.
-
검색 문자열:
historic hotel walk to restaurants and shopping -
벡터 쿼리 문자열(수학적 표현으로 벡터화):
Quintessential lodging near running trails, eateries, retail
Program.cs폴더의VectorSearchExamples파일에서 메서드 호출SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);의 주석을 제거하십시오. 이 메서드는 클래스에서 다음 검색 함수를 실행합니다SearchExamples.cs.public static async Task<SearchResults<Hotel>> SearchHybridVectorAndText(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector) { SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>( "historic hotel walk to restaurants and shopping", new SearchOptions { VectorSearch = new() { Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } } }, Select = { "HotelId", "HotelName", "Description", "Category", "Tags" }, Size = 5, }); Console.WriteLine($"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; }프로젝트를 다시 실행하면 각 문서의 상태가 그 아래에 인쇄됩니다.
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
상호 순위 Fusion(RRF)은 결과를 병합하므로 입력을 검토하는 데 도움이 됩니다. 다음은 전체 텍스트 쿼리에서만 나온 결과입니다. 상위 2개 검색 결과는 Sublime Palace Hotel과 History Lion Resort입니다. Sublime Palace Hotel의 BM25 관련성 점수가 더 높습니다.
{
"@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"
},
HNSW를 사용하여 일치 항목을 찾는 벡터 전용 쿼리에서 Sublime Palace Hotel은 4위로 떨어집니다. 전체 텍스트 검색에서 2위, 벡터 검색에서 3위를 차지했던 Historic Lion은 동일한 변동 범위를 가지지 않으므로 균질화된 결과 집합에서 상위 일치 항목으로 표시됩니다.
"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"
}
]
필터를 사용한 의미 체계 하이브리드 검색
의미 체계 순위가 지정된 하이브리드 쿼리는 워싱턴 D.C.의 반경 500km 내에 있는 호텔만 표시하도록 필터링됩니다.
Program.cs폴더의VectorSearchExamples파일에서 메서드 호출SearchExamples.SearchHybridVectoryAndSemantic(searchClient, vectorizedResult);의 주석을 제거하십시오. 이 메서드는 클래스에서 다음 검색 함수를 실행합니다SearchExamples.cs.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(); }프로젝트를 다시 실행하고 셀 아래의 출력을 검토합니다. 응답은 호텔 세 곳으로, 위치별로 필터링되고
StateProvince로 패싯 처리되며 의미 체계 순위가 다시 지정되어 검색 문자열 쿼리(historic hotel walk to restaurants and shopping)에 가장 가까운 결과를 승격시킵니다.이제 Swirling Currents Hotel이 1위로 올라섰습니다. 의미 체계 순위 없이 보면 Nordick's Valley Motel이 1위입니다. 의미 체계 순위 지정을 통해 기계 독해 모델은
historic이 "식사(식당) 및 쇼핑 위치가 도보 거리 내에 있는 호텔"에 적용된다는 것을 인식합니다.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
핵심 내용:
하이브리드 검색에서는 키워드에 대한 전체 텍스트 검색과 벡터 검색을 통합할 수 있습니다. 필터, 맞춤법 검사 및 의미 체계 순위 지정은 텍스트 콘텐츠에만 적용되며 벡터에는 적용되지 않습니다. 이 마지막 쿼리에는 시스템이 충분히 강력한 쿼리를 생성하지 않았기 때문에 의미 체계
answer가 없습니다.실제 결과에는 의미 체계 캡션 및 강조 표시를 포함한 더 자세한 내용이 포함됩니다. 가독성을 위해 결과가 수정되었습니다. 응답의 전체 구조를 확인하려면 REST 클라이언트에서 요청을 실행하세요.
정리
본인 소유의 구독으로 이 모듈을 진행하고 있는 경우에는 프로젝트가 끝날 때 여기서 만든 리소스가 계속 필요한지 확인하는 것이 좋습니다. 계속 실행되는 리소스에는 요금이 부과될 수 있습니다. 리소스를 개별적으로 삭제하거나 리소스 그룹을 삭제하여 전체 리소스 세트를 삭제할 수 있습니다.
가장 왼쪽 창에 있는 모든 리소스 또는 리소스 그룹 링크를 사용하여 Azure Portal에서 리소스를 찾고 관리할 수 있습니다.
다음 단계
- Azure AI Search의 .NET 벡터 탐색 기능에 대한 코드 샘플 리포지토리를 검토하십시오.
- azure-search-dotnet-samples 리포지토리에서 다른 .NET 및 Azure AI Search 코드 샘플을 검토합니다.
이 빠른 시작에서는 Azure AI Search REST API 를 사용하여 벡터를 만들고, 로드하고, 쿼리합니다.
Azure AI 검색에서 벡터 저장소에는 벡터 및 벡터가 아닌 필드를 정의하는 인덱스 스키마, 포함 공간을 만드는 알고리즘에 대한 벡터 검색 구성, 쿼리 시간에 평가되는 벡터 필드 정의에 대한 설정이 있습니다. 인덱스 만들기 REST API는 벡터 저장소를 만듭니다.
참고
이 빠른 시작에서는 벡터화 단계를 생략하고 인라인 포함을 제공합니다. 사용자 고유의 콘텐츠에 기본 제공 데이터 청크 처리 및 벡터화를 추가하려면 종합적인 단계별 안내를 위해 데이터 가져오기(새) 마법사를 사용해 보세요.
필수 구성 요소
활성 구독이 있는 Azure 계정. 무료로 계정을 만듭니다.
Azure AI 검색 서비스입니다. 현재 구독에서 서비스를 만들거나기존 서비스를 찾습니다.
- 이 빠른 시작의 대부분에 대해 무료 검색 서비스를 사용할 수 있지만 더 큰 데이터 파일의 경우 기본 계층 이상을 사용하는 것이 좋습니다.
- 의미 재순위를 호출하는 쿼리 예제를 실행하려면 검색 서비스가 의미 순위 설정이 활성화된 기본 계층 이상에 있어야 합니다.
리소스 정보 검색
검색 엔드포인트에 대한 요청은 인증 및 권한 부여를 받아야 합니다. 이 작업에 API 키 또는 역할을 사용할 수 있습니다. Microsoft Entra ID를 통한 키 없는 연결을 사용하는 것이 좋습니다.
원하는 인증 방법에 해당하는 탭을 선택합니다. 이 빠른 시작의 모든 요청에 동일한 방법을 사용합니다.
Azure Portal에 로그인하고 검색 서비스를 찾습니다.
개요 홈페이지에서 URL을 찾습니다. 엔드포인트의 예는 다음과 같습니다.
https://mydemo.search.windows.net
키 없는 빠른 시작의 단계에 따라 Microsoft Entra 토큰을 가져옵니다.
이전 빠른 시작의 3단계에서
az account get-access-token명령을 실행하면 토큰을 가져옵니다.az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
코드 파일 만들기 또는 다운로드
이 빠른 시작의 모든 요청을 실행하려면 하나의 .rest 또는 .http 파일을 사용합니다. 이 빠른 시작에 대한 코드가 포함된 REST 파일을 다운로드하거나 Visual Studio Code에서 새 파일을 만들고 코드를 복사할 수 있습니다.
Visual Studio Code에서
.rest또는.http파일 확장명으로 새 파일을 만듭니다. 예들 들어az-search-vector-quickstart.rest입니다. Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest 파일의 원시 콘텐츠를 이 새 파일에 복사하여 붙여넣습니다.파일 상단에서
@baseUrl의 자리 표시자 값을 검색 서비스 URL로 바꿉니다. 검색 서비스 URL을 찾는 방법에 대한 지침은 리소스 정보 검색 섹션을 참조하세요.@baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HERE파일 상단에서 인증을 위한 자리 표시자 값을 바꿉니다. Microsoft Entra 토큰 또는 API 키를 가져오는 방법에 대한 지침은 리소스 정보 검색 섹션을 참조하세요.
Microsoft Entra ID를 통한 권장 키 없는 인증의 경우
@apiKey를@token변수로 바꿔야 합니다.@token = PUT-YOUR-MICROSOFT-ENTRA-TOKEN-HEREAPI 키를 사용하려면
@apiKey를 Azure Portal에서 복사한 키로 바꿉니다.@apiKey = PUT-YOUR-ADMIN-KEY-HEREMicrosoft Entra ID를 통한 권장 키 없는 인증의 경우 요청 헤더에서
api-key: {{apiKey}}를Authorization: Bearer {{token}}으로 바꿔야 합니다. 파일에서 찾은api-key: {{apiKey}}의 모든 인스턴스를 바꿉니다.
벡터 인덱스 만들기
인덱스 만들기 REST API를 사용하여 벡터 인덱스를 만들고 검색 서비스에서 실제 데이터 구조를 설정합니다.
이 예의 인덱스 스키마는 호텔 콘텐츠를 중심으로 구성됩니다. 샘플 데이터는 가상의 호텔에 대한 벡터 및 비벡터 설명으로 구성됩니다. 이 스키마에는 벡터 인덱싱 및 쿼리, 의미 체계 순위에 대한 구성이 포함됩니다.
Visual Studio Code에서
az-search-vector-quickstart.rest파일을 엽니다.파일에서
### Create a new index코드 블록을 찾습니다. 이 블록에는 검색 서비스에서hotels-vector-quickstart인덱스를 만들라는 요청이 포함되어 있습니다.### 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" } ] } } ] } }요청 보내기를 선택합니다.
HTTP/1.1 201 Created응답이 있어야 합니다.응답 본문에는 인덱스 스키마의 JSON 표현이 포함되어야 합니다.
{ "@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" } ] } } ] } }
인덱스 만들기 REST API에 대한 핵심 내용:
fields컬렉션에는 텍스트 및 벡터 검색에 필요한 키 필드, 텍스트 및 벡터 필드(예:Description및DescriptionVector)가 포함되어 있습니다. 동일한 인덱스에 벡터 필드와 벡터가 아닌 필드를 같은 위치에 배치하면 하이브리드 쿼리가 가능해집니다. 예를 들어 필터, 의미 체계 순위를 사용하는 텍스트 검색, 벡터를 단일 쿼리 작업으로 결합할 수 있습니다.벡터 필드는 벡터에 사용되는 EDM 데이터 형식 중 하나여야 합니다(예:
type: Collection(Edm.Single).). 벡터 필드에는 속성도 있습니다dimensionsvectorSearchProfile.vectorSearch섹션은 ANN(Approximate Nearest Neighbors) 알고리즘 구성 및 프로필의 배열입니다. 지원되는 알고리즘에는 계층적 탐색 가능한 스몰 월드 및 완전한 K-최근접 이웃이 포함됩니다. 자세한 내용은 벡터 검색의 관련성 점수를 참조하세요.(선택 사항)
semantic구성을 사용하면 검색 결과의 순위 지정을 다시 지정할 수 있습니다. 구성에 지정된 문자열 필드에 대한semantic형식의 쿼리에서 결과 순위를 다시 지정할 수 있습니다. 자세한 내용은 의미 체계 순위 지정 개요를 참조하세요.
문서 업로드
인덱스 만들기 및 로드는 별도의 단계입니다. 이전 단계 에서 인덱스 스키마를 만들었습니다. 이제 인덱스에 문서를 로드해야 합니다.
Azure AI 검색에서 인덱스에는 검색 가능한 모든 데이터가 포함되어 있으며 검색 서비스에서 실행되는 쿼리가 있습니다. REST 호출의 경우 데이터는 JSON 문서로 제공됩니다. 이 작업에는 문서- 인덱스 REST API를 사용합니다.
docs 컬렉션 및 index 작업을 포함하도록 URI가 길어집니다.
문서 업로드 요청을 작성하여 검색 서비스의 인덱스로
hotels-vector-quickstart문서를 업로드합니다.### 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" } } } } ] }요청 보내기를 선택합니다.
HTTP/1.1 200 OK응답이 있어야 합니다. 응답 본문에는 검색 문서의 JSON 표현이 포함되어야 합니다.
문서 - 인덱스 REST API 요청에 대한 핵심 내용:
페이로드의 문서는 인덱스 스키마에 정의된 필드로 구성됩니다.
벡터 필드에는 부동 소수점 값이 포함되어 있습니다. 차원 특성에는 각각 최소 2개와 최대
4096부동 소수점 값이 있습니다. 이 빠른 시작 가이드에서는 Azure OpenAI text-embedding-3-small 모델에서 생성된 포함 크기가 1,536이므로 차원 속성을 1,536으로 설정합니다.
쿼리 실행
이제 문서가 로드되었으므로 문서 - REST(검색 게시물)를 사용하여 벡터 쿼리를 실행할 수 있습니다.
다음 섹션에서는 hotels-vector-quickstart 인덱스에 대해 쿼리를 실행합니다. 쿼리에는 다음이 포함됩니다.
예제 쿼리는 다음 두 문자열을 기반으로 합니다.
-
검색 문자열:
historic hotel walk to restaurants and shopping -
벡터 쿼리 문자열(수학적 표현으로 벡터화):
quintessential lodging near running trails, eateries, retail
벡터 쿼리 문자열은 검색 문자열과 의미 체계상 유사하지만 검색 인덱스에 존재하지 않는 용어를 포함합니다. 키워드 검색 quintessential lodging near running trails, eateries, retail을 수행하는 경우 의미 체계 순위가 없는 순수 키워드 검색의 결과는 0입니다. 이 예제에서는 일치하는 용어가 없더라도 벡터를 사용하여 관련 결과를 얻는 방법을 보여 줍니다.
단일 벡터 검색
요청을 작성합니다. 이 쿼리는 달리기 코스, 먹거리 장터, 소매점 근처의 전형적인 숙박 시설을 1536개의 부동 소수점 형태로 표현한 것입니다. 쿼리는
DescriptionVector을(를) 검색하고 k-5 결과를 반환합니다. "철저한" 오버라이드 매개변수를 사용하여 ANN 대신 인덱스의 전체 검사를 수행합니다. 철저한 검색은 작은 인덱스에 유용합니다.### 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 } ] }문서 - 검색 포스트 REST API의 핵심 요점:
vectorQueries.vector벡터 쿼리 문자열입니다. 이는 러닝 트레일 근처의 가장 이상적인 숙박 시설, 식당, 소매점의 백터 표현으로 이 쿼리에 대해 1,536개의 포함으로 벡터화되었습니다.fields는 검색할 벡터 필드를 결정합니다.kind이vector로 설정된 것은 쿼리 문자열이 벡터임을 의미합니다.kind가text로 설정되었다면, 쿼리 시 사람이 읽을 수 있는 텍스트 문자열을 벡터로 인코딩할 수 있는 추가 기능, 즉 벡터라이저가 필요할 것입니다. 이 빠른 시작에서는 벡터라이저를 생략하여 연습을 단순하게 유지합니다.k는 응답에 반환할 일치 항목 수를 지정합니다. 매개 변수는count인덱스에 있는 일치 항목 수를 지정합니다. 개수를 포함하는 것은 쿼리의 모범 사례이지만 알고리즘이 거의 모든 문서에서 어느 정도 유사성을 찾을 수 있는 유사성 검색에는 유용하지 않습니다.
요청 보내기를 선택합니다.
HTTP/1.1 200 OK응답이 있어야 합니다. 응답 본문에는 검색 결과의 JSON 표현이 포함되어야 합니다.quintessential lodging near running trails, eateries, retail의 벡터 동등 항목에 대한 응답에는 검색 엔진이 7개의 일치 항목을 찾았지만 k-5개의 결과가 포함됩니다. 상위 결과는 쿼리와 가장 의미상 유사한 것으로 간주됩니다. 각 결과는 검색 점수와select에 나열된 필드를 제공합니다. 유사성 검색에서 응답에는 항상 유사성 점수 값을 기준으로 순서가 지정된k개의 결과가 포함됩니다.{ "@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" ] } ] }
필터를 사용한 단일 벡터 검색
필터를 추가할 수 있지만 필터는 인덱스의 벡터가 아닌 콘텐츠에 적용됩니다. 이 예에서는 필터가 Tags 필드에 적용되어 무료 Wi-Fi를 제공하지 않는 호텔을 필터링합니다.
요청을 작성합니다. 이는 추가 필터 및 필터 모드 매개 변수가 있는 이전 요청과 동일합니다.
### 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 }, ] }요청 보내기를 선택합니다.
HTTP/1.1 200 OK응답이 있어야 합니다. 응답 본문에는 검색 결과의 JSON 표현이 포함되어야 합니다.쿼리는 이전 의 단일 벡터 검색 예제와 동일하지만 사후 처리 제외 필터를 포함하고 무료 Wi-Fi가 있는 두 개의 호텔만 반환합니다.
{ "@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" ] } ] }다음은 지역 필터를 사용하여 워싱턴 D.C.에서 300km 이내의 호텔로 결과를 제한하는 또 다른 쿼리입니다.
### 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 }, ] }응답을 검토합니다. 워싱턴 D.C 근처의 두 호텔로 제한되어 있으며, 측면 구조가 집계를 제공합니다.
"@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" } } ]
하이브리드 검색
하이브리드 검색은 단일 검색 요청의 키워드 쿼리와 벡터 쿼리로 구성됩니다. 이 예에서는 벡터 쿼리와 전체 텍스트 검색을 동시에 실행합니다.
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검색 문자열:
historic hotel walk to restaurants and shopping -
벡터 쿼리 문자열(수학적 표현으로 벡터화):
quintessential lodging near running trails, eateries, retail
하이브리드 쿼리를 작성합니다.
### 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 } ] }요청 보내기를 선택합니다.
HTTP/1.1 200 OK응답이 있어야 합니다. 응답 본문에는 검색 결과의 JSON 표현이 포함되어야 합니다.
하이브리드 쿼리이므로 결과는 RRF(Reciprocal Rank Fusion)에 따라 순위가 매겨집니다.
@search.score 값은 기준이 다르며, 모든 값이 균일하게 더 작습니다. RRF는 여러 검색 결과의 검색 점수를 평가하고 그 반대를 취한 다음 결합된 결과를 병합하고 정렬합니다.
top 결과 수가 반환됩니다.
인덱스의 일치하는 문서 7개 중 상위 k-5 일치 항목으로 구성된 응답을 검토합니다.
{
"@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."
}
]
}
RRF는 결과를 병합하므로 입력을 개별적으로 검토하는 데 도움이 됩니다.
쿼리의 전체 텍스트 부분인 역사적인 호텔 도보에서 레스토랑과 쇼핑까지의 결과는 다음과 같습니다. 전체 텍스트 쿼리에서 상위 3개 결과는 숭고한 팰리스 호텔, Stay-Kay 시티 호텔 및 럭셔리 라이온 리조트입니다. Sublime Palace Hotel의 BM25 관련성 점수가 더 높습니다. 융합 쿼리에서 이러한 일치 항목 중 2개만 상위 3위 안에 있으며, 두 번째 매치(Stay-Kay City Hotel)는 상위 5위 안에 전혀 나타나지 않습니다.
"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."
},
...
]
벡터 부분 쿼리(러닝 트레일, 식당, 소매점 근처의 이상적인 숙소)는 서로 다른 문자열이므로 다른 결과를 예상해야 합니다. 이 쿼리는 노르딕의 밸리 모텔, 럭셔리 라이온 리조트, 숭고한 팰리스 호텔을 상위 3개 경기로 반환합니다. 융합 쿼리에서 이러한 결과는 상위 3개에 있지만 역순으로 표시됩니다.
"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."
},
...
]
의미 체계 하이브리드 검색
컬렉션의 마지막 쿼리는 다음과 같습니다. 이 하이브리드 쿼리는 L1 순위 결과에 대한 컴퓨터 읽기 이해력을 적용하는 L2 의미 체계 순위를 추가하여 상위에 더 관련성이 높은 일치 항목을 승격합니다.
요청을 작성합니다.
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"k": 5, "fields": "DescriptionVector", "kind": "vector", "exhaustive": true } ] }요청 보내기를 선택합니다.
HTTP/1.1 200 OK응답이 있어야 합니다. 응답 본문에는 검색 결과의 JSON 표현이 포함되어야 합니다.
하이브리드 쿼리의 RRF로 순위가 매겨진 결과의 의미 기반 재순위 지정을 포함한 응답을 검토합니다. 의미 체계 순위는 텍스트 입력에서 작동합니다. 텍스트 또는 하이브리드 쿼리에서 이 입력은 쿼리의 텍스트 부분입니다(역사적인 호텔이 레스토랑과 쇼핑으로 이동). 첫 번째 예제와 같은 순수 벡터 쿼리에서 의미 체계 순위를 사용하거나 의미 체계 순위에 사용되는 텍스트를 명시적으로 제어하려면 semanticQuery 문자열을 제공합니다.
의미 체계 재배열 후, 스와일링 커런츠 호텔은 쇼핑, 식사, 엔터테인먼트 및 도심까지 걸어서 갈 수 있는 접근성으로 최고의 자리에 올랐습니다. 기계 이해 모델은 도보로 쇼핑과 식사에 접근하는 것을 레스토랑과 쇼핑에 걸어가기와 더 가깝게 일치하도록 촉진합니다.
의미적 재정렬 이전에, Sublime Palace는 도시의 명소와 랜드마크에 가까운 거리에 위치하며, 교회와 건물, 상점, 기념물의 특별한 아름다움에 둘러싸여 있는 점 때문에 1위에 있었다.
{
"@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"
}
]
}
팁 (조언)
의미 체계 순위가 지정된 결과에는 의미 체계 답변, 캡션 및 강조 표시를 비롯한 자세한 정보가 포함될 수 있습니다. 요청에 매개 변수를 더 추가하면 추가 세부 정보가 생성됩니다. 자세한 내용은 의미 체계 쿼리 설정을 참조하세요.
정리
본인 소유의 구독으로 이 모듈을 진행하고 있는 경우에는 프로젝트가 끝날 때 여기서 만든 리소스가 계속 필요한지 확인하는 것이 좋습니다. 계속 실행되는 리소스에는 요금이 부과될 수 있습니다. 리소스를 개별적으로 삭제하거나 리소스 그룹을 삭제하여 전체 리소스 세트를 삭제할 수 있습니다.
가장 왼쪽 창에 있는 모든 리소스 또는 리소스 그룹 링크를 사용하여 Azure Portal에서 리소스를 찾고 관리할 수 있습니다.
검색 서비스는 유지하되 인덱스와 문서를 삭제하려면 REST 클라이언트에서 DELETE 명령을 사용하면 됩니다. 이 명령(az-search-vector-quickstart.rest 파일 끝)은 hotels-vector-quickstart 인덱스를 삭제합니다.
### Delete an index
DELETE {{baseUrl}}/indexes/hotels-vector-quickstart?api-version=2025-09-01 HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}
다음 단계
다음 단계로, API 키 없이 REST API를 호출하는 방법을 알아보는 것이 좋습니다.
Python, C# 또는 JavaScript에 대한 데모 코드를 검토하는 것도 좋습니다.