Mulai cepat: Pencarian vektor

Dalam mulai cepat ini, Anda menggunakan notebook Jupyter untuk membuat, memuat, dan melakukan kueri vektor. Contoh kode melakukan operasi ini dengan menggunakan pustaka klien Azure AI Search. Pustaka menyediakan abstraksi atas REST API untuk akses ke operasi indeks seperti penyerapan data, operasi pencarian, dan operasi manajemen indeks.

Di Azure AI Search, penyimpanan vektor memiliki skema indeks yang menentukan bidang vektor dan nonvektor, konfigurasi pencarian vektor untuk algoritma yang membuat ruang penyematan, dan pengaturan pada definisi bidang vektor yang dievaluasi pada waktu kueri. Buat Indeks REST API membuat penyimpanan vektor.

Catatan

Panduan cepat ini menghilangkan langkah vektorisasi dan menyediakan penyematan langsung. Jika Anda ingin menambahkan pemotongan dan vektorisasi data bawaan melalui konten Anda sendiri, coba wizard Impor data (baru) untuk panduan end-to-end.

Prasyarat

Sebuah akun Azure dengan langganan aktif. Buat akun secara gratis.
Azure AI layanan Pencarian. Buat layanan atau temukan layanan yang sudah ada di langganan Anda saat ini.
- Anda dapat menggunakan layanan pencarian gratis untuk sebagian besar panduan cepat ini, tetapi kami merekomendasikan tingkat Basic atau lebih tinggi untuk file data yang lebih besar.
- Untuk menjalankan contoh kueri yang memanggil reranking semantik, layanan pencarian Anda harus mengaktifkan ranker semantik.
Visual Studio Code dengan ekstensi Python dan paket Jupyter.
Git untuk mengkloning repositori yang berisi notebook Jupyter dan file terkait lainnya.

Mendapatkan informasi layanan

Permintaan ke titik akhir pencarian harus diautentikasi dan diotorisasi. Meskipun dimungkinkan untuk menggunakan kunci api atau peran untuk tugas ini, sebaiknya gunakan koneksi tanpa kunci melalui ID Microsoft Entra.

Panduan cepat ini menggunakan DefaultAzureCredential, yang menyederhanakan autentikasi dalam skenario pengembangan dan produksi. Namun, untuk skenario produksi, Anda mungkin memiliki persyaratan yang lebih canggih yang memerlukan pendekatan yang berbeda. Lihat Mengautentikasi aplikasi Python ke layanan Azure dengan menggunakan Azure SDK untuk Python untuk memahami semua opsi Anda.

Mengkloning kode dan lingkungan penyiapan

Klon repositori yang berisi kode untuk panduan memulai cepat ini.
```
git clone https://github.com/Azure-Samples/azure-search-python-samples
```
Repositori ini memiliki contoh kode Python untuk beberapa artikel masing-masing dalam subfolder terpisah.
Di Visual Studio Code, buka subfolder Quickstart-Vector-Search.

Ada tiga file dalam folder ini:
- vector-search-quickstart.ipynb
- requirements.txt
- sample.env
Ganti nama sample.env file menjadi .env dan ubah nilai dalam .env file.

Gunakan URL layanan pencarian sebagai AZURE_SEARCH_ENDPOINT. Anda dapat menemukan url di portal Microsoft Azure. Buka layanan Pencarian Azure AI Anda, pada halaman Gambaran Umum , cari bidang URL. Contoh titik akhir mungkin terlihat sepertihttps://mydemo.search.windows.net.

Terakhir, pilih nama baru AZURE_SEARCH_INDEX_NAME , atau gunakan nama yang disediakan dalam file.
Dalam Visual Studio Code, berkarya dalam lingkungan. Pada menu Tampilan , pilih Terminal..., atau pilih Ctrl+`.
Jalankan perintah berikut di terminal:
```
python -m venv .venv
source .venv/scripts/activate
where python
```
Catatan

Ini mengasumsikan Anda menggunakan Git Bash di Terminal Anda, dan Anda menjalankan di Windows. Jika Anda menggunakan shell dan/atau sistem operasi yang berbeda, sesuaikan instruksi ini untuk lingkungan spesifik Anda.

Jika diminta, izinkan Visual Studio Code untuk menggunakan lingkungan baru.

Perintah where python memvalidasi bahwa Anda bekerja dari lingkungan virtual dengan mencantumkan python.exe di Quickstart-Vector-Search\.venv\ folder, dan lokasi lain dari direktori komputer Anda.
Instal pustaka yang diperlukan dengan menjalankan perintah berikut:
```
pip install requirements.txt
```
Di Visual Studio Code, buka vector-search-quickstart.ipynb.

Catatan

Jika ini pertama kalinya Anda menggunakan Jupyter Notebook (.ipynb) di Visual Studio Code, Anda akan diminta untuk menginstal kernel Jupyter Notebook dan mungkin alat lainnya. Pilih menginstal alat yang disarankan untuk melanjutkan panduan cepat ini.

Temukan sel di bawah bagian berjudul "Instal paket dan atur variabel" dan pilih tombol Jalankan Sel (Ctrl + Alt + Enter) (yang terlihat seperti tombol jalankan umum) di sebelah kiri sel. Menjalankan sel memuat variabel lingkungan, membuat DefaultAzureCredential, dan mencetak nilai ke output untuk mengonfirmasi bahwa dependensi buku catatan dan .env telah disiapkan dengan benar.

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

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

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

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

Menjalankan sel ini menghasilkan output berikut.

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
...

Ada lebih banyak paket yang dapat Anda lihat dalam elemen yang dapat digulir (lihat pesan di bawah hasil sel).

Membuat indeks vektor

Kode dalam vector-search-quickstart.ipynb menggunakan beberapa metode dari pustaka azure.search.documents untuk membuat indeks vektor dan bidang yang dapat dicari.

Temukan sel di bawah bagian berjudul "Buat indeks" dan jalankan sel untuk membuat indeks.

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')

Jika indeks berhasil dibuat, Anda akan melihat hasil berikut di bawah sel:

vector-search-quickstart created

Poin penting saat membuat indeks vektor dengan azure.search.documents:

Anda menentukan indeks dengan membuat daftar bidang. Setiap bidang dibuat menggunakan metode pembantu yang menentukan jenis bidang dan pengaturannya.
Indeks khusus ini mendukung beberapa kemampuan pencarian, seperti:
- Pencarian kata kunci teks lengkap (SearchableField(name="HotelName", ...), SearchableField(name="Description", ...))
- Pencarian vektor (memungkinkan pencarian hibrid dengan menggabungkan bidang vektor dan nonvektor) (DescriptionVector)
- Pencarian semantik (semantic_search=SemanticSearch(configurations=[semantic_config]))
- Pencarian berfasilitas (facetable=True)
- Pencarian semantik (semantic_search=SemanticSearch(configurations=[semantic_config]))
- Pencarian geo-spasial (Location kolom adalah GeographyPoint)
- Pemfilteran, pengurutan (Banyak bidang ditandai dapat difilter dan dapat diurutkan)

Unggah dokumen

Membuat dan memuat indeks adalah langkah-langkah terpisah. Anda membuat skema indeks di langkah sebelumnya. Sekarang Anda perlu memuat dokumen ke dalam indeks.

Di Pencarian Azure AI, indeks menyimpan semua konten yang dapat dicari, sementara mesin pencari menjalankan kueri terhadap indeks tersebut.

Temukan sel di bawah bagian yang berjudul "Buat payload dokumen" dan jalankan sel tersebut. Kotak ini berisi kode berikut (dipotong untuk singkatnya):

   # 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>
   ]

Sel ini memuat variabel bernama documents dengan objek JSON yang menjelaskan setiap dokumen, bersama dengan versi vektor deskripsi artikel. Vektor ini memungkinkan pencarian kesamaan, di mana pencocokan didasarkan pada arti daripada kata kunci yang tepat.

Penting

Kode dalam contoh ini tidak dapat dijalankan. Beberapa karakter atau garis dihapus untuk keringkasan. Sebagai gantinya, jalankan kode di notebook Jupyter.

Temukan sel di bawah bagian yang berjudul "Unggah dokumen" dan jalankan sel tersebut. Kotak ini berisi kode berikut (dipotong untuk singkatnya):
```
# 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)
```
Ini membuat instans klien pencarian dengan memanggil SearchClient() konstruktor, lalu memanggil upload_documents() metode pada objek.

Setelah Anda menjalankan sel, status setiap dokumen dicetak di bawahnya:
```
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
```
Poin penting tentang metode ini dan contoh upload_documents():
- Kode Anda berinteraksi dengan indeks pencarian tertentu yang dihosting di layanan Azure AI Search Anda melalui SearchClient, yang merupakan objek utama yang disediakan oleh azure-search-documents paket. SearchClient menyediakan akses ke operasi indeks seperti:
  - Pengambilan data - upload_documents(), merge_documents(), delete_documents(), dll.
  - Operasi - search() pencarian, autocomplete(), suggest()
  - Operasi manajemen indeks
- Bidang vektor berisi nilai titik mengambang. Atribut dimensi memiliki minimal 2 dan maksimum 4096 nilai titik pecahan masing-masing. Mulai cepat ini mengatur atribut dimensi ke 1.536 karena itu adalah ukuran penyematan yang dihasilkan oleh model penyematan teks-ada-002 Azure OpenAI.

Jalankan Kueri

Sekarang setelah dokumen dimuat, Anda dapat mengeluarkan kueri vektor terhadapnya dengan memanggil search_client.search() dan meneruskan objek VectorizedQuery, bidang yang ingin Anda kembalikan, jumlah hasil, dan sebagainya.

Di bagian berikutnya, kita menjalankan kueri terhadap hotels-vector-quickstart indeks. Kueri meliputi:

Pencarian vektor tunggal
Pencarian vektor tunggal dengan filter
Pencarian hibrid
Pencarian hibrid semantik

Buatlah kueri string vektor

Contoh kueri vektor didasarkan pada dua string:

String pencarian: historic hotel walk to restaurants and shopping
String kueri vektor (di-vektorisasi ke dalam representasi matematika): quintessential lodging near running trails, eateries, retail

String kueri vektor secara semantik mirip dengan string pencarian, tetapi menyertakan istilah yang tidak ada dalam indeks pencarian. Jika Anda melakukan pencarian kata kunci untuk quintessential lodging near running trails, eateries, retail, hasilnya adalah nol. Kami menggunakan contoh ini untuk menunjukkan bagaimana Anda bisa mendapatkan hasil yang relevan meskipun tidak ada istilah yang cocok.

Temukan sel di bawah bagian berjudul "Buat string kueri vektor" dan jalankan sel. Ini memuat vector variabel dengan data kueri vektorisasi yang diperlukan untuk menjalankan semua pencarian di bagian berikutnya.

Pencarian vektor tunggal

Contoh pertama menunjukkan skenario dasar di mana Anda ingin menemukan deskripsi dokumen yang sangat cocok dengan string pencarian.

Temukan sel di bawah bagian berjudul "Pencarian vektor tunggal" dan jalankan sel. Blok ini berisi permintaan untuk mengkueri indeks pencarian.

# 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.")

Kueri vektor ini dipersingkat untuk brevity. vectorQueries.vector berisi teks vektor input kueri, fields menentukan bidang vektor mana yang dicari, dan k menentukan jumlah tetangga terdekat yang akan dikembalikan.

String kueri vektor adalah quintessential lodging near running trails, eateries, retail, yang di vektorisasi menjadi 1.536 penyematan untuk kueri ini.

Respon untuk vektor quintessential lodging near running trails, eateries, retail yang setara terdiri dari 7 hasil, tetapi kode menentukan top=5, sehingga hanya 5 hasil pertama yang dikembalikan. Selain itu, hanya bidang yang ditentukan oleh select yang dikembalikan.

search_client.search() mengembalikan objek seperti dict. Setiap hasil menyediakan skor pencarian, yang dapat diakses menggunakan score = result.get("@search.score", "N/A"). Meskipun tidak ditampilkan dalam contoh ini, dalam pencarian kesamaan, respons selalu menyertakan k hasil yang diurutkan berdasarkan skor kesamaan nilai.

Setelah Anda menjalankan sel, status setiap dokumen dicetak di bawahnya:

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

Pencarian vektor tunggal dengan filter

Anda dapat menambahkan filter, tetapi filter diterapkan ke konten nonvektor dalam indeks Anda. Dalam contoh ini, filter berlaku untuk Tags bidang untuk memfilter hotel apa pun yang tidak menyediakan Wi-Fi gratis.

Temukan sel di bawah bagian berjudul "Pencarian vektor tunggal dengan filter" dan jalankan sel. Sel ini berisi permintaan untuk mengkueri indeks pencarian.

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.")

Setelah Anda menjalankan sel, status setiap dokumen dicetak di bawahnya:

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']

Kueri sama dengan contoh pencarian vektor tunggal sebelumnya, tetapi mencakup filter pengecualian pasca-pemrosesan dan hanya mengembalikan dua hotel yang memiliki Wi-Fi gratis.

Contoh filter berikutnya menggunakan filter geografis. Jalankan sel di bagian berjudul "Kueri vektor dengan filter geografis". Blok ini berisi permintaan untuk mengkueri indeks pencarian.

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.")

Kueri sama dengan contoh pencarian vektor tunggal sebelumnya, tetapi mencakup filter pengecualian pasca-pemrosesan dan hanya mengembalikan dua hotel dalam jarak 300 kilometer.

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.

Pencarian hibrid

Pencarian hibrid terdiri dari kueri kata kunci dan kueri vektor dalam satu permintaan pencarian. Contoh ini menjalankan kueri vektor dan pencarian teks lengkap secara bersamaan:

String pencarian: historic hotel walk to restaurants and shopping
String kueri vektor (di-vektorisasi ke dalam representasi matematika): quintessential lodging near running trails, eateries, retail

Temukan sel di bawah bagian berjudul "Pencarian hibrid" dan jalankan sel tersebut. Blok ini berisi permintaan untuk mengkueri indeks pencarian.

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.")

Tinjau respons:

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']

Karena Reciprocal Rank Fusion (RRF) menggabungkan hasil, hal ini membantu dalam meninjau input. Hasil berikut hanya berasal dari kueri teks lengkap. Dua hasil teratas adalah Sublime Palace Hotel dan Luxury Lion Resort. Hotel Sublime Palace memiliki skor relevansi BM25 yang lebih kuat.

{
     "@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"
},

Dalam kueri khusus vektor, yang menggunakan HNSW untuk menemukan kecocokan, Hotel Sublime Palace turun ke posisi keempat. Luxury Lion, yang berada di urutan kedua dalam pencarian teks lengkap dan ketiga dalam pencarian vektor, tidak mengalami rentang fluktuasi yang sama, sehingga muncul sebagai kecocokan teratas dalam tataan hasil yang homogen.

"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"
     }
]

Pencarian hibrid semantik

Berikut adalah kueri terakhir dalam koleksi. Kueri hibrid ini menentukan jenis kueri semantik dan konfigurasi semantik, yang menunjukkan bahwa Anda dapat membuat kueri hibrid yang menggunakan reranking semantik.

Temukan sel di bawah bagian berjudul "Pencarian hibrid semantik" dan jalankan sel. Blok kode ini berisi permintaan untuk mengkueri indeks pencarian.

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.")

Tinjau output di bawah sel.

Dengan peringkat semantik, Swirling Currents Hotel sekarang menduduki posisi teratas. 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

Anda dapat menganggap peringkat semantik sebagai cara untuk meningkatkan relevansi hasil pencarian dengan memahami makna di balik kata-kata dalam kueri dan konten dokumen. Dalam hal ini, peringkat semantik membantu mengidentifikasi hotel yang tidak hanya relevan dengan kata kunci tetapi juga cocok dengan niat kueri:

Kesimpulan utama:

Pencarian vektor ditentukan melalui vectors.value properti . Pencarian kata kunci ditentukan melalui search properti .
Dalam pencarian hibrid, Anda dapat mengintegrasikan pencarian vektor dengan pencarian teks lengkap melalui kata kunci. Filter, pemeriksaan ejaan, dan peringkat semantik hanya berlaku untuk konten tekstual, dan bukan vektor. Dalam kueri akhir ini, tidak ada semantik answer karena sistem tidak menghasilkan yang cukup kuat.
Hasil aktual mencakup detail lebih lanjut, termasuk keterangan semantik dan sorotan. Hasil dimodifikasi untuk keterbacaan. Untuk mendapatkan struktur respons lengkap, jalankan permintaan di klien REST.

Penghapusan

Saat bekerja dengan langganan Anda sendiri, sebaiknya identifikasi apakah Anda masih membutuhkan sumber daya yang Anda buat di akhir proyek. Sumber daya yang dibiarkan berjalan dapat menghabiskan uang Anda. Anda dapat menghapus sumber daya satu per satu atau menghapus grup sumber daya untuk menghapus seluruh rangkaian sumber daya.

Anda dapat menemukan dan mengelola sumber daya di portal Azure dengan menggunakan tautan Semua sumber daya atau Grup sumber daya di panel paling kiri.

Jika Anda ingin menyimpan layanan pencarian, tetapi menghapus indeks dan dokumen, Anda dapat menggunakan SearchIndexClient metode objek delete_index() . Temukan sel di bawah bagian yang diberi judul "Bersihkan" dan jalankan sel untuk menghapus indeks hotels-vector-quickstart.

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

Langkah berikutnya

Tinjau repositori sampel kode untuk kemampuan pencarian vektor di Azure AI Search for Python
Tinjau sampel kode Pencarian Python dan Azure AI lainnya di repositori azure-search-python-samples

Dalam mulai cepat ini, Anda menggunakan Java untuk membuat, memuat, dan mengkueri vektor. Contoh kode melakukan operasi ini dengan menggunakan pustaka klien Azure AI Search. Pustaka menyediakan abstraksi atas REST API untuk akses ke operasi indeks seperti penyerapan data, operasi pencarian, dan operasi manajemen indeks.

Catatan

Prasyarat

Sebuah akun Azure dengan langganan aktif. Buat akun secara gratis.
Azure AI layanan Pencarian. Buat layanan atau temukan layanan yang sudah ada di langganan Anda saat ini.
- Peran Search Index Data Contributor yang ditetapkan pada cakupan layanan.
- Anda dapat menggunakan layanan pencarian gratis untuk sebagian besar panduan cepat ini, tetapi kami merekomendasikan tingkat Basic atau lebih tinggi untuk file data yang lebih besar.
- Untuk menjalankan contoh kueri yang memanggil reranking semantik, layanan pencarian Anda harus mengaktifkan ranker semantik.
Java 21 (LTS).
Maven.

Mendapatkan titik akhir layanan

Di bagian yang tersisa, Anda menyiapkan panggilan API ke Azure OpenAI dan Azure AI Search. Dapatkan titik akhir layanan sehingga Anda dapat menyediakannya sebagai variabel dalam kode Anda.

Masuk ke portal Azure.
Temukan layanan pencarian Anda.
Pada beranda Gambaran Umum , salin URL. Contoh titik akhir mungkin terlihat sepertihttps://example.search.windows.net.

Menyiapkan proyek Java

Buat direktori proyek Maven baru.

mkdir vector-quickstart && cd vector-quickstart

Buat pom.xml file dengan dependensi yang diperlukan.

<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>

Kompilasi proyek untuk menyelesaikan dependensi.
```
mvn compile
```

Buat struktur direktori sumber.

mkdir -p src/main/java/com/example/search
mkdir -p src/main/resources

Buat application.properties file di src/main/resources direktori dan berikan titik akhir layanan pencarian Anda.
```
azure.search.endpoint=<YOUR AZURE AI SEARCH ENDPOINT>
azure.search.index-name=hotels-vector-quickstart
```

Anda menggunakan ID Microsoft Entra dan penetapan peran untuk koneksi. Pastikan Anda masuk ke penyewa dan langganan yang sama dengan Azure AI Search dan Azure OpenAI. Anda dapat menggunakan Azure CLI pada baris perintah untuk memperlihatkan properti saat ini, mengubah properti, dan untuk masuk. Untuk informasi selengkapnya, lihat Menyambungkan tanpa kunci.

Jalankan setiap perintah berikut secara berurutan.

az account show

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

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

Anda sekarang harus masuk ke Azure dari perangkat lokal Anda.

Membuat indeks vektor

Di bagian ini, Anda membuat indeks vektor di Azure AI Search dengan SearchIndexClient. createOrUpdateIndex. Skema indeks menentukan bidang, termasuk bidang DescriptionVectorvektor .

Buat CreateIndex.java file di src/main/java/com/example/search direktori.

Salin kode berikut ke dalam file.

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);
    }
}

File kode membuat indeks dan menentukan skema indeks, termasuk bidang DescriptionVectorvektor .

Jalankan file.

mvn compile exec:java -Dexec.mainClass="com.example.search.CreateIndex"

Output kode ini menunjukkan bahwa indeks berhasil dibuat:
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Creating index...
hotels-vector-quickstart created
```
Poin penting saat membuat indeks vektor dengan pustaka azure-search-documents.
- Anda menentukan indeks dengan membuat daftar bidang.
- Indeks khusus ini mendukung beberapa kemampuan pencarian, seperti:
  - Pencarian kata kunci teks lengkap (setSearchable)
  - Pencarian vektor (memungkinkan pencarian hibrid dengan menggabungkan bidang vektor dan nonvektor) (DescriptionVector dengan setVectorSearchProfileName)
  - Pencarian semantik
  - Pencarian berfasilitas (SearchSuggester)
  - Pencarian geo-spasial (kolom Location dengan SearchFieldDataType.GEOGRAPHY_POINT)
  - Pemfilteran, pengurutan (Banyak bidang ditandai dapat difilter dan dapat diurutkan)

Mengunggah dokumen ke indeks

Membuat dan memuat indeks adalah langkah-langkah terpisah. Anda membuat skema indeks di langkah sebelumnya. Sekarang Anda perlu memuat dokumen ke dalam indeks dengan SearchClient. uploadDocuments. Dokumen berisi versi vektor deskripsi artikel, yang memungkinkan pencarian kesamaan berdasarkan arti daripada kata kunci yang tepat.

Di Pencarian Azure AI, indeks menyimpan semua konten yang dapat dicari, sementara mesin pencari menjalankan kueri terhadap indeks tersebut.

Buat UploadDocuments.java file di src/main/java/com/example/search direktori.

Salin kode berikut ke dalam file.

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);
    }
}

Kode ini memuat kumpulan dokumen. Setiap dokumen menyertakan versi vektor artikel Description. Vektor ini memungkinkan pencarian kesamaan, di mana pencocokan didasarkan pada arti daripada kata kunci yang tepat.

Karena artikel mulai cepat ini segera mencari indeks, waitUntilIndexed metode menunggu hingga indeks siap untuk operasi pencarian. Ini penting karena indeks perlu diisi penuh dengan dokumen sebelum Anda dapat melakukan pencarian.

Buat dan jalankan file.

mvn compile exec:java -Dexec.mainClass="com.example.search.UploadDocuments"

Output kode ini menunjukkan bahwa dokumen diindeks dan siap untuk pencarian:
```
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.
```
Poin penting tentang metode ini dan contoh uploadDocuments:
- Kode Anda berinteraksi dengan indeks pencarian tertentu yang dihosting di layanan Azure AI Search Anda melalui SearchClient. menyediakan SearchClient akses ke operasi seperti:
  - Penyerapan data - uploadDocuments, mergeDocuments, deleteDocuments, dan sebagainya.
  - Operasi pencarian - search, autocomplete, suggest
- Bidang vektor berisi nilai titik mengambang. Atribut dimensi memiliki minimal 2 dan maksimum 4096 nilai titik pecahan masing-masing. Mulai cepat ini mengatur atribut dimensi ke 1.536 karena itu adalah ukuran penyematan yang dihasilkan oleh model Azure OpenAI text-embedding-ada-002 .

Membuat kueri sebagai vektor

Contoh kueri vektor didasarkan pada dua string:

String pencarian: historic hotel walk to restaurants and shopping
String kueri vektor (di-vektorisasi ke dalam representasi matematika): quintessential lodging near running trails, eateries, retail

Buat QueryVector.java file di src/main/java/com/example/search direktori dan tambahkan kode untuk membuat vektor kueri.

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;
    }
}

Kode ini digunakan di bagian berikut untuk melakukan pencarian vektor. Vektor kueri dibuat menggunakan model penyematan dari Azure OpenAI.

Membuat satu pencarian vektor

Contoh pertama menunjukkan skenario dasar di mana Anda ingin menemukan deskripsi dokumen yang sangat cocok dengan string pencarian menggunakan SearchClient. cari dan VectorQuery dan SearchOptions.

Buat SearchSingle.java file di src/main/java/com/example/search direktori.

Salin kode berikut ke dalam file.

package com.example.search;

import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.search.documents.SearchClientBuilder;
import com.azure.search.documents.models.SearchOptions;
import com.azure.search.documents.models.SearchResult;
import com.azure.core.util.Context;
import com.azure.search.documents.SearchDocument;
import com.azure.search.documents.models.VectorizedQuery;
import com.azure.search.documents.models.VectorSearchOptions;
import com.azure.search.documents.models.VectorFilterMode;

import java.io.IOException;
import java.util.Properties;

public class SearchSingle {
    private static final String searchEndpoint;
    private static final String indexName;

    static {
        var properties = new Properties();
        try (var in = SearchSingle.class.getClassLoader()
            .getResourceAsStream("application.properties")) {
            if (in != null) properties.load(in);
        } catch (IOException e) {
            System.out.println("Warning: could not read application.properties.");
        }

        searchEndpoint = properties.getProperty("azure.search.endpoint");
        indexName = properties.getProperty("azure.search.index-name");
    }

    public static void main(String[] args) {
        System.out.println("Using Azure Search endpoint: " + searchEndpoint);
        System.out.println("Using index name: " + indexName);

        // Create a SearchClient using DefaultAzureCredential.
        var searchClient = new SearchClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(searchEndpoint)
            .indexName(indexName)
            .buildClient();

        try {
            var vectorQuery = new VectorizedQuery(QueryVector.getVectorList())
                .setKNearestNeighborsCount(5)
                .setFields("DescriptionVector")
                .setExhaustive(true);

            // Create vector search options with the vector query and filter.
            // Use POST_FILTER to apply filter after vector similarity is calculated.
            var vectorSearchOptions = new VectorSearchOptions()
                .setQueries(vectorQuery)
                .setFilterMode(VectorFilterMode.POST_FILTER);

            var searchOptions = new SearchOptions()
                .setTop(7)
                .setIncludeTotalCount(true)
                .setSelect("HotelId", "HotelName", "Description", "Category", "Tags")
                .setVectorSearchOptions(vectorSearchOptions);

            var results = searchClient.search("*", searchOptions, Context.NONE);

            System.out.println();
            Long count = results.getTotalCount();
            System.out.println("Single Vector search found %s"
                .formatted(count == null ? 0 : count));

            for (SearchResult result : results) {
                // Log each result.
                SearchDocument document = result.getDocument(SearchDocument.class);
                Object tags = document.get("Tags");
                var tagsString = (tags == null ? "N/A" : tags.toString());
                System.out.println(
                    "- HotelId: %s, HotelName: %s, Tags: %s, Score: %s".formatted(
                        document.get("HotelId"), document.get("HotelName"),
                        tagsString, result.getScore()));
            }
        } catch (Exception ex) {
            System.err.println("Search failed: " + ex);
        }

        System.exit(0);
    }
}

vectorQuery berisi konfigurasi kueri vektorisasi termasuk teks input kueri yang di-vektorisasi seperti yang diambil melalui QueryVector.getVectorList. setFields menentukan bidang vektor mana yang dicari dan setKNearestNeighborsCount menentukan jumlah tetangga terdekat yang akan dikembalikan.

String kueri vektor adalah quintessential lodging near running trails, eateries, retail, yang di vektorisasi menjadi 1.536 penyematan untuk kueri ini.

Buat dan jalankan file.

mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingle"

Output kode ini menunjukkan dokumen yang relevan untuk kueri quintessential lodging near running trails, eateries, retail.

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 0.57902366

Respons untuk quintessential lodging near running trails, eateries, retail yang setara dengan vektor terdiri dari 5 hasil hanya dengan bidang yang ditentukan oleh pilihan select yang dikembalikan.

Membuat pencarian vektor tunggal dengan filter

Buat SearchSingleWithFilter.java file di src/main/java/com/example/search direktori.

Salin kode berikut ke dalam file.

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);
    }
}

Kode ini memiliki fungsionalitas pencarian yang sama dengan pencarian sebelumnya, tetapi menambahkan filter pengecualian pasca-pemrosesan untuk hotel dengan free wifi.

Buat dan jalankan file.

mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilter"

Output kode ini memperlihatkan dokumen yang relevan untuk kueri dengan filter untuk free wifi diterapkan:

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

Membuat pencarian dengan filter geospasial

Anda dapat menentukan filter geospasial untuk membatasi hasil ke area geografis tertentu. Dalam contoh ini, filter membatasi hasil ke hotel dalam jarak 300 kilometer dari Washington D.C. (koordinat: -77.03241 38.90166). Jarak geospasial selalu dalam kilometer. Pencarian ini menggunakan SearchClient. SearchClient dan SearchOptions.

Buat SearchSingleWithFilterGeo.java file di src/main/java/com/example/search direktori.

Salin kode berikut ke dalam file.

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);
    }
}

Buat dan jalankan file.

mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilterGeo"

Output dari kode ini memperlihatkan dokumen yang relevan untuk kueri dengan diterapkannya filter pengecualian pasca-pemrosesan geospasial.

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

Membuat pencarian hibrid

Pencarian hibrid terdiri dari kueri kata kunci dan kueri vektor dalam satu permintaan pencarian. Contoh ini menjalankan kueri vektor dan pencarian teks lengkap secara bersamaan:

String pencarian: historic hotel walk to restaurants and shopping
String kueri vektor (di-vektorisasi ke dalam representasi matematika): quintessential lodging near running trails, eateries, retail

Pencarian ini menggunakan SearchClient. SearchClient dan SearchOptions.

Buat SearchHybrid.java file di src/main/java/com/example/search direktori.

Salin kode berikut ke dalam file.

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);
    }
}

Buat dan jalankan file.

mvn compile exec:java -Dexec.mainClass="com.example.search.SearchHybrid"

Output kode ini menunjukkan dokumen yang relevan untuk pencarian hibrid.

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"]

   {
       "@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"
   },

"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"
    }
]

Membuat pencarian hibrid semantik

Berikut adalah kueri terakhir dalam koleksi untuk memperluas pencarian hibrida semantik dengan teks pencarian tambahan historic hotel walk to restaurants and shopping.

Pencarian ini menggunakan SearchClient. SearchClient dan SearchOptions.

Buat SearchSemanticHybrid.java file di src/main/java/com/example/search direktori.

Salin kode berikut ke dalam file.

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);
    }
}

Buat dan jalankan file.

mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSemanticHybrid"

Output kode ini menunjukkan dokumen yang relevan untuk pencarian hibrid semantik.

Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>

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

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

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

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

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

Kesimpulan utama:

Pencarian vektor ditentukan melalui VectorSearchOptions kelas . Pencarian kata kunci ditentukan melalui SemanticSearchOptions kelas .
Dalam pencarian hibrid, Anda dapat mengintegrasikan pencarian vektor dengan pencarian teks lengkap melalui kata kunci. Filter, pemeriksaan ejaan, dan peringkat semantik hanya berlaku untuk konten tekstual, dan bukan vektor. Dalam kueri akhir ini, tidak ada semantik answer karena sistem tidak menghasilkan yang cukup kuat.
Hasil aktual mencakup detail lebih lanjut, termasuk keterangan semantik dan sorotan. Hasil dimodifikasi untuk keterbacaan. Untuk mendapatkan struktur respons lengkap, jalankan permintaan di klien REST.

Penghapusan

Anda dapat menemukan dan mengelola sumber daya di portal Azure dengan menggunakan tautan Semua sumber daya atau Grup sumber daya di panel paling kiri.

Jika Anda ingin menyimpan layanan pencarian tetapi menghapus indeks dan dokumennya, Anda dapat menghapus indeks secara terprogram.

Buat DeleteIndex.java file di src/main/java/com/example/search direktori.

Tambahkan kode berikut ke file.

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);
    }
}

Buat dan jalankan file.

mvn compile exec:java -Dexec.mainClass="com.example.search.DeleteIndex"

Langkah berikutnya

Tinjau repositori sampel kode untuk kemampuan pencarian vektor di Azure AI Search for Java

Dalam panduan cepat ini, Anda menggunakan JavaScript untuk membuat, memuat, dan mengajukan kueri vektor. Contoh kode melakukan operasi ini dengan menggunakan pustaka klien Azure AI Search. Pustaka menyediakan abstraksi atas REST API untuk akses ke operasi indeks seperti penyerapan data, operasi pencarian, dan operasi manajemen indeks.

Catatan

Prasyarat

Sebuah akun Azure dengan langganan aktif. Buat akun secara gratis.
Azure AI layanan Pencarian. Buat layanan atau temukan layanan yang sudah ada di langganan Anda saat ini.
- Peran Search Index Data Contributor yang ditetapkan pada cakupan layanan.
- Anda dapat menggunakan layanan pencarian gratis untuk sebagian besar panduan cepat ini, tetapi kami merekomendasikan tingkat Basic atau lebih tinggi untuk file data yang lebih besar.
- Untuk menjalankan contoh kueri yang memanggil reranking semantik, layanan pencarian Anda harus mengaktifkan ranker semantik.
Kode Visual Studio.
Node.JS dengan LTS.

Mendapatkan titik akhir layanan

Di bagian yang tersisa, Anda menyiapkan panggilan API ke Azure OpenAI dan Azure AI Search. Dapatkan titik akhir layanan sehingga Anda dapat menyediakannya sebagai variabel dalam kode Anda.

Masuk ke portal Azure.
Temukan layanan pencarian Anda.
Pada beranda Gambaran Umum , salin URL. Contoh titik akhir mungkin terlihat sepertihttps://example.search.windows.net.

Menyiapkan proyek Node.JS

Siapkan proyek dengan Visual Studio Code dan JavaScript.

Mulai Visual Studio Code di direktori baru.

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

Buat paket baru untuk modul ESM di direktori proyek Anda.
```
npm init -y
npm pkg set type=module
```
Ini membuat berkas package.json dengan nilai default.

Instal paket npm berikut.

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

Buat src direktori di direktori proyek Anda.
```
mkdir src
```

Menyiapkan variabel lingkungan untuk pengembangan lokal

Buat .env file di direktori proyek Anda vector-quickstart .
Tambahkan variabel lingkungan berikut ke .env file, ganti nilai dengan titik akhir dan kunci layanan Anda sendiri.
```
AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart
```

Jalankan setiap perintah berikut secara berurutan.

az account show

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

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

Anda sekarang harus masuk ke Azure dari perangkat lokal Anda.

Membuat indeks vektor

Di bagian ini, Anda membuat indeks vektor di Azure AI Search dengan SearchIndexClient. createOrUpdateIndex. Skema indeks menentukan bidang, termasuk bidang DescriptionVectorvektor .

Buat createIndex.js file di src direktori.

Salin kode berikut ke dalam file.

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`);

File kode menambahkan dependensi, variabel lingkungan, dan jenis JavaScript untuk HotelDocument ke bagian atas file. createIndex Tambahkan fungsi untuk membuat indeks. Fungsi ini mendefinisikan skema indeks, termasuk bidang DescriptionVectorvektor .

Jalankan file:

node -r dotenv/config src/createIndex.js

Output kode ini menunjukkan bahwa indeks berhasil dibuat:
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Creating index...
hotels-vector-quickstart created
```
Poin penting saat membuat indeks vektor dengan pustaka @azure/search-documents.
- Anda menentukan indeks dengan membuat daftar bidang.
- Indeks khusus ini mendukung beberapa kemampuan pencarian, seperti:
  - Pencarian kata kunci teks lengkap (searchable)
  - Pencarian vektor (memungkinkan pencarian hibrid dengan menggabungkan bidang vektor dan nonvektor) (DescriptionVector dengan vectorSearchProfileName)
  - Pencarian semantik
  - Pencarian berfasilitas (searchSuggester)
  - Pencarian geo-spasial (kolom Location dengan geo.distance)
  - Pemfilteran, pengurutan (Banyak bidang ditandai dapat difilter dan dapat diurutkan)

Mengunggah dokumen ke indeks

Di Pencarian Azure AI, indeks menyimpan semua konten yang dapat dicari, sementara mesin pencari menjalankan kueri terhadap indeks tersebut.

Buat uploadDocuments.js file di src direktori.

Salin kode berikut ke dalam file.

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

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

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

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

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

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

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

Kode ini memuat array objek JSON. Setiap dokumen menyertakan versi vektor artikel description. Vektor ini memungkinkan pencarian kesamaan, di mana pencocokan didasarkan pada arti daripada kata kunci yang tepat.

Karena artikel panduan cepat ini segera mencari indeks, fungsi waitUntilIndexed menunggu hingga indeks siap melakukan operasi pencarian. Ini penting karena indeks perlu diisi penuh dengan dokumen sebelum Anda dapat melakukan pencarian.

Buat dan jalankan file:

node -r dotenv/config src/uploadDocuments.js

Output kode ini menunjukkan bahwa dokumen diindeks dan siap untuk pencarian:
```
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.
```
Kunci takeaways tentang metode uploadDocuments() dan contoh ini:
- Kode Anda berinteraksi dengan indeks pencarian tertentu yang dihosting di layanan Azure AI Search Anda melalui SearchClient, yang merupakan objek utama yang disediakan oleh @azure/search-documents paket. SearchClient menyediakan akses ke operasi indeks seperti:
  - Penyerapan data - uploadDocuments(), mergeDocuments(), deleteDocuments(), dll.
  - Operasi pencarian - search(), autocomplete(), suggest()
  - Operasi manajemen indeks seperti createIndex(), deleteIndex(), getIndex(), dll.
- Bidang vektor berisi nilai titik mengambang. Atribut dimensi memiliki minimal 2 dan maksimum 4096 nilai titik pecahan masing-masing. Panduan cepat ini mengatur atribut dimensi ke 1.536 karena itu adalah ukuran penyematan yang dihasilkan oleh model penyematan teks-embedding-ada-002 Azure OpenAI.

Membuat kueri sebagai vektor

Contoh kueri vektor didasarkan pada dua string:

String pencarian: historic hotel walk to restaurants and shopping
String kueri vektor (di-vektorisasi ke dalam representasi matematika): quintessential lodging near running trails, eateries, retail

Buat queryVector.js file di src direktori dan tambahkan kode untuk membuat vektor kueri.

// 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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Kode ini digunakan di bagian berikut untuk melakukan pencarian vektor. Vektor kueri dibuat menggunakan model penyematan dari Azure OpenAI.

Membuat satu pencarian vektor

Contoh pertama menunjukkan skenario dasar di mana Anda ingin menemukan deskripsi dokumen yang sangat cocok dengan string pencarian menggunakan SearchClient. cari dan VectorQuery dan SearchOptions.

Buat searchSingle.js file di src direktori.

Salin kode berikut ke dalam file.

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;
}

VektorQuery berisi konfigurasi kueri vektorisasi termasuk teks input kueri yang di-vektorisasi sebagai vector, fields menentukan bidang vektor mana yang dicari, dan kNearestNeighborsCount menentukan jumlah tetangga terdekat yang akan dikembalikan.

String kueri vektor adalah quintessential lodging near running trails, eateries, retail, yang di vektorisasi menjadi 1.536 penyematan untuk kueri ini.

Buat dan jalankan file:

node -r dotenv/config src/searchSingle.js

Output kode ini menunjukkan dokumen yang relevan untuk kueri quintessential lodging near running trails, eateries, retail.

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 0.57902366

Respons untuk quintessential lodging near running trails, eateries, retail yang setara dengan vektor terdiri dari 5 hasil hanya dengan bidang yang ditentukan oleh pilihan select yang dikembalikan.

Membuat pencarian vektor tunggal dengan filter

Buat searchSingleWithFilter.js file di src direktori.

Salin kode berikut ke dalam file.

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;
}

Tambahkan dependensi, variabel lingkungan sistem, dan fitur pencarian yang sama dengan pencarian sebelumnya, dengan filter pengecualian yang ditambahkan setelah pemrosesan untuk hotel dengan free wifi.

Buat dan jalankan file:

node -r dotenv/config src/searchSingleWithFilter.js

Output kode ini memperlihatkan dokumen yang relevan untuk kueri dengan filter untuk free wifi diterapkan:

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

Membuat pencarian dengan filter geospasial

Buat searchSingleWithFilterGeo.js file di src direktori.

Salin kode berikut ke dalam file.

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;
}

Buat dan jalankan file:

node -r dotenv/config src/searchSingleWithFilterGeo.js

Hasil dari kode ini menunjukkan dokumen yang relevan untuk kueri dengan filter pengecualian geospasial pasca-pemrosesan diterapkan.

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

Membuat pencarian hibrid

Pencarian hibrid terdiri dari kueri kata kunci dan kueri vektor dalam satu permintaan pencarian. Contoh ini menjalankan kueri vektor dan pencarian teks lengkap secara bersamaan:

String pencarian: historic hotel walk to restaurants and shopping
String kueri vektor (di-vektorisasi ke dalam representasi matematika): quintessential lodging near running trails, eateries, retail

Pencarian ini menggunakan SearchClient. cari dan VectorQuery dan SearchOptions.

Buat searchHybrid.js file di src direktori.

Salin kode berikut ke dalam file.

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;
}

Buat dan jalankan file:

node -r dotenv/config src/searchHybrid.js

Output kode ini menunjukkan dokumen yang relevan untuk pencarian hibrid:

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"]

   {
       "@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"
   },

"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"
    }
]

Membuat pencarian hibrid semantik

Berikut adalah kueri terakhir dalam koleksi untuk memperluas pencarian hibrida semantik dengan teks pencarian tambahan historic hotel walk to restaurants and shopping.

Pencarian ini menggunakan SearchClient. cari dan VectorQuery dan SearchOptions.

Buat searchSemanticHybrid.js file di src direktori.

Salin kode berikut ke dalam file.

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;
}

Buat dan jalankan file:

node -r dotenv/config src/searchSemanticHybrid.js

Output kode ini menunjukkan dokumen yang relevan untuk pencarian hibrid semantik:

Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>


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

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

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

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

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

Kesimpulan utama:

Pencarian vektor ditentukan melalui vectorSearchOptions properti . Pencarian kata kunci ditentukan melalui semanticSearchOptions properti .
Dalam pencarian hibrid, Anda dapat mengintegrasikan pencarian vektor dengan pencarian teks lengkap melalui kata kunci. Filter, pemeriksaan ejaan, dan peringkat semantik hanya berlaku untuk konten tekstual, dan bukan vektor. Dalam kueri akhir ini, tidak ada semantik answer karena sistem tidak menghasilkan yang cukup kuat.
Hasil aktual mencakup detail lebih lanjut, termasuk keterangan semantik dan sorotan. Hasil dimodifikasi untuk keterbacaan. Untuk mendapatkan struktur respons lengkap, jalankan permintaan di klien REST.

Penghapusan

Anda dapat menemukan dan mengelola sumber daya di portal Azure dengan menggunakan tautan Semua sumber daya atau Grup sumber daya di panel paling kiri.

Jika Anda ingin menyimpan layanan pencarian tetapi menghapus indeks dan dokumennya, Anda dapat menghapus indeks secara terprogram.

Buat deleteIndex.js file di src direktori.

Tambahkan dependensi, variabel lingkungan, dan kode untuk menghapus indeks.

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);
}

Buat dan jalankan file:

node -r dotenv/config src/deleteIndex.js

Langkah berikutnya

Tinjau repositori sampel kode untuk kemampuan pencarian vektor di Azure AI Search for JavaScript

Dalam panduan cepat ini, Anda menggunakan TypeScript untuk membuat, memuat, dan kueri vektor. Contoh kode melakukan operasi ini dengan menggunakan pustaka klien Azure AI Search. Pustaka menyediakan abstraksi atas REST API untuk akses ke operasi indeks seperti penyerapan data, operasi pencarian, dan operasi manajemen indeks.

Catatan

Prasyarat

Sebuah akun Azure dengan langganan aktif. Buat akun secara gratis.
Azure AI layanan Pencarian. Buat layanan atau temukan layanan yang sudah ada di langganan Anda saat ini.
- Peran Search Index Data Contributor yang ditetapkan pada cakupan layanan.
- Anda dapat menggunakan layanan pencarian gratis untuk sebagian besar panduan cepat ini, tetapi kami merekomendasikan tingkat Basic atau lebih tinggi untuk file data yang lebih besar.
- Untuk menjalankan contoh kueri yang memanggil reranking semantik, layanan pencarian Anda harus mengaktifkan ranker semantik.
Kode Visual Studio.
Node.JS dengan LTS.
TypeScript. Anda dapat menginstal TypeScript secara global menggunakan npm:
```
npm install -g typescript
```

Mendapatkan titik akhir layanan

Di bagian yang tersisa, Anda menyiapkan panggilan API ke Azure OpenAI dan Azure AI Search. Dapatkan titik akhir layanan sehingga Anda dapat menyediakannya sebagai variabel dalam kode Anda.

Masuk ke portal Azure.
Temukan layanan pencarian Anda.
Pada beranda Gambaran Umum , salin URL. Contoh titik akhir mungkin terlihat sepertihttps://example.search.windows.net.

Menyiapkan proyek Node.JS

Siapkan proyek dengan Visual Studio Code dan TypeScript.

Mulai Visual Studio Code di direktori baru.

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

Buat paket baru untuk modul ESM di direktori proyek Anda.
```
npm init -y
npm pkg set type=module
```
Ini membuat berkas package.json dengan nilai default.
Edit package.json untuk menyertakan skrip build. Tambahkan baris berikut ke bagian scripts :
```
"build": "tsc"
```

Instal paket npm berikut.

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

Buat src direktori di direktori proyek Anda.
```
mkdir src
```

Buat tsconfig.json file di direktori proyek untuk ESM dengan konten berikut.

{
  "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"
  ]
}

Menyiapkan variabel lingkungan untuk pengembangan lokal

Buat .env file di direktori proyek Anda vector-quickstart .
Tambahkan variabel lingkungan berikut ke .env file, ganti nilai dengan titik akhir dan kunci layanan Anda sendiri.
```
AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart
```

Jalankan setiap perintah berikut secara berurutan.

az account show

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

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

Anda sekarang harus masuk ke Azure dari perangkat lokal Anda.

Membuat indeks vektor

Di bagian ini, Anda membuat indeks vektor di Azure AI Search dengan SearchIndexClient. createOrUpdateIndex. Skema indeks menentukan bidang, termasuk bidang DescriptionVectorvektor . Setelah indeks dibuat, Anda mengunggah dokumen ke indeks. Dokumen berisi versi vektor deskripsi artikel, yang memungkinkan pencarian kesamaan berdasarkan arti daripada kata kunci yang tepat.

Buat createIndex.ts file di src direktori.

Salin kode berikut ke dalam file.

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`);

Buat dan jalankan file:

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

Output kode ini menunjukkan bahwa indeks berhasil dibuat:
```
Using Azure Search endpoint: https://my-service.search.windows.net
Using index name: hotels-vector-quickstart
Creating index...
hotels-vector-quickstart created
```
Poin penting saat membuat indeks vektor dengan pustaka @azure/search-documents.
- Anda menentukan indeks dengan membuat daftar bidang.
- Indeks khusus ini mendukung beberapa kemampuan pencarian, seperti:
  - Pencarian kata kunci teks lengkap (searchable)
  - Pencarian vektor (memungkinkan pencarian hibrid dengan menggabungkan bidang vektor dan nonvektor) (DescriptionVector dengan vectorSearchProfileName)
  - Pencarian semantik
  - Pencarian berfasilitas (searchSuggester)
  - Pencarian geo-spasial (kolom Location dengan geo.distance)
  - Pemfilteran, pengurutan (Banyak bidang ditandai dapat difilter dan dapat diurutkan)

Mengunggah dokumen ke indeks

Membuat dan memuat indeks adalah langkah-langkah terpisah. Anda membuat skema indeks di langkah sebelumnya. Sekarang Anda perlu memuat dokumen ke dalam indeks dengan SearchClient. uploadDocuments.

Di Pencarian Azure AI, indeks menyimpan semua konten yang dapat dicari, sementara mesin pencari menjalankan kueri terhadap indeks tersebut.

Buat uploadDocuments.ts file di src direktori.

Salin kode berikut ke dalam file.

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

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

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

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

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

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

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

Kode ini memuat dokumen variabel dengan objek JSON yang menjelaskan setiap dokumen, bersama dengan versi vektor deskripsi artikel. Vektor ini memungkinkan pencarian kesamaan, di mana pencocokan didasarkan pada arti daripada kata kunci yang tepat.

Buat dan jalankan file:

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

Output kode ini menunjukkan bahwa dokumen diindeks dan siap untuk pencarian:
```
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.
```
Poin penting mengenai metode upload_documents() dan contoh ini:
- Kode Anda berinteraksi dengan indeks pencarian tertentu yang dihosting di layanan Azure AI Search Anda melalui SearchClient, yang merupakan objek utama yang disediakan oleh @azure/search-documents paket. SearchClient menyediakan akses ke operasi indeks seperti:
  - Penyerapan data - uploadDocuments(), mergeDocuments(), deleteDocuments(), dll.
  - Operasi pencarian - search(), autocomplete(), suggest()
  - Operasi manajemen indeks seperti createIndex(), deleteIndex(), getIndex(), dll.
- Bidang vektor berisi nilai titik mengambang. Atribut dimensi memiliki minimal 2 dan maksimum 4096 nilai titik pecahan masing-masing. Panduan cepat ini mengatur atribut dimensi ke 1.536 karena itu adalah ukuran penyematan yang dihasilkan oleh model penyematan teks-embedding-ada-002 Azure OpenAI.

Membuat kueri sebagai vektor

Contoh kueri vektor didasarkan pada dua string:

String pencarian: historic hotel walk to restaurants and shopping
String kueri vektor (di-vektorisasi ke dalam representasi matematika): quintessential lodging near running trails, eateries, retail

Buat queryVector.ts file di src direktori dan tambahkan kode untuk membuat vektor kueri.

// 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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Kode ini digunakan di bagian berikut untuk melakukan pencarian vektor. Vektor kueri dibuat menggunakan model penyematan dari Azure OpenAI.

Membuat satu pencarian vektor

Contoh pertama menunjukkan skenario dasar di mana Anda ingin menemukan deskripsi dokumen yang sangat cocok dengan string pencarian menggunakan SearchClient. cari dan VectorQuery dan SearchOptions.

Buat searchSingle.ts file di src direktori.

Salin kode berikut ke dalam file.

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;
}

String kueri vektor adalah quintessential lodging near running trails, eateries, retail, yang di vektorisasi menjadi 1.536 penyematan untuk kueri ini.

Buat dan jalankan file:

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

Output kode ini menunjukkan dokumen yang relevan untuk kueri quintessential lodging near running trails, eateries, retail.

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


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

Respons untuk quintessential lodging near running trails, eateries, retail yang setara dengan vektor terdiri dari 5 hasil hanya dengan bidang yang ditentukan oleh pilihan select yang dikembalikan.

Membuat pencarian vektor tunggal dengan filter

Buat searchSingleWithFilter.ts file di src direktori.

Salin kode berikut ke dalam file.

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;
}

Buat dan jalankan file:

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

Output kode ini memperlihatkan dokumen yang relevan untuk kueri dengan filter untuk free wifi diterapkan:

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

Membuat pencarian dengan filter geospasial

Buat searchSingleWithFilterGeo.ts file di src direktori.

Salin kode berikut ke dalam file.

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;
}

Buat dan jalankan file:

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

Hasil dari kode ini menunjukkan dokumen yang relevan untuk kueri dengan filter pengecualian geospasial pasca-pemrosesan diterapkan.

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

Membuat pencarian hibrid

Pencarian hibrid terdiri dari kueri kata kunci dan kueri vektor dalam satu permintaan pencarian. Contoh ini menjalankan kueri vektor dan pencarian teks lengkap secara bersamaan:

String pencarian: historic hotel walk to restaurants and shopping
String kueri vektor (di-vektorisasi ke dalam representasi matematika): quintessential lodging near running trails, eateries, retail

Pencarian ini menggunakan SearchClient. cari dan VectorQuery dan SearchOptions.

Buat searchHybrid.ts file di src direktori.

Salin kode berikut ke dalam file.

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;
}

Buat dan jalankan file:

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

Output kode ini menunjukkan dokumen yang relevan untuk pencarian hibrid:

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"]

   {
       "@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"
   },

"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"
    }
]

Membuat pencarian hibrid semantik

Berikut adalah kueri terakhir dalam koleksi untuk memperluas pencarian hibrida semantik dengan teks pencarian tambahan historic hotel walk to restaurants and shopping.

Pencarian ini menggunakan SearchClient. cari dan VectorQuery dan SearchOptions.

Buat searchSemanticHybrid.ts file di src direktori.

Salin kode berikut ke dalam file.

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;
}

Buat dan jalankan file:

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

Output kode ini menunjukkan dokumen yang relevan untuk pencarian hibrid semantik:

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


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

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

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

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

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

Kesimpulan utama:

Pencarian vektor ditentukan melalui vectorSearchOptions properti . Pencarian kata kunci ditentukan melalui semanticSearchOptions properti .
Dalam pencarian hibrid, Anda dapat mengintegrasikan pencarian vektor dengan pencarian teks lengkap melalui kata kunci. Filter, pemeriksaan ejaan, dan peringkat semantik hanya berlaku untuk konten tekstual, dan bukan vektor. Dalam kueri akhir ini, tidak ada semantik answer karena sistem tidak menghasilkan yang cukup kuat.
Hasil aktual mencakup detail lebih lanjut, termasuk keterangan semantik dan sorotan. Hasil dimodifikasi untuk keterbacaan. Untuk mendapatkan struktur respons lengkap, jalankan permintaan di klien REST.

Penghapusan

Anda dapat menemukan dan mengelola sumber daya di portal Azure dengan menggunakan tautan Semua sumber daya atau Grup sumber daya di panel paling kiri.

Jika Anda ingin menyimpan layanan pencarian tetapi menghapus indeks dan dokumennya, Anda dapat menghapus indeks secara terprogram.

Buat deleteIndex.ts file di src direktori.

Tambahkan dependensi, variabel lingkungan, dan kode untuk menghapus indeks.

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);
}

Buat dan jalankan file:

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

Langkah berikutnya

Tinjau repositori sampel kode untuk kemampuan pencarian vektor di Azure AI Search for JavaScript

Dalam mulai cepat ini, Anda bekerja dengan aplikasi .NET untuk membuat, mengisi, dan melakukan kueri pada vektor. Contoh kode melakukan operasi ini dengan menggunakan pustaka klien Azure AI Search. Pustaka menyediakan abstraksi atas REST API untuk akses ke operasi indeks seperti penyerapan data, operasi pencarian, dan operasi manajemen indeks.

Catatan

Prasyarat

Sebuah akun Azure dengan langganan aktif. Buat akun secara gratis.
Azure AI layanan Pencarian. Buat layanan atau temukan layanan yang sudah ada di langganan Anda saat ini.
- Anda dapat menggunakan layanan pencarian gratis untuk sebagian besar panduan cepat ini, tetapi kami merekomendasikan tingkat Basic atau lebih tinggi untuk file data yang lebih besar.
- Untuk menjalankan contoh kueri yang memanggil reranking semantik, layanan pencarian Anda harus mengaktifkan ranker semantik.
Visual Studio Code atau Visual Studio.
Git untuk mengkloning repositori sampel.

Mendapatkan informasi layanan

Panduan cepat ini menggunakan DefaultAzureCredential, yang menyederhanakan autentikasi dalam skenario pengembangan dan produksi. Namun, untuk skenario produksi, Anda mungkin memiliki persyaratan yang lebih canggih yang memerlukan pendekatan yang berbeda. Lihat Mengautentikasi aplikasi .NET ke layanan Azure dengan menggunakan Azure SDK untuk .NET untuk memahami semua opsi Anda.

Mengkloning kode dan lingkungan penyiapan

Klon repositori yang berisi kode untuk panduan memulai cepat ini.
```
git clone https://github.com/Azure-Samples/azure-search-dotnet-samples
```
Repositori ini memiliki contoh kode .NET untuk beberapa artikel masing-masing dalam subfolder terpisah.
Buka subfolder quickstart-Vector-Search di Visual Studio Code, atau klik dua kali file .sln untuk membuka solusi di Visual Studio.
Buka appsettings.json file di folder VectorSearchExamples dan VectorSearchCreatePopulateIndex. Perbarui nilai berikut ini:
- AZURE_SEARCH_ENDPOINT: Temukan url layanan Pencarian Azure AI Anda di portal Microsoft Azure. Pada halaman Gambaran Umum sumber daya pencarian Anda, cari bidang URL. Contoh titik akhir mungkin terlihat sepertihttps://mydemo.search.windows.net.
- AZURE_SEARCH_INDEX_NAME: Biarkan nilai default yang disediakan dalam file atau masukkan nama indeks Anda sendiri.

Membuat indeks vektor dan mengunggah dokumen

Untuk menjalankan kueri pencarian terhadap layanan Pencarian Azure AI, Anda harus terlebih dahulu membuat indeks pencarian dan mengunggah dokumen ke layanan.

Buka terminal baru di VectorSearchCreatePopulateIndex folder.
Jalankan proyek menggunakan dotnet run perintah :
```
dotnet run
```

Kode berikut dijalankan untuk membuat indeks:

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();
}

Kode berikut mengunggah dokumen berformat JSON dalam hotel-samples.json file ke layanan Pencarian Azure AI:

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);
    }
}

Setelah Anda menjalankan proyek, output berikut dicetak:

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

Kesimpulan utama:

Kode Anda berinteraksi dengan indeks pencarian tertentu yang dihosting di layanan Azure AI Search Anda melalui SearchClient, yang merupakan objek utama yang disediakan oleh azure-search-documents paket. SearchClient menyediakan akses ke operasi indeks seperti:
- Penyerapan data: UploadDocuments(), MergeDocuments(), DeleteDocuments()
- Operasi pencarian: Search(), Autocomplete(), Suggest()
- Operasi manajemen indeks: CreateOrUpdateIndex()
Bidang vektor berisi nilai titik mengambang. Atribut dimensi memiliki minimal 2 dan maksimum 4096 nilai titik pecahan masing-masing. Ukuran penyematan yang dihasilkan oleh model teks-penyematan-3-kecil Azure OpenAI untuk mulai cepat ini adalah 1536.

Menjalankan kueri pencarian

Setelah indeks dibuat dan dokumen dimuat, Anda dapat mengeluarkan kueri vektor terhadapnya dengan memanggil SearchAsync() dengan berbagai parameter.

Di folder VectorSearchExamples, buka file Program.cs.
Buka terminal baru di VectorSearchExamples folder.

Pada bagian berikutnya, Anda menjalankan kueri terhadap indeks hotels-vector-quickstart. Kueri meliputi:

Pencarian vektor tunggal
Pencarian vektor tunggal dengan filter
Pencarian hibrid
Pencarian hibrid semantik dengan filter

Pencarian vektor tunggal

Contoh pertama menunjukkan skenario dasar di mana Anda ingin menemukan deskripsi dokumen yang sangat cocok dengan string pencarian.

Di dalam file Program.cs di folder VectorSearchExamples, batalkan komentar pemanggilan metode SearchExamples.SearchSingleVector(searchClient, vectorizedResult);. Metode ini menjalankan fungsi pencarian berikut di SearchExamples.cs kelas :

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();
}

Jalankan proyek menggunakan dotnet run perintah :

dotnet run

Setelah Anda menjalankan proyek, hasil pencarian dicetak di jendela output:

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

Pencarian vektor tunggal dengan filter

Di dalam file Program.cs di folder VectorSearchExamples, batalkan komentar pemanggilan metode SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);. Metode ini menjalankan fungsi pencarian berikut di SearchExamples.cs kelas :

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();
}

Jalankan proyek lagi, dan status setiap dokumen dicetak di bawahnya:

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

Kueri sama dengan contoh pencarian vektor tunggal sebelumnya, tetapi mencakup filter pengecualian pasca-pemrosesan dan hanya mengembalikan dua hotel yang memiliki Wi-Fi gratis.

Contoh filter berikutnya menggunakan filter geografis. Di dalam file Program.cs di folder VectorSearchExamples, batalkan komentar pemanggilan metode SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult);. Metode ini menjalankan fungsi pencarian berikut di SearchExamples.cs kelas :

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();
}

Kueri sama dengan contoh pencarian vektor tunggal sebelumnya, tetapi mencakup filter pengecualian pasca-pemrosesan dan hanya mengembalikan dua hotel dalam jarak 300 kilometer.

Jalankan proyek lagi, dan status setiap dokumen dicetak di bawahnya:

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.

Pencarian hibrid

Pencarian hibrid terdiri dari kueri kata kunci dan kueri vektor dalam satu permintaan pencarian. Contoh ini menjalankan kueri vektor dan pencarian teks lengkap secara bersamaan:

String pencarian: historic hotel walk to restaurants and shopping
String kueri vektor (di-vektorisasi ke dalam representasi matematika): Quintessential lodging near running trails, eateries, retail

Di dalam file Program.cs di folder VectorSearchExamples, batalkan komentar pemanggilan metode SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);. Metode ini menjalankan fungsi pencarian berikut di SearchExamples.cs kelas :

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;
}

Jalankan proyek lagi, dan status setiap dokumen dicetak di bawahnya:

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

{
    "@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"
},

"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"
    }
]

Pencarian hibrid semantik dengan filter

Kueri hibrid dengan peringkat semantik difilter untuk menunjukkan hanya hotel dalam radius 500 kilometer dari Washington D.C.

Di dalam file Program.cs di folder VectorSearchExamples, batalkan komentar pemanggilan metode SearchExamples.SearchHybridVectoryAndSemantic(searchClient, vectorizedResult);. Metode ini menjalankan fungsi pencarian berikut di SearchExamples.cs kelas :

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();
}

Jalankan proyek lagi, dan tinjau output di bawah sel. Responsnya adalah tiga hotel, yang difilter berdasarkan lokasi dan disaring oleh StateProvince dan direrankasi secara semantik untuk mempromosikan hasil yang paling dekat dengan kueri string pencarian (historic hotel walk to restaurants and shopping).

Hotel Swirling Currents sekarang bergerak ke tempat teratas. Tanpa peringkat semantik, Nordick's Valley Motel adalah nomor satu. Dengan peringkat semantik, model pemahaman mesin mengenali yang historic berlaku untuk "hotel, dalam jarak berjalan kaki ke makan (restoran) dan berbelanja."

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

Kesimpulan utama:

Dalam pencarian hibrid, Anda dapat mengintegrasikan pencarian vektor dengan pencarian teks lengkap melalui kata kunci. Filter, pemeriksaan ejaan, dan peringkat semantik hanya berlaku untuk konten tekstual, dan bukan vektor. Dalam kueri akhir ini, tidak ada semantik answer karena sistem tidak menghasilkan yang cukup kuat.
Hasil aktual mencakup detail lebih lanjut, termasuk keterangan semantik dan sorotan. Hasil dimodifikasi untuk keterbacaan. Untuk mendapatkan struktur respons lengkap, jalankan permintaan di klien REST.

Penghapusan

Anda dapat menemukan dan mengelola sumber daya di portal Azure dengan menggunakan tautan Semua sumber daya atau Grup sumber daya di panel paling kiri.

Langkah berikutnya

Tinjau repositori sampel kode untuk kemampuan pencarian vektor di Azure AI Search for .NET
Tinjau sampel kode .NET dan Azure AI Search lainnya di repositori azure-search-dotnet-samples

Dalam panduan cepat ini, Anda menggunakan API REST Azure AI Search untuk membuat, memuat, dan menanyakan vektor.

Catatan

Prasyarat

Sebuah akun Azure dengan langganan aktif. Buat akun secara gratis.
Azure AI layanan Pencarian. Buat layanan atau temukan layanan yang sudah ada di langganan Anda saat ini.
- Anda dapat menggunakan layanan pencarian gratis untuk sebagian besar panduan cepat ini, tetapi kami merekomendasikan tingkat Basic atau lebih tinggi untuk file data yang lebih besar.
- Untuk menjalankan contoh kueri yang memanggil reranking semantik, layanan pencarian Anda harus mengaktifkan ranker semantik.
Visual Studio Code dengan klien REST.

Mendapatkan informasi layanan

Permintaan ke titik akhir pencarian harus diautentikasi dan diotorisasi. Anda dapat menggunakan kunci atau peran API untuk tugas ini. Sebaiknya gunakan koneksi tanpa kunci melalui ID Microsoft Entra.

Pilih tab yang sesuai dengan metode autentikasi pilihan Anda. Gunakan metode yang sama untuk semua permintaan dalam mulai cepat ini.

Microsoft Entra ID
Kunci API

Masuk ke portal Azure dan temukan layanan pencarian Anda.
Pada beranda Gambaran Umum , temukan URL. Contoh titik akhir mungkin terlihat sepertihttps://mydemo.search.windows.net.
Ikuti langkah-langkah dalam mulai cepat tanpa kunci untuk mendapatkan token Microsoft Entra Anda.

Anda mendapatkan token saat menjalankan az account get-access-token perintah di langkah 3 dari mulai cepat sebelumnya.
```
az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
```

Masuk ke portal Azure dan temukan layanan pencarian Anda.
Pada beranda Gambaran Umum , temukan URL. Contoh titik akhir mungkin terlihat sepertihttps://mydemo.search.windows.net.
Pilih Pengaturan>Kunci. Kunci API atau Keduanya harus diaktifkan. Kunci API admin digunakan untuk menambahkan, memodifikasi, dan menghapus objek. Ada dua kunci admin yang dapat dipertukarkan. Salin salah satu.

Membuat atau mengunduh file kode

Anda menggunakan satu .rest atau .http file untuk menjalankan semua permintaan dalam mulai cepat ini. Anda dapat mengunduh file REST yang berisi kode untuk mulai cepat ini, atau Anda dapat membuat file baru di Visual Studio Code dan menyalin kode ke dalamnya.

Di Visual Studio Code, buat file baru dengan .rest ekstensi file atau .http . Contohnya,az-search-vector-quickstart.rest. Salin dan tempel konten mentah file Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest ke dalam file baru ini.
Di bagian atas file, ganti nilai tempat penampung dengan @baseUrl URL layanan pencarian Anda. Lihat bagian Dapatkan informasi layanan untuk petunjuk tentang cara menemukan URL layanan pencarian Anda.
```
@baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HERE
```
Di bagian atas file, ganti nilai tempat penampung untuk autentikasi. Lihat bagian Dapatkan informasi layanan untuk instruksi tentang cara mendapatkan token Microsoft Entra atau kunci API Anda.

Untuk autentikasi tanpa kunci yang direkomendasikan melalui ID Microsoft Entra, Anda perlu mengganti @apiKey dengan @token variabel .
```
@token = PUT-YOUR-MICROSOFT-ENTRA-TOKEN-HERE
```
Jika Anda lebih suka menggunakan kunci API, ganti @apiKey dengan kunci yang Anda salin dari portal Azure.
```
@apiKey = PUT-YOUR-ADMIN-KEY-HERE
```
Untuk autentikasi tanpa kunci yang direkomendasikan melalui ID Microsoft Entra, Anda perlu mengganti api-key: {{apiKey}} dengan Authorization: Bearer {{token}} di header permintaan. Ganti semua instans api-key: {{apiKey}} yang Anda temukan dalam file.

Membuat indeks vektor

Anda menggunakan Buat Indeks REST API untuk membuat indeks vektor dan menyiapkan struktur data fisik pada layanan pencarian Anda.

Skema indeks dalam contoh ini diatur di sekitar konten hotel. Data sampel terdiri dari deskripsi vektor dan nonvektor hotel fiktif. Skema ini mencakup konfigurasi untuk pengindeksan dan kueri vektor, dan untuk peringkat semantik.

Di Visual Studio Code, buka file yang az-search-vector-quickstart.rest Anda buat sebelumnya.

### Create a new index Temukan blok kode dalam file. Blok ini berisi permintaan untuk membuat hotels-vector-quickstart indeks pada layanan pencarian Anda.

### 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" }
                    ]
                }
            }
        ]
    }
}

Pilih Kirim Permintaan. Anda harus memiliki HTTP/1.1 201 Created respons.

Isi respons harus menyertakan representasi JSON dari skema indeks.

{
    "@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"
            }
            ]
        }
        }
    ]
    }
}

Kunci takeaways tentang Create Index REST API:

Koleksi fields mencakup bidang kunci dan bidang teks dan vektor yang diperlukan (seperti Description dan DescriptionVector) untuk pencarian teks dan vektor. Mengkolokasi bidang vektor dan nonvektor dalam indeks yang sama memungkinkan kueri hibrid. Misalnya, Anda dapat menggabungkan filter, pencarian teks dengan peringkat semantik, dan vektor ke dalam satu operasi kueri.
Bidang vektor harus merupakan salah satu jenis data EDM yang digunakan untuk vektor, seperti type: Collection(Edm.Single). Bidang vektor juga memiliki dimensions properti dan vectorSearchProfile .
Bagian vectorSearch adalah array konfigurasi dan profil algoritma Tetangga Terdekat Perkiraan (ANN). Algoritma yang didukung termasuk Hierarchical Navigable Small World dan ekstensif K-Nearest Neighbor. Untuk informasi selengkapnya, lihat Penilaian relevansi dalam pencarian vektor.
Konfigurasi (opsional) semantic memungkinkan reranking hasil pencarian. Anda dapat melakukan rerank menghasilkan kueri jenis semantic untuk bidang string yang ditentukan dalam konfigurasi. Untuk mempelajari selengkapnya, lihat Gambaran umum peringkat semantik.

Unggah dokumen

Membuat dan memuat indeks adalah langkah-langkah terpisah. Anda membuat skema indeks di langkah sebelumnya. Sekarang Anda perlu memuat dokumen ke dalam indeks.

Di Pencarian Azure AI, indeks berisi semua data dan kueri yang dapat dicari yang dijalankan pada layanan pencarian. Untuk panggilan REST, data disediakan sebagai dokumen JSON. Gunakan Dokumen- Indeks REST API untuk tugas ini. URI diperluas untuk menyertakan docs koleksi dan index operasi.

Merumuskan permintaan unggah dokumen untuk mengunggah dokumen ke hotels-vector-quickstart indeks di layanan pencarian Anda.

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

Pilih Kirim Permintaan. Anda harus memiliki HTTP/1.1 200 OK respons. Isi respons harus menyertakan representasi JSON dari dokumen pencarian.

Kunci takeaways tentang permintaan Dokumen - Indeks REST API :

Dokumen dalam payload terdiri dari bidang yang ditentukan dalam skema indeks.
Bidang vektor berisi nilai titik mengambang. Atribut dimensi memiliki minimal 2 dan maksimum 4096 nilai titik mengambang masing-masing. Mulai cepat ini mengatur atribut dimensi ke 1.536 karena itu adalah ukuran penyematan yang dihasilkan oleh model teks-penyematan-3-kecil Azure OpenAI.

Jalankan Kueri

Setelah dokumen dimuat, Anda dapat menjalankan kueri vektor terhadapnya dengan menggunakan Documents - Search Post (REST).

Di bagian berikutnya, kita menjalankan kueri terhadap hotels-vector-quickstart indeks. Kueri meliputi:

Pencarian vektor tunggal
Pencarian vektor tunggal dengan filter
Pencarian hibrid
Pencarian hibrid semantik

Contoh kueri didasarkan pada dua string:

String pencarian: historic hotel walk to restaurants and shopping
String kueri vektor (di-vektorisasi ke dalam representasi matematika): quintessential lodging near running trails, eateries, retail

String kueri vektor secara semantik mirip dengan string pencarian, tetapi menyertakan istilah yang tidak ada dalam indeks pencarian. Jika Anda melakukan pencarian kata kunci untuk quintessential lodging near running trails, eateries, retail, hasilnya nol dalam pencarian kata kunci murni tanpa peringkat semantik. Kami menggunakan contoh ini untuk menunjukkan bagaimana Anda bisa mendapatkan hasil yang relevan menggunakan vektor, bahkan jika tidak ada istilah yang cocok.

Pencarian vektor tunggal

Merumuskan permintaan. Kueri adalah representasi float 1536 dari akomodasi esensial di dekat jalur lari, restoran, ritel. Kueri mencari DescriptionVector dan mengembalikan hasil k-5. Ini menggunakan parameter penimpaan "lengkap" untuk melakukan pemindaian penuh indeks alih-alih ANN. Pencarian lengkap berguna untuk indeks kecil.

### 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
            }
        ]
    }

Poin penting tentang Documents - Search Post REST API:

vectorQueries.vector adalah string kueri vektor. Ini adalah representasi vektor dari penginapan quintessensial di dekat jalur lari, restoran, dan ritel, yang diubah menjadi 1.536 penyematan vektor untuk kueri ini.
fields menentukan bidang vektor mana yang dicari.
kind diatur ke vector berarti bahwa string kueri adalah vektor. Jika kind diatur ke text, Anda akan memerlukan kemampuan tambahan ( vektorizer) untuk mengodekan string teks yang dapat dibaca manusia ke vektor pada waktu kueri. Vektorizer dihilangkan dari panduan mulai cepat ini agar latihan tetap sederhana.
k menentukan jumlah kecocokan yang akan dikembalikan dalam respons. Parameter count menentukan jumlah kecocokan yang ditemukan dalam indeks. Termasuk hitungan adalah praktik terbaik untuk kueri, tetapi kurang berguna untuk pencarian kesamaan di mana algoritma dapat menemukan beberapa tingkat kesamaan dalam hampir semua dokumen.

Pilih Kirim Permintaan. Anda harus memiliki HTTP/1.1 200 OK respons. Isi respons harus menyertakan representasi JSON dari hasil pencarian.

Respon untuk padanan vektor quintessential lodging near running trails, eateries, retail termasuk k-5 hasil meskipun mesin pencari menemukan 7 kecocokan. Hasil teratas dianggap paling semantik mirip dengan kueri. Setiap hasil menyediakan skor pencarian dan bidang yang tercantum dalam select. Dalam pencarian kesamaan, respons selalu menyertakan k hasil yang diurutkan berdasarkan skor kesamaan nilai.

{
  "@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"
      ]
    }
  ]
}

Pencarian vektor tunggal dengan filter

Merumuskan permintaan. Ini adalah permintaan yang sama dengan yang sebelumnya, dengan parameter filter dan mode filter tambahan.

### 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
        },
    ]
}

Pilih Kirim Permintaan. Anda harus memiliki HTTP/1.1 200 OK respons. Isi respons harus menyertakan representasi JSON dari hasil pencarian.

Kueri sama dengan contoh pencarian vektor tunggal sebelumnya, tetapi mencakup filter pengecualian pasca-pemrosesan dan hanya mengembalikan dua hotel yang memiliki Wi-Fi gratis.

{
  "@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"
      ]
    }
  ]
}

Berikut adalah kueri lain yang menggunakan filter geografis, membatasi hasil ke hotel dalam jarak 300 kilometer di sekitar Washington D.C.

### 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
        },
    ]
}

Tinjau responsnya. Terbatas pada dua hotel di dekat Washington D.C, dengan struktur faset yang mendukung agregasi.

"@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"
    }
  }
]

Pencarian hibrid

Pencarian hibrid terdiri dari kueri kata kunci dan kueri vektor dalam satu permintaan pencarian. Contoh ini menjalankan kueri vektor dan pencarian teks lengkap secara bersamaan:

String pencarian: historic hotel walk to restaurants and shopping
String kueri vektor (di-vektorisasi ke dalam representasi matematika): quintessential lodging near running trails, eateries, retail

Merumuskan kueri hibrid.

### 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
        }
    ]
}

Pilih Kirim Permintaan. Anda harus memiliki HTTP/1.1 200 OK respons. Isi respons harus menyertakan representasi JSON dari hasil pencarian.

Karena ini adalah kueri hibrid, hasil diberi peringkat oleh Reciprocal Rank Fusion (RRF). Perhatikan bahwa nilai-nilai @search.score memiliki dasar yang berbeda dan nilainya secara umum lebih kecil. RRF mengevaluasi skor pencarian dari beberapa hasil pencarian, mengambil kebalikannya, lalu menggabungkan dan mengurutkan hasil gabungan. top Jumlah hasil dikembalikan.

Tinjau respons, yang terdiri dari kecocokan k-5 teratas dari 7 dokumen yang cocok dalam indeks:

{
  "@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."
    }
  ]
}

Karena RRF menggabungkan hasil, RRF membantu meninjau input satu per satu.

Hasil berikut berasal dari bagian teks lengkap kueri: hotel bersejarah yang berjarak berjalan kaki ke restoran dan pusat perbelanjaan. Dalam kueri teks lengkap, tiga hasil teratas adalah Sublime Palace Hotel, Stay-Kay City Hotel, dan Luxury Lion Resort. Hotel Sublime Palace memiliki skor relevansi BM25 yang lebih kuat. Dalam kueri gabungan, hanya dua dari kecocokan ini yang berada di 3 teratas, dan kecocokan kedua (Stay-Kay City Hotel) tidak muncul sama sekali di 5 teratas.

  "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."
    },
    ...
  ]

Dalam kueri bagian vektor (penginapan khas yang dekat dengan jalur lari, restoran, dan tempat perbelanjaan) adalah string yang berbeda sehingga kita harus mengharapkan hasil yang berbeda. Kueri ini mengembalikan Nordick's Valley Motel, Luxury Lion Resort, dan Sublime Palace Hotel sebagai tiga pertandingan teratas. Dalam kueri gabungan, hasil ini berada di tiga teratas, tetapi dalam urutan terbalik.

  "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."
    },
   ...
  ]

Pencarian hibrid semantik

Berikut adalah kueri terakhir dalam koleksi. Kueri hibrid ini menambahkan peringkat semantik L2 yang menerapkan pemahaman pembacaan mesin atas hasil peringkat L1, mempromosikan kecocokan yang lebih relevan ke bagian atas.

Merumuskan permintaan.

### Run a hybrid query with semantic reranking
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2025-09-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

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

Pilih Kirim Permintaan. Anda harus memiliki HTTP/1.1 200 OK respons. Isi respons harus menyertakan representasi JSON dari hasil pencarian.

Tinjau respons, yang terdiri dari pemeringkatan ulang semantik dari hasil kueri hibrid yang sudah diberi peringkat oleh RRF. Peringkat semantik bekerja dari input teks. Dalam teks atau kueri hibrid, input ini adalah bagian teks kueri (hotel bersejarah dekat dengan restoran dan pusat perbelanjaan). Untuk menggunakan peringkat semantik pada kueri vektor murni, seperti contoh pertama, atau untuk secara eksplisit mengontrol teks yang digunakan untuk peringkat semantik, berikan string semantikQuery.

Setelah reranking semantik, Swirling Currents Hotel dengan referensinya untuk akses berjalan kaki ke pusat perbelanjaan, restoran, hiburan, dan pusat kota bergerak ke tempat teratas. Model pemahaman mesin mempromosikan akses berjalan kaki untuk berbelanja dan makan sebagai kecocokan yang lebih dekat untuk berjalan ke restoran dan berbelanja.

Sebelum reranking semantik, Istana Sublime, yang mengacu pada jarak untuk berjalan kaki ke situs dan landmark kota serta dikelilingi oleh keindahan luar biasa gereja, bangunan, toko, dan monumen, adalah nomor satu.

{
  "@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"
    }
  ]
}

Petunjuk / Saran

Hasil berperingkat semantik dapat mencakup lebih banyak detail, termasuk jawaban semantik, keterangan, dan sorotan. Menambahkan lebih banyak parameter ke permintaan menghasilkan detail tambahan. Untuk informasi selengkapnya, lihat Menyiapkan kueri semantik.

Penghapusan

Anda dapat menemukan dan mengelola sumber daya di portal Azure dengan menggunakan tautan Semua sumber daya atau Grup sumber daya di panel paling kiri.

Jika Anda ingin menyimpan layanan pencarian, tetapi menghapus indeks dan dokumen, Anda dapat menggunakan DELETE perintah di klien REST. Perintah ini (di akhir file Anda az-search-vector-quickstart.rest ) menghapus hotels-vector-quickstart indeks:

### Delete an index
DELETE  {{baseUrl}}/indexes/hotels-vector-quickstart?api-version=2025-09-01 HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

Langkah berikutnya

Sebagai langkah selanjutnya, sebaiknya pelajari cara memanggil panggilan REST API tanpa kunci API.

Anda mungkin juga ingin meninjau kode demo untuk Python, C#, atau JavaScript.

Saran dan Komentar

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Last updated on 2025-11-20

Bagikan melalui

Mulai cepat: Pencarian vektor

Prasyarat

Mendapatkan informasi layanan

Mengkloning kode dan lingkungan penyiapan

Membuat indeks vektor

Unggah dokumen

Jalankan Kueri

Buatlah kueri string vektor

Pencarian vektor tunggal

Pencarian vektor tunggal dengan filter

Pencarian hibrid

Pencarian hibrid semantik

Penghapusan

Langkah berikutnya

Prasyarat

Mendapatkan titik akhir layanan

Menyiapkan proyek Java

Masuk ke Azure

Membuat indeks vektor

Mengunggah dokumen ke indeks

Membuat kueri sebagai vektor

Membuat satu pencarian vektor

Membuat pencarian vektor tunggal dengan filter

Membuat pencarian dengan filter geospasial

Membuat pencarian hibrid

Membuat pencarian hibrid semantik

Penghapusan

Langkah berikutnya

Prasyarat

Mendapatkan titik akhir layanan

Menyiapkan proyek Node.JS

Menyiapkan variabel lingkungan untuk pengembangan lokal

Masuk ke Azure

Membuat indeks vektor

Mengunggah dokumen ke indeks

Membuat kueri sebagai vektor

Membuat satu pencarian vektor

Membuat pencarian vektor tunggal dengan filter

Membuat pencarian dengan filter geospasial

Membuat pencarian hibrid

Membuat pencarian hibrid semantik

Penghapusan

Langkah berikutnya

Prasyarat

Mendapatkan titik akhir layanan

Menyiapkan proyek Node.JS

Menyiapkan variabel lingkungan untuk pengembangan lokal

Masuk ke Azure

Membuat indeks vektor

Mengunggah dokumen ke indeks

Membuat kueri sebagai vektor

Membuat satu pencarian vektor

Membuat pencarian vektor tunggal dengan filter

Membuat pencarian dengan filter geospasial

Membuat pencarian hibrid

Membuat pencarian hibrid semantik

Penghapusan

Langkah berikutnya

Prasyarat

Mendapatkan informasi layanan

Mengkloning kode dan lingkungan penyiapan

Membuat indeks vektor dan mengunggah dokumen

Menjalankan kueri pencarian

Pencarian vektor tunggal

Pencarian vektor tunggal dengan filter

Pencarian hibrid

Pencarian hibrid semantik dengan filter

Penghapusan

Langkah berikutnya

Prasyarat

Mendapatkan informasi layanan

Membuat atau mengunduh file kode

Membuat indeks vektor

Unggah dokumen

Jalankan Kueri

Pencarian vektor tunggal

Pencarian vektor tunggal dengan filter

Pencarian hibrid

Pencarian hibrid semantik