Quickstart: Vector search by using REST
Learn how to use the Search REST APIs to create, load, and query vectors in Azure AI Search.
In Azure AI Search, a vector store has an index schema that defines vector and nonvector fields, a vector search configuration for algorithms that create the embedding space, and settings on vector field definitions that are evaluated at query time. The Create Index REST API creates the vector store.
If you don't have an Azure subscription, create a free account before you begin.
Note
This quickstart omits the vectorization step and provides embeddings in sample documents. If you want to add built-in data chunking and vectorization over your own content, try the Import and vectorize data wizard for an end-to-end walkthrough.
Prerequisites
Visual Studio Code with a REST client.
Azure AI Search, in any region and on any tier. Create or find an existing Azure AI Search resource under your current subscription.
- You can use the Free tier for most of this quickstart, but Basic or higher is recommended for larger data files.
- To run the query example that invokes semantic reranking, your search service must be the Basic tier or higher, with semantic ranker enabled.
Retrieve resource information
Requests to the search endpoint must be authenticated and authorized. You can use API keys or roles for this task. We recommend using a keyless connection via Microsoft Entra ID.
Select the tab that corresponds to your preferred authentication method. Use the same method for all requests in this quickstart.
Sign in to the Azure portal and find your search service.
On the Overview home page, find the URL. An example endpoint might look like
https://mydemo.search.windows.net.
Follow the steps in the keyless quickstart to get your Microsoft Entra token.
You get the token when you run the
az account get-access-tokencommand in step 3 of the previous quickstart.az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
Create or download the code file
You use one .rest or .http file to run all the requests in this quickstart. You can download the REST file that contains the code for this quickstart, or you can create a new file in Visual Studio Code and copy the code into it.
In Visual Studio Code, create a new file with a
.restor.httpfile extension. For example,az-search-vector-quickstart.rest. Copy and paste the raw contents of the Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest file into this new file.At the top of the file, replace the placeholder value for
@baseUrlwith your search service URL. See the Retrieve resource information section for instructions on how to find your search service URL.@baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HEREAt the top of the file, replace the placeholder value for authentication. See the Retrieve resource information section for instructions on how to get your Microsoft Entra token or API key.
For the recommended keyless authentication via Microsoft Entra ID, you need to replace
@apiKeywith the@tokenvariable.@token = PUT-YOUR-MICROSOFT-ENTRA-TOKEN-HEREIf you prefer to use an API key, replace
@apiKeywith the key you copied from the Azure portal.@apiKey = PUT-YOUR-ADMIN-KEY-HEREFor the recommended keyless authentication via Microsoft Entra ID, you need to replace
api-key: {{apiKey}}withAuthorization: Bearer {{token}}in the request headers. Replace all instances ofapi-key: {{apiKey}}that you find in the file.
Create a vector index
You use the Create Index REST API to create a vector index and set up the physical data structures on your search service.
The index schema in this example is organized around hotel content. Sample data consists of vector and nonvector names and descriptions of fictitious hotels. This schema includes configurations for vector indexing and queries, and for semantic ranking.
In Visual Studio Code, open the
az-search-vector-quickstart.restfile you created earlier.Find the
### Create a new indexcode block in the file. This block contains the request to create thehotels-vector-quickstartindex on your search service.### Create a new index POST {{baseUrl}}/indexes?api-version=2023-11-01 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": "HotelNameVector", "type": "Collection(Edm.Single)", "searchable": true, "retrievable": true, "dimensions": 1536, "vectorSearchProfile": "my-vector-profile" }, { "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": "Description_fr", "type": "Edm.String", "searchable": true, "filterable": false, "retrievable": true, "sortable": false, "facetable": false, "analyzer": "en.microsoft" }, { "name": "Description_frvector", "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": "my-hnsw-vector-config-1", "kind": "hnsw", "hnswParameters": { "m": 4, "efConstruction": 400, "efSearch": 500, "metric": "cosine" } }, { "name": "my-hnsw-vector-config-2", "kind": "hnsw", "hnswParameters": { "m": 4, "metric": "euclidean" } }, { "name": "my-eknn-vector-config", "kind": "exhaustiveKnn", "exhaustiveKnnParameters": { "metric": "cosine" } } ], "profiles": [ { "name": "my-vector-profile", "algorithm": "my-hnsw-vector-config-1" } ] }, "semantic": { "configurations": [ { "name": "my-semantic-config", "prioritizedFields": { "titleField": { "fieldName": "HotelName" }, "prioritizedContentFields": [ { "fieldName": "Description" } ], "prioritizedKeywordsFields": [ { "fieldName": "Category" } ] } } ] } }Select Send request. You should have an
HTTP/1.1 201 Createdresponse.
The response body should include the JSON representation of the index schema.
{
"@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": "my-hnsw-vector-config-1",
"kind": "hnsw",
"hnswParameters": {
"metric": "cosine",
"m": 4,
"efConstruction": 400,
"efSearch": 500
},
"exhaustiveKnnParameters": null
},
{
"name": "my-hnsw-vector-config-2",
"kind": "hnsw",
"hnswParameters": {
"metric": "euclidean",
"m": 4,
"efConstruction": 400,
"efSearch": 500
},
"exhaustiveKnnParameters": null
},
{
"name": "my-eknn-vector-config",
"kind": "exhaustiveKnn",
"hnswParameters": null,
"exhaustiveKnnParameters": {
"metric": "cosine"
}
}
],
"profiles": [
{
"name": "my-vector-profile",
"algorithm": "my-hnsw-vector-config-1"
}
]
},
"semantic": {
"defaultConfiguration": null,
"configurations": [
{
"name": "my-semantic-config",
"prioritizedFields": {
"titleField": {
"fieldName": "HotelName"
},
"prioritizedContentFields": [
{
"fieldName": "Description"
}
],
"prioritizedKeywordsFields": [
{
"fieldName": "Category"
}
]
}
}
]
}
}
Key takeaways about the Create Index REST API:
The
fieldscollection includes a required key field and text and vector fields (such asDescriptionandDescriptionVector) for text and vector search. Colocating vector and nonvector fields in the same index enables hybrid queries. For instance, you can combine filters, text search with semantic ranking, and vectors into a single query operation.Vector fields must be
type: Collection(Edm.Single)withdimensionsandvectorSearchProfileproperties.The
vectorSearchsection is an array of approximate nearest neighbor algorithm configurations and profiles. Supported algorithms include hierarchical navigable small world and exhaustive k-nearest neighbor. For more information, see Relevance scoring in vector search.The (optional)
semanticconfiguration enables reranking of search results. You can rerank results in queries of typesemanticfor string fields that are specified in the configuration. To learn more, see Semantic ranking overview.
Upload documents
Creating and loading the index are separate steps. You created the index schema in the previous step. Now you need to load documents into the index.
In Azure AI Search, the index contains all searchable data and queries run on the search service. For REST calls, the data is provided as JSON documents. Use Documents- Index REST API for this task. The URI is extended to include the docs collection and the index operation.
In Visual Studio Code, open the
az-search-vector-quickstart.restfile you created earlier.Find the
### Upload documentscode block in the file. This block contains the request to upload documents to thehotels-vector-quickstartindex on your search service.### Upload documents POST {{baseUrl}}/indexes/hotels-quickstart-vectors/docs/index?api-version=2023-11-01 HTTP/1.1 Content-Type: application/json Authorization: Bearer {{token}} { "value": [ { "@search.action": "mergeOrUpload", "HotelId": "1", "HotelName": "Stay-Kay City Hotel", "HotelNameVector": [VECTOR ARRAY OMITTED], "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of New York.", "DescriptionVector": [VECTOR ARRAY OMITTED], "Category": "Boutique", "Tags": [ "pool", "air conditioning", "concierge" ], }, { "@search.action": "mergeOrUpload", "HotelId": "2", "HotelName": "Old Century Hotel", "HotelNameVector": [VECTOR ARRAY OMITTED], "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.", "DescriptionVector": [VECTOR ARRAY OMITTED], "Category": "Boutique", "Tags": [ "pool", "air conditioning", "free wifi", "concierge" ] }, { "@search.action": "mergeOrUpload", "HotelId": "3", "HotelName": "Gastronomic Landscape Hotel", "HotelNameVector": [VECTOR ARRAY OMITTED], "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.", "DescriptionVector": [VECTOR ARRAY OMITTED], "Category": "Resort and Spa", "Tags": [ "air conditioning", "bar", "continental breakfast" ] } { "@search.action": "mergeOrUpload", "HotelId": "4", "HotelName": "Sublime Palace Hotel", "HotelNameVector": [VECTOR ARRAY OMITTED], "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.", "DescriptionVector": [VECTOR ARRAY OMITTED], "Category": "Boutique", "Tags": [ "concierge", "view", "24-hour front desk service" ] }, { "@search.action": "mergeOrUpload", "HotelId": "13", "HotelName": "Luxury Lion Resort", "HotelNameVector": [VECTOR ARRAY OMITTED], "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort", "DescriptionVector": [VECTOR ARRAY OMITTED], "Category": "Resort and Spa", "Tags": [ "view", "free wifi", "pool" ] }, { "@search.action": "mergeOrUpload", "HotelId": "48", "HotelName": "Nordick's Valley Motel", "HotelNameVector": [VECTOR ARRAY OMITTED], "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.", "DescriptionVector": [VECTOR ARRAY OMITTED], "Category": "Boutique", "Tags": [ "continental breakfast", "air conditioning", "free wifi" ], }, { "@search.action": "mergeOrUpload", "HotelId": "49", "HotelName": "Swirling Currents Hotel", "HotelNameVector": [VECTOR ARRAY OMITTED], "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.", "DescriptionVector": [VECTOR ARRAY OMITTED], "Category": "Luxury", "Tags": [ "air conditioning", "laundry service", "24-hour front desk service" ] } ] }Important
The code in this example isn't runable. Several characters or lines are removed for brevity. Use the code in your
az-search-vector-quickstart.restfile to run the request.Select Send request. You should have an
HTTP/1.1 200 OKresponse. The response body should include the JSON representation of the search documents.
Key takeaways about the Documents - Index REST API request:
Documents in the payload consist of fields defined in the index schema.
Vector fields contain floating point values. The dimensions attribute has a minimum of 2 and a maximum of 3,072 floating point values each. This quickstart sets the dimensions attribute to 1,536 because that's the size of embeddings generated by the Azure OpenAI text-embedding-ada-002 model.
Run queries
Now that documents are loaded, you can issue vector queries against them by using Documents - Search Post (REST).
In the next sections, we run queries against the hotels-vector-quickstart index. The queries include:
- Single vector search
- Single vector search with filter
- Hybrid search
- Semantic hybrid search with filter
The example vector queries are based on two strings:
- Search string:
historic hotel walk to restaurants and shopping - Vector query string (vectorized into a mathematical representation):
classic lodging near running trails, eateries, retail
The vector query string is semantically similar to the search string, but it includes terms that don't exist in the search index. If you do a keyword search for classic lodging near running trails, eateries, retail, results are zero. We use this example to show how you can get relevant results even if there are no matching terms.
Single vector search
In Visual Studio Code, open the
az-search-vector-quickstart.restfile you created earlier.Find the
### Run a single vector querycode block in the file. This block contains the request to query the search index.### Run a single vector query POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2023-11-01 HTTP/1.1 Content-Type: application/json Authorization: Bearer {{token}} { "count": true, "select": "HotelId, HotelName, Description, Category", "vectorQueries": [ { "vector"": [0.01944167, 0.0040178085 . . . TRIMMED FOR BREVITY 010858015, -0.017496133], "k": 7, "fields": "DescriptionVector", "kind": "vector", "exhaustive": true } ] }This vector query is shortened for brevity. The
vectorQueries.vectorcontains the vectorized text of the query input,fieldsdetermines which vector fields are searched, andkspecifies the number of nearest neighbors to return.The vector query string is
classic lodging near running trails, eateries, retail, which is vectorized into 1,536 embeddings for this query.Important
The code in this example isn't runable. Several characters or lines are removed for brevity. Use the code in your
az-search-vector-quickstart.restfile to run the request.Select Send request. You should have an
HTTP/1.1 200 OKresponse. The response body should include the JSON representation of the search results.
The response for the vector equivalent of classic lodging near running trails, eateries, retail includes seven results. Each result provides a search score and the fields listed in select. In a similarity search, the response always includes k results ordered by the value similarity score.
{
"@odata.context": "https://my-demo-search.search.windows.net/indexes('hotels-vector-quickstart')/$metadata#docs(*)",
"@odata.count": 7,
"value": [
{
"@search.score": 0.85773647,
"HotelId": "48",
"HotelName": "Nordick's 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": 0.8399132,
"HotelId": "49",
"HotelName": "Old Carrabelle Hotel",
"Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
"Category": "Luxury"
},
{
"@search.score": 0.83839583,
"HotelId": "13",
"HotelName": "Historic 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": 0.82543474,
"HotelId": "4",
"HotelName": "Sublime Cliff Hotel",
"Description": "Sublime Cliff 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 1800 palace.",
"Category": "Boutique"
},
{
"@search.score": 0.82380104,
"HotelId": "1",
"HotelName": "Secret Point 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": 0.8151413,
"HotelId": "2",
"HotelName": "Twin Dome 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": 0.8133767,
"HotelId": "3",
"HotelName": "Triple 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\u2019s restaurant services.",
"Category": "Resort and Spa"
}
]
}
Single vector search with filter
You can add filters, but the filters are applied to the nonvector content in your index. In this example, the filter applies to the Tags field to filter out any hotels that don't provide free Wi-Fi.
In Visual Studio Code, open the
az-search-vector-quickstart.restfile you created earlier.Find the
### Run a vector query with a filtercode block in the file. This block contains the request to query the search index.### Run a vector query with a filter POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2023-11-01 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": [ VECTOR OMITTED ], "k": 7, "fields": "DescriptionVector", "kind": "vector", "exhaustive": true }, ] }Important
The code in this example isn't runable. Several characters or lines are removed for brevity. Use the code in your
az-search-vector-quickstart.restfile to run the request.Select Send request. You should have an
HTTP/1.1 200 OKresponse. The response body should include the JSON representation of the search results.
The query was the same as the previous single vector search example, but it includes a post-processing exclusion filter and returns only the three hotels that have free Wi-Fi.
{
"@odata.context": "https://my-demo-search.search.windows.net/indexes('hotels-vector-quickstart')/$metadata#docs(*)",
"@odata.count": 3,
"value": [
{
"@search.score": 0.85773647,
"HotelId": "48",
"HotelName": "Nordick's 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": 0.83839583,
"HotelId": "13",
"HotelName": "Historic 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",
"Tags": [
"view",
"free wifi",
"pool"
]
},
{
"@search.score": 0.8151413,
"HotelId": "2",
"HotelName": "Twin Dome 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",
"Tags": [
"pool",
"free wifi",
"air conditioning",
"concierge"
]
}
]
}
Hybrid search
Hybrid search consists of keyword queries and vector queries in a single search request. This example runs the vector query and full text search concurrently:
- Search string:
historic hotel walk to restaurants and shopping - Vector query string (vectorized into a mathematical representation):
classic lodging near running trails, eateries, retail
In Visual Studio Code, open the
az-search-vector-quickstart.restfile you created earlier.Find the
### Run a hybrid querycode block in the file. This block contains the request to query the search index.### Run a hybrid query POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2023-11-01 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": [ VECTOR OMITTED], "k": 7, "fields": "DescriptionVector", "kind": "vector", "exhaustive": true } ] }Important
The code in this example isn't runable. Several characters or lines are removed for brevity. Use the code in your
az-search-vector-quickstart.restfile to run the request.Select Send request. You should have an
HTTP/1.1 200 OKresponse. The response body should include the JSON representation of the search results.
Because this is a hybrid query, results are ranked by Reciprocal Rank Fusion (RRF). RRF evaluates search scores of multiple search results, takes the inverse, and then merges and sorts the combined results. The top number of results are returned.
Review the response:
{
"@odata.count": 7,
"value": [
{
"@search.score": 0.03279569745063782,
"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"
},
{
"@search.score": 0.03226646035909653,
"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": 0.03226646035909653,
"HotelName": "Swirling Currents Hotel",
"Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center."
},
{
"@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": 0.03128054738044739,
"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."
},
{
"@search.score": 0.03100961446762085,
"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."
},
{
"@search.score": 0.03077651560306549,
"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."
}
]
}
Because RRF merges results, it helps to review the inputs. The following results are from only the full-text query. The top two results are Sublime Palace Hotel and History Lion Resort. The Sublime Palace Hotel has a stronger BM25 relevance score.
{
"@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"
},
In the vector-only query, which uses HNSW for finding matches, the Sublime Palace Hotel drops to fourth position. Historic Lion, which was second in the full-text search and third in the vector search, doesn't experience the same range of fluctuation, so it appears as a top match in a homogenized result set.
"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"
}
]
Semantic hybrid search with a filter
Here's the last query in the collection. This hybrid query with semantic ranking is filtered to show only the hotels within a 500-kilometer radius of Washington D.C. You can set vectorFilterMode to null, which is equivalent to the default (preFilter for newer indexes and postFilter for older ones).
In Visual Studio Code, open the
az-search-vector-quickstart.restfile you created earlier.Find the
### Run a hybrid query with semantic rerankingcode block in the file. This block contains the request to query the search index.### Run a hybrid query with semantic reranking POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2023-11-01 HTTP/1.1 Content-Type: application/json Authorization: Bearer {{token}} { "count": true, "search": "historic hotel walk to restaurants and shopping", "select": "HotelId, HotelName, Category, Description,Address/City, Address/StateProvince", "filter": "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 500", "vectorFilterMode": null, "facets": [ "Address/StateProvince"], "top": 7, "queryType": "semantic", "answers": "extractive|count-3", "captions": "extractive|highlight-true", "semanticConfiguration": "my-semantic-config", "vectorQueries": [ { "vector": [ VECTOR OMITTED ], "k": 7, "fields": "DescriptionVector", "kind": "vector", "exhaustive": true } ] }Important
The code in this example isn't runable. Several characters or lines are removed for brevity. Use the code in your
az-search-vector-quickstart.restfile to run the request.Select Send request. You should have an
HTTP/1.1 200 OKresponse. The response body should include the JSON representation of the search results.
Review the response. The response is three hotels, which are filtered by location and faceted by StateProvince and semantically reranked to promote results that are closest to the search string query (historic hotel walk to restaurants and shopping).
The Swirling Currents Hotel now moves into the top spot. Without semantic ranking, Nordick's Valley Motel is number one. With semantic ranking, the machine comprehension models recognize that historic applies to "hotel, within walking distance to dining (restaurants) and shopping."
{
"@odata.context": "https://my-demo-search.search.windows.net/indexes('hotels-vector-quickstart')/$metadata#docs(*)",
"@odata.count": 2,
"@search.facets": {
"Address/StateProvince": [
{
"count": 1,
"value": "VA"
}
]
},
"@search.answers": [],
"value": [
{
"@search.score": 0.03306011110544205,
"@search.rerankerScore": 2.8773112297058105,
"HotelId": "49",
"HotelName": "Old Carrabelle Hotel",
"Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
"Category": "Luxury",
"Address": {
"City": "Arlington",
"StateProvince": "VA"
}
},
{
"@search.score": 0.03306011110544205,
"@search.rerankerScore": 2.1703834533691406,
"HotelId": "48",
"HotelName": "Nordick's 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",
"Address": {
"City": "Washington D.C.",
"StateProvince": null
}
}
]
}
Key takeaways about Documents - Search Post REST API:
Vector search is specified through the
vectors.valueproperty. Keyword search is specified through thesearchproperty.In a hybrid search, you can integrate vector search with full-text search over keywords. Filters, spell check, and semantic ranking apply to textual content only, and not vectors. In this final query, there's no semantic
answerbecause the system didn't produce one that was sufficiently strong.Actual results include more detail, including semantic captions and highlights. Results were modified for readability. To get the full structure of the response, run the request in the REST client.
Clean up
When you're working in your own subscription, it's a good idea at the end of a project to identify whether you still need the resources you created. Resources left running can cost you money. You can delete resources individually or delete the resource group to delete the entire set of resources.
You can find and manage resources in the Azure portal by using the All resources or Resource groups link in the leftmost pane.
If you want to keep the search service, but delete the index and documents, you can use the DELETE command in the REST client. This command (at the end of your az-search-vector-quickstart.rest file) deletes the hotels-vector-quickstart index:
### Delete an index
DELETE {{baseUrl}}/indexes/hotels-vector-quickstart?api-version=2023-11-01 HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}
Next steps
As a next step, we recommend learning how to invoke REST API calls without API keys.
You might also want to review the demo code for Python, C#, or JavaScript.