[Search] Vector Search Updates For August Preview

sdk/search/Azure.Search.Documents/CHANGELOG.md

@@ -1,14 +1,10 @@
 # Release History
 
-## 11.5.0-beta.4 (Unreleased)
+## 11.5.0-beta.4 (2023-08-07)
 
 ### Features Added
-
-### Breaking Changes
-
-### Bugs Fixed
-
-### Other Changes
+- Added the ability to perform multiple vectors query searches.
+- Added support for vector queries over multiple fields.
 
 ## 11.5.0-beta.3 (2023-07-11)
 

sdk/search/Azure.Search.Documents/README.md

@@ -352,7 +352,7 @@ foreach (SearchResult<SearchDocument> result in response.GetResults())
     SearchDocument doc = result.Document;
     string id = (string)doc["HotelId"];
     string name = (string)doc["HotelName"];
-    Console.WriteLine("{id}: {name}");
+    Console.WriteLine($"{id}: {name}");
 }
 ```
 

sdk/search/Azure.Search.Documents/api/Azure.Search.Documents.netstandard2.0.cs

@@ -173,7 +173,7 @@ public SearchOptions() { }
         public string SessionId { get { throw null; } set { } }
         public int? Size { get { throw null; } set { } }
         public int? Skip { get { throw null; } set { } }
-        public Azure.Search.Documents.Models.SearchQueryVector Vector { get { throw null; } set { } }
+        public System.Collections.Generic.IList<Azure.Search.Documents.Models.SearchQueryVector> Vectors { get { throw null; } }
     }
     public partial class SuggestOptions
     {
@@ -210,6 +210,8 @@ public SearchableFieldAttribute() { }
         public string IndexAnalyzerName { get { throw null; } set { } }
         public string SearchAnalyzerName { get { throw null; } set { } }
         public string[] SynonymMapNames { get { throw null; } set { } }
+        public string VectorSearchConfiguration { get { throw null; } set { } }
+        public string VectorSearchDimensions { get { throw null; } set { } }
     }
     public partial class SearchIndexClient
     {
@@ -3282,7 +3284,6 @@ public static partial class SearchModelFactory
         public static Azure.Search.Documents.Indexes.Models.SearchIndexerWarning SearchIndexerWarning(string key, string message, string name, string details, string documentationLink) { throw null; }
         public static Azure.Search.Documents.Indexes.Models.SearchIndexStatistics SearchIndexStatistics(long documentCount, long storageSize) { throw null; }
         public static Azure.Search.Documents.Indexes.Models.SearchIndexStatistics SearchIndexStatistics(long documentCount = (long)0, long storageSize = (long)0, long? vectorIndexSize = default(long?)) { throw null; }
-        public static Azure.Search.Documents.Models.SearchQueryVector SearchQueryVector(System.Collections.Generic.IEnumerable<float> value = null, int? kNearestNeighborsCount = default(int?), string fields = null) { throw null; }
         public static Azure.Search.Documents.Indexes.Models.SearchResourceCounter SearchResourceCounter(long usage, long? quota) { throw null; }
         public static Azure.Search.Documents.Models.SearchResultsPage<T> SearchResultsPage<T>(Azure.Search.Documents.Models.SearchResults<T> results) { throw null; }
         public static Azure.Search.Documents.Models.SearchResults<T> SearchResults<T>(System.Collections.Generic.IEnumerable<Azure.Search.Documents.Models.SearchResult<T>> values, long? totalCount, System.Collections.Generic.IDictionary<string, System.Collections.Generic.IList<Azure.Search.Documents.Models.FacetResult>> facets, double? coverage, Azure.Response rawResponse) { throw null; }
@@ -3311,7 +3312,7 @@ public enum SearchQueryType
     public partial class SearchQueryVector
     {
         public SearchQueryVector() { }
-        public string Fields { get { throw null; } set { } }
+        public System.Collections.Generic.IList<string> Fields { get { throw null; } }
         public int? KNearestNeighborsCount { get { throw null; } set { } }
         public System.Collections.Generic.IReadOnlyList<float> Value { get { throw null; } set { } }
     }

sdk/search/Azure.Search.Documents/assets.json

@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "net",
   "TagPrefix": "net/search/Azure.Search.Documents",
-  "Tag": "net/search/Azure.Search.Documents_a3427a4c81"
+  "Tag": "net/search/Azure.Search.Documents_38f84358e8"
 }

sdk/search/Azure.Search.Documents/samples/Sample07_VectorSearch.md

@@ -10,7 +10,7 @@ This sample will show you how to index a vector field and perform vector search
 
 ## Create a Vector Index
 
-Let's consider the example of a `Hotel`. First, we need to create an index for storing hotel information. In this index, we will define a field called `DescriptionVector` as a vector field. To configure the vector field, you need to provide the model dimensions, which indicate the size of the embeddings generated for this field, and the name of the vector search algorithm configuration that specifies the algorithm and any optional parameters for searching the vector field. You can find detailed instructions on how to create a vector index in the [documentation](https://learn.microsoft.com/azure/search/vector-search-how-to-create-index).
+Let's consider the example of a `Hotel`. First, we need to create an index for storing hotel information. In this index, we will define vector fields called `DescriptionVector` and `CategoryVector`. To configure the vector field, you need to provide the model dimensions, which indicate the size of the embeddings generated for this field, and the name of the vector search algorithm configuration that specifies the algorithm and any optional parameters for searching the vector field. You can find detailed instructions on how to create a vector index in the [documentation](https://learn.microsoft.com/azure/search/vector-search-how-to-create-index).
 
 We will create an instace of `SearchIndex` and define `Hotel` fields.
 
@@ -32,7 +32,13 @@ SearchIndex searchIndex = new(indexName)
             VectorSearchDimensions = modelDimensions,
             VectorSearchConfiguration = vectorSearchConfigName
         },
-        new SearchableField("Category") { IsFilterable = true, IsSortable = true, IsFacetable = true }
+        new SearchableField("Category") { IsFilterable = true, IsSortable = true, IsFacetable = true },
+        new SearchField("CategoryVector", SearchFieldDataType.Collection(SearchFieldDataType.Single))
+        {
+            IsSearchable = true,
+            VectorSearchDimensions = modelDimensions,
+            VectorSearchConfiguration = vectorSearchConfigName
+        },
     },
     VectorSearch = new()
     {
@@ -67,6 +73,7 @@ public class Hotel
     public string Description { get; set; }
     public IReadOnlyList<float> DescriptionVector { get; set; }
     public string Category { get; set; }
+    public IReadOnlyList<float> CategoryVector { get; set; }
 }
 ```
 
@@ -87,7 +94,7 @@ Embeddings embeddings = await openAIClient.GetEmbeddingsAsync("EmbeddingsModelNa
 IReadOnlyList<float> descriptionVector = embeddings.Data[0].Embedding;
 ```
 
-In the sample code below, we are using hardcoded embeddings for the `DescriptionVector` field:
+In the sample code below, we are using hardcoded embeddings for the vector fields named `DescriptionVector` and `CategoryVector`:
 
 ```C# Snippet:Azure_Search_Documents_Tests_Samples_Sample07_Vector_Search_Hotel_Document
 public static Hotel[] GetHotelDocuments()
@@ -104,6 +111,7 @@ public static Hotel[] GetHotelDocuments()
                 "the tourist attractions. We highly recommend this hotel.",
             DescriptionVector = VectorSearchEmbeddings.Hotel1VectorizeDescription,
             Category = "Luxury",
+            CategoryVector = VectorSearchEmbeddings.LuxuryVectorizeCategory
         },
         new Hotel()
         {
@@ -112,6 +120,7 @@ public static Hotel[] GetHotelDocuments()
             Description = "Cheapest hotel in town. Infact, a motel.",
             DescriptionVector = VectorSearchEmbeddings.Hotel2VectorizeDescription,
             Category = "Budget",
+            CategoryVector = VectorSearchEmbeddings.BudgetVectorizeCategory
         },
          // Add more hotel documents here...
     };
@@ -142,7 +151,7 @@ IReadOnlyList<float> vectorizedResult = VectorSearchEmbeddings.SearchVectorizeDe
 SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(null,
     new SearchOptions
     {
-        Vector = new() { Value = vectorizedResult, KNearestNeighborsCount = 3, Fields = "DescriptionVector" },
+        Vectors = { new() { Value = vectorizedResult, KNearestNeighborsCount = 3, Fields = { "DescriptionVector" } } },
     });
 
 int count = 0;
@@ -166,7 +175,7 @@ IReadOnlyList<float> vectorizedResult = VectorSearchEmbeddings.SearchVectorizeDe
 SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(null,
     new SearchOptions
     {
-        Vector = new() { Value = vectorizedResult, KNearestNeighborsCount = 3, Fields = "DescriptionVector" },
+        Vectors = { new() { Value = vectorizedResult, KNearestNeighborsCount = 3, Fields = { "DescriptionVector" } } },
         Filter = "Category eq 'Luxury'"
     });
 
@@ -194,7 +203,7 @@ SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
         "Top hotels in town",
         new SearchOptions
         {
-            Vector = new() { Value = vectorizedResult, KNearestNeighborsCount = 3, Fields = "DescriptionVector" },
+            Vectors = { new() { Value = vectorizedResult, KNearestNeighborsCount = 3, Fields = { "DescriptionVector" } } },
         });
 
 int count = 0;
@@ -247,7 +256,7 @@ SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
     "Is there any hotel located on the main commercial artery of the city in the heart of New York?",
     new SearchOptions
     {
-        Vector = new SearchQueryVector { Value = vectorizedResult, KNearestNeighborsCount = 3, Fields = "descriptionVector" },
+        Vectors = { new() { Value = vectorizedResult, KNearestNeighborsCount = 3, Fields = { "descriptionVector" } } },
         QueryType = SearchQueryType.Semantic,
         QueryLanguage = QueryLanguage.EnUs,
         SemanticConfigurationName = "my-semantic-config",
@@ -286,3 +295,58 @@ await foreach (SearchResult<Hotel> result in response.GetResultsAsync())
 }
 Console.WriteLine($"Total number of search results:{count}");
 ```
+
+### Multi-vector Search
+
+You can search containing multiple query vectors using the `SearchOptions.Vectors` property. These queries will be executed concurrently in the search index, with each one searching for similarities in the target vector fields. The result set will be a combination of documents that matched both vector queries. One common use case for this query request is when using models like CLIP for a multi-modal vector search, where the same model can vectorize both image and non-image content.
+
+```C# Snippet:Azure_Search_Documents_Tests_Samples_Sample07_Multi_Vector_Search
+IReadOnlyList<float> vectorizedDescriptionQuery = VectorSearchEmbeddings.SearchVectorizeDescription; // "Top hotels in town"
+IReadOnlyList<float> vectorizedCategoryQuery = VectorSearchEmbeddings.SearchVectorizeCategory; // "Luxury hotels in town"
+
+SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(null,
+    new SearchOptions
+    {
+        Vectors = {
+            new() { Value = vectorizedDescriptionQuery, KNearestNeighborsCount = 3, Fields = { "DescriptionVector" } },
+            new() { Value = vectorizedCategoryQuery, KNearestNeighborsCount = 3, Fields = { "CategoryVector" } }
+        },
+    });
+
+int count = 0;
+Console.WriteLine($"Multi Vector Search Results:");
+await foreach (SearchResult<Hotel> result in response.GetResultsAsync())
+{
+    count++;
+    Hotel doc = result.Document;
+    Console.WriteLine($"{doc.HotelId}: {doc.HotelName}");
+}
+Console.WriteLine($"Total number of search results:{count}");
+```
+
+### Multi-field Vector Search
+
+You can set the `SearchOptions.Vectors.Fields` property to multiple vector fields. For example, we have vector fields named `DescriptionVector` and `CategoryVector`. Your vector query executes over both the `DescriptionVector` and `CategoryVector` fields, which must have the same embedding space since they share the same query vector.
+
+```C# Snippet:Azure_Search_Documents_Tests_Samples_Sample07_Multi_Fields_Vector_Search
+IReadOnlyList<float> vectorizedResult = VectorSearchEmbeddings.SearchVectorizeDescription; // "Top hotels in town"
+
+SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(null,
+    new SearchOptions
+    {
+        Vectors = { new() {
+            Value = vectorizedResult,
+            KNearestNeighborsCount = 3,
+            Fields = { "DescriptionVector", "CategoryVector" } } }
+    });
+
+int count = 0;
+Console.WriteLine($"Multi Fields Vector Search Results:");
+await foreach (SearchResult<Hotel> result in response.GetResultsAsync())
+{
+    count++;
+    Hotel doc = result.Document;
+    Console.WriteLine($"{doc.HotelId}: {doc.HotelName}");
+}
+Console.WriteLine($"Total number of search results:{count}");
+```