[Search] GA Features for API Version 2024-07-01 (#44485)

* [Search] GA Features for API Version 2024-07-01

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

@@ -1,14 +1,42 @@
 # Release History
 
-## 11.6.0-beta.5 (Unreleased)
+## 11.6.0 (2024-07-17)
 
 ### Features Added
+- Added support for `2024-07-01` service version.
+- `SemanticSearchOptions` now supports `SemanticQuery`, which allows for specifying a semantic query that is only used
+  for semantic reranking.
+- `VectorQuery` now supports `Oversampling` and `Weight`, which allows for specifying richer configurations on how
+  vector queries affect search results.
+- Added support for `VectorizableTextQuery`, which allows for passing a text-based query that is vectorized service-side
+  by `VectorSearchVectorizer`s configured on the index so that vectorization doesn't need to happen before querying.
+- Added support for "bring your own endpoint" with `VectorSearchVectorizer`, with implementations `AzureOpenAIVectorizer`
+  and `WebApiVectorizer`. This enables the service to use a user-provided configuration for vectorizing text, rather 
+  than requiring all client-side calls to vectorize before querying, allowing for easier standardization of vectorization.
+- Added support for compression with `VectorSearchCompression`, with implementations `BinaryQuantizationCompression`
+  and `ScalarQuantizationCompression`. This allows for reducing the size of vectors in the index, which can reduce
+  storage costs and improve querying performance.
+- Added support for `VectorEncodingFormat`, which allows for specifying the encoding format of the vector data.
+- Added support for `AzureOpenAIEmbeddingSkill`, which is a skill that uses the Azure OpenAI service to create text 
+  embeddings during indexing.
+- Added support for index projections with `SearchIndexerIndexProjection`, which allows for specifying how indexed 
+  documents are projected in the index (or indexes).
+- Added support for "narrow" types in `SearchFieldDataType`. This allows for specifying smaller types for vector fields
+  to reduce storage costs and improve querying performance.
+- Added support for `SearchIndexerDataIdentity`, which allows for specifying the identity for the data source for the 
+  indexer.
+- `SearchField` and `SearchableField` now support `IsStored` and `VectorEncodingFormat` configurations. `IsStored` allows
+  for specifying behaviors on how the index will retain vector data (enabling the ability to reduce storage costs), and
+  `VectorEncodingFormat` allows for specifying the encoding format of the vector data.
+- `OcrSkill` now supports `LineEnding`, which allows for specifying the line ending character used by the OCR skill.
+- `SplitSkill` now supports `MaximumPagesToTake` and `PageOverlapLength`, which allows for specifying how the split
+  skill behaves when splitting documents into pages.
+- `SearchServiceLimits` now supports `MaxStoragePerIndexInBytes`, which shows the maximum storage allowed per index.
 
 ### Breaking Changes
 
-### Bugs Fixed
-
-### Other Changes
+- All service concepts that have been in preview but not included in the `2024-07-01` GA have been removed. This
+  includes concepts such as index aliases, normalizers, Azure Machine Learning skills, hybrid search, and more.
 
 ## 11.6.0-beta.4 (2024-05-06)
 

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_52c4e21bfa"
+  "Tag": "net/search/Azure.Search.Documents_cd3c397d05"
 }

sdk/search/Azure.Search.Documents/samples/Sample04_FieldBuilderIgnore.md

@@ -102,7 +102,6 @@ SearchIndex index = new SearchIndex("movies")
 SearchableField genreField = new SearchableField("genre")
 {
     AnalyzerName = LexicalAnalyzerName.Values.EnLucene,
-    NormalizerName = LexicalNormalizerName.Lowercase,
     IsFacetable = true,
     IsFilterable = true
 };

sdk/search/Azure.Search.Documents/samples/Sample07_VectorSearch_UsingReducedEmbeddings.md

@@ -13,7 +13,7 @@ We will create an instace of `SearchIndex` and define `Hotel` fields.
 ```C# Snippet:Azure_Search_Documents_Tests_Samples_Sample07_Reduced_Vector_Search_Index
 string vectorSearchProfileName = "my-vector-profile";
 string vectorSearchHnswConfig = "my-hsnw-vector-config";
-string deploymentId = "my-text-embedding-3-small";
+string deploymentName = "my-text-embedding-3-small";
 int modelDimensions = 256; // Here's the reduced model dimensions
 
 string indexName = "hotel";
@@ -34,7 +34,7 @@ SearchIndex searchIndex = new(indexName)
         {
             new VectorSearchProfile(vectorSearchProfileName, vectorSearchHnswConfig)
             {
-                Vectorizer = "openai"
+                VectorizerName = "openai"
             }
         },
         Algorithms =
@@ -45,11 +45,11 @@ SearchIndex searchIndex = new(indexName)
         {
             new AzureOpenAIVectorizer("openai")
             {
-                AzureOpenAIParameters  = new AzureOpenAIParameters()
+                Parameters  = new AzureOpenAIVectorizerParameters()
                 {
                     ResourceUri = new Uri(Environment.GetEnvironmentVariable("OPENAI_ENDPOINT")),
                     ApiKey = Environment.GetEnvironmentVariable("OPENAI_KEY"),
-                    DeploymentId = deploymentId,
+                    DeploymentName = deploymentName,
                     ModelName = AzureOpenAIModelName.TextEmbedding3Small
                 }
             }

sdk/search/Azure.Search.Documents/samples/Sample07_VectorSearch_UsingSemanticHybridQuery.md

@@ -178,7 +178,6 @@ SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
              QueryCaption = new(QueryCaptionType.Extractive),
              QueryAnswer = new(QueryAnswerType.Extractive)
          },
-         QueryLanguage = QueryLanguage.EnUs,
          QueryType = SearchQueryType.Semantic,
      });
 

sdk/search/Azure.Search.Documents/samples/Sample07_VectorSearch_UsingVectorizableTextQuery.md

@@ -11,7 +11,7 @@ We will create an instace of `SearchIndex` and define `Hotel` fields.
 ```C# Snippet:Azure_Search_Documents_Tests_Samples_Sample07_Vector_Search_Index_UsingVectorizableTextQuery
 string vectorSearchProfileName = "my-vector-profile";
 string vectorSearchHnswConfig = "my-hsnw-vector-config";
-string deploymentId = "text-embedding-ada-002";
+string deploymentName = "text-embedding-ada-002";
 int modelDimensions = 1536;
 
 string indexName = "hotel";
@@ -32,7 +32,7 @@ SearchIndex searchIndex = new(indexName)
         {
             new VectorSearchProfile(vectorSearchProfileName, vectorSearchHnswConfig)
             {
-                Vectorizer = "openai"
+                VectorizerName = "openai"
             }
         },
         Algorithms =
@@ -43,11 +43,11 @@ SearchIndex searchIndex = new(indexName)
         {
             new AzureOpenAIVectorizer("openai")
             {
-                AzureOpenAIParameters  = new AzureOpenAIParameters()
+                Parameters  = new AzureOpenAIVectorizerParameters()
                 {
                     ResourceUri = new Uri(Environment.GetEnvironmentVariable("OPENAI_ENDPOINT")),
                     ApiKey = Environment.GetEnvironmentVariable("OPENAI_KEY"),
-                    DeploymentId = deploymentId,
+                    DeploymentName = deploymentName,
                     ModelName = AzureOpenAIModelName.TextEmbeddingAda002
                 }
             }

sdk/search/Azure.Search.Documents/samples/Sample08_SemanticSearch.md

@@ -125,7 +125,6 @@ SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
             QueryCaption = new(QueryCaptionType.Extractive),
             QueryAnswer = new(QueryAnswerType.Extractive)
         },
-        QueryLanguage = QueryLanguage.EnUs,
         QueryType = SearchQueryType.Semantic
     });
 
@@ -177,7 +176,6 @@ SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
             QueryAnswer = new(QueryAnswerType.Extractive),
             SemanticQuery = "Is there any hotel located on the main commercial artery of the city in the heart of New York?"
         },
-        QueryLanguage = QueryLanguage.EnUs,
     });
 
 int count = 0;

sdk/search/Azure.Search.Documents/src/Azure.Search.Documents.csproj

@@ -1,7 +1,7 @@
 <Project Sdk="Microsoft.NET.Sdk">
   <PropertyGroup>
     <AssemblyTitle>Microsoft Azure.Search.Documents client library</AssemblyTitle>
-    <Version>11.6.0-beta.5</Version>
+    <Version>11.6.0</Version>
     <!--The ApiCompatVersion is managed automatically and should not generally be modified manually.-->
     <ApiCompatVersion>11.5.1</ApiCompatVersion>
     <Description>

sdk/search/Azure.Search.Documents/src/Batching/SearchIndexingBufferedSender.cs

@@ -144,7 +144,7 @@ public SearchIndexingBufferedSender(
         /// </summary>
         #pragma warning disable CA1816 // Dispose methods should call SuppressFinalize
         void IDisposable.Dispose() =>
-            DisposeAsync(async: false).EnsureCompleted();
+            DisposeInternalAsync(async: false).EnsureCompleted();
         #pragma warning restore CA1816 // Dispose methods should call SuppressFinalize
 
         /// <summary>
@@ -155,7 +155,7 @@ void IDisposable.Dispose() =>
         /// </returns>
 #pragma warning disable CA1816 // Dispose methods should call SuppressFinalize
         async ValueTask IAsyncDisposable.DisposeAsync() =>
-            await DisposeAsync(async: true).ConfigureAwait(false);
+            await DisposeInternalAsync(async: true).ConfigureAwait(false);
 #pragma warning restore CA1816 // Dispose methods should call SuppressFinalize
 
         /// <summary>
@@ -165,7 +165,7 @@ async ValueTask IAsyncDisposable.DisposeAsync() =>
         /// </summary>
         /// <param name="async">Whether to call this sync or async.</param>
         /// <returns>A Task that will wait until we're disposed.</returns>
-        internal async Task DisposeAsync(bool async)
+        internal async Task DisposeInternalAsync(bool async)
         {
             if (Interlocked.CompareExchange(ref _disposed, 1, 0) == 0)
             {