.Net MEVD Replace VectorSearchResults with IAsyncEnumerable

dotnet/samples/Concepts/Caching/SemanticCachingWithFilters.cs

@@ -199,8 +199,8 @@ public async Task OnPromptRenderAsync(PromptRenderContext context, Func<PromptRe
             await collection.CreateCollectionIfNotExistsAsync();
 
             // Search for similar prompts in cache.
-            var searchResults = await collection.VectorizedSearchAsync(promptEmbedding, top: 1, cancellationToken: context.CancellationToken);
-            var searchResult = (await searchResults.Results.FirstOrDefaultAsync())?.Record;
+            var searchResults = collection.VectorizedSearchAsync(promptEmbedding, top: 1, cancellationToken: context.CancellationToken);
+            var searchResult = (await searchResults.FirstOrDefaultAsync())?.Record;
 
             // If result exists, return it.
             if (searchResult is not null)

dotnet/samples/Concepts/Memory/VectorStoreEmbeddingGeneration/TextEmbeddingVectorStoreRecordCollection.cs

@@ -122,7 +122,7 @@ public async IAsyncEnumerable<TKey> UpsertAsync(IEnumerable<TRecord> records, [E
     }
 
     /// <inheritdoc />
-    public Task<VectorSearchResults<TRecord>> VectorizedSearchAsync<TVector>(TVector vector, int top, VectorSearchOptions<TRecord>? options = null, CancellationToken cancellationToken = default)
+    public IAsyncEnumerable<VectorSearchResult<TRecord>> VectorizedSearchAsync<TVector>(TVector vector, int top, VectorSearchOptions<TRecord>? options = null, CancellationToken cancellationToken = default)
     {
         return this._decoratedVectorStoreRecordCollection.VectorizedSearchAsync(vector, top, options, cancellationToken);
     }
@@ -135,7 +135,7 @@ public IAsyncEnumerable<TRecord> GetAsync(Expression<Func<TRecord, bool>> filter
     public async Task<VectorSearchResults<TRecord>> VectorizableTextSearchAsync(string searchText, int top, VectorSearchOptions<TRecord>? options = null, CancellationToken cancellationToken = default)
     {
         var embeddingValue = await this._textEmbeddingGenerationService.GenerateEmbeddingAsync(searchText, cancellationToken: cancellationToken).ConfigureAwait(false);
-        return await this.VectorizedSearchAsync(embeddingValue, top, options, cancellationToken).ConfigureAwait(false);
+        return new VectorSearchResults<TRecord>(this.VectorizedSearchAsync(embeddingValue, top, options, cancellationToken));
     }
 
     /// <inheritdoc />

dotnet/samples/Concepts/Memory/VectorStore_HybridSearch_Simple_AzureAISearch.cs

@@ -56,8 +56,8 @@ public async Task IngestDataAndUseHybridSearch()
         // Search the collection using a vector search.
         var searchString = "What is an Application Programming Interface";
         var searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        var searchResult = await hybridSearchCollection.HybridSearchAsync(searchVector, ["Application", "Programming", "Interface"], top: 1);
-        var resultRecords = await searchResult.Results.ToListAsync();
+        var searchResult = hybridSearchCollection.HybridSearchAsync(searchVector, ["Application", "Programming", "Interface"], top: 1);
+        var resultRecords = await searchResult.ToListAsync();
 
         Console.WriteLine("Search string: " + searchString);
         Console.WriteLine("Result: " + resultRecords.First().Record.Definition);
@@ -66,8 +66,8 @@ public async Task IngestDataAndUseHybridSearch()
         // Search the collection using a vector search.
         searchString = "What is Retrieval Augmented Generation";
         searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        searchResult = await hybridSearchCollection.HybridSearchAsync(searchVector, ["Retrieval", "Augmented", "Generation"], top: 1);
-        resultRecords = await searchResult.Results.ToListAsync();
+        searchResult = hybridSearchCollection.HybridSearchAsync(searchVector, ["Retrieval", "Augmented", "Generation"], top: 1);
+        resultRecords = await searchResult.ToListAsync();
 
         Console.WriteLine("Search string: " + searchString);
         Console.WriteLine("Result: " + resultRecords.First().Record.Definition);
@@ -76,8 +76,8 @@ public async Task IngestDataAndUseHybridSearch()
         // Search the collection using a vector search with pre-filtering.
         searchString = "What is Retrieval Augmented Generation";
         searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        searchResult = await hybridSearchCollection.HybridSearchAsync(searchVector, ["Retrieval", "Augmented", "Generation"], top: 3, new() { Filter = g => g.Category == "External Definitions" });
-        resultRecords = await searchResult.Results.ToListAsync();
+        searchResult = hybridSearchCollection.HybridSearchAsync(searchVector, ["Retrieval", "Augmented", "Generation"], top: 3, new() { Filter = g => g.Category == "External Definitions" });
+        resultRecords = await searchResult.ToListAsync();
 
         Console.WriteLine("Search string: " + searchString);
         Console.WriteLine("Number of results: " + resultRecords.Count);

dotnet/samples/Concepts/Memory/VectorStore_Langchain_Interop.cs

@@ -96,8 +96,8 @@ private async Task ReadDataFromCollectionAsync(IVectorStore vectorStore, string
         // Search the data set.
         var searchString = "I'm looking for an animal that is loyal and will make a great companion";
         var searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        var searchResult = await collection.VectorizedSearchAsync(searchVector, top: 1);
-        var resultRecords = await searchResult.Results.ToListAsync();
+        var searchResult = collection.VectorizedSearchAsync(searchVector, top: 1);
+        var resultRecords = await searchResult.ToListAsync();
 
         this.Output.WriteLine("Search string: " + searchString);
         this.Output.WriteLine("Source: " + resultRecords.First().Record.Source);

dotnet/samples/Concepts/Memory/VectorStore_VectorSearch_MultiStore_Common.cs

@@ -51,8 +51,8 @@ public async Task IngestDataAndSearchAsync<TKey>(string collectionName, Func<TKe
         // Search the collection using a vector search.
         var searchString = "What is an Application Programming Interface";
         var searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        var searchResult = await collection.VectorizedSearchAsync(searchVector, top: 1);
-        var resultRecords = await searchResult.Results.ToListAsync();
+        var searchResult = collection.VectorizedSearchAsync(searchVector, top: 1);
+        var resultRecords = await searchResult.ToListAsync();
 
         output.WriteLine("Search string: " + searchString);
         output.WriteLine("Result: " + resultRecords.First().Record.Definition);
@@ -61,8 +61,8 @@ public async Task IngestDataAndSearchAsync<TKey>(string collectionName, Func<TKe
         // Search the collection using a vector search.
         searchString = "What is Retrieval Augmented Generation";
         searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        searchResult = await collection.VectorizedSearchAsync(searchVector, top: 1);
-        resultRecords = await searchResult.Results.ToListAsync();
+        searchResult = collection.VectorizedSearchAsync(searchVector, top: 1);
+        resultRecords = await searchResult.ToListAsync();
 
         output.WriteLine("Search string: " + searchString);
         output.WriteLine("Result: " + resultRecords.First().Record.Definition);
@@ -71,8 +71,8 @@ public async Task IngestDataAndSearchAsync<TKey>(string collectionName, Func<TKe
         // Search the collection using a vector search with pre-filtering.
         searchString = "What is Retrieval Augmented Generation";
         searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        searchResult = await collection.VectorizedSearchAsync(searchVector, top: 3, new() { Filter = g => g.Category == "External Definitions" });
-        resultRecords = await searchResult.Results.ToListAsync();
+        searchResult = collection.VectorizedSearchAsync(searchVector, top: 3, new() { Filter = g => g.Category == "External Definitions" });
+        resultRecords = await searchResult.ToListAsync();
 
         output.WriteLine("Search string: " + searchString);
         output.WriteLine("Number of results: " + resultRecords.Count);

dotnet/samples/Concepts/Memory/VectorStore_VectorSearch_MultiVector.cs

@@ -54,12 +54,12 @@ public async Task VectorSearchWithMultiVectorRecordAsync()
         // Search the store using the description embedding.
         var searchString = "I am looking for a reasonably priced coffee maker";
         var searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        var searchResult = await collection.VectorizedSearchAsync(
+        var searchResult = collection.VectorizedSearchAsync(
             searchVector, top: 1, new()
             {
                 VectorProperty = r => r.DescriptionEmbedding
             });
-        var resultRecords = await searchResult.Results.ToListAsync();
+        var resultRecords = await searchResult.ToListAsync();
 
         WriteLine("Search string: " + searchString);
         WriteLine("Result: " + resultRecords.First().Record.Description);
@@ -69,14 +69,14 @@ public async Task VectorSearchWithMultiVectorRecordAsync()
         // Search the store using the feature list embedding.
         searchString = "I am looking for a handheld vacuum cleaner that will remove pet hair";
         searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        searchResult = await collection.VectorizedSearchAsync(
+        searchResult = collection.VectorizedSearchAsync(
             searchVector,
             top: 1,
             new()
             {
                 VectorProperty = r => r.FeatureListEmbedding
             });
-        resultRecords = await searchResult.Results.ToListAsync();
+        resultRecords = await searchResult.ToListAsync();
 
         WriteLine("Search string: " + searchString);
         WriteLine("Result: " + resultRecords.First().Record.Description);

dotnet/samples/Concepts/Memory/VectorStore_VectorSearch_Paging.cs

@@ -47,7 +47,7 @@ public async Task VectorSearchWithPagingAsync()
         while (moreResults)
         {
             // Get the next page of results by asking for 10 results, and using 'Skip' to skip the results from the previous pages.
-            var currentPageResults = await collection.VectorizedSearchAsync(
+            var currentPageResults = collection.VectorizedSearchAsync(
                 searchVector,
                 top: 10,
                 new()
@@ -57,7 +57,7 @@ public async Task VectorSearchWithPagingAsync()
 
             // Print the results.
             var pageCount = 0;
-            await foreach (var result in currentPageResults.Results)
+            await foreach (var result in currentPageResults)
             {
                 Console.WriteLine($"Key: {result.Record.Key}, Text: {result.Record.Text}");
                 pageCount++;

dotnet/samples/Concepts/Memory/VectorStore_VectorSearch_Simple.cs

@@ -50,8 +50,8 @@ public async Task ExampleAsync()
         // Search the collection using a vector search.
         var searchString = "What is an Application Programming Interface";
         var searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        var searchResult = await collection.VectorizedSearchAsync(searchVector, top: 1);
-        var resultRecords = await searchResult.Results.ToListAsync();
+        var searchResult = collection.VectorizedSearchAsync(searchVector, top: 1);
+        var resultRecords = await searchResult.ToListAsync();
 
         Console.WriteLine("Search string: " + searchString);
         Console.WriteLine("Result: " + resultRecords.First().Record.Definition);
@@ -60,8 +60,8 @@ public async Task ExampleAsync()
         // Search the collection using a vector search.
         searchString = "What is Retrieval Augmented Generation";
         searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        searchResult = await collection.VectorizedSearchAsync(searchVector, top: 1);
-        resultRecords = await searchResult.Results.ToListAsync();
+        searchResult = collection.VectorizedSearchAsync(searchVector, top: 1);
+        resultRecords = await searchResult.ToListAsync();
 
         Console.WriteLine("Search string: " + searchString);
         Console.WriteLine("Result: " + resultRecords.First().Record.Definition);
@@ -70,8 +70,8 @@ public async Task ExampleAsync()
         // Search the collection using a vector search with pre-filtering.
         searchString = "What is Retrieval Augmented Generation";
         searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        searchResult = await collection.VectorizedSearchAsync(searchVector, top: 3, new() { Filter = g => g.Category == "External Definitions" });
-        resultRecords = await searchResult.Results.ToListAsync();
+        searchResult = collection.VectorizedSearchAsync(searchVector, top: 3, new() { Filter = g => g.Category == "External Definitions" });
+        resultRecords = await searchResult.ToListAsync();
 
         Console.WriteLine("Search string: " + searchString);
         Console.WriteLine("Number of results: " + resultRecords.Count);

dotnet/samples/Concepts/Memory/VolatileVectorStore_LoadData.cs

@@ -71,8 +71,8 @@ static DataModel CreateRecord(string text, ReadOnlyMemory<float> embedding)
             // Search the collection using a vector search.
             var searchString = "What is the Semantic Kernel?";
             var searchVector = await embeddingGenerationService.GenerateEmbeddingAsync(searchString);
-            var searchResult = await vectorSearch!.VectorizedSearchAsync(searchVector, top: 1);
-            var resultRecords = await searchResult.Results.ToListAsync();
+            var searchResult = vectorSearch!.VectorizedSearchAsync(searchVector, top: 1);
+            var resultRecords = await searchResult.ToListAsync();
 
             Console.WriteLine("Search string: " + searchString);
             Console.WriteLine("Result: " + resultRecords.First().Record.Text);
@@ -116,8 +116,8 @@ static DataModel CreateRecord(TextSearchResult searchResult, ReadOnlyMemory<floa
         // Search the collection using a vector search.
         var searchString = "What is the Semantic Kernel?";
         var searchVector = await embeddingGenerationService.GenerateEmbeddingAsync(searchString);
-        var searchResult = await vectorSearch!.VectorizedSearchAsync(searchVector, top: 1);
-        var resultRecords = await searchResult.Results.ToListAsync();
+        var searchResult = vectorSearch!.VectorizedSearchAsync(searchVector, top: 1);
+        var resultRecords = await searchResult.ToListAsync();
 
         Console.WriteLine("Search string: " + searchString);
         Console.WriteLine("Result: " + resultRecords.First().Record.Text);

dotnet/samples/Concepts/Optimization/FrugalGPTWithFilters.cs

@@ -216,8 +216,8 @@ public async Task OnPromptRenderAsync(PromptRenderContext context, Func<PromptRe
                 var requestEmbedding = await textEmbeddingGenerationService.GenerateEmbeddingAsync(request, cancellationToken: context.CancellationToken);
 
                 // Find top N examples which are similar to original request.
-                var searchResults = await collection.VectorizedSearchAsync(requestEmbedding, top: TopN, cancellationToken: context.CancellationToken);
-                var topNExamples = (await searchResults.Results.ToListAsync(context.CancellationToken)).Select(l => l.Record).ToList();
+                var searchResults = collection.VectorizedSearchAsync(requestEmbedding, top: TopN, cancellationToken: context.CancellationToken);
+                var topNExamples = (await searchResults.ToListAsync(context.CancellationToken)).Select(l => l.Record).ToList();
 
                 // Override arguments to use only top N examples, which will be sent to LLM.
                 context.Arguments["Examples"] = topNExamples.Select(l => l.Example);

dotnet/samples/Concepts/Optimization/PluginSelectionWithFilters.cs

@@ -298,8 +298,8 @@ public async Task<List<KernelFunction>> GetBestFunctionsAsync(
             await collection.CreateCollectionIfNotExistsAsync(cancellationToken);
 
             // Find best functions to call for original request.
-            var searchResults = await collection.VectorizedSearchAsync(requestEmbedding, top: numberOfBestFunctions, cancellationToken: cancellationToken);
-            var recordKeys = (await searchResults.Results.ToListAsync(cancellationToken)).Select(l => l.Record.Id);
+            var searchResults = collection.VectorizedSearchAsync(requestEmbedding, top: numberOfBestFunctions, cancellationToken: cancellationToken);
+            var recordKeys = (await searchResults.ToListAsync(cancellationToken)).Select(l => l.Record.Id);
 
             return plugins
                 .SelectMany(plugin => plugin)

dotnet/samples/Concepts/Search/VectorStore_TextSearch.cs

@@ -149,7 +149,7 @@ public async Task<VectorSearchResults<TRecord>> VectorizableTextSearchAsync(stri
         {
             var vectorizedQuery = await textEmbeddingGeneration!.GenerateEmbeddingAsync(searchText, cancellationToken: cancellationToken).ConfigureAwait(false);
 
-            return await vectorizedSearch.VectorizedSearchAsync(vectorizedQuery, top, options, cancellationToken);
+            return new VectorSearchResults<TRecord>(vectorizedSearch.VectorizedSearchAsync(vectorizedQuery, top, options, cancellationToken));
         }
 
         /// <inheritdoc />

dotnet/samples/Demos/ModelContextProtocolClientServer/MCPServer/Program.cs

@@ -119,12 +119,12 @@ static TextDataModel CreateRecord(string text, ReadOnlyMemory<float> embedding)
             ReadOnlyMemory<float> promptEmbedding = await embeddingGenerationService.GenerateEmbeddingAsync(prompt, cancellationToken: cancellationToken);
 
             // Retrieve top three matching records from the vector store
-            VectorSearchResults<TextDataModel> result = await vsCollection.VectorizedSearchAsync(promptEmbedding, top: 3, cancellationToken: cancellationToken);
+            var result = vsCollection.VectorizedSearchAsync(promptEmbedding, top: 3, cancellationToken: cancellationToken);
 
             // Return the records as resource contents
             List<ResourceContents> contents = [];
 
-            await foreach (var record in result.Results)
+            await foreach (var record in result)
             {
                 contents.Add(new TextResourceContents()
                 {

dotnet/samples/GettingStartedWithVectorStores/Step2_Vector_Search.cs

@@ -43,10 +43,10 @@ internal static async Task<VectorSearchResult<Glossary>> SearchVectorStoreAsync(
         var searchVector = await textEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
 
         // Search the store and get the single most relevant result.
-        var searchResult = await collection.VectorizedSearchAsync(
+        var searchResult = collection.VectorizedSearchAsync(
             searchVector,
             top: 1);
-        var searchResultItems = await searchResult.Results.ToListAsync();
+        var searchResultItems = await searchResult.ToListAsync();
         return searchResultItems.First();
     }
 
@@ -63,14 +63,14 @@ public async Task SearchAnInMemoryVectorStoreWithFilteringAsync()
         var searchVector = await fixture.TextEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
 
         // Search the store with a filter and get the single most relevant result.
-        var searchResult = await collection.VectorizedSearchAsync(
+        var searchResult = collection.VectorizedSearchAsync(
             searchVector,
             top: 1,
             new()
             {
                 Filter = g => g.Category == "AI"
             });
-        var searchResultItems = await searchResult.Results.ToListAsync();
+        var searchResultItems = await searchResult.ToListAsync();
 
         // Write the search result with its score to the console.
         Console.WriteLine(searchResultItems.First().Record.Definition);

dotnet/samples/GettingStartedWithVectorStores/Step5_Use_DynamicDataModel.cs

@@ -56,10 +56,10 @@ public async Task SearchAVectorStoreWithGenericDataModelAsync()
         var searchVector = await fixture.TextEmbeddingGenerationService.GenerateEmbeddingAsync(searchString);
 
         // Search the generic data model collection and get the single most relevant result.
-        var searchResult = await dynamicDataModelCollection.VectorizedSearchAsync(
+        var searchResult = dynamicDataModelCollection.VectorizedSearchAsync(
             searchVector,
             top: 1);
-        var searchResultItems = await searchResult.Results.ToListAsync();
+        var searchResultItems = await searchResult.ToListAsync();
 
         // Write the search result with its score to the console.
         // Note that here we can loop through all the properties