.Net: Improvements for Azure Cosmos DB for MongoDB connector (#6169)

### Motivation and Context

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### Description

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### Contribution Checklist

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- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
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raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄

dotnet/samples/Concepts/Caching/SemanticCachingWithFilters.cs

@@ -87,12 +87,7 @@ public async Task AzureCosmosDBMongoDBCacheAsync()
         var kernel = GetKernelWithCache(_ => new AzureCosmosDBMongoDBMemoryStore(
             TestConfiguration.AzureCosmosDbMongoDb.ConnectionString,
             TestConfiguration.AzureCosmosDbMongoDb.DatabaseName,
-            new()
-            {
-                Kind = AzureCosmosDBVectorSearchType.VectorIVF,
-                Similarity = AzureCosmosDBSimilarityType.Cosine,
-                Dimensions = 1536
-            }));
+            new(dimensions: 1536)));
 
         var result1 = await ExecuteAsync(kernel, "First run", "What's the tallest building in New York?");
         var result2 = await ExecuteAsync(kernel, "Second run", "What is the highest building in New York City?");

dotnet/src/Connectors/Connectors.Memory.AzureCosmosDBMongoDB/AzureCosmosDBMongoDBConfig.cs

@@ -5,82 +5,73 @@
 namespace Microsoft.SemanticKernel.Connectors.AzureCosmosDBMongoDB;
 
 /// <summary>
-/// Get more details about Azure Cosmos Mongo vCore and these configs https://learn.microsoft.com/azure/cosmos-db/mongodb/vcore/vector-search
+/// Azure Cosmos Mongo vCore configuration.
+/// More information here: https://learn.microsoft.com/azure/cosmos-db/mongodb/vcore/vector-search.
 /// </summary>
-public class AzureCosmosDBMongoDBConfig
+/// <remarks>
+/// Initialize the <see cref="AzureCosmosDBMongoDBConfig"/> with default values.
+/// </remarks>
+public class AzureCosmosDBMongoDBConfig(int dimensions)
 {
+    private const string DefaultIndexName = "default_index";
+
     /// <summary>
     /// Application name for the client for tracking and logging
     /// </summary>
-    public string ApplicationName { get; set; }
+    public string ApplicationName { get; set; } = HttpHeaderConstant.Values.UserAgent;
 
     /// <summary>
-    /// Index name for the Mongo vCore DB
+    /// Index name for the Mongo vCore DB. Default is "default_index".
     /// </summary>
-    public string IndexName { get; set; }
+    public string IndexName { get; set; } = DefaultIndexName;
 
     /// <summary>
-    /// Kind: Type of vector index to create.
+    /// Type of vector index to create.
     ///     Possible options are:
-    ///         - vector-ivf
+    ///         - vector-ivf (default)
     ///         - vector-hnsw: available as a preview feature only,
     ///                        to enable visit https://learn.microsoft.com/azure/azure-resource-manager/management/preview-features
     /// </summary>
-    public AzureCosmosDBVectorSearchType Kind { get; set; }
+    public AzureCosmosDBVectorSearchType Kind { get; set; } = AzureCosmosDBVectorSearchType.VectorIVF;
 
     /// <summary>
-    /// NumLists: This integer is the number of clusters that the inverted file (IVF) index uses to group the vector data.
+    /// This integer is the number of clusters that the inverted file (IVF) index uses to group the vector data. Default is 1.
     /// We recommend that numLists is set to documentCount/1000 for up to 1 million documents and to sqrt(documentCount)
     /// for more than 1 million documents. Using a numLists value of 1 is akin to performing brute-force search, which has
     /// limited performance.
     /// </summary>
-    public int NumLists { get; set; }
+    public int NumLists { get; set; } = 1;
 
     /// <summary>
     /// Number of dimensions for vector similarity. The maximum number of supported dimensions is 2000.
     /// </summary>
-    public int Dimensions { get; set; }
+    public int Dimensions { get; set; } = dimensions;
 
     /// <summary>
-    /// Similarity: Similarity metric to use with the IVF index.
+    /// Similarity metric to use with the IVF index.
     ///     Possible options are:
-    ///         - COS (cosine distance),
+    ///         - COS (cosine distance, default),
     ///         - L2 (Euclidean distance), and
     ///         - IP (inner product).
     /// </summary>
-    public AzureCosmosDBSimilarityType Similarity { get; set; }
+    public AzureCosmosDBSimilarityType Similarity { get; set; } = AzureCosmosDBSimilarityType.Cosine;
 
     /// <summary>
-    /// NumberOfConnections: The max number of connections per layer (16 by default, minimum value is 2, maximum value is
+    /// The max number of connections per layer (16 by default, minimum value is 2, maximum value is
     /// 100). Higher m is suitable for datasets with high dimensionality and/or high accuracy requirements.
     /// </summary>
-    public int NumberOfConnections { get; set; }
+    public int NumberOfConnections { get; set; } = 16;
 
     /// <summary>
-    /// EfConstruction: the size of the dynamic candidate list for constructing the graph (64 by default, minimum value is 4,
+    /// The size of the dynamic candidate list for constructing the graph (64 by default, minimum value is 4,
     /// maximum value is 1000). Higher ef_construction will result in better index quality and higher accuracy, but it will
     /// also increase the time required to build the index. EfConstruction has to be at least 2 * m
     /// </summary>
-    public int EfConstruction { get; set; }
+    public int EfConstruction { get; set; } = 64;
 
     /// <summary>
-    /// EfSearch: The size of the dynamic candidate list for search (40 by default). A higher value provides better recall at
+    /// The size of the dynamic candidate list for search (40 by default). A higher value provides better recall at
     /// the cost of speed.
     /// </summary>
-    public int EfSearch { get; set; }
-
-    /// <summary>
-    /// Initialize the AzureCosmosDBMongoDBConfig with default values
-    /// </summary>
-    public AzureCosmosDBMongoDBConfig()
-    {
-        this.ApplicationName = HttpHeaderConstant.Values.UserAgent;
-        this.IndexName = "default_index";
-        this.Kind = AzureCosmosDBVectorSearchType.VectorHNSW;
-        this.NumLists = 1;
-        this.Similarity = AzureCosmosDBSimilarityType.Cosine;
-        this.NumberOfConnections = 16;
-        this.EfConstruction = 64;
-        this.EfSearch = 40;
-    }
+    public int EfSearch { get; set; } = 40;
 }

dotnet/src/IntegrationTests/Connectors/Memory/AzureCosmosDBMongoDB/AzureCosmosDBMongoDBMemoryStoreTests.cs

@@ -30,7 +30,6 @@ public async Task ItCanCreateGetCheckAndDeleteCollectionAsync()
         var collectionName = this._fixture.CollectionName;
         var memoryStore = this._fixture.MemoryStore;
 
-        await memoryStore.CreateCollectionAsync(collectionName);
         var collectionNames = memoryStore.GetCollectionsAsync();
 
         Assert.True(await collectionNames.ContainsAsync(collectionName));

dotnet/src/IntegrationTests/Connectors/Memory/AzureCosmosDBMongoDB/AzureCosmosDBMongoDBMemoryStoreTestsFixture.cs

@@ -35,12 +35,14 @@ public AzureCosmosDBMongoDBMemoryStoreTestsFixture()
         this.MemoryStore = new AzureCosmosDBMongoDBMemoryStore(
             connectionString,
             this.DatabaseName,
-            new AzureCosmosDBMongoDBConfig()
+            new AzureCosmosDBMongoDBConfig(dimensions: 3)
         );
     }
 
     public async Task InitializeAsync()
     {
+        await this.MemoryStore.CreateCollectionAsync(this.CollectionName);
+
         await this
             .MemoryStore.UpsertBatchAsync(this.CollectionName, DataHelper.VectorSearchTestRecords)
             .ToListAsync();