.Net: Redis memory store constructor additions (#2211)

### Motivation and Context

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RediSearch allows for vector similarity queries with a variety of
settings. Previously, the C# Redis connector defaulted all the vector
settings other than the vector size. Also, users only had the option of
passing their own `IDatabase` instance into the constructor.

Closes #2208 
### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

1. Added vector attributes to the `RedisMemoryStore` constructor: type,
indexing algorithm, distance metric, and query dialect. These maintain
the previous defaults but are now parameters that can be changed.
2. Added a new constructor that takes in a connection string to a server
instead of a database instance

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [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)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations

Note: I get `Warnings were encountered while loading the workspace. Set
the verbosity option to the 'diagnostic' level to log warnings.` when I
try to run `dotnet format`. This occurs on a clean clone of the repo as
well. The verbose output mentions how each project in the repo has its
own `.editorconfig`, so no files are formatted.
- [X] All unit tests pass, and I have added new tests where possible
- [X] I didn't break anyone 😄

---------

Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com>
Co-authored-by: Lee Miller <lemiller@microsoft.com>

Author: 3 people

dotnet/samples/KernelSyntaxExamples/Example47_Redis.cs

@@ -6,7 +6,6 @@
 using Microsoft.SemanticKernel.Connectors.Memory.Redis;
 using Microsoft.SemanticKernel.Memory;
 using RepoUtils;
-using StackExchange.Redis;
 
 // ReSharper disable once InconsistentNaming
 public static class Example47_Redis
@@ -15,10 +14,8 @@ public static class Example47_Redis
 
     public static async Task RunAsync()
     {
-        string configuration = TestConfiguration.Redis.Configuration;
-        await using ConnectionMultiplexer connectionMultiplexer = await ConnectionMultiplexer.ConnectAsync(configuration);
-        IDatabase database = connectionMultiplexer.GetDatabase();
-        RedisMemoryStore memoryStore = new(database, vectorSize: 1536);
+        using RedisMemoryStore memoryStore = new(TestConfiguration.Redis.Configuration, vectorSize: 1536);
+
         IKernel kernel = Kernel.Builder
             .WithLogger(ConsoleLogger.Logger)
             .WithOpenAIChatCompletionService(

dotnet/src/Connectors/Connectors.Memory.Redis/RedisMemoryStore.cs

@@ -14,6 +14,7 @@
 using NRedisStack.Search;
 using NRedisStack.Search.Literals.Enums;
 using StackExchange.Redis;
+using static NRedisStack.Search.Schema.VectorField;
 
 namespace Microsoft.SemanticKernel.Connectors.Memory.Redis;
 
@@ -23,18 +24,65 @@ namespace Microsoft.SemanticKernel.Connectors.Memory.Redis;
 /// <remarks>The embedded data is saved to the Redis server database specified in the constructor.
 /// Similarity search capability is provided through the RediSearch module. Use RediSearch's "Index" to implement "Collection".
 /// </remarks>
-public sealed class RedisMemoryStore : IMemoryStore
+public class RedisMemoryStore : IMemoryStore, IDisposable
 {
     /// <summary>
     /// Create a new instance of semantic memory using Redis.
     /// </summary>
-    /// <param name="database">The database of the redis server.</param>
-    /// <param name="vectorSize">Embedding vector size</param>
-    public RedisMemoryStore(IDatabase database, int vectorSize)
+    /// <param name="database">The database of the Redis server.</param>
+    /// <param name="vectorSize">Embedding vector size, defaults to 1536</param>
+    /// <param name="vectorIndexAlgorithm">Indexing algorithm for vectors, defaults to "HNSW"</param>
+    /// <param name="vectorDistanceMetric">Metric for measuring vector distances, defaults to "COSINE"</param>
+    /// <param name="queryDialect">Query dialect, must be 2 or greater for vector similarity searching, defaults to 2</param>
+    public RedisMemoryStore(
+        IDatabase database,
+        int vectorSize = DefaultVectorSize,
+        VectorAlgo vectorIndexAlgorithm = DefaultIndexAlgorithm,
+        VectorDistanceMetric vectorDistanceMetric = DefaultDistanceMetric,
+        int queryDialect = DefaultQueryDialect)
     {
+        if (vectorSize <= 0)
+        {
+            throw new ArgumentException(
+                $"Invalid vector size: {vectorSize}. Vector size must be a positive integer.", nameof(vectorSize));
+        }
+
         this._database = database;
         this._vectorSize = vectorSize;
         this._ft = database.FT();
+        this._vectorIndexAlgorithm = vectorIndexAlgorithm;
+        this._vectorDistanceMetric = vectorDistanceMetric.ToString();
+        this._queryDialect = queryDialect;
+    }
+
+    /// <summary>
+    /// Create a new instance of semantic memory using Redis.
+    /// </summary>
+    /// <param name="connectionString">Provide connection URL to a Redis instance</param>
+    /// <param name="vectorSize">Embedding vector size, defaults to 1536</param>
+    /// <param name="vectorIndexAlgorithm">Indexing algorithm for vectors, defaults to "HNSW"</param>
+    /// <param name="vectorDistanceMetric">Metric for measuring vector distances, defaults to "COSINE"</param>
+    /// <param name="queryDialect">Query dialect, must be 2 or greater for vector similarity searching, defaults to 2</param>
+    public RedisMemoryStore(
+        string connectionString,
+        int vectorSize = DefaultVectorSize,
+        VectorAlgo vectorIndexAlgorithm = DefaultIndexAlgorithm,
+        VectorDistanceMetric vectorDistanceMetric = DefaultDistanceMetric,
+        int queryDialect = DefaultQueryDialect)
+    {
+        if (vectorSize <= 0)
+        {
+            throw new ArgumentException(
+                $"Invalid vector size: {vectorSize}. Vector size must be a positive integer.", nameof(vectorSize));
+        }
+
+        this._connection = ConnectionMultiplexer.Connect(connectionString);
+        this._database = this._connection.GetDatabase();
+        this._vectorSize = vectorSize;
+        this._ft = this._database.FT();
+        this._vectorIndexAlgorithm = vectorIndexAlgorithm;
+        this._vectorDistanceMetric = vectorDistanceMetric.ToString();
+        this._queryDialect = queryDialect;
     }
 
     /// <inheritdoc />
@@ -54,10 +102,10 @@ public async Task CreateCollectionAsync(string collectionName, CancellationToken
             .AddTextField("key")
             .AddTextField("metadata")
             .AddNumericField("timestamp")
-            .AddVectorField("embedding", VECTOR_INDEX_ALGORITHM, new Dictionary<string, object> {
-                    {"TYPE", VECTOR_TYPE},
+            .AddVectorField("embedding", this._vectorIndexAlgorithm, new Dictionary<string, object> {
+                    {"TYPE", DefaultVectorType},
                     {"DIM", this._vectorSize},
-                    {"DISTANCE_METRIC", VECTOR_DISTANCE_METRIC},
+                    {"DISTANCE_METRIC", this._vectorDistanceMetric},
                 });
 
         await this._ft.CreateAsync(collectionName, ftCreateParams, schema).ConfigureAwait(false);
@@ -71,7 +119,7 @@ public async Task<bool> DoesCollectionExistAsync(string collectionName, Cancella
             await this._ft.InfoAsync(collectionName).ConfigureAwait(false);
             return true;
         }
-        catch (RedisServerException ex) when (ex.Message == MESSAGE_WHEN_INDEX_DOES_NOT_EXIST)
+        catch (RedisServerException ex) when (ex.Message.Equals(IndexDoesNotExistErrorMessage, StringComparison.Ordinal))
         {
             return false;
         }
@@ -112,7 +160,7 @@ public async Task<string> UpsertAsync(string collectionName, MemoryRecord record
         await this._database.HashSetAsync(GetRedisKey(collectionName, record.Key), new[] {
             new HashEntry("key", record.Key),
             new HashEntry("metadata", record.GetSerializedMetadata()),
-            new HashEntry("embedding", MemoryMarshal.AsBytes(record.Embedding.Span).ToArray()),
+            new HashEntry("embedding", this.ConvertEmbeddingToBytes(record.Embedding)),
             new HashEntry("timestamp", ToTimestampLong(record.Timestamp))
         }, flags: CommandFlags.None).ConfigureAwait(false);
 
@@ -155,11 +203,11 @@ public async Task RemoveBatchAsync(string collectionName, IEnumerable<string> ke
         }
 
         var query = new Query($"*=>[KNN {limit} @embedding $embedding AS vector_score]")
-                    .AddParam("embedding", MemoryMarshal.AsBytes(embedding.Span).ToArray())
+                    .AddParam("embedding", this.ConvertEmbeddingToBytes(embedding))
                     .SetSortBy("vector_score")
                     .ReturnFields("key", "metadata", "embedding", "timestamp", "vector_score")
                     .Limit(0, limit)
-                    .Dialect(QUERY_DIALECT);
+                    .Dialect(this._queryDialect);
 
         var results = await this._ft.SearchAsync(collectionName, query).ConfigureAwait(false);
 
@@ -198,43 +246,63 @@ public async Task RemoveBatchAsync(string collectionName, IEnumerable<string> ke
             cancellationToken: cancellationToken).FirstOrDefaultAsync(cancellationToken: cancellationToken).ConfigureAwait(false);
     }
 
-    #region constants  ================================================================================
+    public void Dispose()
+    {
+        this.Dispose(true);
+        GC.SuppressFinalize(this);
+    }
+
+    protected virtual void Dispose(bool disposing)
+    {
+        if (disposing)
+        {
+            this._connection?.Dispose();
+        }
+    }
+
+    #region private ================================================================================
 
     /// <summary>
-    /// Vector similarity index algorithm. The default value is "HNSW".
-    /// <see href="https://redis.io/docs/stack/search/reference/vectors/#create-a-vector-field"/>
+    /// Vector similarity index algorithm. Supported algorithms are {FLAT, HNSW}. The default value is "HNSW".
+    /// <see href="https://redis.io/docs/interact/search-and-query/search/vectors/#create-a-vector-field"/>
     /// </summary>
-    internal const Schema.VectorField.VectorAlgo VECTOR_INDEX_ALGORITHM = Schema.VectorField.VectorAlgo.HNSW;
+    private const VectorAlgo DefaultIndexAlgorithm = VectorAlgo.HNSW;
 
     /// <summary>
-    /// Vector type. Supported types are FLOAT32 and FLOAT64. The default value is "FLOAT32".
+    /// Vector type. Available values are {FLOAT32, FLOAT64}.
+    /// Value "FLOAT32" is used by default based on <see cref="MemoryRecord.Embedding"/> <see cref="float"/> type.
     /// </summary>
-    internal const string VECTOR_TYPE = "FLOAT32";
+    private const string DefaultVectorType = "FLOAT32";
 
     /// <summary>
-    /// Supported distance metric, one of {L2, IP, COSINE}. The default value is "COSINE".
+    /// Supported distance metrics are {L2, IP, COSINE}. The default value is "COSINE".
     /// </summary>
-    internal const string VECTOR_DISTANCE_METRIC = "COSINE";
+    private const VectorDistanceMetric DefaultDistanceMetric = VectorDistanceMetric.COSINE;
 
     /// <summary>
     /// Query dialect. To use a vector similarity query, specify DIALECT 2 or higher. The default value is "2".
-    /// <see href="https://redis.io/docs/stack/search/reference/vectors/#querying-vector-fields"/>
+    /// <see href="https://redis.io/docs/interact/search-and-query/search/vectors/#querying-vector-fields"/>
     /// </summary>
-    internal const int QUERY_DIALECT = 2;
+    private const int DefaultQueryDialect = 2;
 
     /// <summary>
-    /// Message when index does not exist.
-    /// <see href="https://github.com/RediSearch/RediSearch/blob/master/src/info_command.c#L96"/>
+    /// Embedding vector size.
     /// </summary>
-    internal const string MESSAGE_WHEN_INDEX_DOES_NOT_EXIST = "Unknown Index name";
-
-    #endregion
+    private const int DefaultVectorSize = 1536;
 
-    #region private ================================================================================
+    /// <summary>
+    /// Message when index does not exist.
+    /// <see href="https://github.com/RediSearch/RediSearch/blob/master/src/info_command.c#L97"/>
+    /// </summary>
+    private const string IndexDoesNotExistErrorMessage = "Unknown Index name";
 
     private readonly IDatabase _database;
     private readonly int _vectorSize;
     private readonly SearchCommands _ft;
+    private readonly ConnectionMultiplexer? _connection;
+    private readonly Schema.VectorField.VectorAlgo _vectorIndexAlgorithm;
+    private readonly string _vectorDistanceMetric;
+    private readonly int _queryDialect;
 
     private static long ToTimestampLong(DateTimeOffset? timestamp)
     {
@@ -295,5 +363,10 @@ private double GetSimilarity(Document document)
         return 1 - vectorScore;
     }
 
+    private byte[] ConvertEmbeddingToBytes(ReadOnlyMemory<float> embedding)
+    {
+        return MemoryMarshal.AsBytes(embedding.Span).ToArray();
+    }
+
     #endregion
 }

dotnet/src/Connectors/Connectors.Memory.Redis/RedisVectorDistanceMetric.cs

@@ -0,0 +1,25 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+namespace Microsoft.SemanticKernel.Connectors.Memory.Redis;
+
+/// <summary>
+/// Supported distance metrics are {L2, IP, COSINE}. The default value is "COSINE".
+/// <see href="https://redis.io/docs/interact/search-and-query/search/vectors/"/>
+/// </summary>
+public enum VectorDistanceMetric
+{
+    /// <summary>
+    /// Euclidean distance between two vectors
+    /// </summary>
+    L2,
+
+    /// <summary>
+    /// Inner product of two vectors
+    /// </summary>
+    IP,
+
+    /// <summary>
+    /// Cosine distance of two vectors
+    /// </summary>
+    COSINE,
+}