removing warnings

src/Redis.OM.Vectorizers.AllMiniLML6V2/SentenceVectorizer.cs

@@ -7,9 +7,15 @@
 
 namespace Redis.OM.Vectorizers.AllMiniLML6V2;
 
+/// <summary>
+/// A vectorizer to Vectorize sentences using ALl Mini LM L6 V2 Model.
+/// </summary>
 public class SentenceVectorizer : IVectorizer<string>
 {
+    /// <inheritdoc />
     public VectorType VectorType => VectorType.FLOAT32;
+
+    /// <inheritdoc />
     public int Dim => 384;
     private static Lazy<TokenizerBase> Tokenizer => new Lazy<TokenizerBase>(AllMiniLML6V2Tokenizer.Create);
     private static Lazy<InferenceSession> InferenceSession => new Lazy<InferenceSession>(LoadInferenceSession);
@@ -27,14 +33,20 @@ private static InferenceSession LoadInferenceSession()
         _ = stream.Read(resourceBytes, 0, resourceBytes.Length);
         return new InferenceSession(resourceBytes);
     }
-
+
+    /// <inheritdoc />
     public byte[] Vectorize(string obj)
     {
         return Encode(new[] { obj })[0].SelectMany(BitConverter.GetBytes).ToArray();
     }
 
      private static Lazy<string[]> OutputNames => new (() => InferenceSession.Value.OutputMetadata.Keys.ToArray());
 
+    /// <summary>
+    /// Vectorizers an array of sentences (which are vectorized individually).
+    /// </summary>
+    /// <param name="sentences">The Sentences</param>
+    /// <returns></returns>
     public static float[][] Encode(string[] sentences)
     {
         const int MaxTokens = 512;
@@ -50,7 +62,7 @@ public static float[][] Encode(string[] sentences)
 
             var tokenIndexes = tokens.Take(MaxTokens).Select(token => (long)token.VocabularyIndex).Concat(padding).ToArray();
             var segmentIndexes = tokens.Take(MaxTokens).Select(token => token.SegmentIndex).Concat(padding).ToArray();
-            var inputMask = tokens.Take(MaxTokens).Select(o => 1L).Concat(padding).ToArray();
+            var inputMask = tokens.Take(MaxTokens).Select(_ => 1L).Concat(padding).ToArray();
             return (tokenIndexes, TokenTypeIds: segmentIndexes, inputMask);
         }).ToList();
         var tokenCount = encoded.First().InputIds.Length;
@@ -77,7 +89,7 @@ public static float[][] Encode(string[] sentences)
 
         var dimensions = new[] { numSentences, tokenCount };
 
-        var input = new NamedOnnxValue[3]
+        var input = new []
         {
             NamedOnnxValue.CreateFromTensor("input_ids",      new DenseTensor<long>(flattenIDs,          dimensions)),
             NamedOnnxValue.CreateFromTensor("attention_mask", new DenseTensor<long>(flattenAttentionMask,dimensions)),
@@ -109,7 +121,7 @@ public static float[][] Encode(string[] sentences)
         return outputFlatten;
     }
 
-     public static DenseTensor<float> Normalize(DenseTensor<float> input_dense, float eps = 1e-12f)
+    internal static DenseTensor<float> Normalize(DenseTensor<float> input_dense, float eps = 1e-12f)
     {
         //Computes sum(abs(x)^2)^(1/2)
 
@@ -142,7 +154,7 @@ public static DenseTensor<float> Normalize(DenseTensor<float> input_dense, float
     }
 
 
-    public static DenseTensor<float> MeanPooling(DenseTensor<float> token_embeddings_dense, List<(long[] InputIds, long[] TokenTypeIds, long[] AttentionMask)> encodedSentences, float eps = 1e-9f)
+    internal static DenseTensor<float> MeanPooling(DenseTensor<float> token_embeddings_dense, List<(long[] InputIds, long[] TokenTypeIds, long[] AttentionMask)> encodedSentences, float eps = 1e-9f)
     {
         var sentencesCount = token_embeddings_dense.Dimensions[0];
         var sentenceLength = token_embeddings_dense.Dimensions[1];

src/Redis.OM.Vectorizers.AllMiniLML6V2/SentenceVectorizerAttribute.cs

@@ -3,11 +3,20 @@
 
 namespace Redis.OM.Vectorizers.AllMiniLML6V2;
 
+/// <summary>
+/// 
+/// </summary>
 public class SentenceVectorizerAttribute : VectorizerAttribute<string>
 {
+    /// <inheritdoc />
     public override VectorType VectorType => Vectorizer.VectorType;
+
+    /// <inheritdoc />
     public override int Dim => Vectorizer.Dim;
+
+    /// <inheritdoc />
     public override byte[] Vectorize(object obj) => Vectorizer.Vectorize((string)obj);
 
+    /// <inheritdoc />
     public override IVectorizer<string> Vectorizer => new SentenceVectorizer();
 }

src/Redis.OM.Vectorizers.AllMiniLML6V2/Tokenizers/CasedTokenizer.cs

@@ -8,7 +8,7 @@ protected CasedTokenizer(string[] vocabulary) : base(vocabulary)
 
     protected override IEnumerable<string> TokenizeSentence(string text)
     {
-        return text.Split(new string[] { " ", "   ", "\r\n" }, StringSplitOptions.None)
+        return text.Split(new [] { " ", "   ", "\r\n" }, StringSplitOptions.None)
             .SelectMany(o => o.SplitAndKeep(".,;:\\/?!#$%()=+-*\"'–_`<>&^@{}[]|~'".ToArray()));
     }
 }

src/Redis.OM.Vectorizers.AllMiniLML6V2/Tokenizers/Tokens.cs

@@ -1,6 +1,6 @@
 namespace Redis.OM.Vectorizers.AllMiniLML6V2.Tokenizers;
 
-public class Tokens
+internal class Tokens
 {
     public const string Padding = "";
     public const string Unknown = "[UNK]";

src/Redis.OM.Vectorizers.AllMiniLML6V2/Tokenizers/UncasedTokenizer.cs

@@ -8,7 +8,7 @@ public UncasedTokenizer(string[] vocabulary) : base(vocabulary)
 
     protected override IEnumerable<string> TokenizeSentence(string text)
     {
-        return text.Split(new string[] { " ", "   ", "\r\n" }, StringSplitOptions.None)
+        return text.Split(new [] { " ", "   ", "\r\n" }, StringSplitOptions.None)
             .SelectMany(o => o.SplitAndKeep(".,;:\\/?!#$%()=+-*\"'–_`<>&^@{}[]|~'".ToArray()))
             .Select(o => o.ToLower());
     }

src/Redis.OM.Vectorizers.Resnet18/ImageModelObjects.cs

@@ -3,14 +3,24 @@
 
 namespace Redis.OM.Vectorizers.Resnet18;
 
-public class ImageInput
+internal class ImageInput
 {
     [ColumnName(@"ImageSource")]
     public string ImageSource { get; set; }
+
+    public ImageInput(string imageSource)
+    {
+        ImageSource = imageSource;
+    }
 }
 
-public class InMemoryImageData
+internal class InMemoryImageData
 {
     [ImageType(224,224)]
     public MLImage Image;
+
+    public InMemoryImageData(MLImage image)
+    {
+        Image = image;
+    }
 }

src/Redis.OM.Vectorizers.Resnet18/ImageVectorizer.cs

@@ -31,7 +31,7 @@ public byte[] Vectorize(string obj)
             };
             var imageStream = Configuration.Instance.Client.Send(request).Content.ReadAsStream();
             var image = MLImage.CreateFromStream(imageStream);
-            var vector = VectorizeBitMaps(new [] { image })[0].SelectMany(BitConverter.GetBytes).ToArray();
+            var vector = VectorizeImages(new [] { image })[0].SelectMany(BitConverter.GetBytes).ToArray();
             return vector;
         }
 
@@ -44,7 +44,7 @@ public byte[] Vectorize(string obj)
         return VectorizeFiles(new[] { obj })[0].SelectMany(BitConverter.GetBytes).ToArray();
     }
 
-    private static Lazy<EstimatorChain<TransformerChain<ColumnCopyingTransformer>>> FilePipeline = new(CreateFilePipeline);
+    private static readonly Lazy<EstimatorChain<TransformerChain<ColumnCopyingTransformer>>> FilePipeline = new(CreateFilePipeline);
 
     private static readonly Lazy<MLContext> MlContext = new(()=>new MLContext());
 
@@ -68,7 +68,7 @@ private static EstimatorChain<TransformerChain<ColumnCopyingTransformer>> Create
     /// <returns></returns>
     public static float[][] VectorizeFiles(IEnumerable<string> imagePaths)
     {
-        var images = imagePaths.Select(x => new ImageInput { ImageSource = x });
+        var images = imagePaths.Select(x => new ImageInput(x));
         var mlContext = MlContext.Value;
         var dataView = mlContext.Data.LoadFromEnumerable(images);
 
@@ -77,7 +77,7 @@ public static float[][] VectorizeFiles(IEnumerable<string> imagePaths)
         return vector;
     }
 
-    public static Lazy<EstimatorChain<TransformerChain<ColumnCopyingTransformer>>> BitmapPipeline = new(CreateBitmapPipeline);
+    private static readonly Lazy<EstimatorChain<TransformerChain<ColumnCopyingTransformer>>> BitmapPipeline = new(CreateBitmapPipeline);
 
     private static EstimatorChain<TransformerChain<ColumnCopyingTransformer>> CreateBitmapPipeline()
     {
@@ -91,9 +91,14 @@ private static EstimatorChain<TransformerChain<ColumnCopyingTransformer>> Create
         return pipeline;
     }
 
-    public static float[][] VectorizeBitMaps(IEnumerable<MLImage> mlImages)
+    /// <summary>
+    /// Encodes a collection of images.
+    /// </summary>
+    /// <param name="mlImages"></param>
+    /// <returns></returns>
+    public static float[][] VectorizeImages(IEnumerable<MLImage> mlImages)
     {
-        var images = mlImages.Select(x => new InMemoryImageData { Image = x });
+        var images = mlImages.Select(x => new InMemoryImageData(x));
         var mlContext = MlContext.Value;
         var dataView = mlContext.Data.LoadFromEnumerable(images);
         var transformedData = BitmapPipeline.Value.Fit(dataView).Transform(dataView);

src/Redis.OM.Vectorizers.Resnet18/ImageVectorizerAttribute.cs

@@ -3,10 +3,20 @@
 
 namespace Redis.OM.Vectorizers.Resnet18;
 
+/// <summary>
+/// A Vectorizer Attribute for encoding images
+/// </summary>
 public class ImageVectorizerAttribute : VectorizerAttribute<string>
 {
+    /// <inheritdoc />
     public override VectorType VectorType => Vectorizer.VectorType;
+
+    /// <inheritdoc />
     public override int Dim => Vectorizer.Dim;
+
+    /// <inheritdoc />
     public override byte[] Vectorize(object obj) => Vectorizer.Vectorize((string)obj);
+
+    /// <inheritdoc />
     public override IVectorizer<string> Vectorizer { get; } = new ImageVectorizer();
 }

src/Redis.OM.Vectorizers/AzureOpenAIVectorizer.cs

@@ -6,12 +6,22 @@
 
 namespace Redis.OM.Vectorizers;
 
+/// <summary>
+/// Vectorizer for Azure's OpenAI REST API
+/// </summary>
 public class AzureOpenAIVectorizer : IVectorizer<string>
 {
     private readonly string _apiKey;
     private readonly string _resourceName;
     private readonly string _deploymentName;
 
+    /// <summary>
+    /// Initializes vectorizer
+    /// </summary>
+    /// <param name="apiKey">The Vectorizers API Key</param>
+    /// <param name="resourceName">The Azure Resource Name.</param>
+    /// <param name="deploymentName">The Azure Deployment Name.</param>
+    /// <param name="dim">The dimensions of the model addressed by this resource/deployment.</param>
     public AzureOpenAIVectorizer(string apiKey, string resourceName, string deploymentName, int dim)
     {
         _apiKey = apiKey;
@@ -20,8 +30,13 @@ public AzureOpenAIVectorizer(string apiKey, string resourceName, string deployme
         Dim = dim;
     }
 
+    /// <inheritdoc />
     public VectorType VectorType => VectorType.FLOAT32;
+
+    /// <inheritdoc />
     public int Dim { get; }
+
+    /// <inheritdoc />
     public byte[] Vectorize(string str) => GetFloats(str, _resourceName, _deploymentName, _apiKey).SelectMany(BitConverter.GetBytes).ToArray();
 
     internal static float[] GetFloats(string s, string resourceName, string deploymentName, string apiKey)

src/Redis.OM.Vectorizers/Configuration.cs

@@ -1,29 +1,67 @@
 using System.Net.Http.Headers;
+using System.Runtime.CompilerServices;
 using Microsoft.Extensions.Configuration;
-
+[assembly: InternalsVisibleTo("Redis.OM.Vectorizers.Resnet18")]
 namespace Redis.OM;
 
-public class Configuration
+/// <summary>
+/// Some Configuration Items.
+/// </summary>
+internal class Configuration
 {
+    /// <summary>
+    /// Gets the configuration item at the given key.
+    /// </summary>
+    /// <param name="str"></param>
     public string? this[string str] => _settings[str];
+
+    /// <summary>
+    /// The bearer authorization token for Hugging Face's model API.
+    /// </summary>
     public string HuggingFaceAuthorizationToken => _settings["REDIS_OM_HF_TOKEN"] ?? string.Empty;
+
+    /// <summary>
+    /// Bearer token for Open AI's API.
+    /// </summary>
     public string OpenAiAuthorizationToken => _settings["REDIS_OM_OAI_TOKEN"] ?? string.Empty;
+
+    /// <summary>
+    /// Azure OpenAI Api Key.
+    /// </summary>
     public string AzureOpenAIApiKey => _settings["REDIS_OM_AZURE_OAI_TOKEN"] ?? string.Empty;
+
+    /// <summary>
+    /// Hugging Face Model Id 
+    /// </summary>
     public string ModelId => _settings["REDIS_OM_HF_MODEL_ID"] ?? string.Empty;
+
+    /// <summary>
+    /// Base Address for Hugging Face Feature Extraction API
+    /// </summary>
     public string HuggingFaceBaseAddress => _settings["REDIS_OM_HF_FEATURE_EXTRACTION_URL"] ?? string.Empty; 
 
     private const string DefaultHuggingFaceApiUrl = "https://api-inference.huggingface.co";
 
     private const string DefaultOpenAiApiUrl = "https://api.openai.com";
+
+    /// <summary>
+    /// URL for OpenAI API.
+    /// </summary>
     public string OpenAiApiUrl => _settings["REDIS_OM_OAI_API_URL"] ?? String.Empty;
 
     private readonly IConfiguration _settings;
 
     private static readonly object LockObject = new ();
     private static Configuration? _instance;
 
+    /// <summary>
+    /// Common HTTP Client.
+    /// </summary>
     public readonly HttpClient Client;
 
+    /// <summary>
+    /// Singleton Instance.
+    /// </summary>
     public static Configuration Instance
     {
         get

src/Redis.OM.Vectorizers/HuggingFaceVectorizer.cs

@@ -5,8 +5,17 @@
 
 namespace Redis.OM.Vectorizers;
 
+/// <summary>
+/// Vectorizer for HuggingFace API.
+/// </summary>
 public class HuggingFaceVectorizer : IVectorizer<string>
 {
+    /// <summary>
+    /// Initializes the Vectorizer.
+    /// </summary>
+    /// <param name="authToken">Auth token.</param>
+    /// <param name="modelId">Model Id.</param>
+    /// <param name="dim">Dimensions for the output tensors of the model.</param>
     public HuggingFaceVectorizer(string authToken, string modelId, int dim)
     {
         _huggingFaceAuthToken = authToken;
@@ -15,16 +24,33 @@ public HuggingFaceVectorizer(string authToken, string modelId, int dim)
     }
 
     private readonly string _huggingFaceAuthToken;
+
+    /// <summary>
+    /// The Model Id.
+    /// </summary>
     public string ModelId { get; }
+
+    /// <inheritdoc />
     public VectorType VectorType => VectorType.FLOAT32;
-
+
+    /// <inheritdoc />
     public int Dim { get; }
+
+    /// <inheritdoc />
     public byte[] Vectorize(string str)
     {
         return GetFloats(str, ModelId, _huggingFaceAuthToken).SelectMany(BitConverter.GetBytes).ToArray();
     }
 
-    public static float[] GetFloats(string s, string modelId, string huggingFaceAuthToken)
+    /// <summary>
+    /// Gets the floats for the sentence.
+    /// </summary>
+    /// <param name="s">the string.</param>
+    /// <param name="modelId">The Model Id.</param>
+    /// <param name="huggingFaceAuthToken">The HF token.</param>
+    /// <returns></returns>
+    /// <exception cref="Exception"></exception>
+    internal static float[] GetFloats(string s, string modelId, string huggingFaceAuthToken)
     {
         var client = Configuration.Instance.Client;
         var requestContent = JsonContent.Create(new