Added 2 new embedding models to Model.cs

OpenAI_API/Embedding/EmbeddingEndpoint.cs

@@ -28,10 +28,11 @@ internal EmbeddingEndpoint(OpenAIAPI api) : base(api) { }
 		/// Ask the API to embedd text using the default embedding model <see cref="Model.AdaTextEmbedding"/>
 		/// </summary>
 		/// <param name="input">Text to be embedded</param>
+		/// <param name="model">Embeddings model to be used</param>
 		/// <returns>Asynchronously returns the embedding result. Look in its <see cref="Data.Embedding"/> property of <see cref="EmbeddingResult.Data"/> to find the vector of floating point numbers</returns>
-		public async Task<EmbeddingResult> CreateEmbeddingAsync(string input)
+		public async Task<EmbeddingResult> CreateEmbeddingAsync(string input, Model model = null)
 		{
-			EmbeddingRequest req = new EmbeddingRequest(DefaultEmbeddingRequestArgs.Model, input);
+			EmbeddingRequest req = new EmbeddingRequest(model ?? DefaultEmbeddingRequestArgs.Model, input);
 			return await CreateEmbeddingAsync(req);
 		}
 
@@ -46,14 +47,25 @@ public async Task<EmbeddingResult> CreateEmbeddingAsync(EmbeddingRequest request
 		}
 
 		/// <summary>
-		/// Ask the API to embedd text using the default embedding model <see cref="Model.AdaTextEmbedding"/>
+		/// Ask the API to embedd text using the default embedding model <see cref="Model.AdaTextEmbedding"/> in case no other model is specified
 		/// </summary>
 		/// <param name="input">Text to be embedded</param>
+		/// <param name="model">Embeddings model to be used</param>
+		/// <returns>Asynchronously returns the first embedding result as an array of floats.</returns>
+		public async Task<float[]> GetEmbeddingsAsync(string input, Model model = null)
+		{
+			EmbeddingRequest req = new EmbeddingRequest(model ?? DefaultEmbeddingRequestArgs.Model, input);
+			return await GetEmbeddingsAsync(req);
+		}
+
+		/// <summary>
+		/// Ask the API to embedd text
+		/// </summary>
+		/// <param name="request">Request to be send</param>
 		/// <returns>Asynchronously returns the first embedding result as an array of floats.</returns>
-		public async Task<float[]> GetEmbeddingsAsync(string input)
+		public async Task<float[]> GetEmbeddingsAsync(EmbeddingRequest request)
 		{
-			EmbeddingRequest req = new EmbeddingRequest(DefaultEmbeddingRequestArgs.Model, input);
-			var embeddingResult = await CreateEmbeddingAsync(req);
+			var embeddingResult = await CreateEmbeddingAsync(request);
 			return embeddingResult?.Data?[0]?.Embedding;
 		}
 	}

OpenAI_API/Embedding/EmbeddingRequest.cs

@@ -20,6 +20,18 @@ public class EmbeddingRequest
 		[JsonProperty("input")]
 		public string Input { get; set; }
 
+		/// <summary>
+		/// The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
+		/// </summary>
+		[JsonProperty("dimensions")]
+		public int? Dimensions { get; set; }
+
+		/// <summary>
+		/// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
+		/// </summary>
+		[JsonProperty("user")]
+		public string User { get; set; }
+
 		/// <summary>
 		/// Cretes a new, empty <see cref="EmbeddingRequest"/>
 		/// </summary>

OpenAI_API/Embedding/IEmbeddingEndpoint.cs

@@ -17,8 +17,9 @@ public interface IEmbeddingEndpoint
         /// Ask the API to embedd text using the default embedding model <see cref="Model.AdaTextEmbedding"/>
         /// </summary>
         /// <param name="input">Text to be embedded</param>
+        /// <param name="model">Embeddings model to be used</param>
         /// <returns>Asynchronously returns the embedding result. Look in its <see cref="Data.Embedding"/> property of <see cref="EmbeddingResult.Data"/> to find the vector of floating point numbers</returns>
-        Task<EmbeddingResult> CreateEmbeddingAsync(string input);
+        Task<EmbeddingResult> CreateEmbeddingAsync(string input, Model model = null);
 
         /// <summary>
         /// Ask the API to embedd text using a custom request
@@ -28,10 +29,18 @@ public interface IEmbeddingEndpoint
         Task<EmbeddingResult> CreateEmbeddingAsync(EmbeddingRequest request);
 
         /// <summary>
-        /// Ask the API to embedd text using the default embedding model <see cref="Model.AdaTextEmbedding"/>
+        /// Ask the API to embedd text using the default embedding model <see cref="Model.AdaTextEmbedding"/> in case no other model is specified
         /// </summary>
         /// <param name="input">Text to be embedded</param>
+        /// <param name="model">Embeddings model to be used</param>
+        /// <returns>Asynchronously returns the first embedding result as an array of floats.</returns>
+        Task<float[]> GetEmbeddingsAsync(string input, Model model = null);
+
+        /// <summary>
+        /// Ask the API to embedd text using the default embedding model <see cref="Model.AdaTextEmbedding"/> in case no other model is specified
+        /// </summary>
+        /// <param name="request">Request to be send</param>
         /// <returns>Asynchronously returns the first embedding result as an array of floats.</returns>
-        Task<float[]> GetEmbeddingsAsync(string input);
+        Task<float[]> GetEmbeddingsAsync(EmbeddingRequest request);
     }
 }

OpenAI_API/Model/Model.cs

@@ -218,9 +218,19 @@ public async Task<Model> RetrieveModelDetailsAsync(OpenAI_API.OpenAIAPI api)
 
 #region Embeddings
 		/// <summary>
-		/// OpenAI offers one second-generation embedding model for use with the embeddings API endpoint.
+		/// This model is not deprecated yet, but OpenAI recommends to use the newer models text-embedding-3-small and text-embedding-3-large.
 		/// </summary>
 		public static Model AdaTextEmbedding => new Model("text-embedding-ada-002") { OwnedBy = "openai" };
+
+		/// <summary>
+		/// Highly efficient embedding model which provides a significant upgrade over its predecessor, the text-embedding-ada-002 model
+		/// </summary>
+		public static Model SmallTextEmbedding => new Model("text-embedding-3-small") { OwnedBy = "openai" };
+
+		/// <summary>
+		/// Next generation larger embedding model which creates embeddings with up to 3072 dimensions
+		/// </summary>
+		public static Model LargeTextEmbedding => new Model("text-embedding-3-large") { OwnedBy = "openai" };
 #endregion
 
 #region Moderation

OpenAI_Tests/EmbeddingEndpointTests.cs

@@ -64,5 +64,19 @@ public void GetSimpleEmbedding()
             Assert.IsNotNull(results);
             Assert.That(results.Length == 1536);
         }
+
+        [Test]
+        public void GetSimpleEmbeddingWithDimensions()
+        {
+            var api = new OpenAI_API.OpenAIAPI();
+
+            Assert.IsNotNull(api.Embeddings);
+
+            var request = new EmbeddingRequest(Model.SmallTextEmbedding, "A test text for embedding");
+            request.Dimensions = 256;
+            var results = api.Embeddings.GetEmbeddingsAsync(request).Result;
+            Assert.IsNotNull(results);
+            Assert.That(results.Length == 256);
+        }
     }
 }