Updated embedding endpoint and result to match new composition style

OpenAI_API/Embedding/EmbeddingEndpoint.cs

@@ -1,77 +1,59 @@
-using Newtonsoft.Json;
-using System;
-using System.Collections.Generic;
-using System.Net.Http;
-using System.Security.Authentication;
-using System.Text;
-using System.Threading.Tasks;
+using System.Threading.Tasks;
 
 namespace OpenAI_API.Embedding
 {
-    /// <summary>
-    /// OpenAI’s text embeddings measure the relatedness of text strings by generating an embedding, which is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.
-    /// </summary>
-    public class EmbeddingEndpoint
-    {
-        OpenAIAPI Api;
-        /// <summary>
-        /// This allows you to send request to the recommended model without needing to specify. Every request uses the <see cref="Model.AdaTextEmbedding"/> model
-        /// </summary>
-        public EmbeddingRequest DefaultEmbeddingRequestArgs { get; set; } = new EmbeddingRequest() { Model = Model.AdaTextEmbedding };
-
-        /// <summary>
-        /// Constructor of the api endpoint.  Rather than instantiating this yourself, access it through an instance of <see cref="OpenAIAPI"/> as <see cref="OpenAIAPI.Embeddings"/>.
-        /// </summary>
-        /// <param name="api"></param>
-        internal EmbeddingEndpoint(OpenAIAPI api)
-        {
-            this.Api = api;
-        }
-
-        /// <summary>
-        /// Ask the API to embedd text using the default embedding model <see cref="Model.AdaTextEmbedding"/>
-        /// </summary>
-        /// <param name="input">Text to be embedded</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)
-        {
-            DefaultEmbeddingRequestArgs.Input = input;
-            return await CreateEmbeddingAsync(DefaultEmbeddingRequestArgs);
-        }
-
-        /// <summary>
-        /// Ask the API to embedd text using a custom request
-        /// </summary>
-        /// <param name="request">Request to be send</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(EmbeddingRequest request)
-        {
-            if (Api.Auth?.ApiKey is null)
-            {
-                throw new AuthenticationException("You must provide API authentication.  Please refer to https://github.com/OkGoDoIt/OpenAI-API-dotnet#authentication for details.");
-            }
-
-            HttpClient client = new HttpClient();
-            client.DefaultRequestHeaders.Authorization = new System.Net.Http.Headers.AuthenticationHeaderValue("Bearer", Api.Auth.ApiKey);
-            client.DefaultRequestHeaders.Add("User-Agent", "okgodoit/dotnet_openai_api");
-
-            string jsonContent = JsonConvert.SerializeObject(request, new JsonSerializerSettings() { NullValueHandling = NullValueHandling.Ignore });
-            var stringContent = new StringContent(jsonContent, Encoding.UTF8, "application/json");
-
-            var response = await client.PostAsync($"https://api.openai.com/v1/embeddings", stringContent);
-            if (response.IsSuccessStatusCode)
-            {
-                string resultAsString = await response.Content.ReadAsStringAsync();
-
-                var res = JsonConvert.DeserializeObject<EmbeddingResult>(resultAsString);
-
-                return res;
-            }
-            else
-            {
-                throw new HttpRequestException("Error calling OpenAi API to get completion.  HTTP status code: " + response.StatusCode.ToString() + ". Request body: " + jsonContent);
-            }
-        }
-
-    }
+	/// <summary>
+	/// OpenAI’s text embeddings measure the relatedness of text strings by generating an embedding, which is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.
+	/// </summary>
+	public class EmbeddingEndpoint : EndpointBase
+	{
+		/// <summary>
+		/// This allows you to send request to the recommended model without needing to specify. Every request uses the <see cref="Model.AdaTextEmbedding"/> model
+		/// </summary>
+		public EmbeddingRequest DefaultEmbeddingRequestArgs { get; set; } = new EmbeddingRequest() { Model = Model.AdaTextEmbedding };
+
+		/// <summary>
+		/// The name of the endpoint, which is the final path segment in the API URL.  For example, "embeddings".
+		/// </summary>
+		protected override string Endpoint { get { return "embeddings"; } }
+
+		/// <summary>
+		/// Constructor of the api endpoint.  Rather than instantiating this yourself, access it through an instance of <see cref="OpenAIAPI"/> as <see cref="OpenAIAPI.Embeddings"/>.
+		/// </summary>
+		/// <param name="api"></param>
+		internal EmbeddingEndpoint(OpenAIAPI api) : base(api) { }
+
+		/// <summary>
+		/// Ask the API to embedd text using the default embedding model <see cref="Model.AdaTextEmbedding"/>
+		/// </summary>
+		/// <param name="input">Text to be embedded</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)
+		{
+			EmbeddingRequest req = new EmbeddingRequest(DefaultEmbeddingRequestArgs.Model, input);
+			return await CreateEmbeddingAsync(req);
+		}
+
+		/// <summary>
+		/// Ask the API to embedd text using a custom request
+		/// </summary>
+		/// <param name="request">Request to be send</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(EmbeddingRequest request)
+		{
+			return await HttpPost<EmbeddingResult>(postData: request);
+		}
+
+		/// <summary>
+		/// Ask the API to embedd text using the default embedding model <see cref="Model.AdaTextEmbedding"/>
+		/// </summary>
+		/// <param name="input">Text to be embedded</param>
+		/// <returns>Asynchronously returns the first embedding result as an array of floats.</returns>
+		public async Task<float[]> GetEmbeddingsAsync(string input)
+		{
+			EmbeddingRequest req = new EmbeddingRequest(DefaultEmbeddingRequestArgs.Model, input);
+			var embeddingResult = await CreateEmbeddingAsync(req);
+			return embeddingResult?.Data?[0]?.Embedding;
+		}
+	}
 }

OpenAI_API/Embedding/EmbeddingRequest.cs

@@ -1,50 +1,51 @@
 using Newtonsoft.Json;
-using System;
-using System.Collections.Generic;
-using System.Text;
 
 namespace OpenAI_API.Embedding
 {
-    /// <summary>
-    /// Represents a request to the Completions API. Matches with the docs at <see href="https://platform.openai.com/docs/api-reference/embeddings">the OpenAI docs</see>
-    /// </summary>
-    public class EmbeddingRequest
-    {
-        /// <summary>
-        /// ID of the model to use. You can use <see cref="ModelsEndpoint.GetModelsAsync()"/> to see all of your available models, or use a standard model like <see cref="Model.AdaTextEmbedding"/>.
-        /// </summary>
-        [JsonIgnore]
-        public Model Model { get; set; }
+	/// <summary>
+	/// Represents a request to the Completions API. Matches with the docs at <see href="https://platform.openai.com/docs/api-reference/embeddings">the OpenAI docs</see>
+	/// </summary>
+	public class EmbeddingRequest
+	{
+		/// <summary>
+		/// ID of the model to use. You can use <see cref="ModelsEndpoint.GetModelsAsync()"/> to see all of your available models, or use a standard model like <see cref="Model.AdaTextEmbedding"/>.
+		/// </summary>
+		[JsonProperty("model")]
+		public string Model { get; set; }
 
-        /// <summary>
-        /// The id/name of the model
-        /// </summary>
-        [JsonProperty("model")]
-        public string ModelName => Model.ModelID;
+		/// <summary>
+		/// Main text to be embedded
+		/// </summary>
+		[JsonProperty("input")]
+		public string Input { get; set; }
 
-        /// <summary>
-        /// Main text to be embedded
-        /// </summary>
-        [JsonProperty("input")]
-        public string Input { get; set; }
+		/// <summary>
+		/// Cretes a new, empty <see cref="EmbeddingRequest"/>
+		/// </summary>
+		public EmbeddingRequest()
+		{
 
-        /// <summary>
-        /// Cretes a new, empty <see cref="EmbeddingRequest"/>
-        /// </summary>
-        public EmbeddingRequest()
-        {
+		}
 
-        }
+		/// <summary>
+		/// Creates a new <see cref="EmbeddingRequest"/> with the specified parameters
+		/// </summary>
+		/// <param name="model">The model to use. You can use <see cref="ModelsEndpoint.GetModelsAsync()"/> to see all of your available models, or use a standard model like <see cref="Model.AdaTextEmbedding"/>.</param>
+		/// <param name="input">The prompt to transform</param>
+		public EmbeddingRequest(Model model, string input)
+		{
+			Model = model;
+			this.Input = input;
+		}
 
-        /// <summary>
-        /// Creates a new <see cref="EmbeddingRequest"/> with the specified parameters
-        /// </summary>
-        /// <param name="model">The model to use. You can use <see cref="ModelsEndpoint.GetModelsAsync()"/> to see all of your available models, or use a standard model like <see cref="Model.AdaTextEmbedding"/>.</param>
-        /// <param name="input">The prompt to transform</param>
-        public EmbeddingRequest(Model model, string input)
-        {
-            Model = model;
-            this.Input = input;
-        }
-    }
+		/// <summary>
+		/// Creates a new <see cref="EmbeddingRequest"/> with the specified input and the <see cref="Model.AdaTextEmbedding"/> model.
+		/// </summary>
+		/// <param name="input">The prompt to transform</param>
+		public EmbeddingRequest(string input)
+		{
+			Model = OpenAI_API.Model.AdaTextEmbedding;
+			this.Input = input;
+		}
+	}
 }

OpenAI_API/Embedding/EmbeddingResult.cs

@@ -1,84 +1,78 @@
 using Newtonsoft.Json;
-using System;
-using System.Collections.Generic;
-using System.Text;
+using System.Linq;
 
 namespace OpenAI_API.Embedding
 {
-    /// <summary>
-    /// Represents an embedding result returned by the Embedding API.  
-    /// </summary>
-    public class EmbeddingResult
-    {
-        /// <summary>
-        /// Type of the response. In case of embeddings, this will be "list"  
-        /// </summary>
-        [JsonProperty("object")]
+	/// <summary>
+	/// Represents an embedding result returned by the Embedding API.  
+	/// </summary>
+	public class EmbeddingResult : ApiResultBase
+	{
+		/// <summary>
+		/// List of results of the embedding
+		/// </summary>
+		[JsonProperty("data")]
+		public Data[] Data { get; set; }
 
-        public string Object { get; set; }
+		/// <summary>
+		/// Usage statistics of how many tokens have been used for this request
+		/// </summary>
+		[JsonProperty("usage")]
+		public Usage Usage { get; set; }
 
-        /// <summary>
-        /// List of results of the embedding
-        /// </summary>
-        [JsonProperty("data")]
-        public Data[] Data { get; set; }
+		/// <summary>
+		/// Allows an EmbeddingResult to be implicitly cast to the array of floats repsresenting the first ebmedding result
+		/// </summary>
+		/// <param name="embeddingResult">The <see cref="EmbeddingResult"/> to cast to an array of floats.</param>
+		public static implicit operator float[](EmbeddingResult embeddingResult)
+		{
+			return embeddingResult.Data.FirstOrDefault()?.Embedding;
+		}
+	}
 
-        /// <summary>
-        /// Name of the model used to generate this embedding
-        /// </summary>
-        [JsonProperty("model")]
-        public string Model { get; set; }
+	/// <summary>
+	/// Data returned from the Embedding API.
+	/// </summary>
+	public class Data
+	{
+		/// <summary>
+		/// Type of the response. In case of Data, this will be "embedding"  
+		/// </summary>
+		[JsonProperty("object")]
 
-        /// <summary>
-        /// Usage statistics of how many tokens have been used for this request
-        /// </summary>
-        [JsonProperty("usage")]
-        public Usage Usage { get; set; }
-    }
+		public string Object { get; set; }
 
-    /// <summary>
-    /// Data returned from the Embedding API.
-    /// </summary>
-    public class Data
-    {
-        /// <summary>
-        /// Type of the response. In case of Data, this will be "embedding"  
-        /// </summary>
-        [JsonProperty("object")]
+		/// <summary>
+		/// The input text represented as a vector (list) of floating point numbers
+		/// </summary>
+		[JsonProperty("embedding")]
+		public float[] Embedding { get; set; }
 
-        public string Object { get; set; }
+		/// <summary>
+		/// Index
+		/// </summary>
+		[JsonProperty("index")]
+		public int Index { get; set; }
 
-        /// <summary>
-        /// The input text represented as a vector (list) of floating point numbers
-        /// </summary>
-        [JsonProperty("embedding")]
-        public float[] Embedding { get; set; }
+	}
 
-        /// <summary>
-        /// Index
-        /// </summary>
-        [JsonProperty("index")]
-        public int Index { get; set; }
+	/// <summary>
+	/// Usage statistics of how many tokens have been used for this request.
+	/// </summary>
+	public class Usage
+	{
+		/// <summary>
+		/// How many tokens did the prompt consist of
+		/// </summary>
+		[JsonProperty("prompt_tokens")]
+		public int PromptTokens { get; set; }
 
-    }
+		/// <summary>
+		/// How many tokens did the request consume total
+		/// </summary>
+		[JsonProperty("total_tokens")]
+		public int TotalTokens { get; set; }
 
-    /// <summary>
-    /// Usage statistics of how many tokens have been used for this request.
-    /// </summary>
-    public class Usage
-    {
-        /// <summary>
-        /// How many tokens did the prompt consist of
-        /// </summary>
-        [JsonProperty("prompt_tokens")]
-        public int PromptTokens { get; set; }
-
-        /// <summary>
-        /// How many tokens did the request consume total
-        /// </summary>
-        [JsonProperty("total_tokens")]
-        public int TotalTokens { get; set; }
-
-    }
+	}
 
 }

OpenAI_API/OpenAIAPI.cs

@@ -10,6 +10,11 @@ namespace OpenAI_API
 	/// </summary>
 	public class OpenAIAPI
 	{
+		/// <summary>
+		/// Base url for OpenAI
+		/// </summary>
+		public string ApiUrlBase = "https://api.openai.com/v1/";
+
 		/// <summary>
 		/// The API authentication information to use for API calls
 		/// </summary>
@@ -24,7 +29,7 @@ public OpenAIAPI(APIAuthentication apiKeys = null)
 			this.Auth = apiKeys.ThisOrDefault();
 			Completions = new CompletionEndpoint(this);
 			Models = new ModelsEndpoint(this);
-			Search = new SearchEndpoint(this);
+			//Search = new SearchEndpoint(this);
 			Embeddings = new EmbeddingEndpoint(this);
 		}