From 9bf6471c9a0ac48e6abcc5630182aa8e55637485 Mon Sep 17 00:00:00 2001
From: Kevin Day <kevin@riskebiz.com>
Date: Wed, 28 Jun 2023 13:02:32 -0700
Subject: [PATCH] Add batch upload capability for CreateEmbeddingAsync.

---
 OpenAI_API/Embedding/EmbeddingEndpoint.cs  | 23 +++++++---
 OpenAI_API/Embedding/EmbeddingRequest.cs   | 50 +++++++++++++++-------
 OpenAI_API/Embedding/IEmbeddingEndpoint.cs |  6 +++
 3 files changed, 57 insertions(+), 22 deletions(-)

diff --git a/OpenAI_API/Embedding/EmbeddingEndpoint.cs b/OpenAI_API/Embedding/EmbeddingEndpoint.cs
index a324de9..c0ea25b 100644
--- a/OpenAI_API/Embedding/EmbeddingEndpoint.cs
+++ b/OpenAI_API/Embedding/EmbeddingEndpoint.cs
@@ -35,12 +35,23 @@ public async Task<EmbeddingResult> CreateEmbeddingAsync(string 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)
+        /// <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 resu
+        public async Task<EmbeddingResult> CreateEmbeddingAsync(string[] batchInput)
+        {
+            EmbeddingRequest req = new EmbeddingRequest(DefaultEmbeddingRequestArgs.Model, batchInput);
+            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);
 		}
diff --git a/OpenAI_API/Embedding/EmbeddingRequest.cs b/OpenAI_API/Embedding/EmbeddingRequest.cs
index 99780eb..16052fb 100644
--- a/OpenAI_API/Embedding/EmbeddingRequest.cs
+++ b/OpenAI_API/Embedding/EmbeddingRequest.cs
@@ -14,16 +14,16 @@ public class EmbeddingRequest
 		[JsonProperty("model")]
 		public string Model { get; set; }
 
-		/// <summary>
-		/// Main text to be embedded
-		/// </summary>
-		[JsonProperty("input")]
-		public string Input { get; set; }
+        /// <summary>
+        /// Main text to be embedded
+        /// </summary>
+        [JsonProperty("input")]
+        public object 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()
 		{
 
 		}
@@ -38,15 +38,33 @@ 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)
+        /// <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="batchInput">The prompt to transform</param>
+        public EmbeddingRequest(Model model, string[] batchInput)
+        {
+            Model = model;
+            Input = batchInput;
+        }
+        /// <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.Models.Model.AdaTextEmbedding;
 			this.Input = input;
 		}
-	}
+        /// <summary>
+        /// Creates a new <see cref="EmbeddingRequest"/> with the specified input and the <see cref="Model.AdaTextEmbedding"/> model.
+        /// </summary>
+        /// <param name="batchInput">The prompt to transform</param>
+        public EmbeddingRequest(string[] batchInput)
+        {
+            Model = OpenAI_API.Models.Model.AdaTextEmbedding;
+            Input = batchInput;
+        }
+    }
 }
diff --git a/OpenAI_API/Embedding/IEmbeddingEndpoint.cs b/OpenAI_API/Embedding/IEmbeddingEndpoint.cs
index acd9069..08c9d54 100644
--- a/OpenAI_API/Embedding/IEmbeddingEndpoint.cs
+++ b/OpenAI_API/Embedding/IEmbeddingEndpoint.cs
@@ -20,6 +20,12 @@ public interface IEmbeddingEndpoint
         /// <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);
 
+        /// <summary>
+        /// Ask the API to embedd text using the default embedding model <see cref="Model.AdaTextEmbedding"/>
+        /// </summary>
+        /// <param name="batchInput">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>
+        Task<EmbeddingResult> CreateEmbeddingAsync(string[] batchInput);
         /// <summary>
         /// Ask the API to embedd text using a custom request
         /// </summary>