Update long-running operation APIs to use LRO subclient pattern - streaming LRO subclient inherits from polling LRO subclient

OpenAI.sln

@@ -6,7 +6,9 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "OpenAI", "src\OpenAI.csproj
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "OpenAI.Examples", "examples\OpenAI.Examples.csproj", "{1F1CD1D4-9932-4B73-99D8-C252A67D4B46}"
 EndProject
-Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "OpenAI.Tests", "tests\OpenAI.Tests.csproj", "{6F156401-2544-41D7-B204-3148C51C1D09}"
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "OpenAI.Tests", "tests\OpenAI.Tests.csproj", "{6F156401-2544-41D7-B204-3148C51C1D09}"
+EndProject
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "System.ClientModel", "..\azure-sdk-for-net\sdk\core\System.ClientModel\src\System.ClientModel.csproj", "{8CDFA283-1045-45D3-B86A-56DE301B331B}"
 EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
@@ -26,11 +28,15 @@ Global
 		{6F156401-2544-41D7-B204-3148C51C1D09}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{6F156401-2544-41D7-B204-3148C51C1D09}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{6F156401-2544-41D7-B204-3148C51C1D09}.Release|Any CPU.Build.0 = Release|Any CPU
+		{8CDFA283-1045-45D3-B86A-56DE301B331B}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{8CDFA283-1045-45D3-B86A-56DE301B331B}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{8CDFA283-1045-45D3-B86A-56DE301B331B}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{8CDFA283-1045-45D3-B86A-56DE301B331B}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE
 	EndGlobalSection
 	GlobalSection(ExtensibilityGlobals) = postSolution
 		SolutionGuid = {A97F4B90-2591-4689-B1F8-5F21FE6D6CAE}
 	EndGlobalSection
-EndGlobal
+EndGlobal

README.md

@@ -115,11 +115,11 @@ When you request a chat completion, the default behavior is for the server to ge
 The client library offers a convenient approach to working with streaming chat completions. If you wanted to re-write the example from the previous section using streaming, rather than calling the `ChatClient`'s `CompleteChat` method, you would call its `CompleteChatStreaming` method instead:
 
 ```csharp
-ResultCollection<StreamingChatCompletionUpdate> updates
+CollectionResult<StreamingChatCompletionUpdate> updates
     = client.CompleteChatStreaming("Say 'this is a test.'");
 ```
 
-Notice that the returned value is a `ResultCollection<StreamingChatCompletionUpdate>` instance, which can be enumerated to process the streaming response chunks as they arrive:
+Notice that the returned value is a `CollectionResult<StreamingChatCompletionUpdate>` instance, which can be enumerated to process the streaming response chunks as they arrive:
 
 ```csharp
 Console.WriteLine($"[ASSISTANT]:");
@@ -132,10 +132,10 @@ foreach (StreamingChatCompletionUpdate update in updates)
 }
 ```
 
-Alternatively, you can do this asynchronously by calling the `CompleteChatStreamingAsync` method to get an `AsyncResultCollection<StreamingChatCompletionUpdate>` and enumerate it using `await foreach`:
+Alternatively, you can do this asynchronously by calling the `CompleteChatStreamingAsync` method to get an `AsyncCollectionResult<StreamingChatCompletionUpdate>` and enumerate it using `await foreach`:
 
 ```csharp
-AsyncResultCollection<StreamingChatCompletionUpdate> updates
+AsyncCollectionResult<StreamingChatCompletionUpdate> updates
     = client.CompleteChatStreamingAsync("Say 'this is a test.'");
 
 Console.WriteLine($"[ASSISTANT]:");
@@ -528,7 +528,7 @@ Finally, you can use the `AssistantClient`'s `GetMessages` method to retrieve th
 For illustrative purposes, you could print the messages to the console and also save any images produced by the assistant to local storage:
 
 ```csharp
-PageableCollection<ThreadMessage> messages = assistantClient.GetMessages(threadRun.ThreadId, ListOrder.OldestFirst);
+PageCollection<ThreadMessage> messages = assistantClient.GetMessages(threadRun.ThreadId, ListOrder.OldestFirst);
 
 foreach (ThreadMessage message in messages)
 {
@@ -640,10 +640,10 @@ AssistantThread thread = assistantClient.CreateThread(new ThreadCreationOptions(
 });
 ```
 
-With the assistant and thread prepared, use the `CreateRunStreaming` method to get an enumerable `ResultCollection<StreamingUpdate>`. You can then iterate over this collection with `foreach`. For async calling patterns, use `CreateRunStreamingAsync` and iterate over the `AsyncResultCollection<StreamingUpdate>` with `await foreach`, instead. Note that streaming variants also exist for `CreateThreadAndRunStreaming` and `SubmitToolOutputsToRunStreaming`.
+With the assistant and thread prepared, use the `CreateRunStreaming` method to get an enumerable `CollectionResult<StreamingUpdate>`. You can then iterate over this collection with `foreach`. For async calling patterns, use `CreateRunStreamingAsync` and iterate over the `AsyncCollectionResult<StreamingUpdate>` with `await foreach`, instead. Note that streaming variants also exist for `CreateThreadAndRunStreaming` and `SubmitToolOutputsToRunStreaming`.
 
 ```csharp
-ResultCollection<StreamingUpdate> streamingUpdates = assistantClient.CreateRunStreaming(
+CollectionResult<StreamingUpdate> streamingUpdates = assistantClient.CreateRunStreaming(
     thread,
     assistant,
     new RunCreationOptions()

examples/Assistants/Example01_RetrievalAugmentedGeneration.cs

@@ -1,149 +1,144 @@
-using NUnit.Framework;
-using OpenAI.Assistants;
-using OpenAI.Files;
-using System;
-using System.ClientModel;
-using System.Collections.Generic;
-using System.IO;
-using System.Threading;
+//using NUnit.Framework;
+//using OpenAI.Assistants;
+//using OpenAI.Files;
+//using System;
+//using System.ClientModel;
+//using System.Collections.Generic;
+//using System.IO;
+//using System.Threading;
 
-namespace OpenAI.Examples;
+//namespace OpenAI.Examples;
 
-public partial class AssistantExamples
-{
-    [Test]
-    public void Example01_RetrievalAugmentedGeneration()
-    {
-        // Assistants is a beta API and subject to change; acknowledge its experimental status by suppressing the matching warning.
-#pragma warning disable OPENAI001
-        OpenAIClient openAIClient = new(Environment.GetEnvironmentVariable("OPENAI_API_KEY"));
-        FileClient fileClient = openAIClient.GetFileClient();
-        AssistantClient assistantClient = openAIClient.GetAssistantClient();
+//public partial class AssistantExamples
+//{
+//    [Test]
+//    public void Example01_RetrievalAugmentedGeneration()
+//    {
+//        // Assistants is a beta API and subject to change; acknowledge its experimental status by suppressing the matching warning.
+//#pragma warning disable OPENAI001
+//        OpenAIClient openAIClient = new(Environment.GetEnvironmentVariable("OPENAI_API_KEY"));
+//        FileClient fileClient = openAIClient.GetFileClient();
+//        AssistantClient assistantClient = openAIClient.GetAssistantClient();
 
-        // First, let's contrive a document we'll use retrieval with and upload it.
-        using Stream document = BinaryData.FromString("""
-            {
-                "description": "This document contains the sale history data for Contoso products.",
-                "sales": [
-                    {
-                        "month": "January",
-                        "by_product": {
-                            "113043": 15,
-                            "113045": 12,
-                            "113049": 2
-                        }
-                    },
-                    {
-                        "month": "February",
-                        "by_product": {
-                            "113045": 22
-                        }
-                    },
-                    {
-                        "month": "March",
-                        "by_product": {
-                            "113045": 16,
-                            "113055": 5
-                        }
-                    }
-                ]
-            }
-            """).ToStream();
+//        // First, let's contrive a document we'll use retrieval with and upload it.
+//        using Stream document = BinaryData.FromString("""
+//            {
+//                "description": "This document contains the sale history data for Contoso products.",
+//                "sales": [
+//                    {
+//                        "month": "January",
+//                        "by_product": {
+//                            "113043": 15,
+//                            "113045": 12,
+//                            "113049": 2
+//                        }
+//                    },
+//                    {
+//                        "month": "February",
+//                        "by_product": {
+//                            "113045": 22
+//                        }
+//                    },
+//                    {
+//                        "month": "March",
+//                        "by_product": {
+//                            "113045": 16,
+//                            "113055": 5
+//                        }
+//                    }
+//                ]
+//            }
+//            """).ToStream();
 
-        OpenAIFileInfo salesFile = fileClient.UploadFile(
-            document,
-            "monthly_sales.json",
-            FileUploadPurpose.Assistants);
+//        OpenAIFileInfo salesFile = fileClient.UploadFile(
+//            document,
+//            "monthly_sales.json",
+//            FileUploadPurpose.Assistants);
 
-        // Now, we'll create a client intended to help with that data
-        AssistantCreationOptions assistantOptions = new()
-        {
-            Name = "Example: Contoso sales RAG",
-            Instructions =
-                "You are an assistant that looks up sales data and helps visualize the information based"
-                + " on user queries. When asked to generate a graph, chart, or other visualization, use"
-                + " the code interpreter tool to do so.",
-            Tools =
-            {
-                new FileSearchToolDefinition(),
-                new CodeInterpreterToolDefinition(),
-            },
-            ToolResources = new()
-            {
-                FileSearch = new()
-                {
-                    NewVectorStores =
-                    {
-                        new VectorStoreCreationHelper([salesFile.Id]),
-                    }
-                }
-            },
-        };
+//        // Now, we'll create a client intended to help with that data
+//        AssistantCreationOptions assistantOptions = new()
+//        {
+//            Name = "Example: Contoso sales RAG",
+//            Instructions =
+//                "You are an assistant that looks up sales data and helps visualize the information based"
+//                + " on user queries. When asked to generate a graph, chart, or other visualization, use"
+//                + " the code interpreter tool to do so.",
+//            Tools =
+//            {
+//                new FileSearchToolDefinition(),
+//                new CodeInterpreterToolDefinition(),
+//            },
+//            ToolResources = new()
+//            {
+//                FileSearch = new()
+//                {
+//                    NewVectorStores =
+//                    {
+//                        new VectorStoreCreationHelper([salesFile.Id]),
+//                    }
+//                }
+//            },
+//        };
 
-        Assistant assistant = assistantClient.CreateAssistant("gpt-4o", assistantOptions);
+//        Assistant assistant = assistantClient.CreateAssistant("gpt-4o", assistantOptions);
 
-        // Now we'll create a thread with a user query about the data already associated with the assistant, then run it
-        ThreadCreationOptions threadOptions = new()
-        {
-            InitialMessages = { "How well did product 113045 sell in February? Graph its trend over time." }
-        };
+//        // Now we'll create a thread with a user query about the data already associated with the assistant, then run it
+//        ThreadCreationOptions threadOptions = new()
+//        {
+//            InitialMessages = { "How well did product 113045 sell in February? Graph its trend over time." }
+//        };
 
-        ThreadRun threadRun = assistantClient.CreateThreadAndRun(assistant.Id, threadOptions);
+//        // Passing ReturnWhen.Completed means CreateThreadAndRun will return control after the run is complete.
+//        ThreadRunOperation runOperation = assistantClient.CreateThreadAndRun(ReturnWhen.Completed, assistant.Id, threadOptions);
 
-        // Check back to see when the run is done
-        do
-        {
-            Thread.Sleep(TimeSpan.FromSeconds(1));
-            threadRun = assistantClient.GetRun(threadRun.ThreadId, threadRun.Id);
-        } while (!threadRun.Status.IsTerminal);
+//        // Finally, we'll print out the full history for the thread that includes the augmented generation
+//        PageCollection<ThreadMessage> messagePages
+//            = assistantClient.GetMessages(runOperation.ThreadId, new MessageCollectionOptions() { Order = ListOrder.OldestFirst });
+//        IEnumerable<ThreadMessage> messages = messagePages.GetAllValues();
 
-        // Finally, we'll print out the full history for the thread that includes the augmented generation
-        PageableCollection<ThreadMessage> messages
-            = assistantClient.GetMessages(threadRun.ThreadId, ListOrder.OldestFirst);
+//        foreach (ThreadMessage message in messages)
+//        {
+//            Console.Write($"[{message.Role.ToString().ToUpper()}]: ");
+//            foreach (MessageContent contentItem in message.Content)
+//            {
+//                if (!string.IsNullOrEmpty(contentItem.Text))
+//                {
+//                    Console.WriteLine($"{contentItem.Text}");
 
-        foreach (ThreadMessage message in messages)
-        {
-            Console.Write($"[{message.Role.ToString().ToUpper()}]: ");
-            foreach (MessageContent contentItem in message.Content)
-            {
-                if (!string.IsNullOrEmpty(contentItem.Text))
-                {
-                    Console.WriteLine($"{contentItem.Text}");
+//                    if (contentItem.TextAnnotations.Count > 0)
+//                    {
+//                        Console.WriteLine();
+//                    }
 
-                    if (contentItem.TextAnnotations.Count > 0)
-                    {
-                        Console.WriteLine();
-                    }
+//                    // Include annotations, if any.
+//                    foreach (TextAnnotation annotation in contentItem.TextAnnotations)
+//                    {
+//                        if (!string.IsNullOrEmpty(annotation.InputFileId))
+//                        {
+//                            Console.WriteLine($"* File citation, file ID: {annotation.InputFileId}");
+//                        }
+//                        if (!string.IsNullOrEmpty(annotation.OutputFileId))
+//                        {
+//                            Console.WriteLine($"* File output, new file ID: {annotation.OutputFileId}");
+//                        }
+//                    }
+//                }
+//                if (!string.IsNullOrEmpty(contentItem.ImageFileId))
+//                {
+//                    OpenAIFileInfo imageInfo = fileClient.GetFile(contentItem.ImageFileId);
+//                    BinaryData imageBytes = fileClient.DownloadFile(contentItem.ImageFileId);
+//                    using FileStream stream = File.OpenWrite($"{imageInfo.Filename}.png");
+//                    imageBytes.ToStream().CopyTo(stream);
 
-                    // Include annotations, if any.
-                    foreach (TextAnnotation annotation in contentItem.TextAnnotations)
-                    {
-                        if (!string.IsNullOrEmpty(annotation.InputFileId))
-                        {
-                            Console.WriteLine($"* File citation, file ID: {annotation.InputFileId}");
-                        }
-                        if (!string.IsNullOrEmpty(annotation.OutputFileId))
-                        {
-                            Console.WriteLine($"* File output, new file ID: {annotation.OutputFileId}");
-                        }
-                    }
-                }
-                if (!string.IsNullOrEmpty(contentItem.ImageFileId))
-                {
-                    OpenAIFileInfo imageInfo = fileClient.GetFile(contentItem.ImageFileId);
-                    BinaryData imageBytes = fileClient.DownloadFile(contentItem.ImageFileId);
-                    using FileStream stream = File.OpenWrite($"{imageInfo.Filename}.png");
-                    imageBytes.ToStream().CopyTo(stream);
+//                    Console.WriteLine($"<image: {imageInfo.Filename}.png>");
+//                }
+//            }
+//            Console.WriteLine();
+//        }
 
-                    Console.WriteLine($"<image: {imageInfo.Filename}.png>");
-                }
-            }
-            Console.WriteLine();
-        }
-
-        // Optionally, delete any persistent resources you no longer need.
-        _ = assistantClient.DeleteThread(threadRun.ThreadId);
-        _ = assistantClient.DeleteAssistant(assistant);
-        _ = fileClient.DeleteFile(salesFile);
-    }
-}
+//        // Optionally, delete any persistent resources you no longer need.
+//        _ = assistantClient.DeleteThread(runOperation.ThreadId);
+//        _ = assistantClient.DeleteAssistant(assistant);
+//        _ = fileClient.DeleteFile(salesFile);
+//    }
+//}