Azure Ai Projects Dotnet

using Azure.Identity;using Azure.AI.Projects;
var endpoint = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>var credential = new DefaultAzureCredential(    DefaultAzureCredential.DefaultEnvironmentVariableName);// Or use a specific credential directly in production:// See https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes// var credential = new ManagedIdentityCredential();AIProjectClient projectClient = new AIProjectClient(    new Uri(endpoint),     credential);

// Get low-level agents client from project clientPersistentAgentsClient agentsClient = projectClient.GetPersistentAgentsClient();
// Create agentPersistentAgent agent = await agentsClient.Administration.CreateAgentAsync(    model: "gpt-4o-mini",    name: "Math Tutor",    instructions: "You are a personal math tutor.");
// Create thread and runPersistentAgentThread thread = await agentsClient.Threads.CreateThreadAsync();await agentsClient.Messages.CreateMessageAsync(thread.Id, MessageRole.User, "Solve 3x + 11 = 14");ThreadRun run = await agentsClient.Runs.CreateRunAsync(thread.Id, agent.Id);
// Poll for completiondo{    await Task.Delay(500);    run = await agentsClient.Runs.GetRunAsync(thread.Id, run.Id);}while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);
// Get messagesawait foreach (var msg in agentsClient.Messages.GetMessagesAsync(thread.Id)){    foreach (var content in msg.ContentItems)    {        if (content is MessageTextContent textContent)            Console.WriteLine(textContent.Text);    }}
// Cleanupawait agentsClient.Threads.DeleteThreadAsync(thread.Id);await agentsClient.Administration.DeleteAgentAsync(agent.Id);

using Azure.AI.Projects.OpenAI;
// Create agent with web search toolPromptAgentDefinition agentDefinition = new(model: "gpt-4o-mini"){    Instructions = "You are a helpful assistant that can search the web",    Tools = {        ResponseTool.CreateWebSearchTool(            userLocation: WebSearchToolLocation.CreateApproximateLocation(                country: "US",                city: "Seattle",                region: "Washington"            )        ),    }};
AgentVersion agentVersion = await projectClient.Agents.CreateAgentVersionAsync(    agentName: "myAgent",    options: new(agentDefinition));
// Get response clientProjectResponsesClient responseClient = projectClient.OpenAI.GetProjectResponsesClientForAgent(agentVersion.Name);
// Create responseResponseResult response = responseClient.CreateResponse("What's the weather in Seattle?");Console.WriteLine(response.GetOutputText());
// CleanupprojectClient.Agents.DeleteAgentVersion(agentName: agentVersion.Name, agentVersion: agentVersion.Version);

// List all connectionsforeach (AIProjectConnection connection in projectClient.Connections.GetConnections()){    Console.WriteLine($"{connection.Name}: {connection.ConnectionType}");}
// Get specific connectionAIProjectConnection conn = projectClient.Connections.GetConnection(    connectionName,     includeCredentials: true);
// Get default connectionAIProjectConnection defaultConn = projectClient.Connections.GetDefaultConnection(    includeCredentials: false);

// List all deploymentsforeach (AIProjectDeployment deployment in projectClient.Deployments.GetDeployments()){    Console.WriteLine($"{deployment.Name}: {deployment.ModelName}");}
// Filter by publisherforeach (var deployment in projectClient.Deployments.GetDeployments(modelPublisher: "Microsoft")){    Console.WriteLine(deployment.Name);}
// Get specific deploymentModelDeployment details = (ModelDeployment)projectClient.Deployments.GetDeployment("gpt-4o-mini");

// Upload single fileFileDataset fileDataset = projectClient.Datasets.UploadFile(    name: "my-dataset",    version: "1.0",    filePath: "data/training.txt",    connectionName: connectionName);
// Upload folderFolderDataset folderDataset = projectClient.Datasets.UploadFolder(    name: "my-dataset",    version: "2.0",    folderPath: "data/training",    connectionName: connectionName,    filePattern: new Regex(".*\\.txt"));
// Get datasetAIProjectDataset dataset = projectClient.Datasets.GetDataset("my-dataset", "1.0");
// Delete datasetprojectClient.Datasets.Delete("my-dataset", "1.0");

// Create Azure AI Search indexAzureAISearchIndex searchIndex = new(aiSearchConnectionName, aiSearchIndexName){    Description = "Sample Index"};
searchIndex = (AzureAISearchIndex)projectClient.Indexes.CreateOrUpdate(    name: "my-index",    version: "1.0",    index: searchIndex);
// List indexesforeach (AIProjectIndex index in projectClient.Indexes.GetIndexes()){    Console.WriteLine(index.Name);}
// Delete indexprojectClient.Indexes.Delete(name: "my-index", version: "1.0");

// Create evaluation configurationvar evaluatorConfig = new EvaluatorConfiguration(id: EvaluatorIDs.Relevance);evaluatorConfig.InitParams.Add("deployment_name", BinaryData.FromObjectAsJson("gpt-4o"));
// Create evaluationEvaluation evaluation = new Evaluation(    data: new InputDataset("<dataset_id>"),    evaluators: new Dictionary<string, EvaluatorConfiguration>     {         { "relevance", evaluatorConfig }     }){    DisplayName = "Sample Evaluation"};
// Run evaluationEvaluation result = projectClient.Evaluations.Create(evaluation: evaluation);
// Get evaluationEvaluation getResult = projectClient.Evaluations.Get(result.Name);
// List evaluationsforeach (var eval in projectClient.Evaluations.GetAll()){    Console.WriteLine($"{eval.DisplayName}: {eval.Status}");}

using Azure.AI.OpenAI;using OpenAI.Chat;
ClientConnection connection = projectClient.GetConnection(typeof(AzureOpenAIClient).FullName!);
if (!connection.TryGetLocatorAsUri(out Uri uri) || uri is null)    throw new InvalidOperationException("Invalid URI.");
uri = new Uri($"https://{uri.Host}");
AzureOpenAIClient azureOpenAIClient = new AzureOpenAIClient(uri, new DefaultAzureCredential());ChatClient chatClient = azureOpenAIClient.GetChatClient("gpt-4o-mini");
ChatCompletion result = chatClient.CompleteChat("List all rainbow colors");Console.WriteLine(result.Content[0].Text);

using Azure;
try{    var result = await projectClient.Evaluations.CreateAsync(evaluation);}catch (RequestFailedException ex){    Console.WriteLine($"Error: {ex.Status} - {ex.ErrorCode}: {ex.Message}");}