chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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// Copyright (c) Microsoft. All rights reserved.
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using Microsoft.SemanticKernel;
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using Microsoft.SemanticKernel.Agents;
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using Microsoft.SemanticKernel.Agents.OpenAI;
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using Microsoft.SemanticKernel.ChatCompletion;
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namespace GettingStarted.OpenAIResponseAgents;
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/// <summary>
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/// This example demonstrates using <see cref="OpenAIResponseAgent"/>.
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/// </summary>
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public class Step01_OpenAIResponseAgent(ITestOutputHelper output) : BaseResponsesAgentTest(output)
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{
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[Fact]
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public async Task UseOpenAIResponseAgentAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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Name = "ResponseAgent",
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Instructions = "Answer all queries in English and French.",
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};
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// Invoke the agent and output the response
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var responseItems = agent.InvokeAsync("What is the capital of France?");
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await foreach (ChatMessageContent responseItem in responseItems)
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{
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WriteAgentChatMessage(responseItem);
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}
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}
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[Fact]
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public async Task UseOpenAIResponseAgentStreamingAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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Name = "ResponseAgent",
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Instructions = "Answer all queries in English and French.",
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};
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// Invoke the agent and output the response
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var responseItems = agent.InvokeStreamingAsync("What is the capital of France?");
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await WriteAgentStreamMessageAsync(responseItems);
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}
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[Fact]
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public async Task UseOpenAIResponseAgentWithThreadedConversationAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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Name = "ResponseAgent",
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Instructions = "Answer all queries in the users preferred language.",
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};
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string[] messages =
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[
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"My name is Bob and my preferred language is French.",
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"What is the capital of France?",
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"What is the capital of Spain?",
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"What is the capital of Italy?"
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];
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// Initial thread can be null as it will be automatically created
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AgentThread? agentThread = null;
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// Invoke the agent and output the response
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foreach (string message in messages)
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{
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Console.Write($"Agent Thread Id: {agentThread?.Id}");
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var responseItems = agent.InvokeAsync(new ChatMessageContent(AuthorRole.User, message), agentThread);
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await foreach (AgentResponseItem<ChatMessageContent> responseItem in responseItems)
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{
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// Update the thread so the previous response id is used
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agentThread = responseItem.Thread;
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WriteAgentChatMessage(responseItem.Message);
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}
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}
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}
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[Fact]
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public async Task UseOpenAIResponseAgentWithThreadedConversationStreamingAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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Name = "ResponseAgent",
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Instructions = "Answer all queries in the users preferred language.",
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};
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string[] messages =
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[
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"My name is Bob and my preferred language is French.",
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"What is the capital of France?",
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"What is the capital of Spain?",
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"What is the capital of Italy?"
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];
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// Initial thread can be null as it will be automatically created
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AgentThread? agentThread = null;
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// Invoke the agent and output the response
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foreach (string message in messages)
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{
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Console.Write($"Agent Thread Id: {agentThread?.Id}");
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var responseItems = agent.InvokeStreamingAsync(new ChatMessageContent(AuthorRole.User, message), agentThread);
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// Update the thread so the previous response id is used
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agentThread = await WriteAgentStreamMessageAsync(responseItems);
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}
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}
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[Fact]
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public async Task UseOpenAIResponseAgentWithImageContentAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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Name = "ResponseAgent",
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Instructions = "Provide a detailed description including the weather conditions.",
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};
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ICollection<ChatMessageContent> messages =
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[
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new ChatMessageContent(
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AuthorRole.User,
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items: [
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new TextContent("What is in this image?"),
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new ImageContent(new Uri("https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"))
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]
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),
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];
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// Invoke the agent and output the response
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var responseItems = agent.InvokeAsync(messages);
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await foreach (ChatMessageContent responseItem in responseItems)
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{
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WriteAgentChatMessage(responseItem);
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}
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}
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}
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+233
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// Copyright (c) Microsoft. All rights reserved.
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using Microsoft.SemanticKernel;
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using Microsoft.SemanticKernel.Agents;
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using Microsoft.SemanticKernel.Agents.OpenAI;
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using Microsoft.SemanticKernel.ChatCompletion;
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namespace GettingStarted.OpenAIResponseAgents;
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/// <summary>
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/// This example demonstrates how to manage conversation state during a model interaction using <see cref="OpenAIResponseAgent"/>.
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/// OpenAI provides a few ways to manage conversation state, which is important for preserving information across multiple messages or turns in a conversation.
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/// See: https://platform.openai.com/docs/guides/conversation-state?api-mode=responses for more information.
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/// </summary>
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public class Step02_OpenAIResponseAgent_ConversationState(ITestOutputHelper output) : BaseResponsesAgentTest(output)
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{
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[Fact]
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public async Task ManuallyConstructPastConversationAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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StoreEnabled = false,
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};
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ICollection<ChatMessageContent> messages =
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[
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new ChatMessageContent(AuthorRole.User, "knock knock."),
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new ChatMessageContent(AuthorRole.Assistant, "Who's there?"),
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new ChatMessageContent(AuthorRole.User, "Orange.")
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];
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foreach (ChatMessageContent message in messages)
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{
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WriteAgentChatMessage(message);
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}
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// Invoke the agent and output the response
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var responseItems = agent.InvokeAsync(messages);
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await foreach (ChatMessageContent responseItem in responseItems)
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{
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WriteAgentChatMessage(responseItem);
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}
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}
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[Fact]
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public async Task ManuallyConstructPastConversationStreamingAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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StoreEnabled = false,
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};
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ICollection<ChatMessageContent> messages =
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[
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new ChatMessageContent(AuthorRole.User, "knock knock."),
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new ChatMessageContent(AuthorRole.Assistant, "Who's there?"),
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new ChatMessageContent(AuthorRole.User, "Orange.")
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];
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foreach (ChatMessageContent message in messages)
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{
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WriteAgentChatMessage(message);
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}
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// Invoke the agent and output the response
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var responseItems = agent.InvokeStreamingAsync(messages);
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Console.Write("\n# assistant: ");
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await foreach (StreamingChatMessageContent responseItem in responseItems)
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{
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Console.Write(responseItem.Content);
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}
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}
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[Fact]
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public async Task ManageConversationStateWithResponseIdAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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StoreEnabled = false,
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};
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string[] messages =
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[
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"Tell me a joke?",
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"Explain why this is funny?",
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];
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// Invoke the agent and output the response
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AgentThread? agentThread = null;
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foreach (string message in messages)
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{
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var userMessage = new ChatMessageContent(AuthorRole.User, message);
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WriteAgentChatMessage(userMessage);
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var responseItems = agent.InvokeAsync(userMessage, agentThread);
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await foreach (AgentResponseItem<ChatMessageContent> responseItem in responseItems)
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{
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agentThread = responseItem.Thread;
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WriteAgentChatMessage(responseItem.Message);
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}
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}
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}
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[Fact]
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public async Task ManageConversationStateWithResponseIdStreamingAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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StoreEnabled = false,
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};
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string[] messages =
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[
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"Tell me a joke?",
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"Explain why this is funny?",
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];
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// Invoke the agent and output the response
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AgentThread? agentThread = null;
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foreach (string message in messages)
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{
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var userMessage = new ChatMessageContent(AuthorRole.User, message);
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WriteAgentChatMessage(userMessage);
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Console.Write("\n# assistant: ");
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var responseItems = agent.InvokeStreamingAsync(userMessage, agentThread);
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await foreach (AgentResponseItem<StreamingChatMessageContent> responseItem in responseItems)
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{
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agentThread = responseItem.Thread;
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Console.Write(responseItem.Message.Content);
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}
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}
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}
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[Fact]
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public async Task StoreConversationStateAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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StoreEnabled = true,
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};
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string[] messages =
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[
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"Tell me a joke?",
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"Explain why this is funny.",
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];
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// Invoke the agent and output the response
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AgentThread? agentThread = null;
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foreach (string message in messages)
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{
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var userMessage = new ChatMessageContent(AuthorRole.User, message);
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WriteAgentChatMessage(userMessage);
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var responseItems = agent.InvokeAsync(userMessage, agentThread);
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await foreach (AgentResponseItem<ChatMessageContent> responseItem in responseItems)
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{
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agentThread = responseItem.Thread;
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WriteAgentChatMessage(responseItem.Message);
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}
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}
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// Display the contents in the latest thread
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if (agentThread is not null)
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{
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this.Output.WriteLine("\n\nResponse Thread Messages\n");
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var responseAgentThread = agentThread as OpenAIResponseAgentThread;
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var threadMessages = responseAgentThread?.GetMessagesAsync();
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if (threadMessages is not null)
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{
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await foreach (var threadMessage in threadMessages)
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{
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WriteAgentChatMessage(threadMessage);
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}
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}
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await agentThread.DeleteAsync();
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}
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}
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[Fact]
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public async Task StoreConversationStateWithStreamingAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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StoreEnabled = true,
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};
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string[] messages =
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[
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"Tell me a joke?",
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"Explain why this is funny.",
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];
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// Invoke the agent and output the response
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AgentThread? agentThread = null;
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foreach (string message in messages)
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{
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var userMessage = new ChatMessageContent(AuthorRole.User, message);
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WriteAgentChatMessage(userMessage);
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Console.Write("\n# assistant: ");
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var responseItems = agent.InvokeStreamingAsync(userMessage, agentThread);
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await foreach (AgentResponseItem<StreamingChatMessageContent> responseItem in responseItems)
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{
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agentThread = responseItem.Thread;
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Console.Write(responseItem.Message.Content);
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}
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}
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// Display the contents in the latest thread
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if (agentThread is not null)
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{
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this.Output.WriteLine("\n\nResponse Thread Messages\n");
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var responseAgentThread = agentThread as OpenAIResponseAgentThread;
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var threadMessages = responseAgentThread?.GetMessagesAsync();
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if (threadMessages is not null)
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{
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await foreach (var threadMessage in threadMessages)
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{
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WriteAgentChatMessage(threadMessage);
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}
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}
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await agentThread.DeleteAsync();
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}
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}
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}
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+106
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// Copyright (c) Microsoft. All rights reserved.
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using Microsoft.SemanticKernel;
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using Microsoft.SemanticKernel.Agents.OpenAI;
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using OpenAI.Responses;
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using Plugins;
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namespace GettingStarted.OpenAIResponseAgents;
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/// <summary>
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/// This example demonstrates using <see cref="OpenAIResponseAgent"/>.
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/// </summary>
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public class Step03_OpenAIResponseAgent_ReasoningModel(ITestOutputHelper output) : BaseResponsesAgentTest(output, "o4-mini")
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{
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[Fact]
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public async Task UseOpenAIResponseAgentWithAReasoningModelAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
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{
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Name = "ResponseAgent",
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Instructions = "Answer all queries with a detailed response.",
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};
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// Invoke the agent and output the response
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var responseItems = agent.InvokeAsync("Which of the last four Olympic host cities has the highest average temperature?");
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await foreach (ChatMessageContent responseItem in responseItems)
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{
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WriteAgentChatMessage(responseItem);
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}
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}
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[Fact]
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public async Task UseOpenAIResponseAgentWithAReasoningModelAndSummariesAsync()
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{
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// Define the agent
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OpenAIResponseAgent agent = new(this.Client, this.ModelId);
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// ResponseCreationOptions allows you to specify tools for the agent.
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OpenAIResponseAgentInvokeOptions invokeOptions = new()
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{
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ResponseCreationOptions = new()
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{
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ReasoningOptions = new()
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{
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ReasoningEffortLevel = ResponseReasoningEffortLevel.High,
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// This parameter cannot be used due to a known issue in the OpenAI .NET SDK.
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// https://github.com/openai/openai-dotnet/issues/457
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// ReasoningSummaryVerbosity = ResponseReasoningSummaryVerbosity.Detailed,
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},
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},
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};
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// Invoke the agent and output the response
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var responseItems = agent.InvokeAsync(
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"""
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Instructions:
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- Given the React component below, change it so that nonfiction books have red
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text.
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- Return only the code in your reply
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- Do not include any additional formatting, such as markdown code blocks
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- For formatting, use four space tabs, and do not allow any lines of code to
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exceed 80 columns
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const books = [
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{ title: 'Dune', category: 'fiction', id: 1 },
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{ title: 'Frankenstein', category: 'fiction', id: 2 },
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{ title: 'Moneyball', category: 'nonfiction', id: 3 },
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];
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export default function BookList() {
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const listItems = books.map(book =>
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<li>
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{book.title}
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</li>
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);
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return (
|
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<ul>{listItems}</ul>
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);
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||||
}
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""", options: invokeOptions);
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await foreach (ChatMessageContent responseItem in responseItems)
|
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{
|
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WriteAgentChatMessage(responseItem);
|
||||
}
|
||||
}
|
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|
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[Fact]
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public async Task UseOpenAIResponseAgentWithAReasoningModelAndToolsAsync()
|
||||
{
|
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// Define the agent
|
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OpenAIResponseAgent agent = new(this.Client, this.ModelId)
|
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{
|
||||
Name = "ResponseAgent",
|
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Instructions = "Answer all queries with a detailed response.",
|
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};
|
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// Create a plugin that defines the tools to be used by the agent.
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KernelPlugin plugin = KernelPluginFactory.CreateFromType<MenuPlugin>();
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agent.Kernel.Plugins.Add(plugin);
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// Invoke the agent and output the response
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var responseItems = agent.InvokeAsync("What is the best value healthy meal?");
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await foreach (ChatMessageContent responseItem in responseItems)
|
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{
|
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WriteAgentChatMessage(responseItem);
|
||||
}
|
||||
}
|
||||
}
|
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+168
@@ -0,0 +1,168 @@
|
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// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
using System.ClientModel;
|
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using System.ClientModel.Primitives;
|
||||
using Microsoft.SemanticKernel;
|
||||
using Microsoft.SemanticKernel.Agents.OpenAI;
|
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using Microsoft.SemanticKernel.ChatCompletion;
|
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using OpenAI.Files;
|
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using OpenAI.Responses;
|
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using OpenAI.VectorStores;
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using Plugins;
|
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using Resources;
|
||||
|
||||
namespace GettingStarted.OpenAIResponseAgents;
|
||||
|
||||
/// <summary>
|
||||
/// This example demonstrates how to use tools during a model interaction using <see cref="OpenAIResponseAgent"/>.
|
||||
/// </summary>
|
||||
public class Step04_OpenAIResponseAgent_Tools(ITestOutputHelper output) : BaseResponsesAgentTest(output)
|
||||
{
|
||||
[Fact]
|
||||
public async Task InvokeAgentWithFunctionToolsAsync()
|
||||
{
|
||||
// Define the agent
|
||||
OpenAIResponseAgent agent = new(this.Client, this.ModelId)
|
||||
{
|
||||
StoreEnabled = false,
|
||||
};
|
||||
|
||||
// Create a plugin that defines the tools to be used by the agent.
|
||||
KernelPlugin plugin = KernelPluginFactory.CreateFromType<MenuPlugin>();
|
||||
agent.Kernel.Plugins.Add(plugin);
|
||||
|
||||
ICollection<ChatMessageContent> messages =
|
||||
[
|
||||
new ChatMessageContent(AuthorRole.User, "What is the special soup and its price?"),
|
||||
new ChatMessageContent(AuthorRole.User, "What is the special drink and its price?"),
|
||||
];
|
||||
foreach (ChatMessageContent message in messages)
|
||||
{
|
||||
WriteAgentChatMessage(message);
|
||||
}
|
||||
|
||||
// Invoke the agent and output the response
|
||||
var responseItems = agent.InvokeAsync(messages);
|
||||
await foreach (ChatMessageContent responseItem in responseItems)
|
||||
{
|
||||
WriteAgentChatMessage(responseItem);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public async Task InvokeAgentWithWebSearchAsync()
|
||||
{
|
||||
// Define the agent
|
||||
OpenAIResponseAgent agent = new(this.Client, this.ModelId)
|
||||
{
|
||||
StoreEnabled = false,
|
||||
};
|
||||
|
||||
// ResponseCreationOptions allows you to specify tools for the agent.
|
||||
CreateResponseOptions creationOptions = new();
|
||||
creationOptions.Tools.Add(ResponseTool.CreateWebSearchTool());
|
||||
OpenAIResponseAgentInvokeOptions invokeOptions = new()
|
||||
{
|
||||
ResponseCreationOptions = creationOptions,
|
||||
};
|
||||
|
||||
// Invoke the agent and output the response
|
||||
var responseItems = agent.InvokeAsync("What was a positive news story from today?", options: invokeOptions);
|
||||
await foreach (ChatMessageContent responseItem in responseItems)
|
||||
{
|
||||
WriteAgentChatMessage(responseItem);
|
||||
}
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public async Task InvokeAgentWithFileSearchAsync()
|
||||
{
|
||||
// Upload a file to the OpenAI File API
|
||||
await using Stream stream = EmbeddedResource.ReadStream("employees.pdf")!;
|
||||
OpenAIFile file = await this.FileClient.UploadFileAsync(stream, filename: "employees.pdf", purpose: FileUploadPurpose.UserData);
|
||||
|
||||
// Create a vector store for the file
|
||||
ClientResult<VectorStore> createStoreOp = await this.VectorStoreClient.CreateVectorStoreAsync(
|
||||
new VectorStoreCreationOptions()
|
||||
{
|
||||
FileIds = { file.Id },
|
||||
});
|
||||
|
||||
// Define the agent
|
||||
OpenAIResponseAgent agent = new(this.Client, this.ModelId)
|
||||
{
|
||||
StoreEnabled = false,
|
||||
};
|
||||
|
||||
// ResponseCreationOptions allows you to specify tools for the agent.
|
||||
CreateResponseOptions creationOptions = new();
|
||||
creationOptions.Tools.Add(ResponseTool.CreateFileSearchTool([createStoreOp.Value.Id], null));
|
||||
OpenAIResponseAgentInvokeOptions invokeOptions = new()
|
||||
{
|
||||
ResponseCreationOptions = creationOptions,
|
||||
};
|
||||
|
||||
// Invoke the agent and output the response
|
||||
ICollection<ChatMessageContent> messages =
|
||||
[
|
||||
new ChatMessageContent(AuthorRole.User, "Who is the youngest employee?"),
|
||||
new ChatMessageContent(AuthorRole.User, "Who works in sales?"),
|
||||
new ChatMessageContent(AuthorRole.User, "I have a customer request, who can help me?"),
|
||||
];
|
||||
foreach (ChatMessageContent message in messages)
|
||||
{
|
||||
WriteAgentChatMessage(message);
|
||||
}
|
||||
|
||||
// Invoke the agent and output the response
|
||||
var responseItems = agent.InvokeAsync(messages, options: invokeOptions);
|
||||
await foreach (ChatMessageContent responseItem in responseItems)
|
||||
{
|
||||
WriteAgentChatMessage(responseItem);
|
||||
}
|
||||
|
||||
// Clean up resources
|
||||
RequestOptions noThrowOptions = new() { ErrorOptions = ClientErrorBehaviors.NoThrow };
|
||||
this.FileClient.DeleteFile(file.Id, noThrowOptions);
|
||||
this.VectorStoreClient.DeleteVectorStore(createStoreOp.Value.Id, noThrowOptions);
|
||||
}
|
||||
|
||||
[Fact]
|
||||
public async Task InvokeAgentWithMultipleToolsAsync()
|
||||
{
|
||||
// Define the agent
|
||||
OpenAIResponseAgent agent = new(this.Client, this.ModelId)
|
||||
{
|
||||
StoreEnabled = false,
|
||||
};
|
||||
|
||||
// Create a plugin that defines the tools to be used by the agent.
|
||||
KernelPlugin plugin = KernelPluginFactory.CreateFromType<MenuPlugin>();
|
||||
agent.Kernel.Plugins.Add(plugin);
|
||||
|
||||
ICollection<ChatMessageContent> messages =
|
||||
[
|
||||
new ChatMessageContent(AuthorRole.User, "What is the special soup and its price?"),
|
||||
new ChatMessageContent(AuthorRole.User, "What is the special drink and its price?"),
|
||||
];
|
||||
foreach (ChatMessageContent message in messages)
|
||||
{
|
||||
WriteAgentChatMessage(message);
|
||||
}
|
||||
|
||||
// ResponseCreationOptions allows you to specify tools for the agent.
|
||||
CreateResponseOptions creationOptions = new();
|
||||
creationOptions.Tools.Add(ResponseTool.CreateWebSearchTool());
|
||||
OpenAIResponseAgentInvokeOptions invokeOptions = new()
|
||||
{
|
||||
ResponseCreationOptions = creationOptions,
|
||||
};
|
||||
|
||||
// Invoke the agent and output the response
|
||||
var responseItems = agent.InvokeAsync(messages, options: invokeOptions);
|
||||
await foreach (ChatMessageContent responseItem in responseItems)
|
||||
{
|
||||
WriteAgentChatMessage(responseItem);
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user