wehub-resource-sync
107 lines
3.9 KiB
C#
107 lines
3.9 KiB
C#
// Copyright (c) Microsoft. All rights reserved.
|
|
using Microsoft.SemanticKernel;
|
|
using Microsoft.SemanticKernel.Agents.OpenAI;
|
|
using OpenAI.Responses;
|
|
using Plugins;
|
|
|
|
namespace GettingStarted.OpenAIResponseAgents;
|
|
|
|
/// <summary>
|
|
/// This example demonstrates using <see cref="OpenAIResponseAgent"/>.
|
|
/// </summary>
|
|
public class Step03_OpenAIResponseAgent_ReasoningModel(ITestOutputHelper output) : BaseResponsesAgentTest(output, "o4-mini")
|
|
{
|
|
[Fact]
|
|
public async Task UseOpenAIResponseAgentWithAReasoningModelAsync()
|
|
{
|
|
// Define the agent
|
|
OpenAIResponseAgent agent = new(this.Client, this.ModelId)
|
|
{
|
|
Name = "ResponseAgent",
|
|
Instructions = "Answer all queries with a detailed response.",
|
|
};
|
|
|
|
// Invoke the agent and output the response
|
|
var responseItems = agent.InvokeAsync("Which of the last four Olympic host cities has the highest average temperature?");
|
|
await foreach (ChatMessageContent responseItem in responseItems)
|
|
{
|
|
WriteAgentChatMessage(responseItem);
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
public async Task UseOpenAIResponseAgentWithAReasoningModelAndSummariesAsync()
|
|
{
|
|
// Define the agent
|
|
OpenAIResponseAgent agent = new(this.Client, this.ModelId);
|
|
|
|
// ResponseCreationOptions allows you to specify tools for the agent.
|
|
OpenAIResponseAgentInvokeOptions invokeOptions = new()
|
|
{
|
|
ResponseCreationOptions = new()
|
|
{
|
|
ReasoningOptions = new()
|
|
{
|
|
ReasoningEffortLevel = ResponseReasoningEffortLevel.High,
|
|
// This parameter cannot be used due to a known issue in the OpenAI .NET SDK.
|
|
// https://github.com/openai/openai-dotnet/issues/457
|
|
// ReasoningSummaryVerbosity = ResponseReasoningSummaryVerbosity.Detailed,
|
|
},
|
|
},
|
|
};
|
|
|
|
// Invoke the agent and output the response
|
|
var responseItems = agent.InvokeAsync(
|
|
"""
|
|
Instructions:
|
|
- Given the React component below, change it so that nonfiction books have red
|
|
text.
|
|
- Return only the code in your reply
|
|
- Do not include any additional formatting, such as markdown code blocks
|
|
- For formatting, use four space tabs, and do not allow any lines of code to
|
|
exceed 80 columns
|
|
const books = [
|
|
{ title: 'Dune', category: 'fiction', id: 1 },
|
|
{ title: 'Frankenstein', category: 'fiction', id: 2 },
|
|
{ title: 'Moneyball', category: 'nonfiction', id: 3 },
|
|
];
|
|
export default function BookList() {
|
|
const listItems = books.map(book =>
|
|
<li>
|
|
{book.title}
|
|
</li>
|
|
);
|
|
return (
|
|
<ul>{listItems}</ul>
|
|
);
|
|
}
|
|
""", options: invokeOptions);
|
|
await foreach (ChatMessageContent responseItem in responseItems)
|
|
{
|
|
WriteAgentChatMessage(responseItem);
|
|
}
|
|
}
|
|
|
|
[Fact]
|
|
public async Task UseOpenAIResponseAgentWithAReasoningModelAndToolsAsync()
|
|
{
|
|
// Define the agent
|
|
OpenAIResponseAgent agent = new(this.Client, this.ModelId)
|
|
{
|
|
Name = "ResponseAgent",
|
|
Instructions = "Answer all queries with a detailed response.",
|
|
};
|
|
|
|
// Create a plugin that defines the tools to be used by the agent.
|
|
KernelPlugin plugin = KernelPluginFactory.CreateFromType<MenuPlugin>();
|
|
agent.Kernel.Plugins.Add(plugin);
|
|
|
|
// Invoke the agent and output the response
|
|
var responseItems = agent.InvokeAsync("What is the best value healthy meal?");
|
|
await foreach (ChatMessageContent responseItem in responseItems)
|
|
{
|
|
WriteAgentChatMessage(responseItem);
|
|
}
|
|
}
|
|
}
|