169 lines
6.1 KiB
C#
169 lines
6.1 KiB
C#
// Copyright (c) Microsoft. All rights reserved.
|
|
|
|
using System.ClientModel;
|
|
using System.ClientModel.Primitives;
|
|
using Microsoft.SemanticKernel;
|
|
using Microsoft.SemanticKernel.Agents.OpenAI;
|
|
using Microsoft.SemanticKernel.ChatCompletion;
|
|
using OpenAI.Files;
|
|
using OpenAI.Responses;
|
|
using OpenAI.VectorStores;
|
|
using Plugins;
|
|
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);
|
|
}
|
|
}
|
|
}
|