Bagaimana: ChatCompletionAgent

Penting

Fitur ini dalam tahap eksperimental. Fitur pada tahap ini sedang dalam pengembangan dan dapat berubah sebelum maju ke tahap pratinjau atau kandidat rilis.

Gambaran Umum

Dalam sampel ini, kita akan menjelajahi cara mengonfigurasi plugin untuk mengakses GitHub API dan memberikan instruksi yang ditemplatkan kepada ChatCompletionAgent untuk menjawab pertanyaan tentang repositori GitHub. Pendekatan akan dipecah selangkah demi selangkah untuk menyoroti bagian-bagian utama dari proses pengodean. Sebagai bagian dari tugas, agen akan memberikan kutipan dokumen dalam respons.

Streaming akan digunakan untuk menyampaikan respons agen. Ini akan memberikan pembaruan real-time saat tugas berlangsung.

Memulai dengan Langkah Awal

Sebelum melanjutkan pengkodian fitur, pastikan lingkungan pengembangan Anda sepenuhnya disiapkan dan dikonfigurasi.

Mulailah dengan membuat proyek Konsol . Kemudian, sertakan referensi paket berikut untuk memastikan semua dependensi yang diperlukan tersedia.

Untuk menambahkan dependensi paket dari baris perintah, gunakan dotnet perintah :

dotnet add package Azure.Identity
dotnet add package Microsoft.Extensions.Configuration
dotnet add package Microsoft.Extensions.Configuration.Binder
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
dotnet add package Microsoft.Extensions.Configuration.EnvironmentVariables
dotnet add package Microsoft.SemanticKernel.Connectors.AzureOpenAI
dotnet add package Microsoft.SemanticKernel.Agents.Core --prerelease

Penting

Jika mengelola paket NuGet di Visual Studio, pastikan Include prerelease dicentang.

File proyek (.csproj) harus berisi definisi berikut PackageReference :

  <ItemGroup>
    <PackageReference Include="Azure.Identity" Version="<stable>" />
    <PackageReference Include="Microsoft.Extensions.Configuration" Version="<stable>" />
    <PackageReference Include="Microsoft.Extensions.Configuration.Binder" Version="<stable>" />
    <PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="<stable>" />
    <PackageReference Include="Microsoft.Extensions.Configuration.EnvironmentVariables" Version="<stable>" />
    <PackageReference Include="Microsoft.SemanticKernel.Agents.Core" Version="<latest>" />
    <PackageReference Include="Microsoft.SemanticKernel.Connectors.AzureOpenAI" Version="<latest>" />
  </ItemGroup>

Agent Framework bersifat eksperimental dan memerlukan pemadaman peringatan. Ini dapat diatasi sebagai properti dalam file proyek (.csproj):

  <PropertyGroup>
    <NoWarn>$(NoWarn);CA2007;IDE1006;SKEXP0001;SKEXP0110;OPENAI001</NoWarn>
  </PropertyGroup>

Selain itu, salin plug-in dan model GitHub (GitHubPlugin.cs dan GitHubModels.cs) dari Proyek Kernel LearnResources Semantik. Tambahkan file ini di folder proyek Anda.

Mulailah dengan membuat folder yang akan menyimpan skrip (.py file) dan sumber daya sampel Anda. Sertakan impor berikut di bagian atas file Anda .py :

import asyncio
import os
import sys
from datetime import datetime

from semantic_kernel.agents import ChatCompletionAgent, ChatHistoryAgentThread
from semantic_kernel.connectors.ai import FunctionChoiceBehavior
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
from semantic_kernel.functions import KernelArguments
from semantic_kernel.kernel import Kernel

# Adjust the sys.path so we can use the GitHubPlugin and GitHubSettings classes
# This is so we can run the code from the samples/learn_resources/agent_docs directory
# If you are running code from your own project, you may not need need to do this.
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))

from plugins.GithubPlugin.github import GitHubPlugin, GitHubSettings  # noqa: E402

Selain itu, salin plug-in dan model GitHub (github.py) dari Proyek Kernel LearnResources Semantik. Tambahkan file ini di folder proyek Anda.

Mulailah dengan membuat proyek konsol Maven. Kemudian, sertakan referensi paket berikut untuk memastikan semua dependensi yang diperlukan tersedia.

Proyek pom.xml harus berisi dependensi berikut:

<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>com.microsoft.semantic-kernel</groupId>
            <artifactId>semantickernel-bom</artifactId>
            <version>[LATEST]</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

<dependencies>
    <dependency>
        <groupId>com.microsoft.semantic-kernel</groupId>
        <artifactId>semantickernel-agents-core</artifactId>
    </dependency>

    <dependency>
        <groupId>com.microsoft.semantic-kernel</groupId>
        <artifactId>semantickernel-aiservices-openai</artifactId>
    </dependency>
</dependencies>

Selain itu, salin plug-in dan model GitHub (GitHubPlugin.java dan GitHubModels.java) dari Proyek Kernel LearnResources Semantik. Tambahkan file ini di folder proyek Anda.

Konfigurasi

Sampel ini memerlukan pengaturan konfigurasi untuk terhubung ke layanan jarak jauh. Anda harus menentukan pengaturan untuk OpenAI atau Azure OpenAI dan juga untuk GitHub.

Nota

Untuk informasi tentang Token Akses Pribadi GitHub, lihat: Mengelola token akses pribadi Anda.

# OpenAI
dotnet user-secrets set "OpenAISettings:ApiKey" "<api-key>"
dotnet user-secrets set "OpenAISettings:ChatModel" "gpt-4o"

# Azure OpenAI
dotnet user-secrets set "AzureOpenAISettings:ApiKey" "<api-key>" # Not required if using token-credential
dotnet user-secrets set "AzureOpenAISettings:Endpoint" "<model-endpoint>"
dotnet user-secrets set "AzureOpenAISettings:ChatModelDeployment" "gpt-4o"

# GitHub
dotnet user-secrets set "GitHubSettings:BaseUrl" "https://api.github.com"
dotnet user-secrets set "GitHubSettings:Token" "<personal access token>"

Kelas berikut digunakan dalam semua contoh Agen. Pastikan untuk menyertakannya dalam proyek Anda untuk memastikan fungsionalitas yang tepat. Kelas ini berfungsi sebagai komponen dasar untuk contoh berikut.

using System.Reflection;
using Microsoft.Extensions.Configuration;

namespace AgentsSample;

public class Settings
{
    private readonly IConfigurationRoot configRoot;

    private AzureOpenAISettings azureOpenAI;
    private OpenAISettings openAI;

    public AzureOpenAISettings AzureOpenAI => this.azureOpenAI ??= this.GetSettings<Settings.AzureOpenAISettings>();
    public OpenAISettings OpenAI => this.openAI ??= this.GetSettings<Settings.OpenAISettings>();

    public class OpenAISettings
    {
        public string ChatModel { get; set; } = string.Empty;
        public string ApiKey { get; set; } = string.Empty;
    }

    public class AzureOpenAISettings
    {
        public string ChatModelDeployment { get; set; } = string.Empty;
        public string Endpoint { get; set; } = string.Empty;
        public string ApiKey { get; set; } = string.Empty;
    }

    public TSettings GetSettings<TSettings>() =>
        this.configRoot.GetRequiredSection(typeof(TSettings).Name).Get<TSettings>()!;

    public Settings()
    {
        this.configRoot =
            new ConfigurationBuilder()
                .AddEnvironmentVariables()
                .AddUserSecrets(Assembly.GetExecutingAssembly(), optional: true)
                .Build();
    }
}

Cara tercepat untuk memulai konfigurasi yang tepat untuk menjalankan kode sampel adalah dengan membuat .env file di akar proyek Anda (tempat skrip Anda dijalankan).

Konfigurasikan pengaturan berikut dalam file .env Anda untuk Azure OpenAI atau OpenAI.

AZURE_OPENAI_API_KEY="..."
AZURE_OPENAI_ENDPOINT="https://<resource-name>.openai.azure.com/"
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="..."
AZURE_OPENAI_API_VERSION="..."

OPENAI_API_KEY="sk-..."
OPENAI_ORG_ID=""
OPENAI_CHAT_MODEL_ID=""

Setelah dikonfigurasi, masing-masing kelas layanan AI akan mengambil variabel yang diperlukan dan menggunakannya selama instansiasi.

Tentukan variabel lingkungan berikut dalam sistem Anda.

# Azure OpenAI
AZURE_OPENAI_API_KEY=""
AZURE_OPENAI_ENDPOINT="https://<resource-name>.openai.azure.com/"
AZURE_CHAT_MODEL_DEPLOYMENT=""

# OpenAI
OPENAI_API_KEY=""
OPENAI_MODEL_ID=""

Di bagian atas file, Anda dapat mengambil nilainya sebagai berikut.

// Azure OpenAI
private static final String AZURE_OPENAI_API_KEY = System.getenv("AZURE_OPENAI_API_KEY");
private static final String AZURE_OPENAI_ENDPOINT = System.getenv("AZURE_OPENAI_ENDPOINT");
private static final String AZURE_CHAT_MODEL_DEPLOYMENT = System.getenv().getOrDefault("AZURE_CHAT_MODEL_DEPLOYMENT", "gpt-4o");

// OpenAI
private static final String OPENAI_API_KEY = System.getenv("OPENAI_API_KEY");
private static final String OPENAI_MODEL_ID = System.getenv().getOrDefault("OPENAI_MODEL_ID", "gpt-4o");

Pengkodean

Proses pengkodian untuk sampel ini melibatkan:

1. Penyiapan - Menginisialisasi pengaturan dan plug-in.
2. Agent Definisi - Buat ChatCompletionAgent dengan instruksi yang sudah ditentukan dan plug-in.
3. The Chat Loop - Tulis perulangan yang mendorong interaksi pengguna dan agen.

Contoh lengkap kode disediakan di bagian Akhir . Lihat bagian tersebut untuk implementasi lengkap.

Penyiapan

Sebelum membuat ChatCompletionAgent, pengaturan konfigurasi, plugin, dan Kernel harus diinisialisasi.

Inisialisasikan kelas Settings yang disebutkan di bagian Konfigurasi sebelumnya.

Settings settings = new();

Inisialisasi plug-in menggunakan pengaturannya.

Di sini, pesan ditampilkan untuk menunjukkan kemajuan.

Console.WriteLine("Initialize plugins...");
GitHubSettings githubSettings = settings.GetSettings<GitHubSettings>();
GitHubPlugin githubPlugin = new(githubSettings);

gh_settings = GitHubSettings(
    token="<PAT value>"
)
kernel.add_plugin(GitHubPlugin(settings=gh_settings), plugin_name="github")

var githubPlugin = new GitHubPlugin(GITHUB_PAT);

Sekarang inisialisasi Kernel instance dengan IChatCompletionService dan GitHubPlugin yang telah dibuat sebelumnya.

Console.WriteLine("Creating kernel...");
IKernelBuilder builder = Kernel.CreateBuilder();

builder.AddAzureOpenAIChatCompletion(
    settings.AzureOpenAI.ChatModelDeployment,
    settings.AzureOpenAI.Endpoint,
    new AzureCliCredential());

builder.Plugins.AddFromObject(githubPlugin);

Kernel kernel = builder.Build();

kernel = Kernel()

# Add the AzureChatCompletion AI Service to the Kernel
service_id = "agent"
kernel.add_service(AzureChatCompletion(service_id=service_id))

settings = kernel.get_prompt_execution_settings_from_service_id(service_id=service_id)
# Configure the function choice behavior to auto invoke kernel functions
settings.function_choice_behavior = FunctionChoiceBehavior.Auto()

OpenAIAsyncClient client = new OpenAIClientBuilder()
    .credential(new AzureKeyCredential(AZURE_OPENAI_API_KEY))
    .endpoint(AZURE_OPENAI_ENDPOINT)
    .buildAsyncClient();

ChatCompletionService chatCompletion = OpenAIChatCompletion.builder()
    .withModelId(AZURE_CHAT_MODEL_DEPLOYMENT)
    .withOpenAIAsyncClient(client)
    .build();

Kernel kernel = Kernel.builder()
    .withAIService(ChatCompletionService.class, chatCompletion)
    .withPlugin(KernelPluginFactory.createFromObject(githubPlugin, "GitHubPlugin"))
    .build();

Pengertian Agen

Akhirnya kita siap untuk membuat instance ChatCompletionAgent dengan Instruksi, Kernel terkait, serta argumen default dan pengaturan eksekusi. Dalam hal ini, kami ingin agar fungsi plugin apa pun dijalankan secara otomatis.

Console.WriteLine("Defining agent...");
ChatCompletionAgent agent =
    new()
    {
        Name = "SampleAssistantAgent",
        Instructions =
            """
            You are an agent designed to query and retrieve information from a single GitHub repository in a read-only manner.
            You are also able to access the profile of the active user.

            Use the current date and time to provide up-to-date details or time-sensitive responses.

            The repository you are querying is a public repository with the following name: {{$repository}}

            The current date and time is: {{$now}}. 
            """,
        Kernel = kernel,
        Arguments =
            new KernelArguments(new AzureOpenAIPromptExecutionSettings() { FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() })
            {
                { "repository", "microsoft/semantic-kernel" }
            }
    };

Console.WriteLine("Ready!");

agent = ChatCompletionAgent(
    kernel=kernel,
    name="SampleAssistantAgent",
    instructions=f"""
        You are an agent designed to query and retrieve information from a single GitHub repository in a read-only 
        manner.
        You are also able to access the profile of the active user.

        Use the current date and time to provide up-to-date details or time-sensitive responses.

        The repository you are querying is a public repository with the following name: microsoft/semantic-kernel

        The current date and time is: {{$now}}. 
        """,
    arguments=KernelArguments(
        settings=settings,
    ),
)

// Invocation context for the agent
InvocationContext invocationContext = InvocationContext.builder()
    .withFunctionChoiceBehavior(FunctionChoiceBehavior.auto(true))
    .build()

ChatCompletionAgent agent = ChatCompletionAgent.builder()
    .withName("SampleAssistantAgent")
    .withKernel(kernel)
    .withInvocationContext(invocationContext)
    .withTemplate(
        DefaultPromptTemplate.build(
            PromptTemplateConfig.builder()
                .withTemplate(
                    """
                    You are an agent designed to query and retrieve information from a single GitHub repository in a read-only manner.
                    You are also able to access the profile of the active user.

                    Use the current date and time to provide up-to-date details or time-sensitive responses.

                    The repository you are querying is a public repository with the following name: {{$repository}}

                    The current date and time is: {{$now}}.
                    """)
                .build()))
    .withKernelArguments(
        KernelArguments.builder()
            .withVariable("repository", "microsoft/semantic-kernel-java")
            .withExecutionSettings(PromptExecutionSettings.builder()
                    .build())
            .build())
    .build();

Perulangan Obrolan

Akhirnya, kita dapat mengoordinasikan interaksi antara pengguna dan Agent. Mulailah dengan membuat ChatHistoryAgentThread objek untuk mempertahankan status percakapan dan membuat perulangan kosong.

ChatHistoryAgentThread agentThread = new();
bool isComplete = false;
do
{
    // processing logic here
} while (!isComplete);

thread: ChatHistoryAgentThread = None
is_complete: bool = False
while not is_complete:
    # processing logic here

AgentThread agentThread = new ChatHistoryAgentThread();
boolean isComplete = false;

while (!isComplete) {
    // processing logic here
}

Sekarang mari kita menangkap input pengguna dalam perulangan sebelumnya. Dalam hal ini, input kosong akan diabaikan dan istilah EXIT akan memberi sinyal bahwa percakapan selesai.

Console.WriteLine();
Console.Write("> ");
string input = Console.ReadLine();
if (string.IsNullOrWhiteSpace(input))
{
    continue;
}
if (input.Trim().Equals("EXIT", StringComparison.OrdinalIgnoreCase))
{
    isComplete = true;
    break;
}

var message = new ChatMessageContent(AuthorRole.User, input);

Console.WriteLine();

user_input = input("User:> ")
if not user_input:
    continue

if user_input.lower() == "exit":
    is_complete = True
    break

Scanner scanner = new Scanner(System.in);

while (!isComplete) {
    System.out.print("> ");

    String input = scanner.nextLine();
    if (input.isEmpty()) {
        continue;
    }

    if (input.equalsIgnoreCase("exit")) {
        isComplete = true;
        break;
    }

}

Untuk menghasilkan Agent respons terhadap input pengguna, panggil agen menggunakan Argumen untuk menyediakan parameter templat akhir yang menentukan tanggal dan waktu saat ini.

Respons Agent kemudian ditampilkan kepada pengguna.

DateTime now = DateTime.Now;
KernelArguments arguments =
    new()
    {
        { "now", $"{now.ToShortDateString()} {now.ToShortTimeString()}" }
    };
await foreach (ChatMessageContent response in agent.InvokeAsync(message, agentThread, options: new() { KernelArguments = arguments }))
{
    Console.WriteLine($"{response.Content}");
}

arguments = KernelArguments(
    now=datetime.now().strftime("%Y-%m-%d %H:%M")
)

async for response in agent.invoke(messages=user_input, thread=thread, arguments=arguments):
    print(f"{response.content}")
    thread = response.thread

var options = AgentInvokeOptions.builder()
    .withKernelArguments(KernelArguments.builder()
            .withVariable("now", OffsetDateTime.now())
            .build())
    .build();

for (var response : agent.invokeAsync(message, agentThread, options).block()) {
    System.out.println(response.getMessage());
    agentThread = response.getThread();
}

Akhir

Menggabungkan semua langkah, kami sudah memiliki kode lengkap untuk contoh ini. Implementasi lengkap disediakan di bawah ini.

Coba gunakan input yang disarankan ini:

1. Apa nama pengguna saya?
2. Jelaskan repositori.
3. Jelaskan masalah terbaru yang dibuat dalam repositori.
4. Cantumkan 10 masalah teratas yang ditutup dalam seminggu terakhir.
5. Bagaimana masalah ini diberi label?
6. Cantumkan 5 masalah yang terakhir dibuka dengan label "Agents"

using System;
using System.Threading.Tasks;
using Azure.Identity;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents;
using Microsoft.SemanticKernel.ChatCompletion;
using Microsoft.SemanticKernel.Connectors.AzureOpenAI;
using Plugins;

namespace AgentsSample;

public static class Program
{
    public static async Task Main()
    {
        // Load configuration from environment variables or user secrets.
        Settings settings = new();

        Console.WriteLine("Initialize plugins...");
        GitHubSettings githubSettings = settings.GetSettings<GitHubSettings>();
        GitHubPlugin githubPlugin = new(githubSettings);

        Console.WriteLine("Creating kernel...");
        IKernelBuilder builder = Kernel.CreateBuilder();

        builder.AddAzureOpenAIChatCompletion(
            settings.AzureOpenAI.ChatModelDeployment,
            settings.AzureOpenAI.Endpoint,
            new AzureCliCredential());

        builder.Plugins.AddFromObject(githubPlugin);

        Kernel kernel = builder.Build();

        Console.WriteLine("Defining agent...");
        ChatCompletionAgent agent =
            new()
            {
                Name = "SampleAssistantAgent",
                Instructions =
                        """
                        You are an agent designed to query and retrieve information from a single GitHub repository in a read-only manner.
                        You are also able to access the profile of the active user.

                        Use the current date and time to provide up-to-date details or time-sensitive responses.

                        The repository you are querying is a public repository with the following name: {{$repository}}

                        The current date and time is: {{$now}}. 
                        """,
                Kernel = kernel,
                Arguments =
                    new KernelArguments(new AzureOpenAIPromptExecutionSettings() { FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() })
                    {
                        { "repository", "microsoft/semantic-kernel" }
                    }
            };

        Console.WriteLine("Ready!");

        ChatHistoryAgentThread agentThread = new();
        bool isComplete = false;
        do
        {
            Console.WriteLine();
            Console.Write("> ");
            string input = Console.ReadLine();
            if (string.IsNullOrWhiteSpace(input))
            {
                continue;
            }
            if (input.Trim().Equals("EXIT", StringComparison.OrdinalIgnoreCase))
            {
                isComplete = true;
                break;
            }

            var message = new ChatMessageContent(AuthorRole.User, input);

            Console.WriteLine();

            DateTime now = DateTime.Now;
            KernelArguments arguments =
                new()
                {
                    { "now", $"{now.ToShortDateString()} {now.ToShortTimeString()}" }
                };
            await foreach (ChatMessageContent response in agent.InvokeAsync(message, agentThread, options: new() { KernelArguments = arguments }))
            {
                // Display response.
                Console.WriteLine($"{response.Content}");
            }

        } while (!isComplete);
    }
}

import asyncio
import os
import sys
from datetime import datetime

from semantic_kernel.agents import ChatCompletionAgent, ChatHistoryAgentThread
from semantic_kernel.connectors.ai import FunctionChoiceBehavior
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
from semantic_kernel.functions import KernelArguments
from semantic_kernel.kernel import Kernel

# Adjust the sys.path so we can use the GitHubPlugin and GitHubSettings classes
# This is so we can run the code from the samples/learn_resources/agent_docs directory
# If you are running code from your own project, you may not need need to do this.
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))

from plugins.GithubPlugin.github import GitHubPlugin, GitHubSettings  # noqa: E402

async def main():
    kernel = Kernel()

    # Add the AzureChatCompletion AI Service to the Kernel
    service_id = "agent"
    kernel.add_service(AzureChatCompletion(service_id=service_id))

    settings = kernel.get_prompt_execution_settings_from_service_id(service_id=service_id)
    # Configure the function choice behavior to auto invoke kernel functions
    settings.function_choice_behavior = FunctionChoiceBehavior.Auto()

    # Set your GitHub Personal Access Token (PAT) value here
    gh_settings = GitHubSettings(token="")  # nosec
    kernel.add_plugin(plugin=GitHubPlugin(gh_settings), plugin_name="GithubPlugin")

    current_time = datetime.now().isoformat()

    # Create the agent
    agent = ChatCompletionAgent(
        kernel=kernel,
        name="SampleAssistantAgent",
        instructions=f"""
            You are an agent designed to query and retrieve information from a single GitHub repository in a read-only 
            manner.
            You are also able to access the profile of the active user.

            Use the current date and time to provide up-to-date details or time-sensitive responses.

            The repository you are querying is a public repository with the following name: microsoft/semantic-kernel

            The current date and time is: {current_time}. 
            """,
        arguments=KernelArguments(settings=settings),
    )

    thread: ChatHistoryAgentThread = None
    is_complete: bool = False
    while not is_complete:
        user_input = input("User:> ")
        if not user_input:
            continue

        if user_input.lower() == "exit":
            is_complete = True
            break

        arguments = KernelArguments(now=datetime.now().strftime("%Y-%m-%d %H:%M"))

        async for response in agent.invoke(messages=user_input, thread=thread, arguments=arguments):
            print(f"{response.content}")
            thread = response.thread


if __name__ == "__main__":
    asyncio.run(main())

Anda mungkin menemukan kode lengkap, seperti yang ditunjukkan di atas, dalam repositori kami.

import com.microsoft.semantickernel.Kernel;
import com.microsoft.semantickernel.agents.AgentInvokeOptions;
import com.microsoft.semantickernel.agents.AgentThread;
import com.microsoft.semantickernel.agents.chatcompletion.ChatCompletionAgent;
import com.microsoft.semantickernel.agents.chatcompletion.ChatHistoryAgentThread;
import com.microsoft.semantickernel.aiservices.openai.chatcompletion.OpenAIChatCompletion;
import com.microsoft.semantickernel.contextvariables.ContextVariableTypeConverter;
import com.microsoft.semantickernel.functionchoice.FunctionChoiceBehavior;
import com.microsoft.semantickernel.implementation.templateengine.tokenizer.DefaultPromptTemplate;
import com.microsoft.semantickernel.orchestration.InvocationContext;
import com.microsoft.semantickernel.orchestration.PromptExecutionSettings;
import com.microsoft.semantickernel.plugin.KernelPluginFactory;
import com.microsoft.semantickernel.samples.plugins.github.GitHubModel;
import com.microsoft.semantickernel.samples.plugins.github.GitHubPlugin;
import com.microsoft.semantickernel.semanticfunctions.KernelArguments;
import com.microsoft.semantickernel.semanticfunctions.PromptTemplateConfig;
import com.microsoft.semantickernel.services.chatcompletion.AuthorRole;
import com.microsoft.semantickernel.services.chatcompletion.ChatCompletionService;
import com.microsoft.semantickernel.services.chatcompletion.ChatMessageContent;
import com.azure.ai.openai.OpenAIAsyncClient;
import com.azure.ai.openai.OpenAIClientBuilder;
import com.azure.core.credential.AzureKeyCredential;

import java.time.OffsetDateTime;
import java.util.Scanner;

public class CompletionAgent {
    // Azure OpenAI
    private static final String AZURE_OPENAI_API_KEY = System.getenv("AZURE_OPENAI_API_KEY");
    private static final String AZURE_OPENAI_ENDPOINT = System.getenv("AZURE_OPENAI_ENDPOINT");
    private static final String AZURE_CHAT_MODEL_DEPLOYMENT = System.getenv().getOrDefault("AZURE_CHAT_MODEL_DEPLOYMENT", "gpt-4o");

    // GitHub Personal Access Token
    private static final String GITHUB_PAT = System.getenv("GITHUB_PAT");

    public static void main(String[] args) {
        System.out.println("======== ChatCompletion Agent ========");

        OpenAIAsyncClient client = new OpenAIClientBuilder()
                .credential(new AzureKeyCredential(AZURE_OPENAI_API_KEY))
                .endpoint(AZURE_OPENAI_ENDPOINT)
                .buildAsyncClient();

        var githubPlugin = new GitHubPlugin(GITHUB_PAT);

        ChatCompletionService chatCompletion = OpenAIChatCompletion.builder()
                .withModelId(AZURE_CHAT_MODEL_DEPLOYMENT)
                .withOpenAIAsyncClient(client)
                .build();

        Kernel kernel = Kernel.builder()
            .withAIService(ChatCompletionService.class, chatCompletion)
            .withPlugin(KernelPluginFactory.createFromObject(githubPlugin, "GitHubPlugin"))
            .build();

        InvocationContext invocationContext = InvocationContext.builder()
            .withFunctionChoiceBehavior(FunctionChoiceBehavior.auto(true))
            .withContextVariableConverter(new ContextVariableTypeConverter<>(
                    GitHubModel.Issue.class,
                    o -> (GitHubModel.Issue) o,
                    o -> o.toString(),
                    s -> null))
            .build();

        ChatCompletionAgent agent = ChatCompletionAgent.builder()
            .withName("SampleAssistantAgent")
            .withKernel(kernel)
            .withInvocationContext(invocationContext)
            .withTemplate(
                DefaultPromptTemplate.build(
                    PromptTemplateConfig.builder()
                        .withTemplate(
                            """
                            You are an agent designed to query and retrieve information from a single GitHub repository in a read-only manner.
                            You are also able to access the profile of the active user.

                            Use the current date and time to provide up-to-date details or time-sensitive responses.

                            The repository you are querying is a public repository with the following name: {{$repository}}

                            The current date and time is: {{$now}}.
                            """)
                        .build()))
            .withKernelArguments(
                KernelArguments.builder()
                    .withVariable("repository", "microsoft/semantic-kernel-java")
                    .withExecutionSettings(PromptExecutionSettings.builder()
                            .build())
                    .build())
            .build();

        AgentThread agentThread = new ChatHistoryAgentThread();
        boolean isComplete = false;

        Scanner scanner = new Scanner(System.in);

        while (!isComplete) {
            System.out.print("> ");

            String input = scanner.nextLine();
            if (input.isEmpty()) {
                continue;
            }

            if (input.equalsIgnoreCase("EXIT")) {
                isComplete = true;
                break;
            }

            var message = new ChatMessageContent<>(AuthorRole.USER, input);

            var options = AgentInvokeOptions.builder()
                .withKernelArguments(KernelArguments.builder()
                        .withVariable("now", OffsetDateTime.now())
                        .build())
                .build();

            for (var response : agent.invokeAsync(message, agentThread, options).block()) {
                System.out.println(response.getMessage());
                agentThread = response.getThread();
            }
        }
    }
}

Langkah Selanjutnya

Panduan: OpenAIAssistantAgent Penerjemah Kode