Начало работы с семантическим ядром

Семантический ядро имеет несколько доступных пакетов NuGet. Однако для большинства сценариев обычно требуется Microsoft.SemanticKernelтолько .

Ее можно установить с помощью следующей команды:

dotnet add package Microsoft.SemanticKernel

Полный список пакетов Nuget см . в статье о поддерживаемых языках.

Инструкции по доступу к пакету SemanticKernel Python доступны здесь. Это так же просто, как:

pip install semantic-kernel

Инструкции по доступу к пакету SemanticKernel Java доступны здесь. Это так же просто, как:

<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>com.microsoft.semantic-kernel</groupId>
            <artifactId>semantickernel-bom</artifactId>
            <version>${sk.version}</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

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

Быстрое начало работы с записными книжками

Если вы являетесь разработчиком Python или C#, вы можете быстро приступить к работе с нашими записными книжками. Эти записные книжки предоставляют пошаговые руководства по использованию семантического ядра для создания агентов ИИ.

Чтобы начать, выполните эти действия:

1. Клонирование репозитория семантического ядра
2. Открытие репозитория в Visual Studio Code
3. Перейдите к _/python/samples/getting_started
4. Откройте 00-get-started.ipynb , чтобы приступить к настройке среды и созданию первого агента ИИ!

Чтобы начать, выполните эти действия:

1. Клонирование репозитория семантического ядра
2. Открытие репозитория в Visual Studio Code
3. Перейдите к _/dotnet/notebooks
4. Откройте 00-get-started.ipynb , чтобы приступить к настройке среды и созданию первого агента ИИ!

// Import packages
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;
using Microsoft.SemanticKernel.Connectors.OpenAI;

// Create a kernel with Azure OpenAI chat completion
var builder = Kernel.CreateBuilder().AddAzureOpenAIChatCompletion(modelId, endpoint, apiKey);

// Add enterprise components
builder.Services.AddLogging(services => services.AddConsole().SetMinimumLevel(LogLevel.Trace));

// Build the kernel
Kernel kernel = builder.Build();
var chatCompletionService = kernel.GetRequiredService<IChatCompletionService>();

// Add a plugin (the LightsPlugin class is defined below)
kernel.Plugins.AddFromType<LightsPlugin>("Lights");

// Enable planning
OpenAIPromptExecutionSettings openAIPromptExecutionSettings = new() 
{
    FunctionChoiceBehavior = FunctionChoiceBehavior.Auto()
};

// Create a history store the conversation
var history = new ChatHistory();

// Initiate a back-and-forth chat
string? userInput;
do {
    // Collect user input
    Console.Write("User > ");
    userInput = Console.ReadLine();

    // Add user input
    history.AddUserMessage(userInput);

    // Get the response from the AI
    var result = await chatCompletionService.GetChatMessageContentAsync(
        history,
        executionSettings: openAIPromptExecutionSettings,
        kernel: kernel);

    // Print the results
    Console.WriteLine("Assistant > " + result);

    // Add the message from the agent to the chat history
    history.AddMessage(result.Role, result.Content ?? string.Empty);
} while (userInput is not null);

import asyncio

from semantic_kernel import Kernel
from semantic_kernel.utils.logging import setup_logging
from semantic_kernel.functions import kernel_function
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
from semantic_kernel.contents.chat_history import ChatHistory
from semantic_kernel.functions.kernel_arguments import KernelArguments

from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.azure_chat_prompt_execution_settings import (
    AzureChatPromptExecutionSettings,
)

async def main():
    # Initialize the kernel
    kernel = Kernel()

    # Add Azure OpenAI chat completion
    chat_completion = AzureChatCompletion(
        deployment_name="your_models_deployment_name",
        api_key="your_api_key",
        base_url="your_base_url",
    )
    kernel.add_service(chat_completion)

    # Set the logging level for  semantic_kernel.kernel to DEBUG.
    setup_logging()
    logging.getLogger("kernel").setLevel(logging.DEBUG)

    # Add a plugin (the LightsPlugin class is defined below)
    kernel.add_plugin(
        LightsPlugin(),
        plugin_name="Lights",
    )

    # Enable planning
    execution_settings = AzureChatPromptExecutionSettings()
    execution_settings.function_call_behavior = FunctionChoiceBehavior.Auto()

    # Create a history of the conversation
    history = ChatHistory()

    # Initiate a back-and-forth chat
    userInput = None
    while True:
        # Collect user input
        userInput = input("User > ")

        # Terminate the loop if the user says "exit"
        if userInput == "exit":
            break

        # Add user input to the history
        history.add_user_message(userInput)

        # Get the response from the AI
        result = await chat_completion.get_chat_message_content(
            chat_history=history,
            settings=execution_settings,
            kernel=kernel,
        )

        # Print the results
        print("Assistant > " + str(result))

        # Add the message from the agent to the chat history
        history.add_message(result)

# Run the main function
if __name__ == "__main__":
    asyncio.run(main())

OpenAIAsyncClient client = new OpenAIClientBuilder()
    .credential(new AzureKeyCredential(AZURE_CLIENT_KEY))
    .endpoint(CLIENT_ENDPOINT)
    .buildAsyncClient();

// Import the LightsPlugin
KernelPlugin lightPlugin = KernelPluginFactory.createFromObject(new LightsPlugin(),
    "LightsPlugin");

// Create your AI service client
ChatCompletionService chatCompletionService = OpenAIChatCompletion.builder()
    .withModelId(MODEL_ID)
    .withOpenAIAsyncClient(client)
    .build();

// Create a kernel with Azure OpenAI chat completion and plugin
Kernel kernel = Kernel.builder()
    .withAIService(ChatCompletionService.class, chatCompletionService)
    .withPlugin(lightPlugin)
    .build();

// Add a converter to the kernel to show it how to serialise LightModel objects into a prompt
ContextVariableTypes
    .addGlobalConverter(
        ContextVariableTypeConverter.builder(LightModel.class)
            .toPromptString(new Gson()::toJson)
            .build());

// Enable planning
InvocationContext invocationContext = new InvocationContext.Builder()
    .withReturnMode(InvocationReturnMode.LAST_MESSAGE_ONLY)
    .withToolCallBehavior(ToolCallBehavior.allowAllKernelFunctions(true))
    .build();

// Create a history to store the conversation
ChatHistory history = new ChatHistory();

// Initiate a back-and-forth chat
Scanner scanner = new Scanner(System.in);
String userInput;
do {
  // Collect user input
  System.out.print("User > ");

  userInput = scanner.nextLine();
  // Add user input
  history.addUserMessage(userInput);

  // Prompt AI for response to users input
  List<ChatMessageContent<?>> results = chatCompletionService
      .getChatMessageContentsAsync(history, kernel, invocationContext)
      .block();

  for (ChatMessageContent<?> result : results) {
    // Print the results
    if (result.getAuthorRole() == AuthorRole.ASSISTANT && result.getContent() != null) {
      System.out.println("Assistant > " + result);
    }
    // Add the message from the agent to the chat history
    history.addMessage(result);
  }
} while (userInput != null && !userInput.isEmpty());

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;
using Microsoft.SemanticKernel.Connectors.OpenAI;

import asyncio

from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
from semantic_kernel.contents.chat_history import ChatHistory
from semantic_kernel.functions.kernel_arguments import KernelArguments

from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.azure_chat_prompt_execution_settings import (
    AzureChatPromptExecutionSettings,
)

import com.microsoft.semantickernel.Kernel;
import com.microsoft.semantickernel.aiservices.openai.chatcompletion.OpenAIChatCompletion;
import com.microsoft.semantickernel.contextvariables.ContextVariableTypeConverter;
import com.microsoft.semantickernel.contextvariables.ContextVariableTypes;
import com.microsoft.semantickernel.orchestration.InvocationContext;
import com.microsoft.semantickernel.orchestration.InvocationReturnMode;
import com.microsoft.semantickernel.orchestration.ToolCallBehavior;
import com.microsoft.semantickernel.plugin.KernelPlugin;
import com.microsoft.semantickernel.plugin.KernelPluginFactory;
import com.microsoft.semantickernel.services.chatcompletion.AuthorRole;
import com.microsoft.semantickernel.services.chatcompletion.ChatCompletionService;
import com.microsoft.semantickernel.services.chatcompletion.ChatHistory;
import com.microsoft.semantickernel.services.chatcompletion.ChatMessageContent;

// Create kernel
var builder = Kernel.CreateBuilder()
builder.AddAzureOpenAIChatCompletion(modelId, endpoint, apiKey);

# Initialize the kernel
kernel = Kernel()

# Add Azure OpenAI chat completion
kernel.add_service(AzureChatCompletion(
    deployment_name="your_models_deployment_name",
    api_key="your_api_key",
    base_url="your_base_url",
))

// Create your AI service client
ChatCompletionService chatCompletionService = OpenAIChatCompletion.builder()
    .withModelId(MODEL_ID)
    .withOpenAIAsyncClient(client)
    .build();

// Create a kernel with Azure OpenAI chat completion and plugin
Kernel kernel = Kernel.builder()
    .withAIService(ChatCompletionService.class, chatCompletionService)
    .withPlugin(lightPlugin)
    .build();

3) Добавление корпоративных служб

Одним из основных преимуществ использования семантического ядра является поддержка служб корпоративного уровня. В этом примере мы добавили службу ведения журнала в ядро для отладки агента ИИ.

builder.Services.AddLogging(services => services.AddConsole().SetMinimumLevel(LogLevel.Trace));

import logging

# Set the logging level for  semantic_kernel.kernel to DEBUG.
logging.basicConfig(
    format="[%(asctime)s - %(name)s:%(lineno)d - %(levelname)s] %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)
logging.getLogger("kernel").setLevel(logging.DEBUG)

После добавления служб мы создадим ядро и получите службу завершения чата для последующего использования.

Kernel kernel = builder.Build();

// Retrieve the chat completion service
var chatCompletionService = kernel.Services.GetRequiredService<IChatCompletionService>();

После настройки ядра мы получите службу завершения чата для последующего использования.

chat_completion : AzureChatCompletion = kernel.get_service(type=ChatCompletionClientBase)

// Create a kernel with Azure OpenAI chat completion and plugin
Kernel kernel = Kernel.builder()
    .withAIService(ChatCompletionService.class, chatCompletionService)
    .withPlugin(lightPlugin)
    .build();

using System.ComponentModel;
using Microsoft.SemanticKernel;

public class LightsPlugin
{
   // Mock data for the lights
   private readonly List<LightModel> lights = new()
   {
      new LightModel { Id = 1, Name = "Table Lamp", IsOn = false },
      new LightModel { Id = 2, Name = "Porch light", IsOn = false },
      new LightModel { Id = 3, Name = "Chandelier", IsOn = true }
   };

   [KernelFunction("get_lights")]
   [Description("Gets a list of lights and their current state")]
   [return: Description("An array of lights")]
   public async Task<List<LightModel>> GetLightsAsync()
   {
      return lights;
   }

   [KernelFunction("change_state")]
   [Description("Changes the state of the light")]
   [return: Description("The updated state of the light; will return null if the light does not exist")]
   public async Task<LightModel?> ChangeStateAsync(int id, bool isOn)
   {
      var light = lights.FirstOrDefault(light => light.Id == id);

      if (light == null)
      {
         return null;
      }

      // Update the light with the new state
      light.IsOn = isOn;

      return light;
   }
}

public class LightModel
{
   [JsonPropertyName("id")]
   public int Id { get; set; }

   [JsonPropertyName("name")]
   public string Name { get; set; }

   [JsonPropertyName("is_on")]
   public bool? IsOn { get; set; }
}

from typing import Annotated
from semantic_kernel.functions import kernel_function

class LightsPlugin:
    lights = [
        {"id": 1, "name": "Table Lamp", "is_on": False},
        {"id": 2, "name": "Porch light", "is_on": False},
        {"id": 3, "name": "Chandelier", "is_on": True},
    ]

    @kernel_function(
        name="get_lights",
        description="Gets a list of lights and their current state",
    )
    def get_state(
        self,
    ) -> Annotated[str, "the output is a string"]:
        """Gets a list of lights and their current state."""
        return self.lights

    @kernel_function(
        name="change_state",
        description="Changes the state of the light",
    )
    def change_state(
        self,
        id: int,
        is_on: bool,
    ) -> Annotated[str, "the output is a string"]:
        """Changes the state of the light."""
        for light in self.lights:
            if light["id"] == id:
                light["is_on"] = is_on
                return light
        return None

public class LightsPlugin {

  // Mock data for the lights
  private final Map<Integer, LightModel> lights = new HashMap<>();

  public LightsPlugin() {
    lights.put(1, new LightModel(1, "Table Lamp", false));
    lights.put(2, new LightModel(2, "Porch light", false));
    lights.put(3, new LightModel(3, "Chandelier", true));
  }

  @DefineKernelFunction(name = "get_lights", description = "Gets a list of lights and their current state")
  public List<LightModel> getLights() {
    System.out.println("Getting lights");
    return new ArrayList<>(lights.values());
  }

  @DefineKernelFunction(name = "change_state", description = "Changes the state of the light")
  public LightModel changeState(
      @KernelFunctionParameter(name = "id", description = "The ID of the light to change") int id,
      @KernelFunctionParameter(name = "isOn", description = "The new state of the light") boolean isOn) {
    System.out.println("Changing light " + id + " " + isOn);
    if (!lights.containsKey(id)) {
      throw new IllegalArgumentException("Light not found");
    }

    lights.get(id).setIsOn(isOn);

    return lights.get(id);
  }
}

// Add the plugin to the kernel
kernel.Plugins.AddFromType<LightsPlugin>("Lights");

# Add the plugin to the kernel
kernel.add_plugin(
    LightsPlugin(),
    plugin_name="Lights",
)

// Import the LightsPlugin
KernelPlugin lightPlugin = KernelPluginFactory.createFromObject(new LightsPlugin(),
    "LightsPlugin");

OpenAIPromptExecutionSettings openAIPromptExecutionSettings = new()
{
    FunctionChoiceBehavior = FunctionChoiceBehavior.Auto()
};

execution_settings = AzureChatPromptExecutionSettings()
execution_settings.function_call_behavior = FunctionChoiceBehavior.Auto()

// Enable planning
InvocationContext invocationContext = new InvocationContext.Builder()
    .withReturnMode(InvocationReturnMode.LAST_MESSAGE_ONLY)
    .withToolCallBehavior(ToolCallBehavior.allowAllKernelFunctions(true))
    .build();

// Create chat history
var history = new ChatHistory();

// Get the response from the AI
var result = await chatCompletionService.GetChatMessageContentAsync(
    history,
    executionSettings: openAIPromptExecutionSettings,
    kernel: kernel
);

# Create a history of the conversation
history = ChatHistory()

# Get the response from the AI
result = (await chat_completion.get_chat_message_contents(
    chat_history=history,
    settings=execution_settings,
    kernel=kernel,
    arguments=KernelArguments(),
))[0]

userInput = scanner.nextLine();
// Add user input
history.addUserMessage(userInput);

// Prompt AI for response to users input
List<ChatMessageContent<?>> results = chatCompletionService
    .getChatMessageContentsAsync(history, kernel, invocationContext)
    .block();