操作说明：使用代理群聊天协调代理协作（实验性）
警告
语义内核代理框架是实验性的，仍在开发中，可能会更改。
概述
在此示例中，我们将探讨 如何使用代理群聊 协调两个不同的代理的排序规则，这些代理正在努力查看和重写用户提供的内容。 为每个代理分配一个不同的角色：
- 审阅者：审阅并提供编写 器的方向。
- 编写器：根据 审阅者 输入更新用户内容。
该方法将分步分解为高光编码过程的关键部分。
入门
在继续执行功能编码之前，请确保开发环境已完全设置和配置。
首先创建 控制台 项目。 然后，包括以下包引用，以确保所有必需的依赖项都可用。
若要从命令行添加包依赖项，请使用 dotnet 以下命令：
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
如果在 Visual Studio 中管理 NuGet 包，请确保
Include prerelease已选中。
项目文件 （.csproj） 应包含以下 PackageReference 定义：
<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>
代理框架是实验性的，需要警告抑制。 这可以作为项目文件（.csproj）：
<PropertyGroup>
<NoWarn>$(NoWarn);CA2007;IDE1006;SKEXP0001;SKEXP0110;OPENAI001</NoWarn>
</PropertyGroup>
import asyncio
import os
import copy
import pyperclip # Install via pip
from semantic_kernel.agents import AgentGroupChat, ChatCompletionAgent
from semantic_kernel.agents.strategies.selection.kernel_function_selection_strategy import (
KernelFunctionSelectionStrategy,
)
from semantic_kernel.agents.strategies.termination.kernel_function_termination_strategy import (
KernelFunctionTerminationStrategy,
)
from semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion import AzureChatCompletion
from semantic_kernel.contents.chat_message_content import ChatMessageContent
from semantic_kernel.contents.utils.author_role import AuthorRole
from semantic_kernel.functions.kernel_function_decorator import kernel_function
from semantic_kernel.functions.kernel_function_from_prompt import KernelFunctionFromPrompt
from semantic_kernel.kernel import Kernel
代理当前在 Java 中不可用。
配置
此示例需要配置设置才能连接到远程服务。 需要定义 Open AI 或 Azure Open AI 的设置。
# Open AI
dotnet user-secrets set "OpenAISettings:ApiKey" "<api-key>"
dotnet user-secrets set "OpenAISettings:ChatModel" "gpt-4o"
# Azure Open AI
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"
以下类在所有代理示例中均使用。 请确保将其包含在项目中，以确保适当的功能。 此类充当以下示例的基础组件。
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();
}
}
运行示例代码的正确配置入门的最快方法是在项目的根目录（运行脚本的位置）创建 .env 文件。
在 .env 文件中为 Azure OpenAI 或 OpenAI 配置以下设置：
AZURE_OPENAI_API_KEY="..."
AZURE_OPENAI_ENDPOINT="https://..."
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="..."
AZURE_OPENAI_API_VERSION="..."
OPENAI_API_KEY="sk-..."
OPENAI_ORG_ID=""
OPENAI_CHAT_MODEL_ID=""
配置后，相应的 AI 服务类将选取所需的变量，并在实例化期间使用这些变量。
代理当前在 Java 中不可用。
编码
此示例的编码过程涉及：
最终部分提供了完整的示例代码。 有关完整实现，请参阅该部分。
安装
在创建任何 聊天完成代理之前，必须初始化配置设置、插件和 内核 。
实例化 Settings 上一 配置 部分中引用的类。
Settings settings = new();
代理当前在 Java 中不可用。
现在使用 IChatCompletionService.. 初始化Kernel实例。
IKernelBuilder builder = Kernel.CreateBuilder();
builder.AddAzureOpenAIChatCompletion(
settings.AzureOpenAI.ChatModelDeployment,
settings.AzureOpenAI.Endpoint,
new AzureCliCredential());
Kernel kernel = builder.Build();
初始化内核对象：
kernel = Kernel()
代理当前在 Java 中不可用。
另外，让我们通过克隆创建第二个内核实例，并添加一个插件，该插件将允许重新放置更新的内容放在剪辑板上。
Kernel toolKernel = kernel.Clone();
toolKernel.Plugins.AddFromType<ClipboardAccess>();
tool_kernel = copy.deepcopy(kernel)
tool_kernel.add_plugin(ClipboardAccess(), plugin_name="clipboard")
代理当前在 Java 中不可用。
剪贴板插件可以定义为示例的一部分。
private sealed class ClipboardAccess
{
[KernelFunction]
[Description("Copies the provided content to the clipboard.")]
public static void SetClipboard(string content)
{
if (string.IsNullOrWhiteSpace(content))
{
return;
}
using Process clipProcess = Process.Start(
new ProcessStartInfo
{
FileName = "clip",
RedirectStandardInput = true,
UseShellExecute = false,
});
clipProcess.StandardInput.Write(content);
clipProcess.StandardInput.Close();
}
}
注意：我们利用名为 pyperclip 的 Python 包。 请安装使用 pip。
class ClipboardAccess:
@kernel_function
def set_clipboard(content: str):
if not content.strip():
return
pyperclip.copy(content)
代理当前在 Java 中不可用。
代理定义
让我们将代理名称声明为const代理名称，以便在代理群聊天策略中引用它们：
const string ReviewerName = "Reviewer";
const string WriterName = "Writer";
REVIEWER_NAME = "Reviewer"
WRITER_NAME = "Writer"
代理当前在 Java 中不可用。
定义审阅者代理使用操作说明：聊天完成代理中探索的模式。
在这里，“审阅者”的角色是响应用户输入、向编写器代理提供方向，以及验证编写器代理的结果。
ChatCompletionAgent agentReviewer =
new()
{
Name = ReviewerName,
Instructions =
"""
Your responsiblity is to review and identify how to improve user provided content.
If the user has providing input or direction for content already provided, specify how to address this input.
Never directly perform the correction or provide example.
Once the content has been updated in a subsequent response, you will review the content again until satisfactory.
Always copy satisfactory content to the clipboard using available tools and inform user.
RULES:
- Only identify suggestions that are specific and actionable.
- Verify previous suggestions have been addressed.
- Never repeat previous suggestions.
""",
Kernel = toolKernel,
Arguments =
new KernelArguments(
new AzureOpenAIPromptExecutionSettings()
{
FunctionChoiceBehavior = FunctionChoiceBehavior.Auto()
})
};
agent_reviewer = ChatCompletionAgent(
service_id=REVIEWER_NAME,
kernel=_create_kernel_with_chat_completion(REVIEWER_NAME),
name=REVIEWER_NAME,
instructions="""
Your responsiblity is to review and identify how to improve user provided content.
If the user has providing input or direction for content already provided, specify how to
address this input.
Never directly perform the correction or provide example.
Once the content has been updated in a subsequent response, you will review the content
again until satisfactory.
Always copy satisfactory content to the clipboard using available tools and inform user.
RULES:
- Only identify suggestions that are specific and actionable.
- Verify previous suggestions have been addressed.
- Never repeat previous suggestions.
""",
)
代理当前在 Java 中不可用。
编写器代理类似，但不需要执行设置的规范，因为它未使用插件进行配置。
此处， 编写器 提供了一项单用途任务，按照说明重写内容。
ChatCompletionAgent agentWriter =
new()
{
Name = WriterName,
Instructions =
"""
Your sole responsiblity is to rewrite content according to review suggestions.
- Always apply all review direction.
- Always revise the content in its entirety without explanation.
- Never address the user.
""",
Kernel = kernel,
};
agent_writer = ChatCompletionAgent(
service_id=COPYWRITER_NAME,
kernel=_create_kernel_with_chat_completion(COPYWRITER_NAME),
name=COPYWRITER_NAME,
instructions="""
Your sole responsiblity is to rewrite content according to review suggestions.
- Always apply all review direction.
- Always revise the content in its entirety without explanation.
- Never address the user.
""",
)
代理当前在 Java 中不可用。
聊天定义
定义 代理群组聊天 需要考虑选择 代理 轮次的策略，并确定何时退出 聊天 循环。 对于这两个注意事项，我们将定义 内核提示函数。
第一个推理 代理 选择：
使用 AgentGroupChat.CreatePromptFunctionForStrategy 提供了一种方便的机制，以避免 对消息参数进行 HTML 编码 。
KernelFunction selectionFunction =
AgentGroupChat.CreatePromptFunctionForStrategy(
$$$"""
Examine the provided RESPONSE and choose the next participant.
State only the name of the chosen participant without explanation.
Never choose the participant named in the RESPONSE.
Choose only from these participants:
- {{{ReviewerName}}}
- {{{WriterName}}}
Always follow these rules when choosing the next participant:
- If RESPONSE is user input, it is {{{ReviewerName}}}'s turn.
- If RESPONSE is by {{{ReviewerName}}}, it is {{{WriterName}}}'s turn.
- If RESPONSE is by {{{WriterName}}}, it is {{{ReviewerName}}}'s turn.
RESPONSE:
{{$lastmessage}}
""",
safeParameterNames: "lastmessage");
selection_function = KernelFunctionFromPrompt(
function_name="selection",
prompt=f"""
Determine which participant takes the next turn in a conversation based on the the most recent participant.
State only the name of the participant to take the next turn.
No participant should take more than one turn in a row.
Choose only from these participants:
- {REVIEWER_NAME}
- {COPYWRITER_NAME}
Always follow these rules when selecting the next participant:
- After user input, it is {COPYWRITER_NAME}'s turn.
- After {COPYWRITER_NAME} replies, it is {REVIEWER_NAME}'s turn.
- After {REVIEWER_NAME} provides feedback, it is {COPYWRITER_NAME}'s turn.
History:
{{{{$history}}}}
""",
)
代理当前在 Java 中不可用。
第二个将评估何时退出 聊天 循环：
const string TerminationToken = "yes";
KernelFunction terminationFunction =
AgentGroupChat.CreatePromptFunctionForStrategy(
$$$"""
Examine the RESPONSE and determine whether the content has been deemed satisfactory.
If content is satisfactory, respond with a single word without explanation: {{{TerminationToken}}}.
If specific suggestions are being provided, it is not satisfactory.
If no correction is suggested, it is satisfactory.
RESPONSE:
{{$lastmessage}}
""",
safeParameterNames: "lastmessage");
TERMINATION_KEYWORD = "yes"
termination_function = KernelFunctionFromPrompt(
function_name="termination",
prompt=f"""
Examine the RESPONSE and determine whether the content has been deemed satisfactory.
If content is satisfactory, respond with a single word without explanation: {TERMINATION_KEYWORD}.
If specific suggestions are being provided, it is not satisfactory.
If no correction is suggested, it is satisfactory.
RESPONSE:
{{{{$history}}}}
""",
)
代理当前在 Java 中不可用。
这 两种策略 只需要了解最新的 聊天 消息。 这将减少令牌使用情况，并帮助提高性能：
ChatHistoryTruncationReducer historyReducer = new(1);
**ChatHistoryReducer is coming soon to Python.**
代理当前在 Java 中不可用。
最后，我们已准备好将代理群聊定义中的所有内容汇集在一起。
创建 AgentGroupChat 涉及：
- 在构造函数中包括这两个代理。
- 使用以前定义的
KernelFunction和Kernel实例定义一个KernelFunctionSelectionStrategy。 - 使用以前定义的
KernelFunction和Kernel实例定义一个KernelFunctionTerminationStrategy。
请注意，每个策略负责分析 KernelFunction 结果。
AgentGroupChat chat =
new(agentReviewer, agentWriter)
{
ExecutionSettings = new AgentGroupChatSettings
{
SelectionStrategy =
new KernelFunctionSelectionStrategy(selectionFunction, kernel)
{
// Always start with the editor agent.
InitialAgent = agentReviewer,
// Save tokens by only including the final response
HistoryReducer = historyReducer,
// The prompt variable name for the history argument.
HistoryVariableName = "lastmessage",
// Returns the entire result value as a string.
ResultParser = (result) => result.GetValue<string>() ?? agentReviewer.Name
},
TerminationStrategy =
new KernelFunctionTerminationStrategy(terminationFunction, kernel)
{
// Only evaluate for editor's response
Agents = [agentReviewer],
// Save tokens by only including the final response
HistoryReducer = historyReducer,
// The prompt variable name for the history argument.
HistoryVariableName = "lastmessage",
// Limit total number of turns
MaximumIterations = 12,
// Customer result parser to determine if the response is "yes"
ResultParser = (result) => result.GetValue<string>()?.Contains(TerminationToken, StringComparison.OrdinalIgnoreCase) ?? false
}
}
};
Console.WriteLine("Ready!");
创建 AgentGroupChat 涉及：
- 在构造函数中包括这两个代理。
- 使用以前定义的
KernelFunction和Kernel实例定义一个KernelFunctionSelectionStrategy。 - 使用以前定义的
KernelFunction和Kernel实例定义一个KernelFunctionTerminationStrategy。
请注意，每个策略负责分析 KernelFunction 结果。
chat = AgentGroupChat(
agents=[agent_writer, agent_reviewer],
selection_strategy=KernelFunctionSelectionStrategy(
function=selection_function,
kernel=_create_kernel_with_chat_completion("selection"),
result_parser=lambda result: str(result.value[0]) if result.value is not None else COPYWRITER_NAME,
agent_variable_name="agents",
history_variable_name="history",
),
termination_strategy=KernelFunctionTerminationStrategy(
agents=[agent_reviewer],
function=termination_function,
kernel=_create_kernel_with_chat_completion("termination"),
result_parser=lambda result: TERMINATION_KEYWORD in str(result.value[0]).lower(),
history_variable_name="history",
maximum_iterations=10,
),
)
代理当前在 Java 中不可用。
聊天循环
最后，我们能够协调用户与 代理群聊之间的交互。 首先创建一个空循环。
注意：与其他示例不同，没有管理外部历史记录或 线程 。 代理群聊 在内部管理聊天历史记录。
bool isComplete = false;
do
{
} while (!isComplete);
is_complete: bool = False
while not is_complete:
# operational logic
代理当前在 Java 中不可用。
现在，让我们在上一循环中捕获用户输入。 在这种情况下：
- 将忽略空输入
EXIT术语将表明对话已完成- 该术语
RESET将清除 代理群组聊天 历史记录 - 从开头
@的任何术语都将被视为文件路径，其内容将作为输入提供 - 有效的输入将作为用户消息添加到代理组聊天中。
Console.WriteLine();
Console.Write("> ");
string input = Console.ReadLine();
if (string.IsNullOrWhiteSpace(input))
{
continue;
}
input = input.Trim();
if (input.Equals("EXIT", StringComparison.OrdinalIgnoreCase))
{
isComplete = true;
break;
}
if (input.Equals("RESET", StringComparison.OrdinalIgnoreCase))
{
await chat.ResetAsync();
Console.WriteLine("[Converation has been reset]");
continue;
}
if (input.StartsWith("@", StringComparison.Ordinal) && input.Length > 1)
{
string filePath = input.Substring(1);
try
{
if (!File.Exists(filePath))
{
Console.WriteLine($"Unable to access file: {filePath}");
continue;
}
input = File.ReadAllText(filePath);
}
catch (Exception)
{
Console.WriteLine($"Unable to access file: {filePath}");
continue;
}
}
chat.AddChatMessage(new ChatMessageContent(AuthorRole.User, input));
user_input = input("User:> ")
if not user_input:
continue
if user_input.lower() == "exit":
is_complete = True
break
if user_input.lower() == "reset":
await chat.reset()
print("[Conversation has been reset]")
continue
if user_input.startswith("@") and len(input) > 1:
file_path = input[1:]
try:
if not os.path.exists(file_path):
print(f"Unable to access file: {file_path}")
continue
with open(file_path) as file:
user_input = file.read()
except Exception:
print(f"Unable to access file: {file_path}")
continue
await chat.add_chat_message(ChatMessageContent(role=AuthorRole.USER, content=user_input))
代理当前在 Java 中不可用。
若要初始化 代理 协作以响应用户输入并显示 代理 响应，请调用 代理组聊天;但是，首先请务必从任何以前的调用重置 完成 状态。
注意：正在捕获并显示服务故障，以避免会话循环崩溃。
chat.IsComplete = false;
try
{
await foreach (ChatMessageContent response in chat.InvokeAsync())
{
Console.WriteLine();
Console.WriteLine($"{response.AuthorName.ToUpperInvariant()}:{Environment.NewLine}{response.Content}");
}
}
catch (HttpOperationException exception)
{
Console.WriteLine(exception.Message);
if (exception.InnerException != null)
{
Console.WriteLine(exception.InnerException.Message);
if (exception.InnerException.Data.Count > 0)
{
Console.WriteLine(JsonSerializer.Serialize(exception.InnerException.Data, new JsonSerializerOptions() { WriteIndented = true }));
}
}
}
chat.is_complete = False
async for response in chat.invoke():
print(f"# {response.role} - {response.name or '*'}: '{response.content}'")
if chat.is_complete:
is_complete = True
break
代理当前在 Java 中不可用。
Final
将所有步骤组合在一起，我们提供了此示例的最终代码。 下面提供了完整的实现。
// Copyright (c) Microsoft. All rights reserved.
using System;
using System.ComponentModel;
using System.Diagnostics;
using System.IO;
using System.Text.Json;
using System.Threading.Tasks;
using Azure.Identity;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents;
using Microsoft.SemanticKernel.Agents.Chat;
using Microsoft.SemanticKernel.Agents.History;
using Microsoft.SemanticKernel.ChatCompletion;
using Microsoft.SemanticKernel.Connectors.AzureOpenAI;
namespace AgentsSample;
public static class Program
{
public static async Task Main()
{
// Load configuration from environment variables or user secrets.
Settings settings = new();
Console.WriteLine("Creating kernel...");
IKernelBuilder builder = Kernel.CreateBuilder();
builder.AddAzureOpenAIChatCompletion(
settings.AzureOpenAI.ChatModelDeployment,
settings.AzureOpenAI.Endpoint,
new AzureCliCredential());
Kernel kernel = builder.Build();
Kernel toolKernel = kernel.Clone();
toolKernel.Plugins.AddFromType<ClipboardAccess>();
Console.WriteLine("Defining agents...");
const string ReviewerName = "Reviewer";
const string WriterName = "Writer";
ChatCompletionAgent agentReviewer =
new()
{
Name = ReviewerName,
Instructions =
"""
Your responsiblity is to review and identify how to improve user provided content.
If the user has providing input or direction for content already provided, specify how to address this input.
Never directly perform the correction or provide example.
Once the content has been updated in a subsequent response, you will review the content again until satisfactory.
Always copy satisfactory content to the clipboard using available tools and inform user.
RULES:
- Only identify suggestions that are specific and actionable.
- Verify previous suggestions have been addressed.
- Never repeat previous suggestions.
""",
Kernel = toolKernel,
Arguments = new KernelArguments(new AzureOpenAIPromptExecutionSettings() { FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() })
};
ChatCompletionAgent agentWriter =
new()
{
Name = WriterName,
Instructions =
"""
Your sole responsiblity is to rewrite content according to review suggestions.
- Always apply all review direction.
- Always revise the content in its entirety without explanation.
- Never address the user.
""",
Kernel = kernel,
};
KernelFunction selectionFunction =
AgentGroupChat.CreatePromptFunctionForStrategy(
$$$"""
Examine the provided RESPONSE and choose the next participant.
State only the name of the chosen participant without explanation.
Never choose the participant named in the RESPONSE.
Choose only from these participants:
- {{{ReviewerName}}}
- {{{WriterName}}}
Always follow these rules when choosing the next participant:
- If RESPONSE is user input, it is {{{ReviewerName}}}'s turn.
- If RESPONSE is by {{{ReviewerName}}}, it is {{{WriterName}}}'s turn.
- If RESPONSE is by {{{WriterName}}}, it is {{{ReviewerName}}}'s turn.
RESPONSE:
{{$lastmessage}}
""",
safeParameterNames: "lastmessage");
const string TerminationToken = "yes";
KernelFunction terminationFunction =
AgentGroupChat.CreatePromptFunctionForStrategy(
$$$"""
Examine the RESPONSE and determine whether the content has been deemed satisfactory.
If content is satisfactory, respond with a single word without explanation: {{{TerminationToken}}}.
If specific suggestions are being provided, it is not satisfactory.
If no correction is suggested, it is satisfactory.
RESPONSE:
{{$lastmessage}}
""",
safeParameterNames: "lastmessage");
ChatHistoryTruncationReducer historyReducer = new(1);
AgentGroupChat chat =
new(agentReviewer, agentWriter)
{
ExecutionSettings = new AgentGroupChatSettings
{
SelectionStrategy =
new KernelFunctionSelectionStrategy(selectionFunction, kernel)
{
// Always start with the editor agent.
InitialAgent = agentReviewer,
// Save tokens by only including the final response
HistoryReducer = historyReducer,
// The prompt variable name for the history argument.
HistoryVariableName = "lastmessage",
// Returns the entire result value as a string.
ResultParser = (result) => result.GetValue<string>() ?? agentReviewer.Name
},
TerminationStrategy =
new KernelFunctionTerminationStrategy(terminationFunction, kernel)
{
// Only evaluate for editor's response
Agents = [agentReviewer],
// Save tokens by only including the final response
HistoryReducer = historyReducer,
// The prompt variable name for the history argument.
HistoryVariableName = "lastmessage",
// Limit total number of turns
MaximumIterations = 12,
// Customer result parser to determine if the response is "yes"
ResultParser = (result) => result.GetValue<string>()?.Contains(TerminationToken, StringComparison.OrdinalIgnoreCase) ?? false
}
}
};
Console.WriteLine("Ready!");
bool isComplete = false;
do
{
Console.WriteLine();
Console.Write("> ");
string input = Console.ReadLine();
if (string.IsNullOrWhiteSpace(input))
{
continue;
}
input = input.Trim();
if (input.Equals("EXIT", StringComparison.OrdinalIgnoreCase))
{
isComplete = true;
break;
}
if (input.Equals("RESET", StringComparison.OrdinalIgnoreCase))
{
await chat.ResetAsync();
Console.WriteLine("[Converation has been reset]");
continue;
}
if (input.StartsWith("@", StringComparison.Ordinal) && input.Length > 1)
{
string filePath = input.Substring(1);
try
{
if (!File.Exists(filePath))
{
Console.WriteLine($"Unable to access file: {filePath}");
continue;
}
input = File.ReadAllText(filePath);
}
catch (Exception)
{
Console.WriteLine($"Unable to access file: {filePath}");
continue;
}
}
chat.AddChatMessage(new ChatMessageContent(AuthorRole.User, input));
chat.IsComplete = false;
try
{
await foreach (ChatMessageContent response in chat.InvokeAsync())
{
Console.WriteLine();
Console.WriteLine($"{response.AuthorName.ToUpperInvariant()}:{Environment.NewLine}{response.Content}");
}
}
catch (HttpOperationException exception)
{
Console.WriteLine(exception.Message);
if (exception.InnerException != null)
{
Console.WriteLine(exception.InnerException.Message);
if (exception.InnerException.Data.Count > 0)
{
Console.WriteLine(JsonSerializer.Serialize(exception.InnerException.Data, new JsonSerializerOptions() { WriteIndented = true }));
}
}
}
} while (!isComplete);
}
private sealed class ClipboardAccess
{
[KernelFunction]
[Description("Copies the provided content to the clipboard.")]
public static void SetClipboard(string content)
{
if (string.IsNullOrWhiteSpace(content))
{
return;
}
using Process clipProcess = Process.Start(
new ProcessStartInfo
{
FileName = "clip",
RedirectStandardInput = true,
UseShellExecute = false,
});
clipProcess.StandardInput.Write(content);
clipProcess.StandardInput.Close();
}
}
}
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from semantic_kernel.agents import AgentGroupChat, ChatCompletionAgent
from semantic_kernel.agents.strategies.selection.kernel_function_selection_strategy import (
KernelFunctionSelectionStrategy,
)
from semantic_kernel.agents.strategies.termination.kernel_function_termination_strategy import (
KernelFunctionTerminationStrategy,
)
from semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion import AzureChatCompletion
from semantic_kernel.contents.chat_message_content import ChatMessageContent
from semantic_kernel.contents.utils.author_role import AuthorRole
from semantic_kernel.functions.kernel_function_decorator import kernel_function
from semantic_kernel.functions.kernel_function_from_prompt import KernelFunctionFromPrompt
from semantic_kernel.kernel import Kernel
###################################################################
# The following sample demonstrates how to create a simple, #
# agent group chat that utilizes a Reviewer Chat Completion #
# Agent along with a Writer Chat Completion Agent to #
# complete a user's task. #
###################################################################
class ClipboardAccess:
@kernel_function
def set_clipboard(content: str):
if not content.strip():
return
pyperclip.copy(content)
REVIEWER_NAME = "Reviewer"
COPYWRITER_NAME = "Writer"
def _create_kernel_with_chat_completion(service_id: str) -> Kernel:
kernel = Kernel()
kernel.add_service(AzureChatCompletion(service_id=service_id))
return kernel
async def main():
agent_reviewer = ChatCompletionAgent(
service_id=REVIEWER_NAME,
kernel=_create_kernel_with_chat_completion(REVIEWER_NAME),
name=REVIEWER_NAME,
instructions="""
Your responsiblity is to review and identify how to improve user provided content.
If the user has providing input or direction for content already provided, specify how to
address this input.
Never directly perform the correction or provide example.
Once the content has been updated in a subsequent response, you will review the content
again until satisfactory.
Always copy satisfactory content to the clipboard using available tools and inform user.
RULES:
- Only identify suggestions that are specific and actionable.
- Verify previous suggestions have been addressed.
- Never repeat previous suggestions.
""",
)
agent_writer = ChatCompletionAgent(
service_id=COPYWRITER_NAME,
kernel=_create_kernel_with_chat_completion(COPYWRITER_NAME),
name=COPYWRITER_NAME,
instructions="""
Your sole responsiblity is to rewrite content according to review suggestions.
- Always apply all review direction.
- Always revise the content in its entirety without explanation.
- Never address the user.
""",
)
selection_function = KernelFunctionFromPrompt(
function_name="selection",
prompt=f"""
Determine which participant takes the next turn in a conversation based on the the most recent participant.
State only the name of the participant to take the next turn.
No participant should take more than one turn in a row.
Choose only from these participants:
- {REVIEWER_NAME}
- {COPYWRITER_NAME}
Always follow these rules when selecting the next participant:
- After user input, it is {COPYWRITER_NAME}'s turn.
- After {COPYWRITER_NAME} replies, it is {REVIEWER_NAME}'s turn.
- After {REVIEWER_NAME} provides feedback, it is {COPYWRITER_NAME}'s turn.
History:
{{{{$history}}}}
""",
)
TERMINATION_KEYWORD = "yes"
termination_function = KernelFunctionFromPrompt(
function_name="termination",
prompt=f"""
Examine the RESPONSE and determine whether the content has been deemed satisfactory.
If content is satisfactory, respond with a single word without explanation: {TERMINATION_KEYWORD}.
If specific suggestions are being provided, it is not satisfactory.
If no correction is suggested, it is satisfactory.
RESPONSE:
{{{{$history}}}}
""",
)
chat = AgentGroupChat(
agents=[agent_writer, agent_reviewer],
selection_strategy=KernelFunctionSelectionStrategy(
function=selection_function,
kernel=_create_kernel_with_chat_completion("selection"),
result_parser=lambda result: str(result.value[0]) if result.value is not None else COPYWRITER_NAME,
agent_variable_name="agents",
history_variable_name="history",
),
termination_strategy=KernelFunctionTerminationStrategy(
agents=[agent_reviewer],
function=termination_function,
kernel=_create_kernel_with_chat_completion("termination"),
result_parser=lambda result: TERMINATION_KEYWORD in str(result.value[0]).lower(),
history_variable_name="history",
maximum_iterations=10,
),
)
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
if user_input.lower() == "reset":
await chat.reset()
print("[Conversation has been reset]")
continue
if user_input.startswith("@") and len(input) > 1:
file_path = input[1:]
try:
if not os.path.exists(file_path):
print(f"Unable to access file: {file_path}")
continue
with open(file_path) as file:
user_input = file.read()
except Exception:
print(f"Unable to access file: {file_path}")
continue
await chat.add_chat_message(ChatMessageContent(role=AuthorRole.USER, content=user_input))
async for response in chat.invoke():
print(f"# {response.role} - {response.name or '*'}: '{response.content}'")
if chat.is_complete:
is_complete = True
break
if __name__ == "__main__":
asyncio.run(main())
代理当前在 Java 中不可用。