mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Restructure Step1
This commit is contained in:
Roger Barreto
@@ -32,7 +32,6 @@
|
||||
<Folder Name="/Samples/GettingStarted/AgentProviders/">
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||||
<File Path="samples/GettingStarted/AgentProviders/README.md" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_A2A/Agent_With_A2A.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_With_AzureAIAgent.csproj" />
|
||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureFoundryAgent/Agent_With_AzureFoundryAgent.csproj" />
|
||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureFoundryModel/Agent_With_AzureFoundryModel.csproj" />
|
||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureOpenAIChatCompletion/Agent_With_AzureOpenAIChatCompletion.csproj" />
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@@ -44,6 +43,26 @@
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_OpenAIChatCompletion/Agent_With_OpenAIChatCompletion.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_OpenAIResponses/Agent_With_OpenAIResponses.csproj" />
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</Folder>
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||||
<Folder Name="/Samples/GettingStarted/AgentProviders/AzureAIAgents/">
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step01_Basics/Agent_Step01_Basics.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step01_Running/Agent_Step01_Running.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step02_MultiturnConversation/Agent_Step02_MultiturnConversation.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step03.1_UsingFunctionTools/Agent_Step03.1_UsingFunctionTools.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step03.2_UsingFunctionTools_FromOpenAPI/Agent_Step03.2_UsingFunctionTools_FromOpenAPI.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step04_UsingFunctionToolsWithApprovals/Agent_Step04_UsingFunctionToolsWithApprovals.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step05_StructuredOutput/Agent_Step05_StructuredOutput.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step06_PersistedConversations/Agent_Step06_PersistedConversations.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step07_3rdPartyThreadStorage/Agent_Step07_3rdPartyThreadStorage.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step08_Observability/Agent_Step08_Observability.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step09_DependencyInjection/Agent_Step09_DependencyInjection.csproj" />
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||||
<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step10_AsMcpTool/Agent_Step10_AsMcpTool.csproj" />
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<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step11_UsingImages/Agent_Step11_UsingImages.csproj" />
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<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step12_AsFunctionTool/Agent_Step12_AsFunctionTool.csproj" />
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<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step13_Memory/Agent_Step13_Memory.csproj" />
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<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step14_Middleware/Agent_Step14_Middleware.csproj" />
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<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step15_Plugins/Agent_Step15_Plugins.csproj" />
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<Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgent/Agent_Step16_ChatReduction/Agent_Step16_ChatReduction.csproj" />
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</Folder>
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<Folder Name="/Samples/GettingStarted/Agents/">
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<File Path="samples/GettingStarted/Agents/README.md" />
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<Project Path="samples/GettingStarted/Agents/Agent_Step01_Running/Agent_Step01_Running.csproj" />
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@@ -283,8 +302,8 @@
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<Project Path="src/Microsoft.Agents.AI.A2A/Microsoft.Agents.AI.A2A.csproj" />
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<Project Path="src/Microsoft.Agents.AI.Abstractions/Microsoft.Agents.AI.Abstractions.csproj" />
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<Project Path="src/Microsoft.Agents.AI.AGUI/Microsoft.Agents.AI.AGUI.csproj" />
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<Project Path="src/Microsoft.Agents.AI.AzureAI/Microsoft.Agents.AI.AzureAI.csproj" />
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<Project Path="src/Microsoft.Agents.AI.AzureAI.Persistent/Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
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<Project Path="src/Microsoft.Agents.AI.AzureAI/Microsoft.Agents.AI.AzureAI.csproj" />
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<Project Path="src/Microsoft.Agents.AI.CopilotStudio/Microsoft.Agents.AI.CopilotStudio.csproj" />
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<Project Path="src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj" />
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<Project Path="src/Microsoft.Agents.AI.Hosting.A2A.AspNetCore/Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj" />
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+1
-1
@@ -15,7 +15,7 @@
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</ItemGroup>
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||||
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<ItemGroup>
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<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
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<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
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</ItemGroup>
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||||
|
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</Project>
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+21
@@ -0,0 +1,21 @@
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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>net9.0</TargetFramework>
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<Nullable>enable</Nullable>
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<ImplicitUsings>enable</ImplicitUsings>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Azure.AI.OpenAI" />
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<PackageReference Include="Azure.Identity" />
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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
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</ItemGroup>
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||||
|
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<ItemGroup>
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<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
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</ItemGroup>
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|
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</Project>
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+26
@@ -0,0 +1,26 @@
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// Copyright (c) Microsoft. All rights reserved.
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// This sample shows how to create and use a simple AI agent with Azure OpenAI as the backend.
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using Azure.AI.OpenAI;
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using Azure.Identity;
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using Microsoft.Agents.AI;
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using OpenAI;
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var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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AIAgent agent = new AzureOpenAIClient(
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new Uri(endpoint),
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new AzureCliCredential())
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.GetChatClient(deploymentName)
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.CreateAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
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// Invoke the agent and output the text result.
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Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
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// Invoke the agent with streaming support.
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await foreach (var update in agent.RunStreamingAsync("Tell me a joke about a pirate."))
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{
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Console.WriteLine(update);
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}
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+21
@@ -0,0 +1,21 @@
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<Project Sdk="Microsoft.NET.Sdk">
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|
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<PropertyGroup>
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||||
<OutputType>Exe</OutputType>
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||||
<TargetFramework>net9.0</TargetFramework>
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||||
|
||||
<Nullable>enable</Nullable>
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||||
<ImplicitUsings>enable</ImplicitUsings>
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</PropertyGroup>
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||||
|
||||
<ItemGroup>
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<PackageReference Include="Azure.AI.OpenAI" />
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<PackageReference Include="Azure.Identity" />
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||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
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||||
</ItemGroup>
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||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
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||||
</ItemGroup>
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||||
|
||||
</Project>
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+33
@@ -0,0 +1,33 @@
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// Copyright (c) Microsoft. All rights reserved.
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||||
|
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// This sample shows how to create and use a simple AI agent with a multi-turn conversation.
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using Azure.AI.OpenAI;
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using Azure.Identity;
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using Microsoft.Agents.AI;
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using OpenAI;
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var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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AIAgent agent = new AzureOpenAIClient(
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new Uri(endpoint),
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new AzureCliCredential())
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.GetChatClient(deploymentName)
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.CreateAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
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// Invoke the agent with a multi-turn conversation, where the context is preserved in the thread object.
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AgentThread thread = agent.GetNewThread();
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Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", thread));
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Console.WriteLine(await agent.RunAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", thread));
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// Invoke the agent with a multi-turn conversation and streaming, where the context is preserved in the thread object.
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thread = agent.GetNewThread();
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await foreach (var update in agent.RunStreamingAsync("Tell me a joke about a pirate.", thread))
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{
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Console.WriteLine(update);
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}
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await foreach (var update in agent.RunStreamingAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", thread))
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{
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Console.WriteLine(update);
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}
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+21
@@ -0,0 +1,21 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
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+34
@@ -0,0 +1,34 @@
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// Copyright (c) Microsoft. All rights reserved.
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|
||||
// This sample demonstrates how to use a ChatClientAgent with function tools.
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// It shows both non-streaming and streaming agent interactions using menu-related tools.
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||||
|
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using System.ComponentModel;
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using Azure.AI.OpenAI;
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||||
using Azure.Identity;
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||||
using Microsoft.Agents.AI;
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||||
using Microsoft.Extensions.AI;
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||||
using OpenAI;
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||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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|
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[Description("Get the weather for a given location.")]
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static string GetWeather([Description("The location to get the weather for.")] string location)
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=> $"The weather in {location} is cloudy with a high of 15°C.";
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|
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// Create the chat client and agent, and provide the function tool to the agent.
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AIAgent agent = new AzureOpenAIClient(
|
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new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
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||||
.CreateAIAgent(instructions: "You are a helpful assistant", tools: [AIFunctionFactory.Create(GetWeather)]);
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// Non-streaming agent interaction with function tools.
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Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?"));
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|
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// Streaming agent interaction with function tools.
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await foreach (var update in agent.RunStreamingAsync("What is the weather like in Amsterdam?"))
|
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{
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||||
Console.WriteLine(update);
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||||
}
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||||
+28
@@ -0,0 +1,28 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
<PackageReference Include="Microsoft.SemanticKernel.Plugins.OpenApi" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<None Update="OpenAPISpec.json">
|
||||
<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
|
||||
</None>
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+354
@@ -0,0 +1,354 @@
|
||||
{
|
||||
"openapi": "3.0.1",
|
||||
"info": {
|
||||
"title": "Github Versions API",
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"servers": [
|
||||
{
|
||||
"url": "https://api.github.com"
|
||||
}
|
||||
],
|
||||
"components": {
|
||||
"schemas": {
|
||||
"basic-error": {
|
||||
"title": "Basic Error",
|
||||
"description": "Basic Error",
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"message": {
|
||||
"type": "string"
|
||||
},
|
||||
"documentation_url": {
|
||||
"type": "string"
|
||||
},
|
||||
"url": {
|
||||
"type": "string"
|
||||
},
|
||||
"status": {
|
||||
"type": "string"
|
||||
}
|
||||
}
|
||||
},
|
||||
"label": {
|
||||
"title": "Label",
|
||||
"description": "Color-coded labels help you categorize and filter your issues (just like labels in Gmail).",
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"id": {
|
||||
"description": "Unique identifier for the label.",
|
||||
"type": "integer",
|
||||
"format": "int64",
|
||||
"example": 208045946
|
||||
},
|
||||
"node_id": {
|
||||
"type": "string",
|
||||
"example": "MDU6TGFiZWwyMDgwNDU5NDY="
|
||||
},
|
||||
"url": {
|
||||
"description": "URL for the label",
|
||||
"example": "https://api.github.com/repositories/42/labels/bug",
|
||||
"type": "string",
|
||||
"format": "uri"
|
||||
},
|
||||
"name": {
|
||||
"description": "The name of the label.",
|
||||
"example": "bug",
|
||||
"type": "string"
|
||||
},
|
||||
"description": {
|
||||
"description": "Optional description of the label, such as its purpose.",
|
||||
"type": "string",
|
||||
"example": "Something isn't working",
|
||||
"nullable": true
|
||||
},
|
||||
"color": {
|
||||
"description": "6-character hex code, without the leading #, identifying the color",
|
||||
"example": "FFFFFF",
|
||||
"type": "string"
|
||||
},
|
||||
"default": {
|
||||
"description": "Whether this label comes by default in a new repository.",
|
||||
"type": "boolean",
|
||||
"example": true
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"id",
|
||||
"node_id",
|
||||
"url",
|
||||
"name",
|
||||
"description",
|
||||
"color",
|
||||
"default"
|
||||
]
|
||||
},
|
||||
"tag": {
|
||||
"title": "Tag",
|
||||
"description": "Tag",
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"example": "v0.1"
|
||||
},
|
||||
"commit": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"sha": {
|
||||
"type": "string"
|
||||
},
|
||||
"url": {
|
||||
"type": "string",
|
||||
"format": "uri"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"sha",
|
||||
"url"
|
||||
]
|
||||
},
|
||||
"zipball_url": {
|
||||
"type": "string",
|
||||
"format": "uri",
|
||||
"example": "https://github.com/octocat/Hello-World/zipball/v0.1"
|
||||
},
|
||||
"tarball_url": {
|
||||
"type": "string",
|
||||
"format": "uri",
|
||||
"example": "https://github.com/octocat/Hello-World/tarball/v0.1"
|
||||
},
|
||||
"node_id": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"name",
|
||||
"node_id",
|
||||
"commit",
|
||||
"zipball_url",
|
||||
"tarball_url"
|
||||
]
|
||||
}
|
||||
},
|
||||
"examples": {
|
||||
"label-items": {
|
||||
"value": [
|
||||
{
|
||||
"id": 208045946,
|
||||
"node_id": "MDU6TGFiZWwyMDgwNDU5NDY=",
|
||||
"url": "https://api.github.com/repos/octocat/Hello-World/labels/bug",
|
||||
"name": "bug",
|
||||
"description": "Something isn't working",
|
||||
"color": "f29513",
|
||||
"default": true
|
||||
},
|
||||
{
|
||||
"id": 208045947,
|
||||
"node_id": "MDU6TGFiZWwyMDgwNDU5NDc=",
|
||||
"url": "https://api.github.com/repos/octocat/Hello-World/labels/enhancement",
|
||||
"name": "enhancement",
|
||||
"description": "New feature or request",
|
||||
"color": "a2eeef",
|
||||
"default": false
|
||||
}
|
||||
]
|
||||
},
|
||||
"tag-items": {
|
||||
"value": [
|
||||
{
|
||||
"name": "v0.1",
|
||||
"commit": {
|
||||
"sha": "c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc",
|
||||
"url": "https://api.github.com/repos/octocat/Hello-World/commits/c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc"
|
||||
},
|
||||
"zipball_url": "https://github.com/octocat/Hello-World/zipball/v0.1",
|
||||
"tarball_url": "https://github.com/octocat/Hello-World/tarball/v0.1",
|
||||
"node_id": "MDQ6VXNlcjE="
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"parameters": {
|
||||
"owner": {
|
||||
"name": "owner",
|
||||
"description": "The account owner of the repository. The name is not case sensitive.",
|
||||
"in": "path",
|
||||
"required": true,
|
||||
"schema": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"repo": {
|
||||
"name": "repo",
|
||||
"description": "The name of the repository without the `.git` extension. The name is not case sensitive.",
|
||||
"in": "path",
|
||||
"required": true,
|
||||
"schema": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"per-page": {
|
||||
"name": "per_page",
|
||||
"description": "The number of results per page (max 100). For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
|
||||
"in": "query",
|
||||
"schema": {
|
||||
"type": "integer",
|
||||
"default": 30
|
||||
}
|
||||
},
|
||||
"page": {
|
||||
"name": "page",
|
||||
"description": "The page number of the results to fetch. For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
|
||||
"in": "query",
|
||||
"schema": {
|
||||
"type": "integer",
|
||||
"default": 1
|
||||
}
|
||||
}
|
||||
},
|
||||
"responses": {
|
||||
"not_found": {
|
||||
"description": "Resource not found",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"$ref": "#/components/schemas/basic-error"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"headers": {
|
||||
"link": {
|
||||
"example": "<https://api.github.com/resource?page=2>; rel=\"next\", <https://api.github.com/resource?page=5>; rel=\"last\"",
|
||||
"schema": {
|
||||
"type": "string"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"paths": {
|
||||
"/repos/{owner}/{repo}/tags": {
|
||||
"get": {
|
||||
"summary": "List repository tags",
|
||||
"description": "",
|
||||
"tags": [
|
||||
"repos"
|
||||
],
|
||||
"operationId": "repos/list-tags",
|
||||
"externalDocs": {
|
||||
"description": "API method documentation",
|
||||
"url": "https://docs.github.com/rest/repos/repos#list-repository-tags"
|
||||
},
|
||||
"parameters": [
|
||||
{
|
||||
"$ref": "#/components/parameters/owner"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/repo"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/per-page"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/page"
|
||||
}
|
||||
],
|
||||
"responses": {
|
||||
"200": {
|
||||
"description": "Response",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/tag"
|
||||
}
|
||||
},
|
||||
"examples": {
|
||||
"default": {
|
||||
"$ref": "#/components/examples/tag-items"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"headers": {
|
||||
"Link": {
|
||||
"$ref": "#/components/headers/link"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"x-github": {
|
||||
"githubCloudOnly": false,
|
||||
"enabledForGitHubApps": true,
|
||||
"category": "repos",
|
||||
"subcategory": "repos"
|
||||
}
|
||||
}
|
||||
},
|
||||
"/repos/{owner}/{repo}/labels": {
|
||||
"get": {
|
||||
"summary": "List labels for a repository",
|
||||
"description": "Lists all labels for a repository.",
|
||||
"tags": [
|
||||
"issues"
|
||||
],
|
||||
"operationId": "issues/list-labels-for-repo",
|
||||
"externalDocs": {
|
||||
"description": "API method documentation",
|
||||
"url": "https://docs.github.com/rest/issues/labels#list-labels-for-a-repository"
|
||||
},
|
||||
"parameters": [
|
||||
{
|
||||
"$ref": "#/components/parameters/owner"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/repo"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/per-page"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/page"
|
||||
}
|
||||
],
|
||||
"responses": {
|
||||
"200": {
|
||||
"description": "Response",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/label"
|
||||
}
|
||||
},
|
||||
"examples": {
|
||||
"default": {
|
||||
"$ref": "#/components/examples/label-items"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"headers": {
|
||||
"Link": {
|
||||
"$ref": "#/components/headers/link"
|
||||
}
|
||||
}
|
||||
},
|
||||
"404": {
|
||||
"$ref": "#/components/responses/not_found"
|
||||
}
|
||||
},
|
||||
"x-github": {
|
||||
"githubCloudOnly": false,
|
||||
"enabledForGitHubApps": true,
|
||||
"category": "issues",
|
||||
"subcategory": "labels"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
+33
@@ -0,0 +1,33 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample demonstrates how to use a ChatClientAgent with function tools provided via an OpenAPI spec.
|
||||
// It uses functionality from Semantic Kernel to parse the OpenAPI spec and create function tools to use with the Agent Framework Agent.
|
||||
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using Microsoft.SemanticKernel;
|
||||
using Microsoft.SemanticKernel.Plugins.OpenApi;
|
||||
using OpenAI;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
// Load the OpenAPI Spec from a file.
|
||||
KernelPlugin plugin = await OpenApiKernelPluginFactory.CreateFromOpenApiAsync("github", "OpenAPISpec.json");
|
||||
|
||||
// Convert the Semantic Kernel plugin to Agent Framework function tools.
|
||||
// This requires a dummy Kernel instance, since KernelFunctions cannot execute without one.
|
||||
Kernel kernel = new();
|
||||
List<AITool> tools = plugin.Select(x => x.WithKernel(kernel)).Cast<AITool>().ToList();
|
||||
|
||||
// Create the chat client and agent, and provide the OpenAPI function tools to the agent.
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(instructions: "You are a helpful assistant", tools: tools);
|
||||
|
||||
// Run the agent with the OpenAPI function tools.
|
||||
Console.WriteLine(await agent.RunAsync("Please list the names, colors and descriptions of all the labels available in the microsoft/agent-framework repository on github."));
|
||||
+21
@@ -0,0 +1,21 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+66
@@ -0,0 +1,66 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample demonstrates how to use a ChatClientAgent with function tools that require a human in the loop for approvals.
|
||||
// It shows both non-streaming and streaming agent interactions using menu-related tools.
|
||||
// If the agent is hosted in a service, with a remote user, combine this sample with the Persisted Conversations sample to persist the chat history
|
||||
// while the agent is waiting for user input.
|
||||
|
||||
using System.ComponentModel;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using OpenAI;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
// Create a sample function tool that the agent can use.
|
||||
[Description("Get the weather for a given location.")]
|
||||
static string GetWeather([Description("The location to get the weather for.")] string location)
|
||||
=> $"The weather in {location} is cloudy with a high of 15°C.";
|
||||
|
||||
// Create the chat client and agent.
|
||||
// Note that we are wrapping the function tool with ApprovalRequiredAIFunction to require user approval before invoking it.
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(instructions: "You are a helpful assistant", tools: [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(GetWeather))]);
|
||||
|
||||
// Call the agent and check if there are any user input requests to handle.
|
||||
AgentThread thread = agent.GetNewThread();
|
||||
var response = await agent.RunAsync("What is the weather like in Amsterdam?", thread);
|
||||
var userInputRequests = response.UserInputRequests.ToList();
|
||||
|
||||
// For streaming use:
|
||||
// var updates = await agent.RunStreamingAsync("What is the weather like in Amsterdam?", thread).ToListAsync();
|
||||
// userInputRequests = updates.SelectMany(x => x.UserInputRequests).ToList();
|
||||
|
||||
while (userInputRequests.Count > 0)
|
||||
{
|
||||
// Ask the user to approve each function call request.
|
||||
// For simplicity, we are assuming here that only function approval requests are being made.
|
||||
var userInputResponses = userInputRequests
|
||||
.OfType<FunctionApprovalRequestContent>()
|
||||
.Select(functionApprovalRequest =>
|
||||
{
|
||||
Console.WriteLine($"The agent would like to invoke the following function, please reply Y to approve: Name {functionApprovalRequest.FunctionCall.Name}");
|
||||
return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false)]);
|
||||
})
|
||||
.ToList();
|
||||
|
||||
// Pass the user input responses back to the agent for further processing.
|
||||
response = await agent.RunAsync(userInputResponses, thread);
|
||||
|
||||
userInputRequests = response.UserInputRequests.ToList();
|
||||
|
||||
// For streaming use:
|
||||
// updates = await agent.RunStreamingAsync(userInputResponses, thread).ToListAsync();
|
||||
// userInputRequests = updates.SelectMany(x => x.UserInputRequests).ToList();
|
||||
}
|
||||
|
||||
Console.WriteLine($"\nAgent: {response}");
|
||||
|
||||
// For streaming use:
|
||||
// Console.WriteLine($"\nAgent: {updates.ToAgentRunResponse()}");
|
||||
+21
@@ -0,0 +1,21 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+74
@@ -0,0 +1,74 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to configure ChatClientAgent to produce structured output.
|
||||
|
||||
using System.ComponentModel;
|
||||
using System.Text.Json;
|
||||
using System.Text.Json.Serialization;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using OpenAI;
|
||||
using OpenAI.Chat;
|
||||
using SampleApp;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
// Create chat client to be used by chat client agents.
|
||||
ChatClient chatClient = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName);
|
||||
|
||||
// Create the ChatClientAgent with the specified name and instructions.
|
||||
ChatClientAgent agent = chatClient.CreateAIAgent(new ChatClientAgentOptions(name: "HelpfulAssistant", instructions: "You are a helpful assistant."));
|
||||
|
||||
// Set PersonInfo as the type parameter of RunAsync method to specify the expected structured output from the agent and invoke the agent with some unstructured input.
|
||||
AgentRunResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
|
||||
|
||||
// Access the structured output via the Result property of the agent response.
|
||||
Console.WriteLine("Assistant Output:");
|
||||
Console.WriteLine($"Name: {response.Result.Name}");
|
||||
Console.WriteLine($"Age: {response.Result.Age}");
|
||||
Console.WriteLine($"Occupation: {response.Result.Occupation}");
|
||||
|
||||
// Create the ChatClientAgent with the specified name, instructions, and expected structured output the agent should produce.
|
||||
ChatClientAgent agentWithPersonInfo = chatClient.CreateAIAgent(new ChatClientAgentOptions(name: "HelpfulAssistant", instructions: "You are a helpful assistant.")
|
||||
{
|
||||
ChatOptions = new()
|
||||
{
|
||||
ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
|
||||
}
|
||||
});
|
||||
|
||||
// Invoke the agent with some unstructured input while streaming, to extract the structured information from.
|
||||
var updates = agentWithPersonInfo.RunStreamingAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");
|
||||
|
||||
// Assemble all the parts of the streamed output, since we can only deserialize once we have the full json,
|
||||
// then deserialize the response into the PersonInfo class.
|
||||
PersonInfo personInfo = (await updates.ToAgentRunResponseAsync()).Deserialize<PersonInfo>(JsonSerializerOptions.Web);
|
||||
|
||||
Console.WriteLine("Assistant Output:");
|
||||
Console.WriteLine($"Name: {personInfo.Name}");
|
||||
Console.WriteLine($"Age: {personInfo.Age}");
|
||||
Console.WriteLine($"Occupation: {personInfo.Occupation}");
|
||||
|
||||
namespace SampleApp
|
||||
{
|
||||
/// <summary>
|
||||
/// Represents information about a person, including their name, age, and occupation, matched to the JSON schema used in the agent.
|
||||
/// </summary>
|
||||
[Description("Information about a person including their name, age, and occupation")]
|
||||
public class PersonInfo
|
||||
{
|
||||
[JsonPropertyName("name")]
|
||||
public string? Name { get; set; }
|
||||
|
||||
[JsonPropertyName("age")]
|
||||
public int? Age { get; set; }
|
||||
|
||||
[JsonPropertyName("occupation")]
|
||||
public string? Occupation { get; set; }
|
||||
}
|
||||
}
|
||||
+21
@@ -0,0 +1,21 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+41
@@ -0,0 +1,41 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to create and use a simple AI agent with a conversation that can be persisted to disk.
|
||||
|
||||
using System.Text.Json;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using OpenAI;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
// Create the agent
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
|
||||
|
||||
// Start a new thread for the agent conversation.
|
||||
AgentThread thread = agent.GetNewThread();
|
||||
|
||||
// Run the agent with a new thread.
|
||||
Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", thread));
|
||||
|
||||
// Serialize the thread state to a JsonElement, so it can be stored for later use.
|
||||
JsonElement serializedThread = thread.Serialize();
|
||||
|
||||
// Save the serialized thread to a temporary file (for demonstration purposes).
|
||||
string tempFilePath = Path.GetTempFileName();
|
||||
await File.WriteAllTextAsync(tempFilePath, JsonSerializer.Serialize(serializedThread));
|
||||
|
||||
// Load the serialized thread from the temporary file (for demonstration purposes).
|
||||
JsonElement reloadedSerializedThread = JsonSerializer.Deserialize<JsonElement>(await File.ReadAllTextAsync(tempFilePath));
|
||||
|
||||
// Deserialize the thread state after loading from storage.
|
||||
AgentThread resumedThread = agent.DeserializeThread(reloadedSerializedThread);
|
||||
|
||||
// Run the agent again with the resumed thread.
|
||||
Console.WriteLine(await agent.RunAsync("Now tell the same joke in the voice of a pirate, and add some emojis to the joke.", resumedThread));
|
||||
+23
@@ -0,0 +1,23 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
<PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" />
|
||||
<PackageReference Include="System.Linq.Async" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+151
@@ -0,0 +1,151 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
#pragma warning disable CA1869 // Cache and reuse 'JsonSerializerOptions' instances
|
||||
|
||||
// This sample shows how to create and use a simple AI agent with a conversation that can be persisted to disk.
|
||||
|
||||
using System.Text.Json;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using Microsoft.Extensions.VectorData;
|
||||
using Microsoft.SemanticKernel.Connectors.InMemory;
|
||||
using OpenAI;
|
||||
using SampleApp;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
// Create a vector store to store the chat messages in.
|
||||
// Replace this with a vector store implementation of your choice if you want to persist the chat history to disk.
|
||||
VectorStore vectorStore = new InMemoryVectorStore();
|
||||
|
||||
// Create the agent
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(new ChatClientAgentOptions
|
||||
{
|
||||
Instructions = "You are good at telling jokes.",
|
||||
Name = "Joker",
|
||||
ChatMessageStoreFactory = ctx =>
|
||||
{
|
||||
// Create a new chat message store for this agent that stores the messages in a vector store.
|
||||
// Each thread must get its own copy of the VectorChatMessageStore, since the store
|
||||
// also contains the id that the thread is stored under.
|
||||
return new VectorChatMessageStore(vectorStore, ctx.SerializedState, ctx.JsonSerializerOptions);
|
||||
}
|
||||
});
|
||||
|
||||
// Start a new thread for the agent conversation.
|
||||
AgentThread thread = agent.GetNewThread();
|
||||
|
||||
// Run the agent with the thread that stores conversation history in the vector store.
|
||||
Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", thread));
|
||||
|
||||
// Serialize the thread state, so it can be stored for later use.
|
||||
// Since the chat history is stored in the vector store, the serialized thread
|
||||
// only contains the guid that the messages are stored under in the vector store.
|
||||
JsonElement serializedThread = thread.Serialize();
|
||||
|
||||
Console.WriteLine("\n--- Serialized thread ---\n");
|
||||
Console.WriteLine(JsonSerializer.Serialize(serializedThread, new JsonSerializerOptions { WriteIndented = true }));
|
||||
|
||||
// The serialized thread can now be saved to a database, file, or any other storage mechanism
|
||||
// and loaded again later.
|
||||
|
||||
// Deserialize the thread state after loading from storage.
|
||||
AgentThread resumedThread = agent.DeserializeThread(serializedThread);
|
||||
|
||||
// Run the agent with the thread that stores conversation history in the vector store a second time.
|
||||
Console.WriteLine(await agent.RunAsync("Now tell the same joke in the voice of a pirate, and add some emojis to the joke.", resumedThread));
|
||||
|
||||
// We can access the VectorChatMessageStore via the thread's GetService method if we need to read the key under which threads are stored.
|
||||
var messageStore = resumedThread.GetService<VectorChatMessageStore>()!;
|
||||
Console.WriteLine($"\nThread is stored in vector store under key: {messageStore.ThreadDbKey}");
|
||||
|
||||
namespace SampleApp
|
||||
{
|
||||
/// <summary>
|
||||
/// A sample implementation of <see cref="ChatMessageStore"/> that stores chat messages in a vector store.
|
||||
/// </summary>
|
||||
internal sealed class VectorChatMessageStore : ChatMessageStore
|
||||
{
|
||||
private readonly VectorStore _vectorStore;
|
||||
|
||||
public VectorChatMessageStore(VectorStore vectorStore, JsonElement serializedStoreState, JsonSerializerOptions? jsonSerializerOptions = null)
|
||||
{
|
||||
this._vectorStore = vectorStore ?? throw new ArgumentNullException(nameof(vectorStore));
|
||||
|
||||
if (serializedStoreState.ValueKind is JsonValueKind.String)
|
||||
{
|
||||
// Here we can deserialize the thread id so that we can access the same messages as before the suspension.
|
||||
this.ThreadDbKey = serializedStoreState.Deserialize<string>();
|
||||
}
|
||||
}
|
||||
|
||||
public string? ThreadDbKey { get; private set; }
|
||||
|
||||
public override async Task AddMessagesAsync(IEnumerable<ChatMessage> messages, CancellationToken cancellationToken = default)
|
||||
{
|
||||
this.ThreadDbKey ??= Guid.NewGuid().ToString("N");
|
||||
|
||||
var collection = this._vectorStore.GetCollection<string, ChatHistoryItem>("ChatHistory");
|
||||
await collection.EnsureCollectionExistsAsync(cancellationToken);
|
||||
|
||||
await collection.UpsertAsync(messages.Select(x => new ChatHistoryItem()
|
||||
{
|
||||
Key = this.ThreadDbKey + x.MessageId,
|
||||
Timestamp = DateTimeOffset.UtcNow,
|
||||
ThreadId = this.ThreadDbKey,
|
||||
SerializedMessage = JsonSerializer.Serialize(x),
|
||||
MessageText = x.Text
|
||||
}), cancellationToken);
|
||||
}
|
||||
|
||||
public override async Task<IEnumerable<ChatMessage>> GetMessagesAsync(CancellationToken cancellationToken = default)
|
||||
{
|
||||
var collection = this._vectorStore.GetCollection<string, ChatHistoryItem>("ChatHistory");
|
||||
await collection.EnsureCollectionExistsAsync(cancellationToken);
|
||||
|
||||
var records = await collection
|
||||
.GetAsync(
|
||||
x => x.ThreadId == this.ThreadDbKey, 10,
|
||||
new() { OrderBy = x => x.Descending(y => y.Timestamp) },
|
||||
cancellationToken)
|
||||
.ToListAsync(cancellationToken);
|
||||
|
||||
var messages = records.ConvertAll(x => JsonSerializer.Deserialize<ChatMessage>(x.SerializedMessage!)!)
|
||||
;
|
||||
messages.Reverse();
|
||||
return messages;
|
||||
}
|
||||
|
||||
public override JsonElement Serialize(JsonSerializerOptions? jsonSerializerOptions = null) =>
|
||||
// We have to serialize the thread id, so that on deserialization we can retrieve the messages using the same thread id.
|
||||
JsonSerializer.SerializeToElement(this.ThreadDbKey);
|
||||
|
||||
/// <summary>
|
||||
/// The data structure used to store chat history items in the vector store.
|
||||
/// </summary>
|
||||
private sealed class ChatHistoryItem
|
||||
{
|
||||
[VectorStoreKey]
|
||||
public string? Key { get; set; }
|
||||
|
||||
[VectorStoreData]
|
||||
public string? ThreadId { get; set; }
|
||||
|
||||
[VectorStoreData]
|
||||
public DateTimeOffset? Timestamp { get; set; }
|
||||
|
||||
[VectorStoreData]
|
||||
public string? SerializedMessage { get; set; }
|
||||
|
||||
[VectorStoreData]
|
||||
public string? MessageText { get; set; }
|
||||
}
|
||||
}
|
||||
}
|
||||
+24
@@ -0,0 +1,24 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Azure.Monitor.OpenTelemetry.Exporter" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
<PackageReference Include="OpenTelemetry" />
|
||||
<PackageReference Include="OpenTelemetry.Exporter.Console" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+44
@@ -0,0 +1,44 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to create and use a simple AI agent with Azure OpenAI as the backend that logs telemetry using OpenTelemetry.
|
||||
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Azure.Monitor.OpenTelemetry.Exporter;
|
||||
using Microsoft.Agents.AI;
|
||||
using OpenAI;
|
||||
using OpenTelemetry;
|
||||
using OpenTelemetry.Trace;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
var applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
|
||||
|
||||
// Create TracerProvider with console exporter
|
||||
// This will output the telemetry data to the console.
|
||||
string sourceName = Guid.NewGuid().ToString("N");
|
||||
var tracerProviderBuilder = Sdk.CreateTracerProviderBuilder()
|
||||
.AddSource(sourceName)
|
||||
.AddConsoleExporter();
|
||||
if (!string.IsNullOrWhiteSpace(applicationInsightsConnectionString))
|
||||
{
|
||||
tracerProviderBuilder.AddAzureMonitorTraceExporter(options => options.ConnectionString = applicationInsightsConnectionString);
|
||||
}
|
||||
using var tracerProvider = tracerProviderBuilder.Build();
|
||||
|
||||
// Create the agent, and enable OpenTelemetry instrumentation.
|
||||
AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(instructions: "You are good at telling jokes.", name: "Joker")
|
||||
.AsBuilder()
|
||||
.UseOpenTelemetry(sourceName: sourceName)
|
||||
.Build();
|
||||
|
||||
// Invoke the agent and output the text result.
|
||||
Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
|
||||
|
||||
// Invoke the agent with streaming support.
|
||||
await foreach (var update in agent.RunStreamingAsync("Tell me a joke about a pirate."))
|
||||
{
|
||||
Console.WriteLine(update);
|
||||
}
|
||||
+22
@@ -0,0 +1,22 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
<PackageReference Include="Microsoft.Extensions.Hosting" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+86
@@ -0,0 +1,86 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
#pragma warning disable CA1812
|
||||
|
||||
// This sample shows how to use dependency injection to register an AIAgent and use it from a hosted service with a user input chat loop.
|
||||
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using Microsoft.Extensions.DependencyInjection;
|
||||
using Microsoft.Extensions.Hosting;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
// Create a host builder that we will register services with and then run.
|
||||
HostApplicationBuilder builder = Host.CreateApplicationBuilder(args);
|
||||
|
||||
// Add agent options to the service collection.
|
||||
builder.Services.AddSingleton(
|
||||
new ChatClientAgentOptions(instructions: "You are good at telling jokes.", name: "Joker"));
|
||||
|
||||
// Add a chat client to the service collection.
|
||||
builder.Services.AddKeyedChatClient("AzureOpenAI", (sp) => new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.AsIChatClient());
|
||||
|
||||
// Add the AI agent to the service collection.
|
||||
builder.Services.AddSingleton<AIAgent>((sp) => new ChatClientAgent(
|
||||
chatClient: sp.GetRequiredKeyedService<IChatClient>("AzureOpenAI"),
|
||||
options: sp.GetRequiredService<ChatClientAgentOptions>()));
|
||||
|
||||
// Add a sample service that will use the agent to respond to user input.
|
||||
builder.Services.AddHostedService<SampleService>();
|
||||
|
||||
// Build and run the host.
|
||||
using IHost host = builder.Build();
|
||||
await host.RunAsync().ConfigureAwait(false);
|
||||
|
||||
/// <summary>
|
||||
/// A sample service that uses an AI agent to respond to user input.
|
||||
/// </summary>
|
||||
internal sealed class SampleService(AIAgent agent, IHostApplicationLifetime appLifetime) : IHostedService
|
||||
{
|
||||
private AgentThread? _thread;
|
||||
|
||||
public async Task StartAsync(CancellationToken cancellationToken)
|
||||
{
|
||||
// Create a thread that will be used for the entirety of the service lifetime so that the user can ask follow up questions.
|
||||
this._thread = agent.GetNewThread();
|
||||
_ = this.RunAsync(appLifetime.ApplicationStopping);
|
||||
}
|
||||
|
||||
public async Task RunAsync(CancellationToken cancellationToken)
|
||||
{
|
||||
// Delay a little to allow the service to finish starting.
|
||||
await Task.Delay(100, cancellationToken);
|
||||
|
||||
while (!cancellationToken.IsCancellationRequested)
|
||||
{
|
||||
Console.WriteLine("\nAgent: Ask me to tell you a joke about a specific topic. To exit just press Ctrl+C or enter without any input.\n");
|
||||
Console.Write("> ");
|
||||
var input = Console.ReadLine();
|
||||
|
||||
// If the user enters no input, signal the application to shut down.
|
||||
if (string.IsNullOrWhiteSpace(input))
|
||||
{
|
||||
appLifetime.StopApplication();
|
||||
break;
|
||||
}
|
||||
|
||||
// Stream the output to the console as it is generated.
|
||||
await foreach (var update in agent.RunStreamingAsync(input, this._thread, cancellationToken: cancellationToken))
|
||||
{
|
||||
Console.Write(update);
|
||||
}
|
||||
|
||||
Console.WriteLine();
|
||||
}
|
||||
}
|
||||
|
||||
public Task StopAsync(CancellationToken cancellationToken) => Task.CompletedTask;
|
||||
}
|
||||
+24
@@ -0,0 +1,24 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
<UserSecretsId>3afc9b74-af74-4d8e-ae96-fa1c511d11ac</UserSecretsId>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.Agents.Persistent" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.Hosting" />
|
||||
<PackageReference Include="ModelContextProtocol" />
|
||||
<PackageReference Include="System.Net.ServerSentEvents" VersionOverride="10.0.0-rc.2.25502.107" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+38
@@ -0,0 +1,38 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to expose an AI agent as an MCP tool.
|
||||
|
||||
using Azure.AI.Agents.Persistent;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.DependencyInjection;
|
||||
using Microsoft.Extensions.Hosting;
|
||||
using ModelContextProtocol.Server;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
var persistentAgentsClient = new PersistentAgentsClient(endpoint, new AzureCliCredential());
|
||||
|
||||
// Create a server side persistent agent
|
||||
var agentMetadata = await persistentAgentsClient.Administration.CreateAgentAsync(
|
||||
model: deploymentName,
|
||||
instructions: "You are good at telling jokes, and you always start each joke with 'Aye aye, captain!'.",
|
||||
name: "Joker",
|
||||
description: "An agent that tells jokes.");
|
||||
|
||||
// Retrieve the server side persistent agent as an AIAgent.
|
||||
AIAgent agent = await persistentAgentsClient.GetAIAgentAsync(agentMetadata.Value.Id);
|
||||
|
||||
// Convert the agent to an AIFunction and then to an MCP tool.
|
||||
// The agent name and description will be used as the mcp tool name and description.
|
||||
McpServerTool tool = McpServerTool.Create(agent.AsAIFunction());
|
||||
|
||||
// Register the MCP server with StdIO transport and expose the tool via the server.
|
||||
HostApplicationBuilder builder = Host.CreateEmptyApplicationBuilder(settings: null);
|
||||
builder.Services
|
||||
.AddMcpServer()
|
||||
.WithStdioServerTransport()
|
||||
.WithTools([tool]);
|
||||
|
||||
await builder.Build().RunAsync();
|
||||
+29
@@ -0,0 +1,29 @@
|
||||
This sample demonstrates how to expose an existing AI agent as an MCP tool.
|
||||
|
||||
## Run the sample
|
||||
|
||||
To run the sample, please use one of the following MCP clients: https://modelcontextprotocol.io/clients
|
||||
|
||||
Alternatively, use the QuickstartClient sample from this repository: https://github.com/modelcontextprotocol/csharp-sdk/tree/main/samples/QuickstartClient
|
||||
|
||||
## Run the sample using MCP Inspector
|
||||
|
||||
To use the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector), follow these steps:
|
||||
|
||||
1. Open a terminal in the Agent_Step10_AsMcpTool project directory.
|
||||
1. Run the `npx @modelcontextprotocol/inspector dotnet run` command to start the MCP Inspector. Make sure you have [node.js](https://nodejs.org/en/download/) and npm installed.
|
||||
```bash
|
||||
npx @modelcontextprotocol/inspector dotnet run
|
||||
```
|
||||
1. When the inspector is running, it will display a URL in the terminal, like this:
|
||||
```
|
||||
MCP Inspector is up and running at http://127.0.0.1:6274
|
||||
```
|
||||
1. Open a web browser and navigate to the URL displayed in the terminal. If not opened automatically, this will open the MCP Inspector interface.
|
||||
1. In the MCP Inspector interface, add the following environment variables to allow your MCP server to access Azure AI Foundry Project to create and run the agent:
|
||||
- AZURE_FOUNDRY_PROJECT_ENDPOINT = https://your-resource.openai.azure.com/ # Replace with your Azure AI Foundry Project endpoint
|
||||
- AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME = gpt-4o-mini # Replace with your model deployment name
|
||||
1. Find and click the `Connect` button in the MCP Inspector interface to connect to the MCP server.
|
||||
1. As soon as the connection is established, open the `Tools` tab in the MCP Inspector interface and select the `Joker` tool from the list.
|
||||
1. Specify your prompt as a value for the `query` argument, for example: `Tell me a joke about a pirate` and click the `Run Tool` button to run the tool.
|
||||
1. The agent will process the request and return a response in accordance with the provided instructions that instruct it to always start each joke with 'Aye aye, captain!'.
|
||||
+20
@@ -0,0 +1,20 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+29
@@ -0,0 +1,29 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to use Image Multi-Modality with an AI agent.
|
||||
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Extensions.AI;
|
||||
using OpenAI;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o";
|
||||
|
||||
var agent = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(
|
||||
name: "VisionAgent",
|
||||
instructions: "You are a helpful agent that can analyze images");
|
||||
|
||||
ChatMessage message = new(ChatRole.User, [
|
||||
new TextContent("What do you see in this image?"),
|
||||
new UriContent("https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg", "image/jpeg")
|
||||
]);
|
||||
|
||||
var thread = agent.GetNewThread();
|
||||
|
||||
await foreach (var update in agent.RunStreamingAsync(message, thread))
|
||||
{
|
||||
Console.WriteLine(update);
|
||||
}
|
||||
+52
@@ -0,0 +1,52 @@
|
||||
# Using Images with AI Agents
|
||||
|
||||
This sample demonstrates how to use image multi-modality with an AI agent. It shows how to create a vision-enabled agent that can analyze and describe images using Azure OpenAI.
|
||||
|
||||
## What this sample demonstrates
|
||||
|
||||
- Creating a persistent AI agent with vision capabilities
|
||||
- Sending both text and image content to an agent in a single message
|
||||
- Using `UriContent` to Uri referenced images
|
||||
- Processing multimodal input (text + image) with an AI agent
|
||||
|
||||
## Key features
|
||||
|
||||
- **Vision Agent**: Creates an agent specifically instructed to analyze images
|
||||
- **Multimodal Input**: Combines text questions with image uri in a single message
|
||||
- **Azure OpenAI Integration**: Uses AzureOpenAI LLM agents
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Before running this sample, ensure you have:
|
||||
|
||||
1. An Azure OpenAI project set up
|
||||
2. A compatible model deployment (e.g., gpt-4o)
|
||||
3. Azure CLI installed and authenticated
|
||||
|
||||
## Environment Variables
|
||||
|
||||
Set the following environment variables:
|
||||
|
||||
```powershell
|
||||
$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI endpoint
|
||||
$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o" # Replace with your model deployment name (optional, defaults to gpt-4o)
|
||||
```
|
||||
|
||||
## Run the sample
|
||||
|
||||
Navigate to the sample directory and run:
|
||||
|
||||
```powershell
|
||||
cd Agent_Step11_UsingImages
|
||||
dotnet run
|
||||
```
|
||||
|
||||
## Expected behavior
|
||||
|
||||
The sample will:
|
||||
|
||||
1. Create a vision-enabled agent named "VisionAgent"
|
||||
2. Send a message containing both text ("What do you see in this image?") and a Uri image of a green walk
|
||||
3. The agent will analyze the image and provide a description
|
||||
4. Clean up resources by deleting the thread and agent
|
||||
|
||||
+22
@@ -0,0 +1,22 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
<UserSecretsId>3afc9b74-af74-4d8e-ae96-fa1c511d11ac</UserSecretsId>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.Hosting" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+38
@@ -0,0 +1,38 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to create and use a Azure OpenAI AI agent as a function tool.
|
||||
|
||||
using System.ComponentModel;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using OpenAI;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
[Description("Get the weather for a given location.")]
|
||||
static string GetWeather([Description("The location to get the weather for.")] string location)
|
||||
=> $"The weather in {location} is cloudy with a high of 15°C.";
|
||||
|
||||
// Create the chat client and agent, and provide the function tool to the agent.
|
||||
AIAgent weatherAgent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(
|
||||
instructions: "You answer questions about the weather.",
|
||||
name: "WeatherAgent",
|
||||
description: "An agent that answers questions about the weather.",
|
||||
tools: [AIFunctionFactory.Create(GetWeather)]);
|
||||
|
||||
// Create the main agent, and provide the weather agent as a function tool.
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(instructions: "You are a helpful assistant who responds in French.", tools: [weatherAgent.AsAIFunction()]);
|
||||
|
||||
// Invoke the agent and output the text result.
|
||||
Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?"));
|
||||
+21
@@ -0,0 +1,21 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+158
@@ -0,0 +1,158 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to add a basic custom memory component to an agent.
|
||||
// The memory component subscribes to all messages added to the conversation and
|
||||
// extracts the user's name and age if provided.
|
||||
// The component adds a prompt to ask for this information if it is not already known
|
||||
// and provides it to the model before each invocation if known.
|
||||
|
||||
using System.Text;
|
||||
using System.Text.Json;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using OpenAI;
|
||||
using OpenAI.Chat;
|
||||
using SampleApp;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
ChatClient chatClient = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName);
|
||||
|
||||
// Create the agent and provide a factory to add our custom memory component to
|
||||
// all threads created by the agent. Here each new memory component will have its own
|
||||
// user info object, so each thread will have its own memory.
|
||||
// In real world applications/services, where the user info would be persisted in a database,
|
||||
// and preferably shared between multiple threads used by the same user, ensure that the
|
||||
// factory reads the user id from the current context and scopes the memory component
|
||||
// and its storage to that user id.
|
||||
AIAgent agent = chatClient.CreateAIAgent(new ChatClientAgentOptions()
|
||||
{
|
||||
Instructions = "You are a friendly assistant. Always address the user by their name.",
|
||||
AIContextProviderFactory = ctx => new UserInfoMemory(chatClient.AsIChatClient(), ctx.SerializedState, ctx.JsonSerializerOptions)
|
||||
});
|
||||
|
||||
// Create a new thread for the conversation.
|
||||
AgentThread thread = agent.GetNewThread();
|
||||
|
||||
Console.WriteLine(">> Use thread with blank memory\n");
|
||||
|
||||
// Invoke the agent and output the text result.
|
||||
Console.WriteLine(await agent.RunAsync("Hello, what is the square root of 9?", thread));
|
||||
Console.WriteLine(await agent.RunAsync("My name is Ruaidhrí", thread));
|
||||
Console.WriteLine(await agent.RunAsync("I am 20 years old", thread));
|
||||
|
||||
// We can serialize the thread. The serialized state will include the state of the memory component.
|
||||
var threadElement = thread.Serialize();
|
||||
|
||||
Console.WriteLine("\n>> Use deserialized thread with previously created memories\n");
|
||||
|
||||
// Later we can deserialize the thread and continue the conversation with the previous memory component state.
|
||||
var deserializedThread = agent.DeserializeThread(threadElement);
|
||||
Console.WriteLine(await agent.RunAsync("What is my name and age?", deserializedThread));
|
||||
|
||||
Console.WriteLine("\n>> Read memories from memory component\n");
|
||||
|
||||
// It's possible to access the memory component via the thread's GetService method.
|
||||
var userInfo = deserializedThread.GetService<UserInfoMemory>()?.UserInfo;
|
||||
|
||||
// Output the user info that was captured by the memory component.
|
||||
Console.WriteLine($"MEMORY - User Name: {userInfo?.UserName}");
|
||||
Console.WriteLine($"MEMORY - User Age: {userInfo?.UserAge}");
|
||||
|
||||
Console.WriteLine("\n>> Use new thread with previously created memories\n");
|
||||
|
||||
// It is also possible to set the memories in a memory component on an individual thread.
|
||||
// This is useful if we want to start a new thread, but have it share the same memories as a previous thread.
|
||||
var newThread = agent.GetNewThread();
|
||||
if (userInfo is not null && newThread.GetService<UserInfoMemory>() is UserInfoMemory newThreadMemory)
|
||||
{
|
||||
newThreadMemory.UserInfo = userInfo;
|
||||
}
|
||||
|
||||
// Invoke the agent and output the text result.
|
||||
// This time the agent should remember the user's name and use it in the response.
|
||||
Console.WriteLine(await agent.RunAsync("What is my name and age?", newThread));
|
||||
|
||||
namespace SampleApp
|
||||
{
|
||||
/// <summary>
|
||||
/// Sample memory component that can remember a user's name and age.
|
||||
/// </summary>
|
||||
internal sealed class UserInfoMemory : AIContextProvider
|
||||
{
|
||||
private readonly IChatClient _chatClient;
|
||||
|
||||
public UserInfoMemory(IChatClient chatClient, UserInfo? userInfo = null)
|
||||
{
|
||||
this._chatClient = chatClient;
|
||||
this.UserInfo = userInfo ?? new UserInfo();
|
||||
}
|
||||
|
||||
public UserInfoMemory(IChatClient chatClient, JsonElement serializedState, JsonSerializerOptions? jsonSerializerOptions = null)
|
||||
{
|
||||
this._chatClient = chatClient;
|
||||
|
||||
this.UserInfo = serializedState.ValueKind == JsonValueKind.Object ?
|
||||
serializedState.Deserialize<UserInfo>(jsonSerializerOptions)! :
|
||||
new UserInfo();
|
||||
}
|
||||
|
||||
public UserInfo UserInfo { get; set; }
|
||||
|
||||
public override async ValueTask InvokedAsync(InvokedContext context, CancellationToken cancellationToken = default)
|
||||
{
|
||||
// Try and extract the user name and age from the message if we don't have it already and it's a user message.
|
||||
if ((this.UserInfo.UserName is null || this.UserInfo.UserAge is null) && context.RequestMessages.Any(x => x.Role == ChatRole.User))
|
||||
{
|
||||
var result = await this._chatClient.GetResponseAsync<UserInfo>(
|
||||
context.RequestMessages,
|
||||
new ChatOptions()
|
||||
{
|
||||
Instructions = "Extract the user's name and age from the message if present. If not present return nulls."
|
||||
},
|
||||
cancellationToken: cancellationToken);
|
||||
|
||||
this.UserInfo.UserName ??= result.Result.UserName;
|
||||
this.UserInfo.UserAge ??= result.Result.UserAge;
|
||||
}
|
||||
}
|
||||
|
||||
public override ValueTask<AIContext> InvokingAsync(InvokingContext context, CancellationToken cancellationToken = default)
|
||||
{
|
||||
StringBuilder instructions = new();
|
||||
|
||||
// If we don't already know the user's name and age, add instructions to ask for them, otherwise just provide what we have to the context.
|
||||
instructions
|
||||
.AppendLine(
|
||||
this.UserInfo.UserName is null ?
|
||||
"Ask the user for their name and politely decline to answer any questions until they provide it." :
|
||||
$"The user's name is {this.UserInfo.UserName}.")
|
||||
.AppendLine(
|
||||
this.UserInfo.UserAge is null ?
|
||||
"Ask the user for their age and politely decline to answer any questions until they provide it." :
|
||||
$"The user's age is {this.UserInfo.UserAge}.");
|
||||
|
||||
return new ValueTask<AIContext>(new AIContext
|
||||
{
|
||||
Instructions = instructions.ToString()
|
||||
});
|
||||
}
|
||||
|
||||
public override JsonElement Serialize(JsonSerializerOptions? jsonSerializerOptions = null)
|
||||
{
|
||||
return JsonSerializer.SerializeToElement(this.UserInfo, jsonSerializerOptions);
|
||||
}
|
||||
}
|
||||
|
||||
internal sealed class UserInfo
|
||||
{
|
||||
public string? UserName { get; set; }
|
||||
public int? UserAge { get; set; }
|
||||
}
|
||||
}
|
||||
+22
@@ -0,0 +1,22 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.Extensions.Logging.Console" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+260
@@ -0,0 +1,260 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows multiple middleware layers working together with Azure OpenAI:
|
||||
// chat client (global/per-request), agent run (PII filtering and guardrails),
|
||||
// function invocation (logging and result overrides), and human-in-the-loop
|
||||
// approval workflows for sensitive function calls.
|
||||
|
||||
using System.ComponentModel;
|
||||
using System.Text.RegularExpressions;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
|
||||
// Get Azure AI Foundry configuration from environment variables
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o";
|
||||
|
||||
// Get a client to create/retrieve server side agents with
|
||||
var azureOpenAIClient = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
|
||||
.GetChatClient(deploymentName);
|
||||
|
||||
[Description("Get the weather for a given location.")]
|
||||
static string GetWeather([Description("The location to get the weather for.")] string location)
|
||||
=> $"The weather in {location} is cloudy with a high of 15°C.";
|
||||
|
||||
[Description("The current datetime offset.")]
|
||||
static string GetDateTime()
|
||||
=> DateTimeOffset.Now.ToString();
|
||||
|
||||
// Adding middleware to the chat client level and building an agent on top of it
|
||||
var originalAgent = azureOpenAIClient.AsIChatClient()
|
||||
.AsBuilder()
|
||||
.Use(getResponseFunc: ChatClientMiddleware, getStreamingResponseFunc: null)
|
||||
.BuildAIAgent(
|
||||
instructions: "You are an AI assistant that helps people find information.",
|
||||
tools: [AIFunctionFactory.Create(GetDateTime, name: nameof(GetDateTime))]);
|
||||
|
||||
// Adding middleware to the agent level
|
||||
var middlewareEnabledAgent = originalAgent
|
||||
.AsBuilder()
|
||||
.Use(FunctionCallMiddleware)
|
||||
.Use(FunctionCallOverrideWeather)
|
||||
.Use(PIIMiddleware, null)
|
||||
.Use(GuardrailMiddleware, null)
|
||||
.Build();
|
||||
|
||||
var thread = middlewareEnabledAgent.GetNewThread();
|
||||
|
||||
Console.WriteLine("\n\n=== Example 1: Wording Guardrail ===");
|
||||
var guardRailedResponse = await middlewareEnabledAgent.RunAsync("Tell me something harmful.");
|
||||
Console.WriteLine($"Guard railed response: {guardRailedResponse}");
|
||||
|
||||
Console.WriteLine("\n\n=== Example 2: PII detection ===");
|
||||
var piiResponse = await middlewareEnabledAgent.RunAsync("My name is John Doe, call me at 123-456-7890 or email me at john@something.com");
|
||||
Console.WriteLine($"Pii filtered response: {piiResponse}");
|
||||
|
||||
Console.WriteLine("\n\n=== Example 3: Agent function middleware ===");
|
||||
|
||||
// Agent function middleware support is limited to agents that wraps a upstream ChatClientAgent or derived from it.
|
||||
|
||||
// Add Per-request tools
|
||||
var options = new ChatClientAgentRunOptions(new()
|
||||
{
|
||||
Tools = [AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather))]
|
||||
});
|
||||
|
||||
var functionCallResponse = await middlewareEnabledAgent.RunAsync("What's the current time and the weather in Seattle?", thread, options);
|
||||
Console.WriteLine($"Function calling response: {functionCallResponse}");
|
||||
|
||||
// Special per-request middleware agent.
|
||||
Console.WriteLine("\n\n=== Example 4: Per-request middleware with human in the loop function approval ===");
|
||||
|
||||
var optionsWithApproval = new ChatClientAgentRunOptions(new()
|
||||
{
|
||||
// Adding a function with approval required
|
||||
Tools = [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather)))],
|
||||
})
|
||||
{
|
||||
ChatClientFactory = (chatClient) => chatClient
|
||||
.AsBuilder()
|
||||
.Use(PerRequestChatClientMiddleware, null) // Using the non-streaming for handling streaming as well
|
||||
.Build()
|
||||
};
|
||||
|
||||
// var response = middlewareAgent // Using per-request middleware pipeline in addition to existing agent-level middleware
|
||||
var response = await originalAgent // Using per-request middleware pipeline without existing agent-level middleware
|
||||
.AsBuilder()
|
||||
.Use(PerRequestFunctionCallingMiddleware)
|
||||
.Use(ConsolePromptingApprovalMiddleware, null)
|
||||
.Build()
|
||||
.RunAsync("What's the current time and the weather in Seattle?", thread, optionsWithApproval);
|
||||
|
||||
Console.WriteLine($"Per-request middleware response: {response}");
|
||||
|
||||
// Function invocation middleware that logs before and after function calls.
|
||||
async ValueTask<object?> FunctionCallMiddleware(AIAgent agent, FunctionInvocationContext context, Func<FunctionInvocationContext, CancellationToken, ValueTask<object?>> next, CancellationToken cancellationToken)
|
||||
{
|
||||
Console.WriteLine($"Function Name: {context!.Function.Name} - Middleware 1 Pre-Invoke");
|
||||
var result = await next(context, cancellationToken);
|
||||
Console.WriteLine($"Function Name: {context!.Function.Name} - Middleware 1 Post-Invoke");
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
// Function invocation middleware that overrides the result of the GetWeather function.
|
||||
async ValueTask<object?> FunctionCallOverrideWeather(AIAgent agent, FunctionInvocationContext context, Func<FunctionInvocationContext, CancellationToken, ValueTask<object?>> next, CancellationToken cancellationToken)
|
||||
{
|
||||
Console.WriteLine($"Function Name: {context!.Function.Name} - Middleware 2 Pre-Invoke");
|
||||
|
||||
var result = await next(context, cancellationToken);
|
||||
|
||||
if (context.Function.Name == nameof(GetWeather))
|
||||
{
|
||||
// Override the result of the GetWeather function
|
||||
result = "The weather is sunny with a high of 25°C.";
|
||||
}
|
||||
Console.WriteLine($"Function Name: {context!.Function.Name} - Middleware 2 Post-Invoke");
|
||||
return result;
|
||||
}
|
||||
|
||||
// There's no difference per-request middleware, except it's added to the agent and used for a single agent run.
|
||||
// This middleware logs function names before and after they are invoked.
|
||||
async ValueTask<object?> PerRequestFunctionCallingMiddleware(AIAgent agent, FunctionInvocationContext context, Func<FunctionInvocationContext, CancellationToken, ValueTask<object?>> next, CancellationToken cancellationToken)
|
||||
{
|
||||
Console.WriteLine($"Agent Id: {agent.Id}");
|
||||
Console.WriteLine($"Function Name: {context!.Function.Name} - Per-Request Pre-Invoke");
|
||||
var result = await next(context, cancellationToken);
|
||||
Console.WriteLine($"Function Name: {context!.Function.Name} - Per-Request Post-Invoke");
|
||||
return result;
|
||||
}
|
||||
|
||||
// This middleware redacts PII information from input and output messages.
|
||||
async Task<AgentRunResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, AgentThread? thread, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
|
||||
{
|
||||
// Redact PII information from input messages
|
||||
var filteredMessages = FilterMessages(messages);
|
||||
Console.WriteLine("Pii Middleware - Filtered Messages Pre-Run");
|
||||
|
||||
var response = await innerAgent.RunAsync(filteredMessages, thread, options, cancellationToken).ConfigureAwait(false);
|
||||
|
||||
// Redact PII information from output messages
|
||||
response.Messages = FilterMessages(response.Messages);
|
||||
|
||||
Console.WriteLine("Pii Middleware - Filtered Messages Post-Run");
|
||||
|
||||
return response;
|
||||
|
||||
static IList<ChatMessage> FilterMessages(IEnumerable<ChatMessage> messages)
|
||||
{
|
||||
return messages.Select(m => new ChatMessage(m.Role, FilterPii(m.Text))).ToList();
|
||||
}
|
||||
|
||||
static string FilterPii(string content)
|
||||
{
|
||||
// Regex patterns for PII detection (simplified for demonstration)
|
||||
Regex[] piiPatterns = [
|
||||
new(@"\b\d{3}-\d{3}-\d{4}\b", RegexOptions.Compiled), // Phone number (e.g., 123-456-7890)
|
||||
new(@"\b[\w\.-]+@[\w\.-]+\.\w+\b", RegexOptions.Compiled), // Email address
|
||||
new(@"\b[A-Z][a-z]+\s[A-Z][a-z]+\b", RegexOptions.Compiled) // Full name (e.g., John Doe)
|
||||
];
|
||||
|
||||
foreach (var pattern in piiPatterns)
|
||||
{
|
||||
content = pattern.Replace(content, "[REDACTED: PII]");
|
||||
}
|
||||
|
||||
return content;
|
||||
}
|
||||
}
|
||||
|
||||
// This middleware enforces guardrails by redacting certain keywords from input and output messages.
|
||||
async Task<AgentRunResponse> GuardrailMiddleware(IEnumerable<ChatMessage> messages, AgentThread? thread, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
|
||||
{
|
||||
// Redact keywords from input messages
|
||||
var filteredMessages = FilterMessages(messages);
|
||||
|
||||
Console.WriteLine("Guardrail Middleware - Filtered messages Pre-Run");
|
||||
|
||||
// Proceed with the agent run
|
||||
var response = await innerAgent.RunAsync(filteredMessages, thread, options, cancellationToken);
|
||||
|
||||
// Redact keywords from output messages
|
||||
response.Messages = FilterMessages(response.Messages);
|
||||
|
||||
Console.WriteLine("Guardrail Middleware - Filtered messages Post-Run");
|
||||
|
||||
return response;
|
||||
|
||||
List<ChatMessage> FilterMessages(IEnumerable<ChatMessage> messages)
|
||||
{
|
||||
return messages.Select(m => new ChatMessage(m.Role, FilterContent(m.Text))).ToList();
|
||||
}
|
||||
|
||||
static string FilterContent(string content)
|
||||
{
|
||||
foreach (var keyword in new[] { "harmful", "illegal", "violence" })
|
||||
{
|
||||
if (content.Contains(keyword, StringComparison.OrdinalIgnoreCase))
|
||||
{
|
||||
return "[REDACTED: Forbidden content]";
|
||||
}
|
||||
}
|
||||
|
||||
return content;
|
||||
}
|
||||
}
|
||||
|
||||
// This middleware handles Human in the loop console interaction for any user approval required during function calling.
|
||||
async Task<AgentRunResponse> ConsolePromptingApprovalMiddleware(IEnumerable<ChatMessage> messages, AgentThread? thread, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
|
||||
{
|
||||
var response = await innerAgent.RunAsync(messages, thread, options, cancellationToken);
|
||||
|
||||
var userInputRequests = response.UserInputRequests.ToList();
|
||||
|
||||
while (userInputRequests.Count > 0)
|
||||
{
|
||||
// Ask the user to approve each function call request.
|
||||
// For simplicity, we are assuming here that only function approval requests are being made.
|
||||
|
||||
// Pass the user input responses back to the agent for further processing.
|
||||
response.Messages = userInputRequests
|
||||
.OfType<FunctionApprovalRequestContent>()
|
||||
.Select(functionApprovalRequest =>
|
||||
{
|
||||
Console.WriteLine($"The agent would like to invoke the following function, please reply Y to approve: Name {functionApprovalRequest.FunctionCall.Name}");
|
||||
return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false)]);
|
||||
})
|
||||
.ToList();
|
||||
|
||||
response = await innerAgent.RunAsync(response.Messages, thread, options, cancellationToken);
|
||||
|
||||
userInputRequests = response.UserInputRequests.ToList();
|
||||
}
|
||||
|
||||
return response;
|
||||
}
|
||||
|
||||
// This middleware handles chat client lower level invocations.
|
||||
// This is useful for handling agent messages before they are sent to the LLM and also handle any response messages from the LLM before they are sent back to the agent.
|
||||
async Task<ChatResponse> ChatClientMiddleware(IEnumerable<ChatMessage> message, ChatOptions? options, IChatClient innerChatClient, CancellationToken cancellationToken)
|
||||
{
|
||||
Console.WriteLine("Chat Client Middleware - Pre-Chat");
|
||||
var response = await innerChatClient.GetResponseAsync(message, options, cancellationToken);
|
||||
Console.WriteLine("Chat Client Middleware - Post-Chat");
|
||||
|
||||
return response;
|
||||
}
|
||||
|
||||
// There's no difference per-request middleware, except it's added to the chat client and used for a single agent run.
|
||||
// This middleware handles chat client lower level invocations.
|
||||
// This is useful for handling agent messages before they are sent to the LLM and also handle any response messages from the LLM before they are sent back to the agent.
|
||||
async Task<ChatResponse> PerRequestChatClientMiddleware(IEnumerable<ChatMessage> message, ChatOptions? options, IChatClient innerChatClient, CancellationToken cancellationToken)
|
||||
{
|
||||
Console.WriteLine("Per-Request Chat Client Middleware - Pre-Chat");
|
||||
var response = await innerChatClient.GetResponseAsync(message, options, cancellationToken);
|
||||
Console.WriteLine("Per-Request Chat Client Middleware - Post-Chat");
|
||||
|
||||
return response;
|
||||
}
|
||||
+41
@@ -0,0 +1,41 @@
|
||||
# Agent Middleware
|
||||
|
||||
This sample demonstrates how to add middleware to intercept:
|
||||
- Chat client calls (global and per‑request)
|
||||
- Agent runs (guardrails and PII filtering)
|
||||
- Function calling (logging/override)
|
||||
|
||||
## What This Sample Shows
|
||||
|
||||
1. Azure OpenAI integration via `AzureOpenAIClient` and `AzureCliCredential`
|
||||
2. Chat client middleware using `ChatClientBuilder.Use(...)`
|
||||
3. Agent run middleware (PII redaction and wording guardrails)
|
||||
4. Function invocation middleware (logging and overriding a tool result)
|
||||
5. Per‑request chat client middleware
|
||||
6. Per‑request function pipeline with approval
|
||||
7. Combining agent‑level and per‑request middleware
|
||||
|
||||
## Function Invocation Middleware
|
||||
|
||||
Not all agents support function invocation middleware.
|
||||
|
||||
Attempting to use function middleware on agents that do not wrap a ChatClientAgent or derives from it will throw an InvalidOperationException.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Environment variables:
|
||||
- `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI endpoint
|
||||
- `AZURE_OPENAI_DEPLOYMENT_NAME`: Chat deployment name (optional; defaults to `gpt-4o`)
|
||||
2. Sign in with Azure CLI (PowerShell):
|
||||
```powershell
|
||||
az login
|
||||
```
|
||||
|
||||
## Running the Sample
|
||||
|
||||
Use PowerShell:
|
||||
```powershell
|
||||
cd dotnet/samples/GettingStarted/Agents/Agent_Step14_Middleware
|
||||
dotnet run
|
||||
```
|
||||
|
||||
+24
@@ -0,0 +1,24 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
<NoWarn>$(NoWarn);CA1812</NoWarn>
|
||||
<RootNamespace>Agent_Step15_Plugins</RootNamespace>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Microsoft.Extensions.Logging.Console" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+130
@@ -0,0 +1,130 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to use plugins with an AI agent. Plugin classes can
|
||||
// depend on other services that need to be injected. In this sample, the
|
||||
// AgentPlugin class uses the WeatherProvider and CurrentTimeProvider classes
|
||||
// to get weather and current time information. Both services are registered
|
||||
// in the service collection and injected into the plugin.
|
||||
// Plugin classes may have many methods, but only some are intended to be used
|
||||
// as AI functions. The AsAITools method of the plugin class shows how to specify
|
||||
// which methods should be exposed to the AI agent.
|
||||
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using Microsoft.Extensions.DependencyInjection;
|
||||
using OpenAI;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
// Create a service collection to hold the agent plugin and its dependencies.
|
||||
ServiceCollection services = new();
|
||||
services.AddSingleton<WeatherProvider>();
|
||||
services.AddSingleton<CurrentTimeProvider>();
|
||||
services.AddSingleton<AgentPlugin>(); // The plugin depends on WeatherProvider and CurrentTimeProvider registered above.
|
||||
|
||||
IServiceProvider serviceProvider = services.BuildServiceProvider();
|
||||
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(
|
||||
instructions: "You are a helpful assistant that helps people find information.",
|
||||
name: "Assistant",
|
||||
tools: [.. serviceProvider.GetRequiredService<AgentPlugin>().AsAITools()],
|
||||
services: serviceProvider); // Pass the service provider to the agent so it will be available to plugin functions to resolve dependencies.
|
||||
|
||||
Console.WriteLine(await agent.RunAsync("Tell me current time and weather in Seattle."));
|
||||
|
||||
/// <summary>
|
||||
/// The agent plugin that provides weather and current time information.
|
||||
/// </summary>
|
||||
/// <param name="weatherProvider">The weather provider to get weather information.</param>
|
||||
internal sealed class AgentPlugin(WeatherProvider weatherProvider)
|
||||
{
|
||||
/// <summary>
|
||||
/// Gets the weather information for the specified location.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// This method demonstrates how to use the dependency that was injected into the plugin class.
|
||||
/// </remarks>
|
||||
/// <param name="location">The location to get the weather for.</param>
|
||||
/// <returns>The weather information for the specified location.</returns>
|
||||
public string GetWeather(string location)
|
||||
{
|
||||
return weatherProvider.GetWeather(location);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Gets the current date and time for the specified location.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// This method demonstrates how to resolve a dependency using the service provider passed to the method.
|
||||
/// </remarks>
|
||||
/// <param name="sp">The service provider to resolve the <see cref="CurrentTimeProvider"/>.</param>
|
||||
/// <param name="location">The location to get the current time for.</param>
|
||||
/// <returns>The current date and time as a <see cref="DateTimeOffset"/>.</returns>
|
||||
public DateTimeOffset GetCurrentTime(IServiceProvider sp, string location)
|
||||
{
|
||||
// Resolve the CurrentTimeProvider from the service provider
|
||||
var currentTimeProvider = sp.GetRequiredService<CurrentTimeProvider>();
|
||||
|
||||
return currentTimeProvider.GetCurrentTime(location);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Returns the functions provided by this plugin.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// In real world scenarios, a class may have many methods and only a subset of them may be intended to be exposed as AI functions.
|
||||
/// This method demonstrates how to explicitly specify which methods should be exposed to the AI agent.
|
||||
/// </remarks>
|
||||
/// <returns>The functions provided by this plugin.</returns>
|
||||
public IEnumerable<AITool> AsAITools()
|
||||
{
|
||||
yield return AIFunctionFactory.Create(this.GetWeather);
|
||||
yield return AIFunctionFactory.Create(this.GetCurrentTime);
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// The weather provider that returns weather information.
|
||||
/// </summary>
|
||||
internal sealed class WeatherProvider
|
||||
{
|
||||
/// <summary>
|
||||
/// Gets the weather information for the specified location.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// The weather information is hardcoded for demonstration purposes.
|
||||
/// In a real application, this could call a weather API to get actual weather data.
|
||||
/// </remarks>
|
||||
/// <param name="location">The location to get the weather for.</param>
|
||||
/// <returns>The weather information for the specified location.</returns>
|
||||
public string GetWeather(string location)
|
||||
{
|
||||
return $"The weather in {location} is cloudy with a high of 15°C.";
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Provides the current date and time.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// This class returns the current date and time using the system's clock.
|
||||
/// </remarks>
|
||||
internal sealed class CurrentTimeProvider
|
||||
{
|
||||
/// <summary>
|
||||
/// Gets the current date and time.
|
||||
/// </summary>
|
||||
/// <param name="location">The location to get the current time for (not used in this implementation).</param>
|
||||
/// <returns>The current date and time as a <see cref="DateTimeOffset"/>.</returns>
|
||||
public DateTimeOffset GetCurrentTime(string location)
|
||||
{
|
||||
return DateTimeOffset.Now;
|
||||
}
|
||||
}
|
||||
+21
@@ -0,0 +1,21 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+48
@@ -0,0 +1,48 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to use a chat history reducer to keep the context within model size limits.
|
||||
// Any implementation of Microsoft.Extensions.AI.IChatReducer can be used to customize how the chat history is reduced.
|
||||
// NOTE: this feature is only supported where the chat history is stored locally, such as with OpenAI Chat Completion.
|
||||
// Where the chat history is stored server side, such as with Azure Foundry Agents, the service must manage the chat history size.
|
||||
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using OpenAI;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
// Construct the agent, and provide a factory to create an in-memory chat message store with a reducer that keeps only the last 2 non-system messages.
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(new ChatClientAgentOptions
|
||||
{
|
||||
Instructions = "You are good at telling jokes.",
|
||||
Name = "Joker",
|
||||
ChatMessageStoreFactory = ctx => new InMemoryChatMessageStore(new MessageCountingChatReducer(2), ctx.SerializedState, ctx.JsonSerializerOptions)
|
||||
});
|
||||
|
||||
AgentThread thread = agent.GetNewThread();
|
||||
|
||||
// Invoke the agent and output the text result.
|
||||
Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", thread));
|
||||
|
||||
// Get the chat history to see how many messages are stored.
|
||||
IList<ChatMessage>? chatHistory = thread.GetService<IList<ChatMessage>>();
|
||||
Console.WriteLine($"\nChat history has {chatHistory?.Count} messages.\n");
|
||||
|
||||
// Invoke the agent a few more times.
|
||||
Console.WriteLine(await agent.RunAsync("Tell me a joke about a robot.", thread));
|
||||
Console.WriteLine($"\nChat history has {chatHistory?.Count} messages.\n");
|
||||
Console.WriteLine(await agent.RunAsync("Tell me a joke about a lemur.", thread));
|
||||
Console.WriteLine($"\nChat history has {chatHistory?.Count} messages.\n");
|
||||
|
||||
// At this point, the chat history has exceeded the limit and the original message will not exist anymore,
|
||||
// so asking a follow up question about it will not work as expected.
|
||||
Console.WriteLine(await agent.RunAsync("Tell me the joke about the pirate again, but add emojis and use the voice of a parrot.", thread));
|
||||
|
||||
Console.WriteLine($"\nChat history has {chatHistory?.Count} messages.\n");
|
||||
Reference in New Issue
Block a user