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.NET: Add more console based getting started samples (#507)
* Add more console based getting started samples * Simplify function calling and approavls samples and some minor renaming based on PR feedback. * Cover streaming with comments for aprovals sample. * Remove extra line break. * Update getting started samples list in readme. * Address PR comments * Address PR comments.
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@@ -24,7 +24,14 @@ Before you begin, ensure you have the following prerequisites:
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|Sample|Description|
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|---|---|
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|[Running a simple agent](./Step01_ChatClientAgent_Running/)|This sample demonstrates how to create and run a basic agent with instructions|
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|[Multi-turn conversation with a simple agent](./Step02_ChatClientAgent_MultiTurn/)|This sample demonstrates how to implement a multi-turn conversation with a simple agent|
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|[Multi-turn conversation with a simple agent](./Step02_ChatClientAgent_MultiturnConversation/)|This sample demonstrates how to implement a multi-turn conversation with a simple agent|
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|[Using function tools with a simple agent](./Step03_ChatClientAgent_UsingFunctionTools/)|This sample demonstrates how to use function tools with a simple agent|
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|[Using function tools with approvals](./Step04_ChatClientAgent_UsingFunctionToolsWithApprovals/)|This sample demonstrates how to use function tools where approvals require human in the loop approvals before execution|
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|[Structured output with a simple agent](./Step05_ChatClientAgent_StructuredOutput/)|This sample demonstrates how to use structured output with a simple agent|
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|[Persisted conversations with a simple agent](./Step06_ChatClientAgent_PersistedConversations/)|This sample demonstrates how to persist conversations and reload them later. This is useful for cases where an agent is hosted in a stateless service|
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|[3rd party thread storage with a simple agent](./Step07_ChatClientAgent_3rdPartyThreadStorage/)|This sample demonstrates how to store conversation history in a 3rd party storage solution|
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|[Telemetry with a simple agent](./Step08_ChatClientAgent_Telemetry/)|This sample demonstrates how to add telemetry to a simple agent|
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|[Dependency injection with a simple agent](./Step09_ChatClientAgent_DependencyInjection/)|This sample demonstrates how to add and resolve an agent with a dependency injection container|
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## Running the samples from the console
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@@ -8,25 +8,23 @@ using Azure.Identity;
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using Microsoft.Extensions.AI.Agents;
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using OpenAI;
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var azureOpenAIEndpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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var azureOpenAIDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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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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const string JokerName = "Joker";
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const string JokerInstructions = "You are good at telling jokes.";
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AIAgent agent = new AzureOpenAIClient(
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new Uri(azureOpenAIEndpoint),
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new Uri(endpoint),
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new AzureCliCredential())
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.GetChatClient(azureOpenAIDeploymentName)
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.GetChatClient(deploymentName)
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.CreateAIAgent(JokerInstructions, JokerName);
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// Invoke the agent and output the text result.
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Console.WriteLine("--- Run the agent ---\n");
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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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Console.WriteLine("\n--- Run the agent with streaming ---\n");
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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.Write(update);
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Console.WriteLine(update);
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}
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+4
-6
@@ -8,26 +8,24 @@ using Azure.Identity;
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using Microsoft.Extensions.AI.Agents;
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using OpenAI;
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var azureOpenAIEndpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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var azureOpenAIDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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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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const string JokerName = "Joker";
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const string JokerInstructions = "You are good at telling jokes.";
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AIAgent agent = new AzureOpenAIClient(
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new Uri(azureOpenAIEndpoint),
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new Uri(endpoint),
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new AzureCliCredential())
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.GetChatClient(azureOpenAIDeploymentName)
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.GetChatClient(deploymentName)
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.CreateAIAgent(JokerInstructions, JokerName);
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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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Console.WriteLine("\n--- Run with a thread (context preserved) ---\n");
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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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Console.WriteLine("\n--- Run with a thread and streaming (context preserved) ---\n");
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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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+35
@@ -0,0 +1,35 @@
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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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using System;
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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.Extensions.AI;
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using Microsoft.Extensions.AI.Agents;
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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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[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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// 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),
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new AzureCliCredential())
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.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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// 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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+23
@@ -0,0 +1,23 @@
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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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<LangVersion>12</LangVersion>
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<Nullable>enable</Nullable>
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<ImplicitUsings>disable</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>
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<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents.OpenAI\Microsoft.Extensions.AI.Agents.OpenAI.csproj" />
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<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents\Microsoft.Extensions.AI.Agents.csproj" />
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</ItemGroup>
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</Project>
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+68
@@ -0,0 +1,68 @@
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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 that require a human in the loop for approvals.
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// It shows both non-streaming and streaming agent interactions using menu-related tools.
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// 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
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// while the agent is waiting for user input.
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using System;
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using System.ComponentModel;
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using System.Linq;
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using Azure.AI.OpenAI;
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using Azure.Identity;
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using Microsoft.Extensions.AI;
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using Microsoft.Extensions.AI.Agents;
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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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// Create a sample function tool that the agent can use.
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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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// Create the chat client and agent.
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// Note that we are wrapping the function tool with ApprovalRequiredAIFunction to require user approval before invoking it.
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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 a helpful assistant", tools: [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(GetWeather))]);
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// Call the agent and check if there are any user input requests to handle.
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AgentThread thread = agent.GetNewThread();
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var response = await agent.RunAsync("What is the weather like in Amsterdam?", thread);
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var userInputRequests = response.UserInputRequests.ToList();
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// For streaming use:
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// var updates = await agent.RunStreamingAsync("What is the weather like in Amsterdam?", thread).ToListAsync();
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// userInputRequests = updates.SelectMany(x => x.UserInputRequests).ToList();
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while (userInputRequests.Count > 0)
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{
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// Ask the user to approve each function call request.
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// For simplicity, we are assuming here that only function approval requests are being made.
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var userInputResponses = userInputRequests
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.OfType<FunctionApprovalRequestContent>()
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.Select(functionApprovalRequest =>
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{
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Console.WriteLine($"The agent would like to invoke the following function, please reply Y to approve: Name {functionApprovalRequest.FunctionCall.Name}");
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return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false)]);
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})
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.ToList();
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// Pass the user input responses back to the agent for further processing.
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response = await agent.RunAsync(userInputResponses, thread);
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userInputRequests = response.UserInputRequests.ToList();
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// For streaming use:
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// updates = await agent.RunStreamingAsync(userInputResponses, thread).ToListAsync();
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// userInputRequests = updates.SelectMany(x => x.UserInputRequests).ToList();
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}
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Console.WriteLine($"\nAgent: {response}");
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// For streaming use:
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// Console.WriteLine($"\nAgent: {updates.ToAgentRunResponse()}");
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+24
@@ -0,0 +1,24 @@
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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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<LangVersion>12</LangVersion>
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<Nullable>enable</Nullable>
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<ImplicitUsings>disable</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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<PackageReference Include="System.Linq.Async" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents.OpenAI\Microsoft.Extensions.AI.Agents.OpenAI.csproj" />
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<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents\Microsoft.Extensions.AI.Agents.csproj" />
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</ItemGroup>
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</Project>
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@@ -0,0 +1,76 @@
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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, to produce structured output using JSON schema from a class.
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using System;
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using System.Text.Json;
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using System.Text.Json.Serialization;
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using Azure.AI.OpenAI;
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using Azure.Identity;
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using Microsoft.Extensions.AI;
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using Microsoft.Extensions.AI.Agents;
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using OpenAI;
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using SampleApp;
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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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// Create the agent options, specifying the response format to use a JSON schema based on the PersonInfo class.
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ChatClientAgentOptions agentOptions = new(name: "HelpfulAssistant", instructions: "You are a helpful assistant.")
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{
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ChatOptions = new()
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{
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ResponseFormat = ChatResponseFormatJson.ForJsonSchema(
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schema: AIJsonUtilities.CreateJsonSchema(typeof(PersonInfo)),
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schemaName: "PersonInfo",
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schemaDescription: "Information about a person including their name, age, and occupation")
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}
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};
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// Create the agent using Azure OpenAI.
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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(agentOptions);
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// Invoke the agent with some unstructured input, to extract the structured information from.
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var response = await agent.RunAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");
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// Deserialize the response into the PersonInfo class.
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var personInfo = response.Deserialize<PersonInfo>(JsonSerializerOptions.Web);
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Console.WriteLine("Assistant Output:");
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Console.WriteLine($"Name: {personInfo.Name}");
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Console.WriteLine($"Age: {personInfo.Age}");
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Console.WriteLine($"Occupation: {personInfo.Occupation}");
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// Invoke the agent with some unstructured input while streaming, to extract the structured information from.
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var updates = agent.RunStreamingAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");
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// Assemble all the parts of the streamed output, since we can only deserialize once we have the full json,
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// then deserialize the response into the PersonInfo class.
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personInfo = (await updates.ToAgentRunResponseAsync()).Deserialize<PersonInfo>(JsonSerializerOptions.Web);
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Console.WriteLine("Assistant Output:");
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Console.WriteLine($"Name: {personInfo.Name}");
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Console.WriteLine($"Age: {personInfo.Age}");
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Console.WriteLine($"Occupation: {personInfo.Occupation}");
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namespace SampleApp
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{
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/// <summary>
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/// Represents information about a person, including their name, age, and occupation, matched to the JSON schema used in the agent.
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/// </summary>
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public class PersonInfo
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{
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[JsonPropertyName("name")]
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public string? Name { get; set; }
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[JsonPropertyName("age")]
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public int? Age { get; set; }
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[JsonPropertyName("occupation")]
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public string? Occupation { get; set; }
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}
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}
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+23
@@ -0,0 +1,23 @@
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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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<LangVersion>12</LangVersion>
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<Nullable>enable</Nullable>
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<ImplicitUsings>disable</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>
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<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents.OpenAI\Microsoft.Extensions.AI.Agents.OpenAI.csproj" />
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<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents\Microsoft.Extensions.AI.Agents.csproj" />
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</ItemGroup>
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</Project>
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+46
@@ -0,0 +1,46 @@
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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 a conversation that can be persisted to disk.
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using System;
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using System.IO;
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using System.Text.Json;
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using Azure.AI.OpenAI;
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using Azure.Identity;
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using Microsoft.Extensions.AI.Agents;
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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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const string JokerName = "Joker";
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const string JokerInstructions = "You are good at telling jokes.";
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// Create the agent
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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(JokerInstructions, JokerName);
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// Start a new thread for the agent conversation.
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AgentThread thread = agent.GetNewThread();
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// Run the agent with a new thread.
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Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", thread));
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// Serialize the thread state to a JsonElement, so it can be stored for later use.
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JsonElement serializedThread = await thread.SerializeAsync();
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// Save the serialized thread to a temporary file (for demonstration purposes).
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string tempFilePath = Path.GetTempFileName();
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await File.WriteAllTextAsync(tempFilePath, JsonSerializer.Serialize(serializedThread));
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// Load the serialized thread from the temporary file (for demonstration purposes).
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JsonElement reloadedSerializedThread = JsonSerializer.Deserialize<JsonElement>(await File.ReadAllTextAsync(tempFilePath));
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// Deserialize the thread state after loading from storage.
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AgentThread resumedThread = await agent.DeserializeThreadAsync(reloadedSerializedThread);
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// Run the agent again with the resumed thread.
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Console.WriteLine(await agent.RunAsync("Now tell the same joke in the voice of a pirate, and add some emojis to the joke.", resumedThread));
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+23
@@ -0,0 +1,23 @@
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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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<LangVersion>12</LangVersion>
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<Nullable>enable</Nullable>
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<ImplicitUsings>disable</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>
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<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents.OpenAI\Microsoft.Extensions.AI.Agents.OpenAI.csproj" />
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<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents\Microsoft.Extensions.AI.Agents.csproj" />
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</ItemGroup>
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</Project>
|
||||
+155
@@ -0,0 +1,155 @@
|
||||
// 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;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using System.Text.Json;
|
||||
using System.Threading;
|
||||
using System.Threading.Tasks;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Extensions.AI;
|
||||
using Microsoft.Extensions.AI.Agents;
|
||||
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";
|
||||
|
||||
const string JokerName = "Joker";
|
||||
const string JokerInstructions = "You are good at telling jokes.";
|
||||
|
||||
// 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
|
||||
{
|
||||
Name = JokerName,
|
||||
Instructions = JokerInstructions,
|
||||
ChatMessageStoreFactory = () =>
|
||||
{
|
||||
// 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);
|
||||
}
|
||||
});
|
||||
|
||||
// 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 = await thread.SerializeAsync();
|
||||
|
||||
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 = await agent.DeserializeThreadAsync(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));
|
||||
|
||||
namespace SampleApp
|
||||
{
|
||||
/// <summary>
|
||||
/// A sample implementation of <see cref="IChatMessageStore"/> that stores chat messages in a vector store.
|
||||
/// </summary>
|
||||
/// <param name="vectorStore">The vector store to store the messages in.</param>
|
||||
internal sealed class VectorChatMessageStore(VectorStore vectorStore) : IChatMessageStore
|
||||
{
|
||||
private string? _threadId;
|
||||
|
||||
public string? ThreadId => this._threadId;
|
||||
|
||||
public async Task AddMessagesAsync(IReadOnlyCollection<ChatMessage> messages, CancellationToken cancellationToken)
|
||||
{
|
||||
this._threadId ??= Guid.NewGuid().ToString();
|
||||
|
||||
var collection = vectorStore.GetCollection<string, ChatHistoryItem>("ChatHistory");
|
||||
await collection.EnsureCollectionExistsAsync(cancellationToken);
|
||||
|
||||
await collection.UpsertAsync(messages.Select(x => new ChatHistoryItem()
|
||||
{
|
||||
Key = this._threadId + x.MessageId,
|
||||
Timestamp = DateTimeOffset.UtcNow,
|
||||
ThreadId = this._threadId,
|
||||
SerializedMessage = JsonSerializer.Serialize(x),
|
||||
MessageText = x.Text
|
||||
}), cancellationToken);
|
||||
}
|
||||
|
||||
public async Task<IEnumerable<ChatMessage>> GetMessagesAsync(CancellationToken cancellationToken)
|
||||
{
|
||||
var collection = vectorStore.GetCollection<string, ChatHistoryItem>("ChatHistory");
|
||||
await collection.EnsureCollectionExistsAsync(cancellationToken);
|
||||
|
||||
var records = await collection
|
||||
.GetAsync(
|
||||
x => x.ThreadId == this._threadId, 10,
|
||||
new() { OrderBy = x => x.Descending(y => y.Timestamp) },
|
||||
cancellationToken)
|
||||
.ToListAsync(cancellationToken);
|
||||
|
||||
var messages = records
|
||||
.Select(x => JsonSerializer.Deserialize<ChatMessage>(x.SerializedMessage!)!)
|
||||
.ToList();
|
||||
messages.Reverse();
|
||||
return messages;
|
||||
}
|
||||
|
||||
public ValueTask<JsonElement?> SerializeStateAsync(JsonSerializerOptions? jsonSerializerOptions = null, CancellationToken cancellationToken = default)
|
||||
{
|
||||
// We have to serialize the thread id, so that on deserialization we can retrieve the messages using the same thread id.
|
||||
return new ValueTask<JsonElement?>(JsonSerializer.SerializeToElement(this._threadId));
|
||||
}
|
||||
|
||||
public ValueTask DeserializeStateAsync(JsonElement? serializedStoreState, JsonSerializerOptions? jsonSerializerOptions = null, CancellationToken cancellationToken = default)
|
||||
{
|
||||
// Here we can deserialize the thread id so that we can access the same messages as before the suspension.
|
||||
this._threadId = JsonSerializer.Deserialize<string>((JsonElement)serializedStoreState!);
|
||||
return new ValueTask();
|
||||
}
|
||||
|
||||
/// <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; }
|
||||
}
|
||||
}
|
||||
}
|
||||
+25
@@ -0,0 +1,25 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
<LangVersion>12</LangVersion>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>disable</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.Extensions.AI.Agents.OpenAI\Microsoft.Extensions.AI.Agents.OpenAI.csproj" />
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents\Microsoft.Extensions.AI.Agents.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
@@ -0,0 +1,45 @@
|
||||
// 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 System;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Extensions.AI.Agents;
|
||||
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";
|
||||
|
||||
const string JokerName = "Joker";
|
||||
const string JokerInstructions = "You are good at telling jokes.";
|
||||
|
||||
// Enable telemetry
|
||||
AppContext.SetSwitch("Microsoft.Extensions.AI.Agents.EnableTelemetry", true);
|
||||
|
||||
// Create TracerProvider with console exporter
|
||||
// This will output the telemetry data to the console.
|
||||
string sourceName = Guid.NewGuid().ToString();
|
||||
using var tracerProvider = Sdk.CreateTracerProviderBuilder()
|
||||
.AddSource(sourceName)
|
||||
.AddConsoleExporter()
|
||||
.Build();
|
||||
|
||||
// Create the agent, and enable OpenTelemetry instrumentation.
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(JokerInstructions, JokerName)
|
||||
.WithOpenTelemetry(sourceName: sourceName);
|
||||
|
||||
// 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);
|
||||
}
|
||||
+25
@@ -0,0 +1,25 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
<LangVersion>12</LangVersion>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>disable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
<PackageReference Include="OpenTelemetry" />
|
||||
<PackageReference Include="OpenTelemetry.Exporter.Console" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents.OpenAI\Microsoft.Extensions.AI.Agents.OpenAI.csproj" />
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents\Microsoft.Extensions.AI.Agents.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
+99
@@ -0,0 +1,99 @@
|
||||
// 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 System;
|
||||
using System.Threading;
|
||||
using System.Threading.Tasks;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Extensions.AI;
|
||||
using Microsoft.Extensions.AI.Agents;
|
||||
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.
|
||||
const string JokerName = "Joker";
|
||||
const string JokerInstructions = "You are good at telling jokes.";
|
||||
builder.Services.AddSingleton(new ChatClientAgentOptions(JokerInstructions, JokerName));
|
||||
|
||||
// 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<SampleApp.SampleService>();
|
||||
|
||||
// Create a cancellation token and source to pass to the sample service that can
|
||||
// be used to signal shutdown of the application.
|
||||
CancellationTokenSource appShutdownCancellationTokenSource = new();
|
||||
CancellationToken appShutdownCancellationToken = appShutdownCancellationTokenSource.Token;
|
||||
builder.Services.AddKeyedSingleton("AppShutdown", appShutdownCancellationTokenSource);
|
||||
|
||||
// Build and run the host.
|
||||
using IHost host = builder.Build();
|
||||
await host.RunAsync(appShutdownCancellationToken).ConfigureAwait(false);
|
||||
|
||||
namespace SampleApp
|
||||
{
|
||||
/// <summary>
|
||||
/// A sample service that uses an AI agent to respond to user input.
|
||||
/// </summary>
|
||||
internal sealed class SampleService(AIAgent agent, [FromKeyedServices("AppShutdown")] CancellationTokenSource appShutdownCancellationTokenSource) : 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(cancellationToken);
|
||||
}
|
||||
|
||||
public async Task RunAsync(CancellationToken cancellationToken)
|
||||
{
|
||||
// Delay a little to allow the service to finish starting.
|
||||
await Task.Delay(100, cancellationToken);
|
||||
|
||||
while (cancellationToken.IsCancellationRequested is false)
|
||||
{
|
||||
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))
|
||||
{
|
||||
appShutdownCancellationTokenSource.Cancel();
|
||||
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>
|
||||
<LangVersion>12</LangVersion>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>disable</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.Extensions.AI.Agents.OpenAI\Microsoft.Extensions.AI.Agents.OpenAI.csproj" />
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Extensions.AI.Agents\Microsoft.Extensions.AI.Agents.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
Reference in New Issue
Block a user