wehub-resource-sync
459 lines
17 KiB
C#
459 lines
17 KiB
C#
// Copyright (c) Microsoft. All rights reserved.
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using Azure.Core;
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using Azure.Identity;
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using Microsoft.Extensions.DependencyInjection;
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using Microsoft.SemanticKernel;
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using Microsoft.SemanticKernel.Agents;
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using Microsoft.SemanticKernel.Agents.AzureAI;
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using Microsoft.SemanticKernel.ChatCompletion;
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using Plugins;
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namespace GettingStarted.AzureAgents;
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/// <summary>
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/// This example demonstrates how to declaratively create instances of <see cref="AzureAIAgent"/>.
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/// </summary>
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public class Step08_AzureAIAgent_Declarative : BaseAzureAgentTest
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{
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/// <summary>
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/// Demonstrates creating and using a Chat Completion Agent with a Kernel.
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/// </summary>
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[Fact]
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public async Task AzureAIAgentWithConfiguration()
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{
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var text =
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"""
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type: foundry_agent
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name: MyAgent
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description: My helpful agent.
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instructions: You are helpful agent.
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model:
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id: ${AzureAI:ChatModelId}
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connection:
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connection_string: ${AzureAI:ConnectionString}
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""";
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AzureAIAgentFactory factory = new();
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var builder = Kernel.CreateBuilder();
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builder.Services.AddSingleton(this.Client);
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builder.Services.AddSingleton<TokenCredential>(new AzureCliCredential());
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var kernel = builder.Build();
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var agent = await factory.CreateAgentFromYamlAsync(text, new() { Kernel = kernel }, TestConfiguration.ConfigurationRoot);
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await InvokeAgentAsync(agent!, "Could you please create a bar chart for the operating profit using the following data and provide the file to me? Company A: $1.2 million, Company B: $2.5 million, Company C: $3.0 million, Company D: $1.8 million");
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}
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[Fact]
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public async Task AzureAIAgentWithKernel()
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{
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var text =
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"""
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type: foundry_agent
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name: MyAgent
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description: My helpful agent.
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instructions: You are helpful agent.
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model:
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id: ${AzureOpenAI:ChatModelId}
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""";
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AzureAIAgentFactory factory = new();
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var agent = await factory.CreateAgentFromYamlAsync(text, new() { Kernel = this._kernel }, TestConfiguration.ConfigurationRoot);
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await InvokeAgentAsync(agent!, "Could you please create a bar chart for the operating profit using the following data and provide the file to me? Company A: $1.2 million, Company B: $2.5 million, Company C: $3.0 million, Company D: $1.8 million");
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}
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[Fact]
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public async Task AzureAIAgentWithId()
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{
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var text =
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"""
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id: ${AzureAI:AgentId}
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type: foundry_agent
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instructions: You are helpful agent who always responds in French.
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""";
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AzureAIAgentFactory factory = new();
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var agent = await factory.CreateAgentFromYamlAsync(text, new() { Kernel = this._kernel }, TestConfiguration.ConfigurationRoot);
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await InvokeAgentAsync(
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agent!,
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"Could you please create a bar chart for the operating profit using the following data and provide the file to me? Company A: $1.2 million, Company B: $2.5 million, Company C: $3.0 million, Company D: $1.8 million",
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deleteAgent: false);
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}
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[Fact]
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public async Task AzureAIAgentWithCodeInterpreter()
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{
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var text =
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"""
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type: foundry_agent
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name: CodeInterpreterAgent
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instructions: Use the code interpreter tool to answer questions which require code to be generated and executed.
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description: Agent with code interpreter tool.
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model:
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id: ${AzureAI:ChatModelId}
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tools:
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- type: code_interpreter
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""";
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AzureAIAgentFactory factory = new();
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var agent = await factory.CreateAgentFromYamlAsync(text, new() { Kernel = this._kernel }, TestConfiguration.ConfigurationRoot);
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await InvokeAgentAsync(agent!, "Use code to determine the values in the Fibonacci sequence that that are less then the value of 101?");
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}
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[Fact]
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public async Task AzureAIAgentWithFunctions()
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{
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var text =
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"""
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type: foundry_agent
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name: FunctionCallingAgent
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instructions: Use the provided functions to answer questions about the menu.
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description: This agent uses the provided functions to answer questions about the menu.
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model:
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id: ${AzureAI:ChatModelId}
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options:
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temperature: 0.4
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tools:
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- id: GetSpecials
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type: function
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description: Get the specials from the menu.
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- id: GetItemPrice
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type: function
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description: Get the price of an item on the menu.
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options:
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parameters:
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- name: menuItem
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type: string
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required: true
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description: The name of the menu item.
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""";
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AzureAIAgentFactory factory = new();
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KernelPlugin plugin = KernelPluginFactory.CreateFromType<MenuPlugin>();
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this._kernel.Plugins.Add(plugin);
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var agent = await factory.CreateAgentFromYamlAsync(text, new() { Kernel = this._kernel }, TestConfiguration.ConfigurationRoot);
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await InvokeAgentAsync(agent!, "What is the special soup and how much does it cost?");
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}
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[Fact]
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public async Task AzureAIAgentWithBingGrounding()
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{
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var text =
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"""
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type: foundry_agent
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name: BingAgent
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instructions: Answer questions using Bing to provide grounding context.
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description: This agent answers questions using Bing to provide grounding context.
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model:
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id: ${AzureAI:ChatModelId}
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options:
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temperature: 0.4
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tools:
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- type: bing_grounding
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options:
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tool_connections:
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- ${AzureAI:BingConnectionId}
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""";
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AzureAIAgentFactory factory = new();
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KernelPlugin plugin = KernelPluginFactory.CreateFromType<MenuPlugin>();
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this._kernel.Plugins.Add(plugin);
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var agent = await factory.CreateAgentFromYamlAsync(text, new() { Kernel = this._kernel }, TestConfiguration.ConfigurationRoot);
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await InvokeAgentAsync(agent!, "What is the latest new about the Semantic Kernel?");
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}
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[Fact]
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public async Task AzureAIAgentWithFileSearch()
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{
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var text =
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"""
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type: foundry_agent
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name: FileSearchAgent
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instructions: Answer questions using available files to provide grounding context.
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description: This agent answers questions using available files to provide grounding context.
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model:
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id: ${AzureAI:ChatModelId}
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optisons:
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temperature: 0.4
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tools:
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- type: file_search
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description: Grounding with available files.
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options:
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vector_store_ids:
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- ${AzureAI.VectorStoreId}
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""";
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AzureAIAgentFactory factory = new();
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var agent = await factory.CreateAgentFromYamlAsync(text, new() { Kernel = this._kernel }, TestConfiguration.ConfigurationRoot);
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await InvokeAgentAsync(agent!, "What are the key features of the Semantic Kernel?");
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}
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[Fact]
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public async Task AzureAIAgentWithOpenAPI()
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{
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var text =
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"""
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type: foundry_agent
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name: WeatherAgent
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instructions: Answer questions about the weather. For all other questions politely decline to answer.
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description: This agent answers question about the weather.
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model:
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id: ${AzureAI:ChatModelId}
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options:
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temperature: 0.4
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tools:
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- type: openapi
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id: GetCurrentWeather
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description: Retrieves current weather data for a location based on wttr.in.
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options:
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specification: |
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{
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"openapi": "3.1.0",
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"info": {
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"title": "Get Weather Data",
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"description": "Retrieves current weather data for a location based on wttr.in.",
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"version": "v1.0.0"
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},
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"servers": [
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{
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"url": "https://wttr.in"
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}
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],
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"auth": [],
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"paths": {
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"/{location}": {
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"get": {
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"description": "Get weather information for a specific location",
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"operationId": "GetCurrentWeather",
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"parameters": [
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{
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"name": "location",
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"in": "path",
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"description": "City or location to retrieve the weather for",
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"required": true,
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"schema": {
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"type": "string"
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}
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},
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{
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"name": "format",
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"in": "query",
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"description": "Always use j1 value for this parameter",
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"required": true,
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"schema": {
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"type": "string",
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"default": "j1"
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}
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}
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],
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"responses": {
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"200": {
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"description": "Successful response",
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"content": {
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"text/plain": {
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"schema": {
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"type": "string"
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}
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}
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}
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},
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"404": {
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"description": "Location not found"
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}
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},
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"deprecated": false
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}
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}
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},
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"components": {
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"schemes": {}
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}
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}
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""";
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AzureAIAgentFactory factory = new();
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var agent = await factory.CreateAgentFromYamlAsync(text, new() { Kernel = this._kernel }, TestConfiguration.ConfigurationRoot);
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await InvokeAgentAsync(agent!, "What is the current weather in Dublin?");
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}
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[Fact]
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public async Task AzureAIAgentWithOpenAPIYaml()
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{
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var text =
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"""
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type: foundry_agent
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name: WeatherAgent
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instructions: Answer questions about the weather. For all other questions politely decline to answer.
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description: This agent answers question about the weather.
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model:
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id: ${AzureAI:ChatModelId}
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options:
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temperature: 0.4
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tools:
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- type: openapi
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id: GetCurrentWeather
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description: Retrieves current weather data for a location based on wttr.in.
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options:
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specification:
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openapi: "3.1.0"
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info:
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title: "Get Weather Data"
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description: "Retrieves current weather data for a location based on wttr.in."
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version: "v1.0.0"
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servers:
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- url: "https://wttr.in"
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auth: []
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paths:
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/{location}:
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get:
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description: "Get weather information for a specific location"
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operationId: "GetCurrentWeather"
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parameters:
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- name: "location"
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in: "path"
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description: "City or location to retrieve the weather for"
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required: true
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schema:
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type: "string"
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- name: "format"
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in: "query"
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description: "Always use j1 value for this parameter"
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required: true
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schema:
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type: "string"
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default: "j1"
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responses:
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"200":
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description: "Successful response"
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content:
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text/plain:
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schema:
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type: "string"
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"404":
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description: "Location not found"
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deprecated: false
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components:
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schemes: {}
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""";
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AzureAIAgentFactory factory = new();
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var agent = await factory.CreateAgentFromYamlAsync(text, new() { Kernel = this._kernel }, TestConfiguration.ConfigurationRoot);
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await InvokeAgentAsync(agent!, "What is the current weather in Dublin?");
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}
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[Fact]
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public async Task AzureAIAgentWithTemplate()
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{
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var text =
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"""
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type: foundry_agent
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name: StoryAgent
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description: A agent that generates a story about a topic.
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instructions: Tell a story about {{$topic}} that is {{$length}} sentences long.
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model:
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id: ${AzureAI:ChatModelId}
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inputs:
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topic:
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description: The topic of the story.
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required: true
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default: Cats
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length:
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description: The number of sentences in the story.
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required: true
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default: 2
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outputs:
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output1:
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description: output1 description
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template:
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format: semantic-kernel
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""";
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AzureAIAgentFactory factory = new();
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var promptTemplateFactory = new KernelPromptTemplateFactory();
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var agent =
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await factory.CreateAgentFromYamlAsync(text, new() { Kernel = this._kernel }, TestConfiguration.ConfigurationRoot) ??
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throw new InvalidOperationException("Unable to create agent");
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var options = new AgentInvokeOptions()
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{
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KernelArguments = new()
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{
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{ "topic", "Dogs" },
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{ "length", "3" },
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}
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};
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Microsoft.SemanticKernel.Agents.AgentThread? agentThread = null;
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try
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{
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await foreach (var response in agent!.InvokeAsync(Array.Empty<ChatMessageContent>(), agentThread, options))
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{
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agentThread = response.Thread;
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this.WriteAgentChatMessage(response);
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}
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}
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finally
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{
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var azureaiAgent = (AzureAIAgent)agent;
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await azureaiAgent.Client.Administration.DeleteAgentAsync(azureaiAgent.Id);
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if (agentThread is not null)
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{
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await agentThread.DeleteAsync();
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}
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}
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}
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public Step08_AzureAIAgent_Declarative(ITestOutputHelper output) : base(output)
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{
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var builder = Kernel.CreateBuilder();
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builder.Services.AddSingleton(this.Client);
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builder.Services.AddSingleton(this.CreateFoundryProjectClient());
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this._kernel = builder.Build();
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}
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#region private
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private readonly Kernel _kernel;
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/// <summary>
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/// Invoke the agent with the user input.
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/// </summary>
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private async Task InvokeAgentAsync(Agent agent, string input, bool? deleteAgent = true)
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{
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Microsoft.SemanticKernel.Agents.AgentThread? agentThread = null;
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try
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{
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await foreach (AgentResponseItem<ChatMessageContent> response in agent.InvokeAsync(new ChatMessageContent(AuthorRole.User, input)))
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{
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agentThread = response.Thread;
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WriteAgentChatMessage(response);
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}
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}
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finally
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{
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if (deleteAgent ?? true)
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{
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var azureaiAgent = agent as AzureAIAgent;
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Assert.NotNull(azureaiAgent);
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await azureaiAgent.Client.Administration.DeleteAgentAsync(azureaiAgent.Id);
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if (agentThread is not null)
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{
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await agentThread.DeleteAsync();
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}
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}
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}
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}
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#endregion
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}
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