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.NET: Add sample to show how to do RAG using Foundry's built-in service (#2324)
* Add sample to show how to do RAG using Foundry's built-in service * Update README.me * Update dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/Program.cs Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com> --------- Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>
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<Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step01_BasicTextRAG/AgentWithRAG_Step01_BasicTextRAG.csproj" />
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<Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/AgentWithRAG_Step02_CustomVectorStoreRAG.csproj" />
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<Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/AgentWithRAG_Step03_CustomRAGDataSource.csproj" />
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<Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/AgentWithRAG_Step04_FoundryServiceRAG.csproj" />
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</Folder>
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<Folder Name="/Samples/GettingStarted/FoundryAgents/">
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<File Path="samples/GettingStarted/FoundryAgents/README.md" />
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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.Projects" />
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<PackageReference Include="Azure.Identity" />
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</ItemGroup>
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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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</ItemGroup>
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<ItemGroup>
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<None Update="contoso-outdoors-knowledge-base.md">
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<CopyToOutputDirectory>Always</CopyToOutputDirectory>
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</None>
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</ItemGroup>
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</Project>
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// Copyright (c) Microsoft. All rights reserved.
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// This sample shows how to use the built in RAG capabilities that the Foundry service provides when using AI Agents provided by Foundry.
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using System.ClientModel;
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using Azure.AI.Projects;
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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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using OpenAI.Files;
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using OpenAI.VectorStores;
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var endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
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var deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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// Create an AI Project client and get an OpenAI client that works with the foundry service.
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AIProjectClient aiProjectClient = new(
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new Uri(endpoint),
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new AzureCliCredential());
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OpenAIClient openAIClient = aiProjectClient.GetProjectOpenAIClient();
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// Upload the file that contains the data to be used for RAG to the Foundry service.
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OpenAIFileClient fileClient = openAIClient.GetOpenAIFileClient();
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ClientResult<OpenAIFile> uploadResult = await fileClient.UploadFileAsync(
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filePath: "contoso-outdoors-knowledge-base.md",
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purpose: FileUploadPurpose.Assistants);
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// Create a vector store in the Foundry service using the uploaded file.
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VectorStoreClient vectorStoreClient = openAIClient.GetVectorStoreClient();
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ClientResult<VectorStore> vectorStoreCreate = await vectorStoreClient.CreateVectorStoreAsync(options: new VectorStoreCreationOptions()
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{
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Name = "contoso-outdoors-knowledge-base",
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FileIds = { uploadResult.Value.Id }
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});
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var fileSearchTool = new HostedFileSearchTool() { Inputs = [new HostedVectorStoreContent(vectorStoreCreate.Value.Id)] };
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AIAgent agent = await aiProjectClient
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.CreateAIAgentAsync(
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model: deploymentName,
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name: "AskContoso",
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instructions: "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
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tools: [fileSearchTool]);
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AgentThread thread = agent.GetNewThread();
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Console.WriteLine(">> Asking about returns\n");
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Console.WriteLine(await agent.RunAsync("Hi! I need help understanding the return policy.", thread));
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Console.WriteLine("\n>> Asking about shipping\n");
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Console.WriteLine(await agent.RunAsync("How long does standard shipping usually take?", thread));
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Console.WriteLine("\n>> Asking about product care\n");
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Console.WriteLine(await agent.RunAsync("What is the best way to maintain the TrailRunner tent fabric?", thread));
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// Cleanup
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await fileClient.DeleteFileAsync(uploadResult.Value.Id);
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await vectorStoreClient.DeleteVectorStoreAsync(vectorStoreCreate.Value.Id);
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await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
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+19
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# Contoso Outdoors Knowledge Base
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## Contoso Outdoors Return Policy
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Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection.
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## Contoso Outdoors Shipping Guide
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Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout.
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## Product Information
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### TrailRunner Tent
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The TrailRunner Tent is a lightweight, 2-person tent designed for easy setup and durability. It features waterproof materials, ventilation windows, and a compact carry bag.
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#### Care Instructions
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Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating.
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@@ -7,3 +7,4 @@ These samples show how to create an agent with the Agent Framework that uses Ret
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|[Basic Text RAG](./AgentWithRAG_Step01_BasicTextRAG/)|This sample demonstrates how to create and run a basic agent with simple text Retrieval Augmented Generation (RAG).|
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|[RAG with Vector Store and custom schema](./AgentWithRAG_Step02_CustomVectorStoreRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a vector store. It also uses a custom schema for the documents stored in the vector store.|
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|[RAG with custom RAG data source](./AgentWithRAG_Step03_CustomRAGDataSource/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a custom RAG data source.|
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|[RAG with Foundry VectorStore service](./AgentWithRAG_Step04_FoundryServiceRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with the Foundry VectorStore service.|
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