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.NET: Add sample to show how to do RAG using Foundry's built-in service (#2324)

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* 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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dotnet/samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/Program.cs
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// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to use the built in RAG capabilities that the Foundry service provides when using AI Agents provided by Foundry.
using System.ClientModel;
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OpenAI;
using OpenAI.Files;
using OpenAI.VectorStores;
var endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
// Create an AI Project client and get an OpenAI client that works with the foundry service.
AIProjectClient aiProjectClient = new(
new Uri(endpoint),
new AzureCliCredential());
OpenAIClient openAIClient = aiProjectClient.GetProjectOpenAIClient();
// Upload the file that contains the data to be used for RAG to the Foundry service.
OpenAIFileClient fileClient = openAIClient.GetOpenAIFileClient();
ClientResult<OpenAIFile> uploadResult = await fileClient.UploadFileAsync(
filePath: "contoso-outdoors-knowledge-base.md",
purpose: FileUploadPurpose.Assistants);
// Create a vector store in the Foundry service using the uploaded file.
VectorStoreClient vectorStoreClient = openAIClient.GetVectorStoreClient();
ClientResult<VectorStore> vectorStoreCreate = await vectorStoreClient.CreateVectorStoreAsync(options: new VectorStoreCreationOptions()
{
Name = "contoso-outdoors-knowledge-base",
FileIds = { uploadResult.Value.Id }
});
var fileSearchTool = new HostedFileSearchTool() { Inputs = [new HostedVectorStoreContent(vectorStoreCreate.Value.Id)] };
AIAgent agent = await aiProjectClient
.CreateAIAgentAsync(
model: deploymentName,
name: "AskContoso",
instructions: "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
tools: [fileSearchTool]);
AgentThread thread = agent.GetNewThread();
Console.WriteLine(">> Asking about returns\n");
Console.WriteLine(await agent.RunAsync("Hi! I need help understanding the return policy.", thread));
Console.WriteLine("\n>> Asking about shipping\n");
Console.WriteLine(await agent.RunAsync("How long does standard shipping usually take?", thread));
Console.WriteLine("\n>> Asking about product care\n");
Console.WriteLine(await agent.RunAsync("What is the best way to maintain the TrailRunner tent fabric?", thread));
// Cleanup
await fileClient.DeleteFileAsync(uploadResult.Value.Id);
await vectorStoreClient.DeleteVectorStoreAsync(vectorStoreCreate.Value.Id);
await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);