chore: import upstream snapshot with attribution

2026-07-13 13:39:25 +08:00
commit db620d33df
5151 changed files with 925932 additions and 0 deletions
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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.AI.Projects.Agents;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Foundry;
+using OpenAI;
+using OpenAI.Files;
+using OpenAI.Responses;
+using OpenAI.VectorStores;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// Create an AI Project client and get an OpenAI client that works with the foundry service.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(
+    new Uri(endpoint),
+    new DefaultAzureCredential());
+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 }
+});
+
+// Use the native OpenAI SDK FileSearchTool directly with the vector store ID.
+#pragma warning disable OPENAI001
+FileSearchTool fileSearchTool = new([vectorStoreCreate.Value.Id]);
+#pragma warning restore OPENAI001
+
+ProjectsAgentVersion agentVersion = await aiProjectClient.AgentAdministrationClient.CreateAgentVersionAsync(
+    "AskContoso",
+    new ProjectsAgentVersionCreationOptions(
+        new DeclarativeAgentDefinition(model: deploymentName)
+        {
+            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 }
+        }));
+FoundryAgent agent = aiProjectClient.AsAIAgent(agentVersion);
+
+AgentSession session = await agent.CreateSessionAsync();
+
+Console.WriteLine(">> Asking about returns\n");
+Console.WriteLine(await agent.RunAsync("Hi! I need help understanding the return policy.", session));
+
+Console.WriteLine("\n>> Asking about shipping\n");
+Console.WriteLine(await agent.RunAsync("How long does standard shipping usually take?", session));
+
+Console.WriteLine("\n>> Asking about product care\n");
+Console.WriteLine(await agent.RunAsync("What is the best way to maintain the TrailRunner tent fabric?", session));
+
+// Cleanup
+await fileClient.DeleteFileAsync(uploadResult.Value.Id);
+await vectorStoreClient.DeleteVectorStoreAsync(vectorStoreCreate.Value.Id);
+await aiProjectClient.AgentAdministrationClient.DeleteAgentAsync(agent.Name);