azure-ai-agents-persistent-dotnet
microsoft/skills
Erstellen und verwalten Sie persistente KI-Agenten mit Threads, Nachrichten, Ausführungsläufen und Tools mithilfe des Azure AI Agents SDK für .NET.
...Alle erweiternAzure.AI.Agents.Persistent (.NET)
Low-Level-SDK zum Erstellen und Verwalten persistenter KI-Agenten mit Threads, Nachrichten, Ausführungsläufen und Tools.
Installation
dotnet add package Azure.AI.Agents.Persistent --prerelease
dotnet add package Azure.Identity
Aktuelle Versionen: Stable v1.1.0, Preview v1.2.0-beta.8
Umgebungsvariablen
PROJECT_ENDPOINT=https://.services.ai.azure.com/api/projects/ # Erforderlich: Endpunkt des Azure AI-Projekts
MODEL_DEPLOYMENT_NAME=gpt-4o-mini # Erforderlich: Name der Modellbereitstellung
AZURE_BING_CONNECTION_ID= # Erforderlich: Bing-Verbindungs-Ressourcen-ID
AZURE_AI_SEARCH_CONNECTION_ID= # Erforderlich: Azure AI Search-Verbindungs-Ressourcen-ID
AZURE_TOKEN_CREDENTIALS=prod # Nur erforderlich, wenn „DefaultAzureCredential“ in der Produktion verwendet wird
Authentifizierung
using Azure.AI.Agents.Persistent;
using Azure.Identity;
var projectEndpoint = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
// Lokale Entwicklung: „DefaultAzureCredential“. Produktion: Setzen Sie AZURE_TOKEN_CREDENTIALS=prod oder AZURE_TOKEN_CREDENTIALS=
var credential = new DefaultAzureCredential(
DefaultAzureCredential.DefaultEnvironmentVariableName
);
// Oder verwenden Sie in der Produktion direkt eine bestimmte Anmeldeinformation:
// Siehe https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
PersistentAgentsClient client = new(projectEndpoint, credential);
Client-Hierarchie
PersistentAgentsClient
├── Verwaltung → CRUD-Operationen für Agenten
├── Threads → Thread-Verwaltung
├── Nachrichten → Nachrichtenoperationen
├── Läufe → Ausführung und Streaming von Läufen
├── Dateien → Datei-Upload/Download
└── VectorStores → Verwaltung von Vector-Speichern
Kern-Workflow
1. Agent erstellen
var modelDeploymentName = Environment.GetEnvironmentVariable("MODEL_DEPLOYMENT_NAME");
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Math Tutor",
instructions: "Du bist ein persönlicher Mathe-Nachhilfelehrer. Schreibe und führe Code aus, um mathematische Fragen zu beantworten.",
tools: [new CodeInterpreterToolDefinition()]
);
2. Thread und Nachricht erstellen
// Thread erstellen
PersistentAgentThread thread = await client.Threads.CreateThreadAsync();
// Nachricht erstellen
await client.Messages.CreateMessageAsync(
thread.Id,
MessageRole.User,
"Ich muss die Gleichung `3x + 11 = 14` lösen. Kannst du mir helfen?"
);
3. Agenten ausführen (Polling)
// Lauf erstellen
ThreadRun run = await client.Runs.CreateRunAsync(
thread.Id,
agent.Id,
additionalInstructions: "Bitte sprich den Benutzer mit Jane Doe an."
);
// Auf Abschluss abfragen
do
{
await Task.Delay(TimeSpan.FromMilliseconds(500));
run = await client.Runs.GetRunAsync(thread.Id, run.Id);
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);
// Nachrichten abrufen
await foreach (PersistentThreadMessage message in client.Messages.GetMessagesAsync(
threadId: thread.Id,
order: ListSortOrder.Ascending))
{
Console.Write($"{message.Role}: ");
foreach (MessageContent content in message.ContentItems)
{
if (content is MessageTextContent textContent)
Console.WriteLine(textContent.Text);
}
}
4. Streaming-Antwort
AsyncCollectionResult stream = client.Runs.CreateRunStreamingAsync(
thread.Id,
agent.Id
);
await foreach (StreamingUpdate update in stream)
{
if (update.UpdateKind == StreamingUpdateReason.RunCreated)
{
Console.WriteLine("--- Lauf gestartet! ---");
}
else if (update is MessageContentUpdate contentUpdate)
{
Console.Write(contentUpdate.Text);
}
else if (update.UpdateKind == StreamingUpdateReason.RunCompleted)
{
Console.WriteLine("\n--- Lauf abgeschlossen! ---");
}
}
5. Funktionsaufruf
// Funktionstool definieren
FunctionToolDefinition weatherTool = new(
name: "getCurrentWeather",
description: "Ruft das aktuelle Wetter an einem Ort ab.",
parameters: BinaryData.FromObjectAsJson(new
{
Type = "object",
Properties = new
{
Location = new { Type = "string", Description = "Stadt und Bundesstaat, z. B. San Francisco, CA" },
Unit = new { Type = "string", Enum = new[] { "c", "f" } }
},
Required = new[] { „location“ }
}, new JsonSerializerOptions { PropertyNamingPolicy = JsonNamingPolicy.CamelCase })
);
// Agenten mit Funktion erstellen
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Weather Bot",
instructions: "Du bist ein Wetter-Bot.",
tools: [weatherTool]
);
// Funktionsaufrufe während des Pollings verarbeiten
do
{
await Task.Delay(500);
run = await client.Runs.GetRunAsync(thread.Id, run.Id);
if (run.Status == RunStatus.RequiresAction
&& run.RequiredAction ist SubmitToolOutputsAction submitAction)
{
List outputs = [];
foreach (RequiredToolCall toolCall in submitAction.ToolCalls)
{
if (toolCall ist RequiredFunctionToolCall funcCall)
{
// Funktion ausführen und Ergebnis abrufen
string result = ExecuteFunction(funcCall.Name, funcCall.Arguments);
outputs.Add(new ToolOutput(toolCall, result));
}
}
run = await client.Runs.SubmitToolOutputsToRunAsync(run, outputs, toolApprovals: null);
}
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);
6. Dateisuche mit Vector Store
// Datei hochladen
PersistentAgentFileInfo file = await client.Files.UploadFileAsync(
filePath: "document.txt",
purpose: PersistentAgentFilePurpose.Agents
);
// Vektorspeicher erstellen
PersistentAgentsVectorStore vectorStore = await client.VectorStores.CreateVectorStoreAsync(
fileIds: [file.Id],
name: "my_vector_store"
);
// Dateisuchressource erstellen
FileSearchToolResource fileSearchResource = new();
fileSearchResource.VectorStoreIds.Add(vectorStore.Id);
// Agent mit Dateisuche erstellen
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Document Assistant",
instructions: "Du hilfst Benutzern dabei, Informationen in Dokumenten zu finden.",
tools: [new FileSearchToolDefinition()],
toolResources: new ToolResources { FileSearch = fileSearchResource }
);
7. Bing-Grounding
var bingConnectionId = Environment.GetEnvironmentVariable("AZURE_BING_CONNECTION_ID");
BingGroundingToolDefinition bingTool = new(
new BingGroundingSearchToolParameters(
[new BingGroundingSearchConfiguration(bingConnectionId)]
)
);
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: „Search Agent“,
instructions: „Verwende Bing, um Fragen zu aktuellen Ereignissen zu beantworten.“,
tools: [bingTool]
);
8. Azure AI Search
AzureAISearchToolResource searchResource = new(
connectionId: searchConnectionId,
indexName: "my_index",
topK: 5,
filter: "category eq 'documentation'",
queryType: AzureAISearchQueryType.Simple
);
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: „Search Agent“,
Anweisungen: „Durchsuche den Dokumentationsindex, um Fragen zu beantworten.“,
tools: [new AzureAISearchToolDefinition()],
toolResources: new ToolResources { AzureAISearch = searchResource }
);
9. Aufräumen
await client.Threads.DeleteThreadAsync(thread.Id);
await client.Administration.DeleteAgentAsync(agent.Id);
await client.VectorStores.DeleteVectorStoreAsync(vectorStore.Id);
await client.Files.DeleteFileAsync(file.Id);
Verfügbare Tools
| Tool | Klasse | Zweck |
|---|---|---|
| Code-Interpreter | CodeInterpreterToolDefinition |
Python-Code ausführen, Visualisierungen erstellen |
| Dateisuche | Dateisuche-Tool-Definition |
Hochgeladene Dateien über Vektorspeicher durchsuchen |
| Funktionsaufruf | FunctionToolDefinition |
Benutzerdefinierte Funktionen aufrufen |
| Bing-Grounding | BingGroundingToolDefinition |
Websuche über Bing |
| Azure AI-Suche | AzureAISearchToolDefinition |
Azure AI Search-Indizes durchsuchen |
| OpenAPI | OpenApiToolDefinition |
Aufruf externer APIs über die OpenAPI-Spezifikation |
| Azure Functions | AzureFunctionToolDefinition |
Azure Functions aufrufen |
| MCP | MCPToolDefinition |
Modellkontext-Protokoll-Tools |
| SharePoint | SharePointToolDefinition |
Auf SharePoint-Inhalte zugreifen |
| Microsoft Fabric | MicrosoftFabricToolDefinition |
Auf Fabric-Daten zugreifen |
Streaming-Aktualisierungstypen
| Aktualisierungstyp | Beschreibung |
|---|---|
StreamingUpdateReason.RunCreated |
Lauf gestartet |
StreamingUpdateReason.RunInProgress |
Lauf wird verarbeitet |
StreamingUpdateReason.RunCompleted |
Lauf abgeschlossen |
StreamingUpdateReason.RunFailed |
Ausführung fehlgeschlagen |
MessageContentUpdate |
Textinhaltsblock |
Ausführungsschritt-Aktualisierung |
Änderung des Schrittstatus |
Referenz zu Schlüsseltypen
| Typ | Zweck |
|---|---|
PersistentAgentsClient |
Hauptzugangspunkt |
PersistentAgent |
Agent mit Modell, Anweisungen und Werkzeugen |
PersistentAgentThread |
Konversations-Thread |
PersistentThreadMessage |
Nachricht im Thread |
ThreadRun |
Ausführung des Agenten im Thread |
Ausführungsstatus |
In der Warteschlange, In Bearbeitung, Erfordert Aktion, Abgeschlossen, Fehlgeschlagen |
Tool-Ressourcen |
Kombinierte Tool-Ressourcen |
Tool-Ausgabe |
Antwort auf Funktionsaufruf |
Bewährte Verfahren
- Clients immer freigeben — Verwenden Sie
„using“-Anweisungenoder explizite Freigabe - Führen Sie Abfragen mit angemessenen Verzögerungen durch – empfohlen werden 500 ms zwischen den Statusprüfungen
- Ressourcen bereinigen – Threads und Agenten nach Abschluss löschen
- Alle Ausführungsstatus behandeln – Auf
„RequiresAction“,„Failed“und„Cancelled“prüfen - Verwenden Sie Streaming für eine Echtzeit-Benutzererfahrung – Bessere Benutzererfahrung als bei Abfragen
- Speichern Sie IDs statt Objekte – Verweisen Sie auf Agenten/Threads anhand ihrer ID
- Asynchrone Methoden verwenden – Alle Operationen sollten asynchron sein
Fehlerbehandlung
using Azure;
try
{
var agent = await client.Administration.CreateAgentAsync(...);
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
Console.WriteLine("Ressource nicht gefunden");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"Fehler: {ex.Status} - {ex.ErrorCode}: {ex.Message}");
}
Zugehörige SDKs
| SDK | Zweck | Installieren |
|---|---|---|
Azure.AI.Agents.Persistent |
Low-Level-Agenten (dieses SDK) | dotnet add package Azure.AI.Agents.Persistent |
Azure.AI.Projects |
High-Level-Projekt-Client | dotnet add package Azure.AI.Projects |
Referenzlinks
| Ressource | URL |
|---|---|
| NuGet-Paket | https://www.nuget.org/packages/Azure.AI.Agents.Persistent |
| API-Referenz | https://learn.microsoft.com/dotnet/api/azure.ai.agents.persistent |
| GitHub-Quellcode | https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent |
| Beispiele | https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent/samples |
---
name: azure-ai-agents-persistent-dotnet
description: Create and manage persistent AI agents with threads, messages, runs, and tools using the Azure AI Agents SDK for .NET.
license: MIT
---
# Azure.AI.Agents.Persistent (.NET)
Low-level SDK for creating and managing persistent AI agents with threads, messages, runs, and tools.
## Installation
```bash
dotnet add package Azure.AI.Agents.Persistent --prerelease
dotnet add package Azure.Identity
```
**Current Versions**: Stable v1.1.0, Preview v1.2.0-beta.8
## Environment Variables
```bash
PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project> # Required: Azure AI project endpoint
MODEL_DEPLOYMENT_NAME=gpt-4o-mini # Required: model deployment name
AZURE_BING_CONNECTION_ID=<bing-connection-resource-id> # Required: Bing connection resource ID
AZURE_AI_SEARCH_CONNECTION_ID=<search-connection-resource-id> # Required: Azure AI Search connection resource ID
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
```
## Authentication
```csharp
using Azure.AI.Agents.Persistent;
using Azure.Identity;
var projectEndpoint = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
var credential = new DefaultAzureCredential(
DefaultAzureCredential.DefaultEnvironmentVariableName
);
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
PersistentAgentsClient client = new(projectEndpoint, credential);
```
## Client Hierarchy
```
PersistentAgentsClient
├── Administration → Agent CRUD operations
├── Threads → Thread management
├── Messages → Message operations
├── Runs → Run execution and streaming
├── Files → File upload/download
└── VectorStores → Vector store management
```
## Core Workflow
### 1. Create Agent
```csharp
var modelDeploymentName = Environment.GetEnvironmentVariable("MODEL_DEPLOYMENT_NAME");
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Math Tutor",
instructions: "You are a personal math tutor. Write and run code to answer math questions.",
tools: [new CodeInterpreterToolDefinition()]
);
```
### 2. Create Thread and Message
```csharp
// Create thread
PersistentAgentThread thread = await client.Threads.CreateThreadAsync();
// Create message
await client.Messages.CreateMessageAsync(
thread.Id,
MessageRole.User,
"I need to solve the equation `3x + 11 = 14`. Can you help me?"
);
```
### 3. Run Agent (Polling)
```csharp
// Create run
ThreadRun run = await client.Runs.CreateRunAsync(
thread.Id,
agent.Id,
additionalInstructions: "Please address the user as Jane Doe."
);
// Poll for completion
do
{
await Task.Delay(TimeSpan.FromMilliseconds(500));
run = await client.Runs.GetRunAsync(thread.Id, run.Id);
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);
// Retrieve messages
await foreach (PersistentThreadMessage message in client.Messages.GetMessagesAsync(
threadId: thread.Id,
order: ListSortOrder.Ascending))
{
Console.Write($"{message.Role}: ");
foreach (MessageContent content in message.ContentItems)
{
if (content is MessageTextContent textContent)
Console.WriteLine(textContent.Text);
}
}
```
### 4. Streaming Response
```csharp
AsyncCollectionResult<StreamingUpdate> stream = client.Runs.CreateRunStreamingAsync(
thread.Id,
agent.Id
);
await foreach (StreamingUpdate update in stream)
{
if (update.UpdateKind == StreamingUpdateReason.RunCreated)
{
Console.WriteLine("--- Run started! ---");
}
else if (update is MessageContentUpdate contentUpdate)
{
Console.Write(contentUpdate.Text);
}
else if (update.UpdateKind == StreamingUpdateReason.RunCompleted)
{
Console.WriteLine("\n--- Run completed! ---");
}
}
```
### 5. Function Calling
```csharp
// Define function tool
FunctionToolDefinition weatherTool = new(
name: "getCurrentWeather",
description: "Gets the current weather at a location.",
parameters: BinaryData.FromObjectAsJson(new
{
Type = "object",
Properties = new
{
Location = new { Type = "string", Description = "City and state, e.g. San Francisco, CA" },
Unit = new { Type = "string", Enum = new[] { "c", "f" } }
},
Required = new[] { "location" }
}, new JsonSerializerOptions { PropertyNamingPolicy = JsonNamingPolicy.CamelCase })
);
// Create agent with function
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Weather Bot",
instructions: "You are a weather bot.",
tools: [weatherTool]
);
// Handle function calls during polling
do
{
await Task.Delay(500);
run = await client.Runs.GetRunAsync(thread.Id, run.Id);
if (run.Status == RunStatus.RequiresAction
&& run.RequiredAction is SubmitToolOutputsAction submitAction)
{
List<ToolOutput> outputs = [];
foreach (RequiredToolCall toolCall in submitAction.ToolCalls)
{
if (toolCall is RequiredFunctionToolCall funcCall)
{
// Execute function and get result
string result = ExecuteFunction(funcCall.Name, funcCall.Arguments);
outputs.Add(new ToolOutput(toolCall, result));
}
}
run = await client.Runs.SubmitToolOutputsToRunAsync(run, outputs, toolApprovals: null);
}
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);
```
### 6. File Search with Vector Store
```csharp
// Upload file
PersistentAgentFileInfo file = await client.Files.UploadFileAsync(
filePath: "document.txt",
purpose: PersistentAgentFilePurpose.Agents
);
// Create vector store
PersistentAgentsVectorStore vectorStore = await client.VectorStores.CreateVectorStoreAsync(
fileIds: [file.Id],
name: "my_vector_store"
);
// Create file search resource
FileSearchToolResource fileSearchResource = new();
fileSearchResource.VectorStoreIds.Add(vectorStore.Id);
// Create agent with file search
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Document Assistant",
instructions: "You help users find information in documents.",
tools: [new FileSearchToolDefinition()],
toolResources: new ToolResources { FileSearch = fileSearchResource }
);
```
### 7. Bing Grounding
```csharp
var bingConnectionId = Environment.GetEnvironmentVariable("AZURE_BING_CONNECTION_ID");
BingGroundingToolDefinition bingTool = new(
new BingGroundingSearchToolParameters(
[new BingGroundingSearchConfiguration(bingConnectionId)]
)
);
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Search Agent",
instructions: "Use Bing to answer questions about current events.",
tools: [bingTool]
);
```
### 8. Azure AI Search
```csharp
AzureAISearchToolResource searchResource = new(
connectionId: searchConnectionId,
indexName: "my_index",
topK: 5,
filter: "category eq 'documentation'",
queryType: AzureAISearchQueryType.Simple
);
PersistentAgent agent = await client.Administration.CreateAgentAsync(
model: modelDeploymentName,
name: "Search Agent",
instructions: "Search the documentation index to answer questions.",
tools: [new AzureAISearchToolDefinition()],
toolResources: new ToolResources { AzureAISearch = searchResource }
);
```
### 9. Cleanup
```csharp
await client.Threads.DeleteThreadAsync(thread.Id);
await client.Administration.DeleteAgentAsync(agent.Id);
await client.VectorStores.DeleteVectorStoreAsync(vectorStore.Id);
await client.Files.DeleteFileAsync(file.Id);
```
## Available Tools
| Tool | Class | Purpose |
|------|-------|---------|
| Code Interpreter | `CodeInterpreterToolDefinition` | Execute Python code, generate visualizations |
| File Search | `FileSearchToolDefinition` | Search uploaded files via vector stores |
| Function Calling | `FunctionToolDefinition` | Call custom functions |
| Bing Grounding | `BingGroundingToolDefinition` | Web search via Bing |
| Azure AI Search | `AzureAISearchToolDefinition` | Search Azure AI Search indexes |
| OpenAPI | `OpenApiToolDefinition` | Call external APIs via OpenAPI spec |
| Azure Functions | `AzureFunctionToolDefinition` | Invoke Azure Functions |
| MCP | `MCPToolDefinition` | Model Context Protocol tools |
| SharePoint | `SharepointToolDefinition` | Access SharePoint content |
| Microsoft Fabric | `MicrosoftFabricToolDefinition` | Access Fabric data |
## Streaming Update Types
| Update Type | Description |
|-------------|-------------|
| `StreamingUpdateReason.RunCreated` | Run started |
| `StreamingUpdateReason.RunInProgress` | Run processing |
| `StreamingUpdateReason.RunCompleted` | Run finished |
| `StreamingUpdateReason.RunFailed` | Run errored |
| `MessageContentUpdate` | Text content chunk |
| `RunStepUpdate` | Step status change |
## Key Types Reference
| Type | Purpose |
|------|---------|
| `PersistentAgentsClient` | Main entry point |
| `PersistentAgent` | Agent with model, instructions, tools |
| `PersistentAgentThread` | Conversation thread |
| `PersistentThreadMessage` | Message in thread |
| `ThreadRun` | Execution of agent against thread |
| `RunStatus` | Queued, InProgress, RequiresAction, Completed, Failed |
| `ToolResources` | Combined tool resources |
| `ToolOutput` | Function call response |
## Best Practices
1. **Always dispose clients** — Use `using` statements or explicit disposal
2. **Poll with appropriate delays** — 500ms recommended between status checks
3. **Clean up resources** — Delete threads and agents when done
4. **Handle all run statuses** — Check for `RequiresAction`, `Failed`, `Cancelled`
5. **Use streaming for real-time UX** — Better user experience than polling
6. **Store IDs not objects** — Reference agents/threads by ID
7. **Use async methods** — All operations should be async
## Error Handling
```csharp
using Azure;
try
{
var agent = await client.Administration.CreateAgentAsync(...);
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
Console.WriteLine("Resource not found");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"Error: {ex.Status} - {ex.ErrorCode}: {ex.Message}");
}
```
## Related SDKs
| SDK | Purpose | Install |
|-----|---------|---------|
| `Azure.AI.Agents.Persistent` | Low-level agents (this SDK) | `dotnet add package Azure.AI.Agents.Persistent` |
| `Azure.AI.Projects` | High-level project client | `dotnet add package Azure.AI.Projects` |
## Reference Links
| Resource | URL |
|----------|-----|
| NuGet Package | https://www.nuget.org/packages/Azure.AI.Agents.Persistent |
| API Reference | https://learn.microsoft.com/dotnet/api/azure.ai.agents.persistent |
| GitHub Source | https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent |
| Samples | https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent/samples |
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