fixing tab errors

Author: fosteramanda

articles/ai-services/agents/how-to/tools/file-search.md

@@ -85,253 +85,16 @@ As a fallback, there's a 60-second maximum wait in the run object when the threa
 
 ::: zone pivot="upload-files-code-examples"
 
-## Quickstart – Upload Local Files with file search 
-
-In this example, we’ll use Azure AI Agent Service to create an agent that can help answer questions on information you upload from local files.  
-
-##  Prerequisites 
-Complete the [agent setup](../../quickstart.md).
-
-## Step 1: Create a project client
-
-Create a client object, which will contain the connection string for connecting to your AI project and other resources. 
-
-# [Python](#tab/python)
-
-```python
-import os
-from azure.ai.projects import AIProjectClient
-from azure.ai.projects.models import FileSearchTool, VectorStoreDataSource, VectorStoreDataSourceAssetType
-from azure.identity import DefaultAzureCredential
-
-
-# Create an Azure AI Client from a connection string, copied from your AI Studio project.
-# At the moment, it should be in the format "<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<ProjectName>"
-# Customer needs to login to Azure subscription via Azure CLI and set the environment variables
-
-credential = DefaultAzureCredential()
-project_client = AIProjectClient.from_connection_string(
-    credential=credential, conn_str=os.environ["PROJECT_CONNECTION_STRING"] 
-)
-```
-
-# [C#](#tab/csharp)
-
-```csharp
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Threading.Tasks;
-using Azure.Core.TestFramework;
-using NUnit.Framework;
-
-// Create an Azure AI Client from a connection string, copied from your AI Studio project.
-// At the moment, it should be in the format "<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<ProjectName>"
-// Customer needs to login to Azure subscription via Azure CLI and set the environment variables
-var connectionString = TestEnvironment.AzureAICONNECTIONSTRING;
-AgentsClient client = new AgentsClient(connectionString, new DefaultAzureCredential());
-```
-
----
-
-## Step 2: Upload files and add them to a Vector Store
-
-To access your files, the file search tool uses the vector store object. Upload your files and create a vector store to contain them. Once the vector store is created, you should poll its status until all files are out of the `in_progress` state to ensure that all content has finished processing. The SDK provides helpers for uploading and polling.
-
-Vector stores are created using message attachments that have a default expiration policy of seven days after they were last active (defined as the last time the vector store was part of a run). This default exists to help you manage your vector storage costs. You can override these expiration policies at any time.
-
-# [Python](#tab/python)
-
-```python
-# We will upload the local file to Azure and will use it for vector store creation.
-_, asset_uri = project_client.upload_file("./data/product_info_1.md")
-print(f"Uploaded file, asset URI: {asset_uri}")
-# create a vector store with no file and wait for it to be processed
-ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
-vector_store = project_client.agents.create_vector_store_and_poll(data_sources=[ds], name="sample_vector_store")
-print(f"Created vector store, vector store ID: {vector_store.id}")
-```
-
-# [C#](#tab/csharp)
-
-```csharp
-// Upload a file and wait for it to be processed
-File.WriteAllText(
-    path: "sample_file_for_upload.txt",
-    contents: "The word 'apple' uses the code 442345, while the word 'banana' uses the code 673457.");
-Response<AgentFile> uploadAgentFileResponse = await client.UploadFileAsync(
-    filePath: "sample_file_for_upload.txt",
-    purpose: AgentFilePurpose.Agents);
-
-AgentFile uploadedAgentFile = uploadAgentFileResponse.Value;
-
-// Create a vector store with the file and wait for it to be processed.
-// If you do not specify a vector store, create_message will create a vector store with a default expiration policy of seven days after they were last active
-VectorStore vectorStore = await client.CreateVectorStoreAsync(
-    fileIds:  new List<string> { uploadedAgentFile.Id },
-    name: "my_vector_store");
-```
----
-
-## Step 3: Enable file search
-
-To make the files accessible to your agent, create a `FileSearchTool` object with the `vector_store` ID, and attach `tools` and `tool_resources` to the agent.
-
-# [Python](#tab/python)
-
-```python
-
-# create a file search tool
-file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])
-
-# notices that FileSearchTool as tool and tool_resources must be added or the agent will be unable to search the file
-agent = project_client.agents.create_agent(
-    model="gpt-4o-mini",
-    name="my-agent",
-    instructions="You are a helpful agent",
-    tools=file_search_tool.definitions,
-    tool_resources=file_search_tool.resources,
-)
-print(f"Created agent, agent ID: {agent.id}")
-```
-
-# [C#](#tab/csharp)
-
-```csharp
-FileSearchToolResource fileSearchToolResource = new FileSearchToolResource();
-fileSearchToolResource.VectorStoreIds.Add(vectorStore.Id);
-
-// Create an agent with toolResources and process assistant run
-Response<Agent> agentResponse = await client.CreateAgentAsync(
-        model: "gpt-4o-mini",
-        name: "SDK Test Agent - Retrieval",
-        instructions: "You are a helpful agent that can help fetch data from files you know about.",
-        tools: new List<ToolDefinition> { new FileSearchToolDefinition() },
-        toolResources: new ToolResources() { FileSearch = fileSearchToolResource });
-Agent agent = agentResponse.Value;
-```
-
----
-
-## Step 4: Create a thread
-
-# [Python](#tab/python)
-
-```python
-thread = project_client.agents.create_thread()
-print(f"Created thread, thread ID: {thread.id}")
-
-message = project_client.agents.create_message(
-    thread_id=thread.id, role="user", content="What feature does Smart Eyewear offer?"
-)
-print(f"Created message, message ID: {message.id}")
-```
-
-# [C#](#tab/csharp)
-
-```csharp
-// Create thread for communication
-Response<AgentThread> threadResponse = await client.CreateThreadAsync();
-AgentThread thread = threadResponse.Value;
-
-// Create message to thread
-Response<ThreadMessage> messageResponse = await client.CreateMessageAsync(
-    thread.Id,
-    MessageRole.User,
-    "Can you give me the documented codes for 'banana' and 'orange'?");
-ThreadMessage message = messageResponse.Value;
-```
----
-
-## Step 5: Create a run and check the output
-Create a run and observe that the model uses the file search tool to provide a response to the user's question.
-# [Python](#tab/python)
-```python
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
-print(f"Created run, run ID: {run.id}")
-
-project_client.agents.delete_vector_store(vector_store.id)
-print("Deleted vector store")
-
-project_client.agents.delete_agent(agent.id)
-print("Deleted agent")
-
-messages = project_client.agents.list_messages(thread_id=thread.id)
-print(f"Messages: {messages}")
-```
-
-# [C#](#tab/csharp)
-
-```csharp
-// Run the agent
-Response<ThreadRun> runResponse = await client.CreateRunAsync(thread, agent);
-
-do
-{
-    await Task.Delay(TimeSpan.FromMilliseconds(500));
-    runResponse = await client.GetRunAsync(thread.Id, runResponse.Value.Id);
-}
-while (runResponse.Value.Status == RunStatus.Queued
-    || runResponse.Value.Status == RunStatus.InProgress);
-
-Response<PageableList<ThreadMessage>> afterRunMessagesResponse
-    = await client.GetMessagesAsync(thread.Id);
-IReadOnlyList<ThreadMessage> messages = afterRunMessagesResponse.Value.Data;
-
-// Note: messages iterate from newest to oldest, with the messages[0] being the most recent
-foreach (ThreadMessage threadMessage in messages)
-{
-    Console.Write($"{threadMessage.CreatedAt:yyyy-MM-dd HH:mm:ss} - {threadMessage.Role,10}: ");
-    foreach (MessageContent contentItem in threadMessage.ContentItems)
-    {
-        if (contentItem is MessageTextContent textItem)
-        {
-            Console.Write(textItem.Text);
-        }
-        else if (contentItem is MessageImageFileContent imageFileItem)
-        {
-            Console.Write($"<image from ID: {imageFileItem.FileId}");
-        }
-        Console.WriteLine();
-    }
-}
-```
----
-
+[!INCLUDE [upload-files-code-examples](../../includes/file-search/upload-files-code-examples.md)]
 
 ::: zone-end
 
 ::: zone pivot="azure-blob-storage-code-examples"
-## Quickstart – Use existing files in Azure Blob Storage with file search
-In this example, we’ll use Azure AI Agent Service to create an agent that can help answer questions on information from files in Azure Blob Storage.
 
-###  Prerequisites 
-Complete the [standard agent setup](../../quickstart.md).
-
-> [!NOTE]
-> Azure Blob Storage is only available with the standard agent setup. The basic agent setup does not support this file source.
-
-### Step 1: Create a project client
-# [Python](#tab/python)
-
-```python
-import os
-from azure.ai.projects import AIProjectClient
-from azure.ai.projects.models import FileSearchTool, VectorStoreDataSource, VectorStoreDataSourceAssetType
-from azure.identity import DefaultAzureCredential
-
-
-# Create an Azure AI Client from a connection string, copied from your AI Studio project.
-# At the moment, it should be in the format "<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<ProjectName>"
-# Customer needs to login to Azure subscription via Azure CLI and set the environment variables
-
-credential = DefaultAzureCredential()
-project_client = AIProjectClient.from_connection_string(
-    credential=credential, conn_str=os.environ["PROJECT_CONNECTION_STRING"]
-)
-```
+[!INCLUDE [azure-blob-storage-code-examples](../../includes/file-search/azure-blob-storage-code-examples.md)]
 
 ::: zone-end
+
 ::: zone pivot="supported-filetypes"
 
 ### Supported file types