Merge branch 'MicrosoftDocs:main' into samuel100/quickstart-patch1

Author: samuel100

.openpublishing.redirection.json

@@ -6,6 +6,11 @@
       "redirect_document_id": false
     },
     {
+      "source_path": "articles/ai-foundry/how-to/develop/vscode.md",
+      "redirect_url": "/azure/ai-foundry/how-to/develop/get-started-projects-vs-code",
+      "redirect_document_id": false
+    },
+        {
       "source_path": "articles/ai-foundry/azure-openai-in-azure-ai-foundry.md",
       "redirect_url": "/azure/ai-foundry/what-is-azure-ai-foundry",
       "redirect_document_id": false

articles/ai-foundry/agents/how-to/connected-agents.md

@@ -275,11 +275,11 @@ To create a multi-agent setup, follow these steps:
 1. Create a thread and add a message to it.
 
     ```python
-    thread = project_client.agents.create_thread()
+    thread = project_client.agents.threads.create()
     print(f"Created thread, ID: {thread.id}")
 
     # Create message to thread
-    message = project_client.agents.create_message(
+    message = project_client.agents.messages.create(
         thread_id=thread.id,
         role=MessageRole.USER,
         content="What is the stock price of Microsoft?",
@@ -293,7 +293,7 @@ To create a multi-agent setup, follow these steps:
     ```python
 
     # Create and process Agent run in thread with tools
-    run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
+    run = project_client.agents.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
     print(f"Run finished with status: {run.status}")
 
     if run.status == "failed":
@@ -312,7 +312,7 @@ To create a multi-agent setup, follow these steps:
 
     ```python
     # Print the Agent's response message with optional citation
-    response_message = project_client.agents.list_messages(thread_id=thread.id).get_last_message_by_role(
+    response_message = project_client.agents.messages.list(thread_id=thread.id).get_last_message_by_role(
         MessageRole.AGENT
     )
     if response_message:

articles/ai-foundry/agents/how-to/tools/bing-grounding.md

@@ -89,7 +89,7 @@ When you add the Grounding with Bing Search tool to your agent, you can pass the
 According to Grounding with Bing's [terms of use and use and display requirements](https://www.microsoft.com/en-us/bing/apis/grounding-legal#use-and-display-requirements), you need to display both website URLs and Bing search query URLs in your custom interface. You can find website URLs through `annotations` parameter in API response and Bing search query URLs through `runstep` details. To render the webpage, we recommend you replace the endpoint of Bing search query URLs with `www.bing.com` and your Bing search query URL would look like "https://www.bing.com/search?q={search query}"
 
 ```python
-run_steps = project_client.agents.list_run_steps(run_id=run.id, thread_id=thread.id)
+run_steps = project_client.agents.runs_steps.list(run_id=run.id, thread_id=thread.id)
 run_steps_data = run_steps['data']
 print(f"Last run step detail: {run_steps_data}")
 ```

articles/ai-foundry/agents/how-to/tools/code-interpreter-samples.md

@@ -66,7 +66,7 @@ project_client = AIProjectClient(
 The sample uploads a data file for analysis:
 
 ```python
-file = project_client.agents.upload_file_and_poll(
+file = project_client.agents.files.upload_and_poll(
     file_path="nifty_500_quarterly_results.csv", 
     purpose=FilePurpose.AGENTS
 )
@@ -122,7 +122,7 @@ messages = project_client.agents.messages.list(thread_id=thread.id)
 for image_content in messages.image_contents:
     file_id = image_content.image_file.file_id
     file_name = f"{file_id}_image_file.png"
-    project_client.agents.save_file(file_id=file_id, file_name=file_name)
+    project_client.agents.files.save(file_id=file_id, file_name=file_name)
 
 # Process file path annotations
 for file_path_annotation in messages.file_path_annotations:
@@ -136,7 +136,7 @@ for file_path_annotation in messages.file_path_annotations:
 After completing the interaction, the code properly cleans up resources:
 
 ```python
-project_client.agents.delete_file(file.id)
+project_client.agents.files.delete(file.id)
 project_client.agents.delete_agent(agent.id)
 ```
 

articles/ai-foundry/agents/how-to/tools/file-search-upload-files.md

@@ -136,7 +136,7 @@ if run.status == "failed":
 project_client.agents.vector_stores.delete(vector_store.id)
 print("Deleted vector store")
 
-project_client.agents.delete_file(file_id=file.id)
+project_client.agents.files.delete(file_id=file.id)
 print("Deleted file")
 
 project_client.agents.delete_agent(agent.id)

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

@@ -98,11 +98,11 @@ Files can also be added to a vector store after it's created by creating vector
 ```python
 
 # create a vector store with no file and wait for it to be processed
-vector_store = project_client.agents.create_vector_store_and_poll(data_sources=[], name="sample_vector_store")
+vector_store = project_client.agents.vector_stores.create_and_poll(data_sources=[], name="sample_vector_store")
 print(f"Created vector store, vector store ID: {vector_store.id}")
 
 # add the file to the vector store or you can supply file ids in the vector store creation
-vector_store_file_batch = project_client.agents.create_vector_store_file_batch_and_poll(
+vector_store_file_batch = project_client.agents.vector_store_file_batches.create_and_poll(
     vector_store_id=vector_store.id, file_ids=[file.id]
 )
 print(f"Created vector store file batch, vector store file batch ID: {vector_store_file_batch.id}")
@@ -112,7 +112,7 @@ print(f"Created vector store file batch, vector store file batch ID: {vector_sto
 Alternatively, you can add several files to a vector store by creating batches of up to 500 files.
 
 ```python
-batch = project_client.agents.create_vector_store_file_batch_and_poll(
+batch = project_client.agents.vector_store_file_batches.create_and_poll(
   vector_store_id=vector_store.id,
   file_ids=[file_1.id, file_2.id, file_3.id, file_4.id, file_5.id]
 )
@@ -144,7 +144,7 @@ print(f"Updated agent, agent ID: {agent.id}")
 
 ## Deleting vector stores
 ```python
-project_client.agents.delete_vector_store(vector_store.id)
+project_client.agents.vector_stores.delete(vector_store.id)
 print("Deleted vector store")
 ```
 
@@ -155,7 +155,7 @@ For basic agent setup, the `file_search` tool uses the `vector_stores`  object a
 To help you manage the costs associated with these vector_store objects, we added support for expiration policies in the `vector_store` object. You can set these policies when creating or updating the `vector_store` object.
 
 ```python
-vector_store = project_client.agents.create_vector_store_and_poll(
+vector_store = project_client.agents.vector_stores.create_and_poll(
   name="Product Documentation",
   file_ids=[file_1.id],
   expires_after={

articles/ai-foundry/agents/how-to/tools/model-context-protocol-samples.md

@@ -139,7 +139,7 @@ Create the run, check the output, and examine what tools were called during the
 
 ## Perform cleanup
 
-After the interaction is complete, the script performs cleanup by deleting the created agent resource via `agents_client.delete_agent()` to avoid leaving unused resources. It also fetches and prints the entire message history from the thread by using `agents_client.list_messages()` for review or logging.
+After the interaction is complete, the script performs cleanup by deleting the created agent resource via `agents_client.delete_agent()` to avoid leaving unused resources. It also fetches and prints the entire message history from the thread by using `agents_client.messages.list()` for review or logging.
 
 ```python
         # Delete the agent resource to clean up

articles/ai-foundry/agents/how-to/tools/openapi-spec-samples.md

@@ -155,7 +155,7 @@ Create the run, check the output, and examine what tools were called during the
 
 
 ## Cleanup
-After the interaction is complete, the script performs cleanup by deleting the created agent resource using `agents_client.delete_agent()` to avoid leaving unused resources. It also fetches and prints the entire message history from the thread using `agents_client.list_messages()` for review or logging.
+After the interaction is complete, the script performs cleanup by deleting the created agent resource using `agents_client.delete_agent()` to avoid leaving unused resources. It also fetches and prints the entire message history from the thread using `agents_client.messages.list()` for review or logging.
 
 ```python
         # Delete the agent resource to clean up

articles/ai-foundry/how-to/continuous-evaluation-agents.md

@@ -58,16 +58,16 @@ agent = project_client.agents.create_agent(
 )
 
 # Create thread and process user message
-thread = project_client.agents.create_thread()
-project_client.agents.create_message(thread_id=thread.id, role="user", content="Hello, what Contoso products do you know?")
-run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
+thread = project_client.agents.threads.create()
+project_client.agents.messages.create(thread_id=thread.id, role="user", content="Hello, what Contoso products do you know?")
+run = project_client.agents.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
 
 # Handle run status
 if run.status == "failed":
     print(f"Run failed: {run.last_error}")
 
 # Print thread messages
-for message in project_client.agents.list_messages(thread_id=thread.id).text_messages:
+for message in project_client.agents.messages.list(thread_id=thread.id).text_messages:
     print(message)
 
 ```

articles/ai-foundry/how-to/costs-plan-manage.md

@@ -59,7 +59,7 @@ When you create resources for a hub, resources for other Azure services are also
 | --- | --- | 
 | [Azure AI services](https://azure.microsoft.com/pricing/details/cognitive-services/) | You pay to use services such as Azure OpenAI, Speech, Content Safety, Vision, Document Intelligence, and Language. Costs vary for each service and for some features within each service. For more information about provisioning of Azure AI services, see [Azure AI Foundry hubs](../concepts/ai-resources.md).| 
 | [Azure AI Search](https://azure.microsoft.com/pricing/details/search/) | An example use case is to store data in a [vector search index](./index-add.md). |
-| [Azure Machine Learning](https://azure.microsoft.com/pricing/details/machine-learning/) | Compute instances are needed to run [Visual Studio Code (Web or Desktop)](./develop/vscode.md) and [prompt flow](./prompt-flow.md) via Azure AI Foundry.<br/><br/>When you create a compute instance, the virtual machine (VM) stays on so it's available for your work.<br/><br/>Enable idle shutdown to save on cost when the VM is idle for a specified time period.<br/><br/>Or set up a schedule to automatically start and stop the compute instance to save cost when you aren't planning to use it. | 
+| [Azure Machine Learning](https://azure.microsoft.com/pricing/details/machine-learning/) | Compute instances are needed to run [prompt flow](./prompt-flow.md) via Azure AI Foundry.<br/><br/>When you create a compute instance, the virtual machine (VM) stays on so it's available for your work.<br/><br/>Enable idle shutdown to save on cost when the VM is idle for a specified time period.<br/><br/>Or set up a schedule to automatically start and stop the compute instance to save cost when you aren't planning to use it. | 
 | [Azure Virtual Machine](https://azure.microsoft.com/pricing/details/virtual-machines/) | Azure Virtual Machines gives you the flexibility of virtualization for a wide range of computing solutions with support for Linux, Windows Server, SQL Server, Oracle, IBM, SAP, and more. |
 | [Azure Container Registry Basic account](https://azure.microsoft.com/pricing/details/container-registry) | Provides storage of private Docker container images, enabling fast, scalable retrieval, and network-close deployment of container workloads on Azure. |
 | [Azure Blob Storage](https://azure.microsoft.com/pricing/details/storage/blobs/) | Can be used to store Azure AI Foundry project files. |