Merge pull request #6381 from dargilco/main

Update Python Agents code snippets

Author: prmerger-automator[bot]

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/search/search-agentic-retrieval-how-to-pipeline.md

@@ -203,7 +203,7 @@ def agentic_retrieval() -> str:
         Be sure to use the same format in your agent's response
     """
     # Take the last 5 messages in the conversation
-    messages = project_client.agents.list_messages(thread.id, limit=5, order=ListSortOrder.DESCENDING)
+    messages = project_client.agents.messages.list(thread.id, limit=5, order=ListSortOrder.DESCENDING)
     # Reverse the order so the most recent message is last
     messages.data.reverse()
     retrieval_result = retrieval_result = agent_client.retrieve(