Merge pull request #3299 from dargilco/dargilco/rename-to-agent-id

Update `assistant_id` to `agent_id`, when using Agents in azure-ai-projects SDK

Author: AnnaMHuff

articles/ai-services/agents/how-to/tools/azure-functions.md

@@ -232,7 +232,7 @@ agent = project_client.agents.create_agent(
 ```
 
 # [REST API](#tab/rest)
-Follow the [REST API Quickstart](../../quickstart.md?pivots=rest-api) to set the right values for the environment variables `AZURE_AI_AGENTS_TOKEN` and `AZURE_AI_AGENTS_ENDPOINT`. The create the agent using:
+Follow the [REST API Quickstart](../../quickstart.md?pivots=rest-api) to set the right values for the environment variables `AZURE_AI_AGENTS_TOKEN` and `AZURE_AI_AGENTS_ENDPOINT`. Then create the agent using:
 ```console
 curl $AZURE_AI_AGENTS_ENDPOINT/assistants?api-version=2024-12-01-preview \
   -H "Authorization: Bearer $AZURE_AI_AGENTS_TOKEN" \
@@ -309,7 +309,7 @@ message = project_client.agents.create_message(
 print(f"Created message, message ID: {message.id}")
 
 # Run the agent
-run = project_client.agents.create_run(thread_id=thread.id, assistant_id=agent.id)
+run = project_client.agents.create_run(thread_id=thread.id, agent_id=agent.id)
 # Monitor and process the run status. The function call should be placed on the input queue by the Agent Service for the Azure Function to pick up when requires_action is returned
 while run.status in ["queued", "in_progress", "requires_action"]:
     time.sleep(1)

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

@@ -344,7 +344,7 @@ Create a run and observe that the model uses the Grounding with Bing Search tool
 
 ```python
 # Create and process agent run in thread with tools
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
 print(f"Run finished with status: {run.status}")
 
 # Retrieve run step details to get Bing Search query link

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

@@ -260,7 +260,7 @@ message = project_client.agents.create_message(
 print(f"Created message, message ID: {message.id}")
 
 # create and execute a run
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
 print(f"Run finished with status: {run.status}")
 
 if run.status == "failed":

articles/ai-services/agents/how-to/tools/function-calling.md

@@ -369,7 +369,7 @@ curl $AZURE_AI_AGENTS_ENDPOINT/threads/thread_abc123/messages?api-version=2024-1
 
 ```python
 # Create and process agent run in thread with tools
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
 print(f"Run finished with status: {run.status}")
 
 if run.status == "failed":

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

@@ -321,7 +321,7 @@ Create a run and observe that the model uses the OpenAPI Spec tool to provide a
     print(f"Created message, ID: {message.id}")
 
     # Create and process agent run in thread with tools
-    run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+    run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
     print(f"Run finished with status: {run.status}")
 
     if run.status == "failed":

articles/ai-services/agents/how-to/virtual-networks.md

@@ -246,7 +246,7 @@ with project_client:
     print(f"Created message, message ID: {message.id}")
 
     # Run the agent
-    run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+    run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
     print(f"Run finished with status: {run.status}")
 
     if run.status == "failed":

articles/ai-services/agents/includes/azure-search/code-examples.md

@@ -262,7 +262,7 @@ message = project_client.agents.create_message(
 print(f"Created message, message ID: {message.id}")
 
 # Run the agent
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
 print(f"Run finished with status: {run.status}")
 
 if run.status == "failed":

articles/ai-services/agents/includes/file-search/azure-blob-storage-code-examples.md

@@ -78,7 +78,7 @@ message = project_client.agents.create_message(
 )
 print(f"Created message, message ID: {message.id}")
 
-run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent_1.id)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent_1.id)
 
 project_client.agents.delete_vector_store(vector_store.id)
 print("Deleted vector store")

articles/ai-services/agents/includes/file-search/deep-dive.md

@@ -38,7 +38,7 @@ batch = project_client.agents.create_vector_store_file_batch_and_poll(
 Files can be removed from a vector store by either:
 
 * Deleting the vector store file object or,
-* Deleting the underlying file object, which removes the file it from all vector_store and code_interpreter configurations across all agents and threads in your organization
+* Deleting the underlying file object, which removes the file from all vector_store and code_interpreter configurations across all agents and threads in your organization
 
 The maximum file size is 512 MB. Each file should contain no more than 5,000,000 tokens per file (computed automatically when you attach a file).
 
@@ -52,7 +52,7 @@ file_search_tool.remove_vector_store(vector_store.id)
 print(f"Removed vector store from file search, vector store ID: {vector_store.id}")
 
 project_client.agents.update_agent(
-    assistant_id=agent.id, tools=file_search_tool.definitions, tool_resources=file_search_tool.resources
+    agent_id=agent.id, tools=file_search_tool.definitions, tool_resources=file_search_tool.resources
 )
 print(f"Updated agent, agent ID: {agent.id}")
 

articles/ai-services/agents/includes/file-search/upload-files-code-examples.md

@@ -310,7 +310,7 @@ curl $AZURE_AI_AGENTS_ENDPOINT/threads/thread_abc123/messages?api-version=2024-1
 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)
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
 print(f"Created run, run ID: {run.id}")
 
 project_client.agents.delete_vector_store(vector_store.id)