upse agent endpoints

sdk/ai/azure-ai-projects/assets.json

@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "python",
   "TagPrefix": "python/ai/azure-ai-projects",
-  "Tag": "python/ai/azure-ai-projects_c84bf9341a"
+  "Tag": "python/ai/azure-ai-projects_f3cdb78ac6"
 }

sdk/ai/azure-ai-projects/samples/agents/__init__.py

@@ -0,0 +1 @@
+# Package marker for sample type-checking.

sdk/ai/azure-ai-projects/samples/agents/sample_agent_basic.py

@@ -25,58 +25,87 @@
        page of your Microsoft Foundry portal.
     2) FOUNDRY_MODEL_NAME - The deployment name of the AI model, as found under the "Name" column in
        the "Models + endpoints" tab in your Microsoft Foundry project.
+    3) FOUNDRY_AGENT_NAME - Optional. The name of the AI agent. If not set, defaults to "MyAgent".
 """
 
 import os
 from dotenv import load_dotenv
 from azure.identity import DefaultAzureCredential
 from azure.ai.projects import AIProjectClient
-from azure.ai.projects.models import PromptAgentDefinition
+from azure.ai.projects.models import (
+    AgentEndpointConfig,
+    FixedRatioVersionSelectionRule,
+    PromptAgentDefinition,
+    ProtocolConfiguration,
+    ResponsesProtocolConfiguration,
+    VersionSelector,
+)
 
 load_dotenv()
 
 endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+agent_name = os.environ.get("FOUNDRY_AGENT_NAME", "MyAgent")
 
 with (
     DefaultAzureCredential() as credential,
     AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
 ):
+    created_version = None
+    original_agent_endpoint = None
 
-    with project_client.get_openai_client() as openai_client:
-        agent = project_client.agents.create_version(
-            agent_name="MyAgent",
+    try:
+        created_version = project_client.agents.create_version(
+            agent_name=agent_name,
             definition=PromptAgentDefinition(
                 model=os.environ["FOUNDRY_MODEL_NAME"],
                 instructions="You are a helpful assistant that answers general questions",
             ),
         )
-        print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
 
-        conversation = openai_client.conversations.create(
-            items=[{"type": "message", "role": "user", "content": "What is the size of France in square miles?"}],
-        )
-        print(f"Created conversation with initial user message (id: {conversation.id})")
-
-        response = openai_client.responses.create(
-            conversation=conversation.id,
-            extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
-        )
-        print(f"Response output: {response.output_text}")
-
-        openai_client.conversations.items.create(
-            conversation_id=conversation.id,
-            items=[{"type": "message", "role": "user", "content": "And what is the capital city?"}],
-        )
-        print("Added a second user message to the conversation")
-
-        response = openai_client.responses.create(
-            conversation=conversation.id,
-            extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
+        original_agent_endpoint = project_client.agents.get(agent_name=agent_name).agent_endpoint
+        endpoint_config = AgentEndpointConfig(
+            version_selector=VersionSelector(
+                version_selection_rules=[
+                    FixedRatioVersionSelectionRule(agent_version=created_version.version, traffic_percentage=100),
+                ]
+            ),
+            protocol_configuration=ProtocolConfiguration(responses=ResponsesProtocolConfiguration()),
         )
-        print(f"Response output: {response.output_text}")
-
-        openai_client.conversations.delete(conversation_id=conversation.id)
-        print("Conversation deleted")
-
-    project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
-    print("Agent deleted")
+        project_client.agents.update_details(agent_name=agent_name, agent_endpoint=endpoint_config)
+        print(f"Agent endpoint configured for version {created_version.version}")
+
+        with project_client.get_openai_client(agent_name=agent_name) as openai_client:
+            agent = project_client.agents.get(agent_name=agent_name)
+            print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.versions.latest.version})")
+
+            conversation = openai_client.conversations.create(
+                items=[{"type": "message", "role": "user", "content": "What is the size of France in square miles?"}],
+            )
+            print(f"Created conversation with initial user message (id: {conversation.id})")
+
+            response = openai_client.responses.create(
+                conversation=conversation.id,
+            )
+            print(f"Response output: {response.output_text}")
+
+            openai_client.conversations.items.create(
+                conversation_id=conversation.id,
+                items=[{"type": "message", "role": "user", "content": "And what is the capital city?"}],
+            )
+            print("Added a second user message to the conversation")
+
+            response = openai_client.responses.create(
+                conversation=conversation.id,
+            )
+            print(f"Response output: {response.output_text}")
+
+            openai_client.conversations.delete(conversation_id=conversation.id)
+            print("Conversation deleted")
+    finally:
+        if original_agent_endpoint is not None:
+            project_client.agents.update_details(agent_name=agent_name, agent_endpoint=original_agent_endpoint)
+
+        if created_version is not None:
+            project_client.agents.delete_version(
+                agent_name=agent_name, agent_version=created_version.version, force=True
+            )

sdk/ai/azure-ai-projects/samples/agents/sample_agent_basic_async.py

@@ -25,64 +25,96 @@
        page of your Microsoft Foundry portal.
     2) FOUNDRY_MODEL_NAME - The deployment name of the AI model, as found under the "Name" column in
        the "Models + endpoints" tab in your Microsoft Foundry project.
+    3) FOUNDRY_AGENT_NAME - Optional. The name of the AI agent. If not set, defaults to "MyAgent".
 """
 
 import asyncio
 import os
 from dotenv import load_dotenv
 from azure.identity.aio import DefaultAzureCredential
 from azure.ai.projects.aio import AIProjectClient
-from azure.ai.projects.models import PromptAgentDefinition
+from azure.ai.projects.models import (
+    AgentEndpointConfig,
+    FixedRatioVersionSelectionRule,
+    PromptAgentDefinition,
+    ProtocolConfiguration,
+    ResponsesProtocolConfiguration,
+    VersionSelector,
+)
 
 load_dotenv()
 
 endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+agent_name = os.environ.get("FOUNDRY_AGENT_NAME", "MyAgent")
 
 
 async def main() -> None:
     async with (
         DefaultAzureCredential() as credential,
         AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
-        project_client.get_openai_client() as openai_client,
     ):
-
-        agent = await project_client.agents.create_version(
-            agent_name="MyAgent",
-            definition=PromptAgentDefinition(
-                model=os.environ["FOUNDRY_MODEL_NAME"],
-                instructions="You are a helpful assistant that answers general questions.",
-            ),
-        )
-        print(f"Agent created (name: {agent.name}, id: {agent.id}, version: {agent.version})")
-
-        conversation = await openai_client.conversations.create(
-            items=[{"type": "message", "role": "user", "content": "What is the size of France in square miles?"}],
-        )
-        print(f"Created conversation with initial user message (id: {conversation.id})")
-
-        response = await openai_client.responses.create(
-            conversation=conversation.id,
-            extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
-        )
-        print(f"Response output: {response.output_text}")
-
-        await openai_client.conversations.items.create(
-            conversation_id=conversation.id,
-            items=[{"type": "message", "role": "user", "content": "And what is the capital city?"}],
-        )
-        print("Added a second user message to the conversation")
-
-        response = await openai_client.responses.create(
-            conversation=conversation.id,
-            extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
-        )
-        print(f"Response output: {response.output_text}")
-
-        await openai_client.conversations.delete(conversation_id=conversation.id)
-        print("Conversation deleted")
-
-        await project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
-        print("Agent deleted")
+        created_version = None
+        original_agent_endpoint = None
+
+        try:
+            created_version = await project_client.agents.create_version(
+                agent_name=agent_name,
+                definition=PromptAgentDefinition(
+                    model=os.environ["FOUNDRY_MODEL_NAME"],
+                    instructions="You are a helpful assistant that answers general questions.",
+                ),
+            )
+
+            original_agent_endpoint = (await project_client.agents.get(agent_name=agent_name)).agent_endpoint
+            endpoint_config = AgentEndpointConfig(
+                version_selector=VersionSelector(
+                    version_selection_rules=[
+                        FixedRatioVersionSelectionRule(agent_version=created_version.version, traffic_percentage=100),
+                    ]
+                ),
+                protocol_configuration=ProtocolConfiguration(responses=ResponsesProtocolConfiguration()),
+            )
+            await project_client.agents.update_details(agent_name=agent_name, agent_endpoint=endpoint_config)
+            print(f"Agent endpoint configured for version {created_version.version}")
+
+            openai_client = project_client.get_openai_client(agent_name=agent_name)
+
+            agent = await project_client.agents.get(agent_name=agent_name)
+            print(f"Agent created (name: {agent.name}, id: {agent.id}, version: {agent.versions.latest.version})")
+
+            conversation = await openai_client.conversations.create(
+                items=[{"type": "message", "role": "user", "content": "What is the size of France in square miles?"}],
+            )
+            print(f"Created conversation with initial user message (id: {conversation.id})")
+
+            response = await openai_client.responses.create(
+                conversation=conversation.id,
+            )
+            print(f"Response output: {response.output_text}")
+
+            await openai_client.conversations.items.create(
+                conversation_id=conversation.id,
+                items=[{"type": "message", "role": "user", "content": "And what is the capital city?"}],
+            )
+            print("Added a second user message to the conversation")
+
+            response = await openai_client.responses.create(
+                conversation=conversation.id,
+            )
+            print(f"Response output: {response.output_text}")
+
+            await openai_client.conversations.delete(conversation_id=conversation.id)
+            print("Conversation deleted")
+        finally:
+            if original_agent_endpoint is not None:
+                await project_client.agents.update_details(
+                    agent_name=agent_name, agent_endpoint=original_agent_endpoint
+                )
+
+            if created_version is not None:
+                await project_client.agents.delete_version(
+                    agent_name=agent_name, agent_version=created_version.version, force=True
+                )
 
 
 if __name__ == "__main__":

sdk/ai/azure-ai-projects/samples/agents/sample_agent_retrieve_basic.py

@@ -26,37 +26,43 @@
        page of your Microsoft Foundry portal.
     2) FOUNDRY_MODEL_NAME - The deployment name of the AI model, as found under the "Name" column in
        the "Models + endpoints" tab in your Microsoft Foundry project.
+    3) FOUNDRY_AGENT_NAME - Optional. The name of the AI agent. If not set, defaults to "MyAgent".
 """
 
 import os
 from dotenv import load_dotenv
-from agent_retrieve_helper import create_and_retrieve_agent_and_conversation  # pylint: disable=import-error
+from util import create_version_with_endpoint
 from azure.identity import DefaultAzureCredential
 from azure.ai.projects import AIProjectClient
+from azure.ai.projects.models import PromptAgentDefinition
 
 load_dotenv()
 
 endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
 model = os.environ["FOUNDRY_MODEL_NAME"]
-
+agent_name = os.environ.get("FOUNDRY_AGENT_NAME", "MyAgent")
 with (
     DefaultAzureCredential() as credential,
     AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
-    # Creates prerequisite resources and yields (agent_name, conversation_id).
-    # Then automatically deletes the created agent version when this context manager exits.
-    create_and_retrieve_agent_and_conversation(project_client=project_client, model=model) as (
-        agent_name,
-        conversation_id,
+    create_version_with_endpoint(
+        project_client=project_client,
+        agent_name=agent_name,
+        definition=PromptAgentDefinition(
+            model=model,
+            instructions="You are a helpful assistant.",
+        ),
     ),
-    project_client.get_openai_client() as openai_client,
+    project_client.get_openai_client(agent_name=agent_name) as openai_client,
 ):
+    agent = project_client.agents.get(agent_name=agent_name)
+    conversation = openai_client.conversations.create()
+    print(f"Conversation created (id: {conversation.id})")
 
     # Retrieve latest version for the prerequisite agent.
-    agent = project_client.agents.get(agent_name=agent_name)
     print(f"Agent retrieved (id: {agent.id}, name: {agent.name}, version: {agent.versions.latest.version})")
 
     # Retrieve the prerequisite conversation.
-    conversation = openai_client.conversations.retrieve(conversation_id=conversation_id)
+    conversation = openai_client.conversations.retrieve(conversation_id=conversation.id)
     print(f"Retrieved conversation (id: {conversation.id})")
 
     # Add a new user text message to the conversation
@@ -68,6 +74,5 @@
 
     response = openai_client.responses.create(
         conversation=conversation.id,
-        extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
     )
     print(f"Response output: {response.output_text}")