adding response format samples

sdk/ai/azure-ai-agents/samples/agents_response_formats/sample_agents_json_object_response_format.py

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+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    This sample demonstrates how to create an agent with json response format.
+
+USAGE:
+    python sample_agents_json_object_response_format.py
+
+    Before running the sample:
+
+    pip install azure-ai-projects azure-ai-agents azure-identity
+
+    Set these environment variables with your own values:
+    1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
+                          page of your Azure AI Foundry portal.
+    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
+       the "Models + endpoints" tab in your Azure AI Foundry project.
+"""
+
+import os, time
+from azure.ai.projects import AIProjectClient
+from azure.identity import DefaultAzureCredential
+from azure.ai.agents.models import ListSortOrder, AgentsResponseFormat
+
+project_client = AIProjectClient(
+    endpoint=os.environ["PROJECT_ENDPOINT"],
+    credential=DefaultAzureCredential(),
+)
+
+with project_client:
+    agents_client = project_client.agents
+
+    agent = agents_client.create_agent(
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
+        name="my-agent",
+        instructions="You are helpful agent. You will respond with a JSON object.",
+        response_format=AgentsResponseFormat(type="json_object")
+    )
+    print(f"Created agent, agent ID: {agent.id}")
+
+    thread = agents_client.threads.create()
+    print(f"Created thread, thread ID: {thread.id}")
+
+    # List all threads for the agent
+    threads = agents_client.threads.list()
+
+    message = agents_client.messages.create(thread_id=thread.id, role="user", content="Hello, give me a list of planets in our solar system.")
+    print(f"Created message, message ID: {message.id}")
+
+    run = agents_client.runs.create(thread_id=thread.id, agent_id=agent.id)
+
+    # Poll the run as long as run status is queued or in progress
+    while run.status in ["queued", "in_progress", "requires_action"]:
+        # Wait for a second
+        time.sleep(1)
+        run = agents_client.runs.get(thread_id=thread.id, run_id=run.id)
+        print(f"Run status: {run.status}")
+
+    if run.status == "failed":
+        print(f"Run error: {run.last_error}")
+
+    agents_client.delete_agent(agent.id)
+    print("Deleted agent")
+
+    messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
+    for msg in messages:
+        if msg.text_messages:
+            last_text = msg.text_messages[-1]
+            print(f"{msg.role}: {last_text.text.value}")

sdk/ai/azure-ai-agents/samples/agents_response_formats/sample_agents_json_schema_response_format.py

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+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    This sample demonstrates how to use agents with JSON schema output format.
+
+USAGE:
+    python sample_agents_json_schema_response_format.py
+
+    Before running the sample:
+
+    pip install azure-ai-projects azure-ai-agents azure-identity pydantic
+
+    Set these environment variables with your own values:
+    1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
+                          page of your Azure AI Foundry portal.
+    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
+       the "Models + endpoints" tab in your Azure AI Foundry project.
+"""
+
+import os
+
+from enum import Enum
+from pydantic import BaseModel, TypeAdapter
+from azure.ai.projects import AIProjectClient
+from azure.identity import DefaultAzureCredential
+from azure.ai.agents.models import (
+    ListSortOrder,
+    MessageTextContent,
+    MessageRole,
+    ResponseFormatJsonSchema,
+    ResponseFormatJsonSchemaType,
+    RunStatus,
+)
+
+
+# Create the pydantic model to represent the planet names and there masses.
+class Planets(str, Enum):
+    Earth = "Earth"
+    Mars = "Mars"
+    Mercury = "Mercury"
+
+class Planet(BaseModel):
+    planet: Planets
+    mass: float
+
+
+project_client = AIProjectClient(
+    endpoint=os.environ["PROJECT_ENDPOINT"],
+    credential=DefaultAzureCredential(),
+)
+
+with project_client:
+    agents_client = project_client.agents
+
+    agent = agents_client.create_agent(
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
+        name="my-agent",
+        instructions="Extract the information about planets.",
+        response_format=ResponseFormatJsonSchemaType(
+            json_schema=ResponseFormatJsonSchema(
+                name="planet_mass",
+                description="Extract planet mass.",
+                schema=Planet.model_json_schema(),
+            )
+        ),
+    )
+    print(f"Created agent, agent ID: {agent.id}")
+
+    thread = agents_client.threads.create()
+    print(f"Created thread, thread ID: {thread.id}")
+
+    message = agents_client.messages.create(
+        thread_id=thread.id,
+        role="user",
+        content=("The mass of the Mars is 6.4171E23 kg; the mass of the Earth is 5.972168E24 kg;"),
+    )
+    print(f"Created message, message ID: {message.id}")
+
+    run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
+
+    if run.status != RunStatus.COMPLETED:
+        print(f"The run did not succeed: {run.status=}.")
+
+    agents_client.delete_agent(agent.id)
+    print("Deleted agent")
+
+    messages = agents_client.messages.list(
+        thread_id=thread.id,
+        order=ListSortOrder.ASCENDING,
+    )
+
+    for msg in messages:
+        if msg.role == MessageRole.AGENT:
+            last_part = msg.content[-1]
+            if isinstance(last_part, MessageTextContent):
+                planet = TypeAdapter(Planet).validate_json(last_part.text.value)
+                print(f"The mass of {planet.planet} is {planet.mass} kg.")

sdk/ai/azure-ai-agents/samples/agents_response_formats/sample_agents_text_response_format.py

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+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    This sample demonstrates how to create an agent with text response format.
+
+USAGE:
+    python sample_agents_text_response_format.py
+
+    Before running the sample:
+
+    pip install azure-ai-projects azure-ai-agents azure-identity
+
+    Set these environment variables with your own values:
+    1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
+                          page of your Azure AI Foundry portal.
+    2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
+       the "Models + endpoints" tab in your Azure AI Foundry project.
+"""
+
+import os, time
+from azure.ai.projects import AIProjectClient
+from azure.identity import DefaultAzureCredential
+from azure.ai.agents.models import ListSortOrder, AgentsResponseFormat
+
+project_client = AIProjectClient(
+    endpoint=os.environ["PROJECT_ENDPOINT"],
+    credential=DefaultAzureCredential(),
+)
+
+with project_client:
+    agents_client = project_client.agents
+
+    agent = agents_client.create_agent(
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
+        name="my-agent",
+        instructions="You are helpful agent.",
+        response_format=AgentsResponseFormat(type="text")
+    )
+    print(f"Created agent, agent ID: {agent.id}")
+
+    thread = agents_client.threads.create()
+    print(f"Created thread, thread ID: {thread.id}")
+
+    # List all threads for the agent
+    threads = agents_client.threads.list()
+
+    message = agents_client.messages.create(thread_id=thread.id, role="user", content="Hello, give me a list of planets in our solar system.")
+    print(f"Created message, message ID: {message.id}")
+
+    run = agents_client.runs.create(thread_id=thread.id, agent_id=agent.id)
+
+    # Poll the run as long as run status is queued or in progress
+    while run.status in ["queued", "in_progress", "requires_action"]:
+        # Wait for a second
+        time.sleep(1)
+        run = agents_client.runs.get(thread_id=thread.id, run_id=run.id)
+        print(f"Run status: {run.status}")
+
+    if run.status == "failed":
+        print(f"Run error: {run.last_error}")
+
+    agents_client.delete_agent(agent.id)
+    print("Deleted agent")
+
+    messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
+    for msg in messages:
+        if msg.text_messages:
+            last_text = msg.text_messages[-1]
+            print(f"{msg.role}: {last_text.text.value}")