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Add basic Agent streaming sample (#44251)
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‎sdk/ai/azure-ai-projects/samples/agents/sample_agent_stream_events.py‎

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# ------------------------------------
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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# ------------------------------------
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"""
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DESCRIPTION:
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This sample demonstrates how to run basic Prompt Agent operations
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using the synchronous AIProjectClient and OpenAI clients. The response
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is streamed by setting `stream=True` in the `.responses.create` call.
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The OpenAI compatible Responses and Conversation calls in this sample are made using
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the OpenAI client from the `openai` package. See https://platform.openai.com/docs/api-reference
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for more information.
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USAGE:
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python sample_agent_stream_events.py
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Before running the sample:
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pip install "azure-ai-projects>=2.0.0b1" python-dotenv
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Set these environment variables with your own values:
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1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
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page of your Microsoft Foundry portal.
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2) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
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the "Models + endpoints" tab in your Microsoft Foundry project.
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"""
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import os
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from dotenv import load_dotenv
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from azure.identity import DefaultAzureCredential
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from azure.ai.projects import AIProjectClient
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from azure.ai.projects.models import PromptAgentDefinition
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load_dotenv()
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endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
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with (
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DefaultAzureCredential() as credential,
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AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
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project_client.get_openai_client() as openai_client,
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):
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agent = project_client.agents.create_version(
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agent_name="MyAgent",
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definition=PromptAgentDefinition(
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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instructions="You are a helpful assistant that answers general questions",
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),
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)
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print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
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conversation = openai_client.conversations.create(
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items=[{"type": "message", "role": "user", "content": "Tell me about the capital city of France"}],
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)
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print(f"Created conversation with initial user message (id: {conversation.id})")
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with openai_client.responses.create(
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conversation=conversation.id,
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extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
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input="",
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stream=True,
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) as response_stream_events:
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for event in response_stream_events:
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if event.type == "response.created":
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print(f"Stream response created with ID: {event.response.id}\n")
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elif event.type == "response.output_text.delta":
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print(event.delta, end="", flush=True)
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elif event.type == "response.text.done":
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print(f"\n\nResponse text done. Access final text in 'event.text'")
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elif event.type == "response.completed":
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print(f"\n\nResponse completed. Access final text in 'event.response.output_text'")
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openai_client.conversations.delete(conversation_id=conversation.id)
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print("Conversation deleted")
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project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
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print("Agent deleted")

‎sdk/ai/azure-ai-projects/samples/responses/sample_responses_stream_events.py‎

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# ------------------------------------
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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# ------------------------------------
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"""
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DESCRIPTION:
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This sample demonstrates how to run a basic streaming responses operation
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using OpenAI client `.responses.create()` method with `stream=True`.
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See also https://platform.openai.com/docs/guides/streaming-responses?api-mode=responses&lang=python
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Note also the alternative streaming approach shown in sample_responses_stream_manager.py.
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USAGE:
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python sample_responses_stream_events.py
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Before running the sample:
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pip install "azure-ai-projects>=2.0.0b1" python-dotenv
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Set these environment variables with your own values:
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1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
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page of your Microsoft Foundry portal.
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2) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
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the "Models + endpoints" tab in your Microsoft Foundry project.
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"""
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import os
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from dotenv import load_dotenv
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from azure.identity import DefaultAzureCredential
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from azure.ai.projects import AIProjectClient
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load_dotenv()
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endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
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with (
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DefaultAzureCredential() as credential,
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AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
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project_client.get_openai_client() as openai_client,
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):
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with openai_client.responses.create(
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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input=[
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{"role": "user", "content": "Tell me about the capital city of France"},
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],
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stream=True,
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) as response_stream_events:
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for event in response_stream_events:
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if event.type == "response.created":
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print(f"Stream response created with ID: {event.response.id}\n")
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elif event.type == "response.output_text.delta":
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print(event.delta, end="", flush=True)
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elif event.type == "response.text.done":
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print(f"\n\nResponse text done. Access final text in 'event.text'")
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elif event.type == "response.completed":
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print(f"\n\nResponse completed. Access final text in 'event.response.output_text'")

‎sdk/ai/azure-ai-projects/samples/responses/sample_responses_stream_manager.py‎

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"""
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DESCRIPTION:
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This sample demonstrates how to stream through responses.stream that returns a responses stream manager.
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This sample demonstrates how to run a basic streaming responses operation
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using OpenAI client `.responses.stream()` method.
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See also https://platform.openai.com/docs/api-reference/responses/create?lang=python
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Note also the alternative streaming approach shown in sample_responses_stream_events.py.
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USAGE:
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python sample_responses_stream_method.py
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python sample_responses_stream_manager.py
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Before running the sample:
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import os
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from dotenv import load_dotenv
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from azure.identity import DefaultAzureCredential, get_bearer_token_provider
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from azure.identity import DefaultAzureCredential
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from azure.ai.projects import AIProjectClient
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load_dotenv()
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AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
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project_client.get_openai_client() as openai_client,
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):
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# [START response_stream_method]
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response = openai_client.responses.create(
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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input=[
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{"role": "user", "content": "What is the size of France in square miles?"},
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],
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stream=False, # Create non-streaming response
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)
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print(f"Initial response: {response.output_text}")
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print(f"Response ID: {response.id}")
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# Now create a streaming version using the same input but with stream=True
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# This demonstrates an alternative approach since response.stream() may not be available
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with openai_client.responses.stream(
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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input=[
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{"role": "user", "content": "Now tell me about the capital city of France."},
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{"role": "user", "content": "Tell me about the capital city of France"},
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],
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previous_response_id=response.id, # Continue the conversation
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) as responses_stream_manager:
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) as response_stream_manager:
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# Process streaming events as they arrive
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for event in responses_stream_manager:
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for event in response_stream_manager:
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if event.type == "response.created":
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print(f"Stream response created with ID: {event.response.id}")
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print(f"Stream response created with ID: {event.response.id}\n")
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elif event.type == "response.output_text.delta":
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print(f"Delta: {event.delta}")
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print(event.delta, end="", flush=True)
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elif event.type == "response.text.done":
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print(f"Response done with full message: {event.text}")
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print(f"\n\nResponse text done. Access final text in 'event.text'")
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elif event.type == "response.completed":
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print(f"Response completed with full message: {event.response.output_text}")
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# [END response_stream_method]
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print(f"\n\nResponse completed. Access final text in 'event.response.output_text'")

‎sdk/ai/azure-ai-projects/samples/responses/sample_responses_streaming.py‎

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This file was deleted.

‎sdk/ai/azure-ai-projects/tests/agents/tools/multitool/test_agent_file_search_and_function.py‎

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vector_store = openai_client.vector_stores.create(name="ResearchStore")
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print(f"Vector store created: {vector_store.id}")
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file1 = BytesIO(doc1_content.encode("utf-8"))
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file1.name = "ml_healthcare.txt"
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file2 = BytesIO(doc2_content.encode("utf-8"))

‎sdk/ai/azure-ai-projects/tests/responses/test_responses_async.py‎

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from devtools_testutils.aio import recorded_by_proxy_async
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from devtools_testutils import RecordedTransport
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class TestResponsesAsync(TestBase):
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# To run this test:

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