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howieleung merged 2 commits into from
Feb 24, 2026
Merged

recordings #45318

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f3b8144
recordings
howieleung Feb 24, 2026
f71f85e
recording
howieleung Feb 24, 2026
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2 changes: 1 addition & 1 deletion sdk/ai/azure-ai-projects/assets.json
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Expand Up @@ -2,5 +2,5 @@
"AssetsRepo": "Azure/azure-sdk-assets",
"AssetsRepoPrefixPath": "python",
"TagPrefix": "python/ai/azure-ai-projects",
"Tag": "python/ai/azure-ai-projects_b88e0edce8"
"Tag": "python/ai/azure-ai-projects_8a24536cc3"
}
16 changes: 13 additions & 3 deletions sdk/ai/azure-ai-projects/samples/datasets/sample_datasets.py
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Expand Up @@ -20,7 +20,7 @@
Set these environment variables with your own values:
1) AZURE_AI_PROJECT_ENDPOINT - Required. The Azure AI Project endpoint, as found in the overview page of your
Microsoft Foundry project.
2) CONNECTION_NAME - Required. The name of the Azure Storage Account connection to use for uploading files.
2) CONNECTION_NAME - Optional. The name of the Azure Storage Account connection to use for uploading files.
3) DATASET_NAME - Optional. The name of the Dataset to create and use in this sample.
4) DATASET_VERSION_1 - Optional. The first version of the Dataset to create and use in this sample.
5) DATASET_VERSION_2 - Optional. The second version of the Dataset to create and use in this sample.
Expand All @@ -32,12 +32,12 @@
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import DatasetVersion
from azure.ai.projects.models import DatasetVersion, ConnectionType

load_dotenv()

endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
connection_name = os.environ["CONNECTION_NAME"]
connection_name = os.environ.get("CONNECTION_NAME")
dataset_name = os.environ.get("DATASET_NAME", "dataset-test")
dataset_version_1 = os.environ.get("DATASET_VERSION_1", "1.0")
dataset_version_2 = os.environ.get("DATASET_VERSION_2", "2.0")
Expand All @@ -52,6 +52,16 @@
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
):

if not connection_name:
try:
connection_name = project_client.connections.get_default(ConnectionType.AZURE_STORAGE_ACCOUNT).name
except Exception as e:
raise ValueError(
"Unable to resolve a default Azure Storage connection from this project endpoint. "
"Use a project endpoint configured with an Azure Storage account connection, "
"or set CONNECTION_NAME explicitly."
) from e

# [START datasets_sample]
print(
f"Upload a single file and create a new Dataset `{dataset_name}`, version `{dataset_version_1}`, to reference the file."
Expand Down
18 changes: 15 additions & 3 deletions sdk/ai/azure-ai-projects/samples/datasets/sample_datasets_async.py
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Expand Up @@ -20,7 +20,7 @@
Set these environment variables with your own values:
1) AZURE_AI_PROJECT_ENDPOINT - Required. The Azure AI Project endpoint, as found in the overview page of your
Microsoft Foundry project.
2) CONNECTION_NAME - Required. The name of the Azure Storage Account connection to use for uploading files.
2) CONNECTION_NAME - Optional. The name of the Azure Storage Account connection to use for uploading files.
3) DATASET_NAME - Optional. The name of the Dataset to create and use in this sample.
4) DATASET_VERSION_1 - Optional. The first version of the Dataset to create and use in this sample.
5) DATASET_VERSION_2 - Optional. The second version of the Dataset to create and use in this sample.
Expand All @@ -33,7 +33,7 @@
from dotenv import load_dotenv
from azure.identity.aio import DefaultAzureCredential
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import DatasetVersion
from azure.ai.projects.models import DatasetVersion, ConnectionType

load_dotenv()

Expand All @@ -46,7 +46,7 @@
async def main() -> None:

endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
connection_name = os.environ["CONNECTION_NAME"]
connection_name = os.environ.get("CONNECTION_NAME")
dataset_name = os.environ.get("DATASET_NAME", "dataset-test")
dataset_version_1 = os.environ.get("DATASET_VERSION_1", "1.0")
dataset_version_2 = os.environ.get("DATASET_VERSION_2", "2.0")
Expand All @@ -56,6 +56,18 @@ async def main() -> None:
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
):

if not connection_name:
try:
connection_name = (
await project_client.connections.get_default(ConnectionType.AZURE_STORAGE_ACCOUNT)
).name
except Exception as e:
raise ValueError(
"Unable to resolve a default Azure Storage connection from this project endpoint. "
"Use a project endpoint configured with an Azure Storage account connection, "
"or set CONNECTION_NAME explicitly."
) from e

print(
f"Upload a single file and create a new Dataset `{dataset_name}`, version `{dataset_version_1}`, to reference the file."
)
Expand Down
16 changes: 12 additions & 4 deletions sdk/ai/azure-ai-projects/samples/datasets/sample_datasets_download.py
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Expand Up @@ -21,7 +21,7 @@
Set these environment variables with your own values:
1) AZURE_AI_PROJECT_ENDPOINT - Required. The Azure AI Project endpoint, as found in the overview page of your
Microsoft Foundry project.
2) CONNECTION_NAME - Required. The name of the Azure Storage Account connection to use for uploading files.
2) CONNECTION_NAME - Optional. The name of the Azure Storage Account connection to use for uploading files.
3) DATASET_NAME - Optional. The name of the Dataset to create and use in this sample.
4) DATASET_VERSION - Optional. The version of the Dataset to create and use in this sample.
6) DATA_FOLDER - Optional. The folder path where the data files for upload are located.
Expand All @@ -30,18 +30,20 @@

import os
import re
import tempfile
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import ConnectionType
from azure.storage.blob import ContainerClient

load_dotenv()

endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
connection_name = os.environ["CONNECTION_NAME"]
connection_name = os.environ.get("CONNECTION_NAME")
dataset_name = os.environ.get("DATASET_NAME", "dataset-test")
dataset_version = os.environ.get("DATASET_VERSION", "1.0")
download_folder = os.environ.get("DOWNLOAD_FOLDER", "downloaded_blobs")
download_folder = os.environ.get("DOWNLOAD_FOLDER", os.path.join(tempfile.gettempdir(), "downloaded_blobs"))

# Construct the paths to the data folder and data file used in this sample
script_dir = os.path.dirname(os.path.abspath(__file__))
Expand All @@ -52,6 +54,10 @@
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
):

connection_name = (
connection_name or project_client.connections.get_default(ConnectionType.AZURE_STORAGE_ACCOUNT).name
)

print(
f"Upload files in a folder (including sub-folders) and create a dataset named `{dataset_name}` version `{dataset_version}`, to reference the files."
)
Expand Down Expand Up @@ -85,7 +91,9 @@
with open(file_path, "wb") as f:
f.write(blob_client.download_blob().readall())

print(f"Downloaded: {blob_name}")
print(f"Downloaded: {blob_name} -> {file_path}")

print(f"All files were downloaded to: {os.path.abspath(download_folder)}")

print("Delete the dataset created above:")
project_client.datasets.delete(name=dataset_name, version=dataset_version)
21 changes: 17 additions & 4 deletions sdk/ai/azure-ai-projects/tests/conftest.py
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Expand Up @@ -169,9 +169,22 @@ def sanitize_url_paths():
# Sanitize API key from service response (this includes Application Insights connection string)
add_body_key_sanitizer(json_path="credentials.key", value="sanitized-api-key")

# Sanitize SAS URI from Datasets get credential response
add_body_key_sanitizer(json_path="blobReference.credential.sasUri", value="sanitized-sas-uri")
add_body_key_sanitizer(json_path="blobReferenceForConsumption.credential.sasUri", value="sanitized-sas-uri")
# Sanitize GitHub personal access tokens that may appear in connection credentials
add_general_regex_sanitizer(regex=r"github_pat_[A-Za-z0-9_]+", value="sanitized-github-pat")
add_body_key_sanitizer(
json_path="$..authorization",
value="Bearer sanitized-github-pat",
regex=r"(?i)^Bearer\s+github_pat_[A-Za-z0-9_]+$",
)

# Sanitize Azure Blob account host while preserving container path and SAS shape.
# This avoids creating inconsistent recordings where sasUri points to a different
# container than the corresponding blob RequestUri entries.
add_general_regex_sanitizer(
regex=r"https://([a-z0-9-]+)\.blob\.core\.windows\.net",
value="Sanitized",
group_for_replace="1",
)

add_body_key_sanitizer(
json_path="$..project_connection_id",
Expand All @@ -183,7 +196,7 @@ def sanitize_url_paths():
add_body_key_sanitizer(
json_path="$.input",
value="sanitized-print-output",
regex=r"print contents array = .*",
regex=r"(?s)print contents array = .*",
)

# Remove Stainless headers from OpenAI client requests, since they include platform and OS specific info, which we can't have in recorded requests.
Expand Down
87 changes: 86 additions & 1 deletion sdk/ai/azure-ai-projects/tests/samples/test_samples.py
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Expand Up @@ -16,6 +16,7 @@
agent_tools_instructions,
agents_instructions,
memories_instructions,
resource_management_instructions,
get_sample_environment_variables_map,
)

Expand Down Expand Up @@ -47,7 +48,6 @@ class TestSamples(AzureRecordedTestCase):
"sample_agent_azure_function.py",
"sample_agent_computer_use.py",
"sample_agent_browser_automation.py",
"sample_agent_openapi.py",
],
),
)
Expand Down Expand Up @@ -102,3 +102,88 @@ def test_agents_samples(self, sample_path: str, **kwargs) -> None:
project_endpoint=kwargs["azure_ai_project_endpoint"],
model=kwargs["azure_ai_model_deployment_name"],
)

@pytest.mark.parametrize(
"sample_path",
get_sample_paths(
"connections",
samples_to_skip=[],
),
)
@servicePreparer()
@SamplePathPasser()
@recorded_by_proxy(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
def test_connections_samples(self, sample_path: str, **kwargs) -> None:
kwargs = kwargs.copy()
kwargs["connection_name"] = "mcp"
env_var_mapping = get_sample_environment_variables_map(kwargs)
executor = SyncSampleExecutor(self, sample_path, env_var_mapping=env_var_mapping, **kwargs)
executor.execute()
executor.validate_print_calls_by_llm(
instructions=resource_management_instructions,
project_endpoint=kwargs["azure_ai_project_endpoint"],
model=kwargs["azure_ai_model_deployment_name"],
)

@pytest.mark.parametrize(
"sample_path",
get_sample_paths(
"files",
samples_to_skip=[],
),
)
@servicePreparer()
@SamplePathPasser()
@recorded_by_proxy(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
def test_files_samples(self, sample_path: str, **kwargs) -> None:
env_var_mapping = get_sample_environment_variables_map(kwargs)
executor = SyncSampleExecutor(self, sample_path, env_var_mapping=env_var_mapping, **kwargs)
executor.execute()
executor.validate_print_calls_by_llm(
instructions=resource_management_instructions,
project_endpoint=kwargs["azure_ai_project_endpoint"],
model=kwargs["azure_ai_model_deployment_name"],
)

@pytest.mark.parametrize(
"sample_path",
get_sample_paths(
"deployments",
samples_to_skip=[],
),
)
@servicePreparer()
@SamplePathPasser()
@recorded_by_proxy(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
def test_deployments_samples(self, sample_path: str, **kwargs) -> None:
env_var_mapping = get_sample_environment_variables_map(kwargs)
executor = SyncSampleExecutor(self, sample_path, env_var_mapping=env_var_mapping, **kwargs)
executor.execute()
executor.validate_print_calls_by_llm(
instructions=resource_management_instructions,
project_endpoint=kwargs["azure_ai_project_endpoint"],
model=kwargs["azure_ai_model_deployment_name"],
)

@pytest.mark.parametrize(
"sample_path",
get_sample_paths(
"datasets",
samples_to_skip=[],
),
)
@servicePreparer()
@SamplePathPasser()
@recorded_by_proxy(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
def test_datasets_samples(self, sample_path: str, **kwargs) -> None:
env_var_mapping = get_sample_environment_variables_map(kwargs)
executor = SyncSampleExecutor(self, sample_path, env_var_mapping=env_var_mapping, **kwargs)
executor.execute()
if self.is_live:
# Don't replay LLM validation since there probably a defect in proxy server fail to replay
# Proxy server probably not able to parse the captured print content
executor.validate_print_calls_by_llm(
instructions=resource_management_instructions,
project_endpoint=kwargs["azure_ai_project_endpoint"],
model=kwargs["azure_ai_model_deployment_name"],
)
86 changes: 86 additions & 0 deletions sdk/ai/azure-ai-projects/tests/samples/test_samples_async.py
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Expand Up @@ -16,6 +16,7 @@
agent_tools_instructions,
memories_instructions,
agents_instructions,
resource_management_instructions,
get_sample_environment_variables_map,
)

Expand Down Expand Up @@ -89,3 +90,88 @@ async def test_agents_samples(self, sample_path: str, **kwargs) -> None:
project_endpoint=kwargs["azure_ai_project_endpoint"],
model=kwargs["azure_ai_model_deployment_name"],
)

@pytest.mark.parametrize(
"sample_path",
get_async_sample_paths(
"connections",
samples_to_skip=[],
),
)
@servicePreparer()
@SamplePathPasser()
@recorded_by_proxy_async(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
async def test_connections_samples(self, sample_path: str, **kwargs) -> None:
kwargs = kwargs.copy()
kwargs["connection_name"] = "mcp"
env_var_mapping = get_sample_environment_variables_map(kwargs)
executor = AsyncSampleExecutor(self, sample_path, env_var_mapping=env_var_mapping, **kwargs)
await executor.execute_async()
await executor.validate_print_calls_by_llm_async(
instructions=resource_management_instructions,
project_endpoint=kwargs["azure_ai_project_endpoint"],
model=kwargs["azure_ai_model_deployment_name"],
)

@pytest.mark.parametrize(
"sample_path",
get_async_sample_paths(
"files",
samples_to_skip=[],
),
)
@servicePreparer()
@SamplePathPasser()
@recorded_by_proxy_async(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
async def test_files_samples(self, sample_path: str, **kwargs) -> None:
env_var_mapping = get_sample_environment_variables_map(kwargs)
executor = AsyncSampleExecutor(self, sample_path, env_var_mapping=env_var_mapping, **kwargs)
await executor.execute_async()
await executor.validate_print_calls_by_llm_async(
instructions=resource_management_instructions,
project_endpoint=kwargs["azure_ai_project_endpoint"],
model=kwargs["azure_ai_model_deployment_name"],
)

@pytest.mark.parametrize(
"sample_path",
get_async_sample_paths(
"deployments",
samples_to_skip=[],
),
)
@servicePreparer()
@SamplePathPasser()
@recorded_by_proxy_async(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
async def test_deployments_samples(self, sample_path: str, **kwargs) -> None:
env_var_mapping = get_sample_environment_variables_map(kwargs)
executor = AsyncSampleExecutor(self, sample_path, env_var_mapping=env_var_mapping, **kwargs)
await executor.execute_async()
await executor.validate_print_calls_by_llm_async(
instructions=resource_management_instructions,
project_endpoint=kwargs["azure_ai_project_endpoint"],
model=kwargs["azure_ai_model_deployment_name"],
)

@pytest.mark.parametrize(
"sample_path",
get_async_sample_paths(
"datasets",
samples_to_skip=[],
),
)
@servicePreparer()
@SamplePathPasser()
@recorded_by_proxy_async(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
async def test_datasets_samples(self, sample_path: str, **kwargs) -> None:
env_var_mapping = get_sample_environment_variables_map(kwargs)
executor = AsyncSampleExecutor(self, sample_path, env_var_mapping=env_var_mapping, **kwargs)
await executor.execute_async()
if self.is_live:
# Don't replay LLM validation since there probably a defect in proxy server fail to replay
# Proxy server probably not able to parse the captured print content
await executor.validate_print_calls_by_llm_async(
instructions=resource_management_instructions,
project_endpoint=kwargs["azure_ai_project_endpoint"],
model=kwargs["azure_ai_model_deployment_name"],
)
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