Address Azure SDK review meeting comment and other fixes / minor changes

sdk/ai/azure-ai-projects/README.md

@@ -154,7 +154,7 @@ If not present the default Azure OpenAI connection will be used.
 <!-- SNIPPET:sample_chat_completions_with_azure_openai_client.aoai_sample-->
 
 ```python
-with project_client.inference.get_azure_openai_client(api_version="2024-06-01") as client:
+with project_client.inference.get_azure_openai_client(api_version="2024-10-21") as client:
 
     response = client.chat.completions.create(
         model=model_deployment_name,
@@ -239,7 +239,7 @@ print(
 dataset: DatasetVersion = project_client.datasets.upload_file(
     name=dataset_name,
     version=dataset_version_1,
-    file="sample_folder/sample_file1.txt",
+    file_path="sample_folder/sample_file1.txt",
 )
 print(dataset)
 
@@ -280,7 +280,9 @@ folder in the [package samples][samples].
 <!-- SNIPPET:sample_indexes.indexes_sample-->
 
 ```python
-print(f"Create Index `{index_name}` with version `{index_version}`, referencing an existing AI Search resource:")
+print(
+    f"Create Index `{index_name}` with version `{index_version}`, referencing an existing AI Search resource:"
+)
 index = project_client.indexes.create_or_update(
     name=index_name,
     version=index_version,

sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/_patch_datasets_async.py

@@ -100,7 +100,7 @@ async def _create_dataset_and_get_its_container_client(
         )
 
     @distributed_trace_async
-    async def upload_file(self, *, name: str, version: str, file: str, **kwargs: Any) -> DatasetVersion:
+    async def upload_file(self, *, name: str, version: str, file_path: str, **kwargs: Any) -> DatasetVersion:
         """Upload file to a blob storage, and create a dataset that references this file.
         This method uses the `ContainerClient.upload_blob` method from the azure-storage-blob package
         to upload the file. Any keyword arguments provided will be passed to the `upload_blob` method.
@@ -109,31 +109,31 @@ async def upload_file(self, *, name: str, version: str, file: str, **kwargs: Any
         :paramtype name: str
         :keyword version: The version identifier for the dataset. Required.
         :paramtype version: str
-        :keyword file: The file name (including optional path) to be uploaded. Required.
-        :paramtype file: str
+        :keyword file_path: The file name (including optional path) to be uploaded. Required.
+        :paramtype file_path: str
         :return: The created dataset version.
         :rtype: ~azure.ai.projects.models.DatasetVersion
         :raises ~azure.core.exceptions.HttpResponseError: If an error occurs during the HTTP request.
         """
 
-        path_file = Path(file)
-        if not path_file.exists():
-            raise ValueError("The provided file does not exist.")
-        if path_file.is_dir():
-            raise ValueError("The provided file is actually a folder. Use method `create_and_upload_folder` instead")
+        pathlib_file_path = Path(file_path)
+        if not pathlib_file_path.exists():
+            raise ValueError(f"The provided file `{file_path}` does not exist.")
+        if pathlib_file_path.is_dir():
+            raise ValueError("The provided file is actually a folder. Use method `upload_folder` instead")
 
         container_client, output_version = await self._create_dataset_and_get_its_container_client(
             name=name, input_version=version
         )
 
         async with container_client:
 
-            with open(file=file, mode="rb") as data:  # TODO: What is the best async options for file reading?
+            with open(file=file_path, mode="rb") as data:  # TODO: What is the best async options for file reading?
 
-                blob_name = path_file.name  # Extract the file name from the path.
+                blob_name = pathlib_file_path.name  # Extract the file name from the path.
                 logger.debug(
                     "[upload_file] Start uploading file `%s` as blob `%s`.",
-                    file,
+                    file_path,
                     blob_name,
                 )
 
@@ -173,9 +173,9 @@ async def upload_folder(self, *, name: str, version: str, folder: str, **kwargs:
         """
         path_folder = Path(folder)
         if not Path(path_folder).exists():
-            raise ValueError("The provided folder does not exist.")
+            raise ValueError(f"The provided folder `{folder}` does not exist.")
         if Path(path_folder).is_file():
-            raise ValueError("The provided folder is actually a file. Use method `create_and_upload_file` instead.")
+            raise ValueError("The provided folder is actually a file. Use method `upload_file` instead.")
 
         container_client, output_version = await self._create_dataset_and_get_its_container_client(
             name=name, input_version=version

sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/_patch_inference_async.py

@@ -250,7 +250,7 @@ async def get_azure_openai_client(
         # If the connection uses API key authentication, we need to make another service call to get
         # the connection with API key populated.
         if connection.credentials.type == CredentialType.API_KEY:
-            connection = await self._outer_instance.connections.get_with_credentials(name=connection_name, **kwargs)
+            connection = await self._outer_instance.connections._get_with_credentials(name=connection_name, **kwargs) # pylint: disable=protected-access
 
         logger.debug("[InferenceOperations.get_azure_openai_client] connection = %s", str(connection))
 

sdk/ai/azure-ai-projects/azure/ai/projects/aio/operations/_patch_telemetry_async.py

@@ -48,17 +48,17 @@ async def get_connection_string(self) -> str:
             # Returns an empty Iterable if no connections exits.
             connections: AsyncIterable[Connection] = self._outer_instance.connections.list(
                 connection_type=ConnectionType.APPLICATION_INSIGHTS,
-                default_connection=True,
             )
 
+            # Note: there can't be more than one AppInsights connection.
             connection_name: Optional[str] = None
             async for connection in connections:
                 connection_name = connection.name
                 break
             if not connection_name:
                 raise ResourceNotFoundError("No Application Insights connection found.")
 
-            connection = await self._outer_instance.connections.get_with_credentials(name=connection_name)
+            connection = await self._outer_instance.connections._get_with_credentials(name=connection_name)  # pylint: disable=protected-access
 
             if isinstance(connection.credentials, ApiKeyCredentials):
                 if not connection.credentials.api_key:

sdk/ai/azure-ai-projects/azure/ai/projects/models/_models.py

@@ -321,7 +321,7 @@ class ApiKeyCredentials(BaseCredentials, discriminator="ApiKey"):
 
     type: Literal[CredentialType.API_KEY] = rest_discriminator(name="type", visibility=["read"])  # type: ignore
     """The credentail type. Required. API Key credential"""
-    api_key: Optional[str] = rest_field(name="key", visibility=["read"])
+    api_key: Optional[str] = rest_field(name="Key", visibility=["read"])
     """API Key."""
 
     @overload

sdk/ai/azure-ai-projects/azure/ai/projects/operations/_patch_datasets.py

@@ -99,7 +99,7 @@ def _create_dataset_and_get_its_container_client(
         )
 
     @distributed_trace
-    def upload_file(self, *, name: str, version: str, file: str, **kwargs: Any) -> DatasetVersion:
+    def upload_file(self, *, name: str, version: str, file_path: str, **kwargs: Any) -> DatasetVersion:
         """Upload file to a blob storage, and create a dataset that references this file.
         This method uses the `ContainerClient.upload_blob` method from the azure-storage-blob package
         to upload the file. Any keyword arguments provided will be passed to the `upload_blob` method.
@@ -108,31 +108,31 @@ def upload_file(self, *, name: str, version: str, file: str, **kwargs: Any) -> D
         :paramtype name: str
         :keyword version: The version identifier for the dataset. Required.
         :paramtype version: str
-        :keyword file: The file name (including optional path) to be uploaded. Required.
-        :paramtype file: str
+        :keyword file_path: The file name (including optional path) to be uploaded. Required.
+        :paramtype file_path: str
         :return: The created dataset version.
         :rtype: ~azure.ai.projects.models.DatasetVersion
         :raises ~azure.core.exceptions.HttpResponseError: If an error occurs during the HTTP request.
         """
 
-        path_file = Path(file)
-        if not path_file.exists():
-            raise ValueError("The provided file does not exist.")
-        if path_file.is_dir():
-            raise ValueError("The provided file is actually a folder. Use method `create_and_upload_folder` instead")
+        pathlib_file_path = Path(file_path)
+        if not pathlib_file_path.exists():
+            raise ValueError(f"The provided file `{file_path}` does not exist.")
+        if pathlib_file_path.is_dir():
+            raise ValueError("The provided file is actually a folder. Use method `upload_folder` instead")
 
         container_client, output_version = self._create_dataset_and_get_its_container_client(
             name=name, input_version=version
         )
 
         with container_client:
 
-            with open(file=file, mode="rb") as data:
+            with open(file=file_path, mode="rb") as data:
 
-                blob_name = path_file.name  # Extract the file name from the path.
+                blob_name = pathlib_file_path.name  # Extract the file name from the path.
                 logger.debug(
                     "[upload_file] Start uploading file `%s` as blob `%s`.",
-                    file,
+                    file_path,
                     blob_name,
                 )
 
@@ -172,9 +172,9 @@ def upload_folder(self, *, name: str, version: str, folder: str, **kwargs: Any)
         """
         path_folder = Path(folder)
         if not Path(path_folder).exists():
-            raise ValueError("The provided folder does not exist.")
+            raise ValueError(f"The provided folder `{folder}` does not exist.")
         if Path(path_folder).is_file():
-            raise ValueError("The provided folder is actually a file. Use method `create_and_upload_file` instead.")
+            raise ValueError("The provided folder is actually a file. Use method `upload_file` instead.")
 
         container_client, output_version = self._create_dataset_and_get_its_container_client(
             name=name, input_version=version

sdk/ai/azure-ai-projects/azure/ai/projects/operations/_patch_inference.py

@@ -246,7 +246,7 @@ def get_azure_openai_client(
         # If the connection uses API key authentication, we need to make another service call to get
         # the connection with API key populated.
         if connection.credentials.type == CredentialType.API_KEY:
-            connection = self._outer_instance.connections.get_with_credentials(name=connection_name, **kwargs)
+            connection = self._outer_instance.connections._get_with_credentials(name=connection_name, **kwargs)  # pylint: disable=protected-access
 
         logger.debug("[InferenceOperations.get_azure_openai_client] connection = %s", str(connection))
 

sdk/ai/azure-ai-projects/azure/ai/projects/operations/_patch_telemetry.py

@@ -44,17 +44,17 @@ def get_connection_string(self) -> str:
             # Returns an empty Iterable if no connections exits.
             connections: Iterable[Connection] = self._outer_instance.connections.list(
                 connection_type=ConnectionType.APPLICATION_INSIGHTS,
-                default_connection=True,
             )
 
+            # Note: there can't be more than one AppInsights connection.
             connection_name: Optional[str] = None
             for connection in connections:
                 connection_name = connection.name
                 break
             if not connection_name:
                 raise ResourceNotFoundError("No Application Insights connection found.")
 
-            connection = self._outer_instance.connections.get_with_credentials(name=connection_name)
+            connection = self._outer_instance.connections._get_with_credentials(name=connection_name)  # pylint: disable=protected-access
 
             if isinstance(connection.credentials, ApiKeyCredentials):
                 if not connection.credentials.api_key:

sdk/ai/azure-ai-projects/samples/datasets/sample_datasets.py

@@ -46,7 +46,7 @@
         dataset: DatasetVersion = project_client.datasets.upload_file(
             name=dataset_name,
             version=dataset_version_1,
-            file="sample_folder/sample_file1.txt",
+            file_path="sample_folder/sample_file1.txt",
         )
         print(dataset)
 

sdk/ai/azure-ai-projects/samples/datasets/sample_datasets_async.py

@@ -49,7 +49,7 @@ async def sample_datasets_async() -> None:
             dataset: DatasetVersion = await project_client.datasets.upload_file(
                 name=dataset_name,
                 version=dataset_version_1,
-                file="sample_folder/sample_file1.txt",
+                file_path="sample_folder/sample_file1.txt",
             )
             print(dataset)
 

sdk/ai/azure-ai-projects/samples/deployments/sample_deployments.py

@@ -18,7 +18,7 @@
     Set these environment variables with your own values:
     1) PROJECT_ENDPOINT - Required. The Azure AI Project endpoint, as found in the overview page of your
        Azure AI Foundry project.
-    2) DEPLOYMENT_NAME - Required. The name of the deployment to retrieve.
+    2) MODEL_DEPLOYMENT_NAME - Required. The name of the deployment to retrieve.
     3) MODEL_PUBLISHER - Required. The publisher of the model to filter by.
 """
 

sdk/ai/azure-ai-projects/samples/deployments/sample_deployments_async.py

@@ -18,7 +18,7 @@
     Set these environment variables with your own values:
     1) PROJECT_ENDPOINT - Required. The Azure AI Project endpoint, as found in the overview page of your
        Azure AI Foundry project.
-    2) DEPLOYMENT_NAME - Required. The name of the deployment to retrieve.
+    2) MODEL_DEPLOYMENT_NAME - Required. The name of the deployment to retrieve.
     3) MODEL_PUBLISHER - Required. The publisher of the model to filter by.       
 """
 

sdk/ai/azure-ai-projects/samples/inference/async_samples/sample_chat_completions_with_azure_ai_inference_client_async.py

@@ -20,7 +20,7 @@
     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 project.
-    2) DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
+    2) MODEL_DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
 """
 
 import os

sdk/ai/azure-ai-projects/samples/inference/async_samples/sample_chat_completions_with_azure_openai_client_async.py

@@ -19,7 +19,7 @@
     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 project.
-    2) DEPLOYMENT_NAME - The model deployment name, as found in your AI Foundry project.
+    2) MODEL_DEPLOYMENT_NAME - The model deployment name, as found in your AI Foundry project.
 
     Update the Azure OpenAI api-version as needed (see `api_version=` below). Values can be found here:
     https://learn.microsoft.com/azure/ai-services/openai/reference#api-specs
@@ -41,7 +41,7 @@ async def sample_chat_completions_with_azure_openai_client_async():
         async with AIProjectClient(endpoint=endpoint, credential=credential) as project_client:
 
             # Get an authenticated AsyncAzureOpenAI client for your default Azure OpenAI connection:
-            async with await project_client.inference.get_azure_openai_client(api_version="2024-06-01") as client:
+            async with await project_client.inference.get_azure_openai_client(api_version="2024-10-21") as client:
 
                 response = await client.chat.completions.create(
                     model=model_deployment_name,

sdk/ai/azure-ai-projects/samples/inference/async_samples/sample_image_embeddings_with_azure_ai_inference_client_async.py

@@ -21,7 +21,7 @@
     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 project.
-    2) DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
+    2) MODEL_DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
 """
 
 import os

sdk/ai/azure-ai-projects/samples/inference/async_samples/sample_text_embeddings_with_azure_ai_inference_client_async.py

@@ -20,7 +20,7 @@
     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 project.
-    2) DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
+    2) MODEL_DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
 """
 
 import os

sdk/ai/azure-ai-projects/samples/inference/sample_chat_completions_with_azure_ai_inference_client.py

@@ -20,7 +20,7 @@
     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 project.
-    2) DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
+    2) MODEL_DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
 """
 
 import os

sdk/ai/azure-ai-projects/samples/inference/sample_chat_completions_with_azure_ai_inference_client_and_azure_monitor_tracing.py

@@ -21,7 +21,7 @@
     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 project.
-    2) DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
+    2) MODEL_DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
     3) AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED - Optional. Set to `true` to trace the content of chat
        messages, which may contain personal data. False by default.
 """

sdk/ai/azure-ai-projects/samples/inference/sample_chat_completions_with_azure_ai_inference_client_and_prompt_string.py

@@ -23,7 +23,7 @@
     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 project.
-    2) DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
+    2) MODEL_DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
 """
 
 import os

sdk/ai/azure-ai-projects/samples/inference/sample_chat_completions_with_azure_ai_inference_client_and_prompty_file.py

@@ -22,7 +22,7 @@
     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 project.
-    2) DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
+    2) MODEL_DEPLOYMENT_NAME - The AI model deployment name, as found in your AI Foundry project.
 """
 
 import os

sdk/ai/azure-ai-projects/samples/inference/sample_chat_completions_with_azure_openai_client.py

@@ -18,7 +18,7 @@
     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 project.
-    2) DEPLOYMENT_NAME - The model deployment name, as found in your AI Foundry project.
+    2) MODEL_DEPLOYMENT_NAME - The model deployment name, as found in your AI Foundry project.
 
     Update the Azure OpenAI api-version as needed (see `api_version=` below). Values can be found here:
     https://learn.microsoft.com/azure/ai-services/openai/reference#api-specs
@@ -36,7 +36,7 @@
     with AIProjectClient(endpoint=endpoint, credential=credential) as project_client:
 
         # [START aoai_sample]
-        with project_client.inference.get_azure_openai_client(api_version="2024-06-01") as client:
+        with project_client.inference.get_azure_openai_client(api_version="2024-10-21") as client:
 
             response = client.chat.completions.create(
                 model=model_deployment_name,

sdk/ai/azure-ai-projects/samples/inference/sample_chat_completions_with_azure_openai_client_and_azure_monitor_tracing.py

@@ -21,7 +21,7 @@
     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 project.
-    2) DEPLOYMENT_NAME - The model deployment name, as found in your AI Foundry project.
+    2) MODEL_DEPLOYMENT_NAME - The model deployment name, as found in your AI Foundry project.
     3) OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT - Optional. Set to `true` to trace the content of chat
        messages, which may contain personal data. False by default.
 
@@ -54,7 +54,7 @@
 
         configure_azure_monitor(connection_string=application_insights_connection_string)
 
-        with project_client.inference.get_azure_openai_client(api_version="2024-06-01") as client:
+        with project_client.inference.get_azure_openai_client(api_version="2024-10-21") as client:
 
             response = client.chat.completions.create(
                 model=model_deployment_name,

sdk/ai/azure-ai-projects/samples/inference/sample_chat_completions_with_azure_openai_client_and_console_tracing.py

@@ -53,7 +53,7 @@
 
     with AIProjectClient(endpoint=endpoint, credential=credential) as project_client:
 
-        with project_client.inference.get_azure_openai_client(api_version="2024-06-01") as client:
+        with project_client.inference.get_azure_openai_client(api_version="2024-10-21") as client:
 
             response = client.chat.completions.create(
                 model=model_deployment_name,