Merge pull request #258034 from MicrosoftDocs/main

11/7/2023 PM Publish

Author: Taojunshen

.openpublishing.publish.config.json

@@ -230,6 +230,12 @@
       "branch": "master",
       "branch_mapping": {}
     },
+    {
+      "path_to_root": "function-app-arm-templates",
+      "url": "https://github.com/Azure-Samples/function-app-arm-templates",
+      "branch": "main",
+      "branch_mapping": {}
+    },    
     {
       "path_to_root": "functions-azure-product",
       "url": "https://github.com/Azure/Azure-Functions",

articles/ai-services/openai/how-to/migration.md

@@ -28,7 +28,7 @@ OpenAI has just released a new version of the [OpenAI Python API library](https:
 
 ## Known issues
 
-- The latest release of the [OpenAI Python library](https://pypi.org/project/openai/) doesn't currently support DALL-E when used with Azure OpenAI. DALL-E with Azure OpenAI is still supported with `0.28.1`.
+- The latest release of the [OpenAI Python library](https://pypi.org/project/openai/) doesn't currently support DALL-E when used with Azure OpenAI. DALL-E with Azure OpenAI is still supported with `0.28.1`. For those who can't wait for native support for DALL-E and Azure OpenAI we're providing [two code examples](#dall-e-fix) which can be used as a workaround.
 - `embeddings_utils.py` which was used to provide functionality like cosine similarity for semantic text search is [no longer part of the OpenAI Python API library](https://github.com/openai/openai-python/issues/676).
 - You should also check the active [GitHub Issues](https://github.com/openai/openai-python/issues/703) for the OpenAI Python library.
 
@@ -252,6 +252,171 @@ completion = client.chat.completions.create(
 print(completion.model_dump_json(indent=2))
 ```
 
+## DALL-E fix
+
+# [DALLE-Fix](#tab/dalle-fix)
+
+```python
+import time
+import json
+import httpx
+import openai
+
+
+class CustomHTTPTransport(httpx.HTTPTransport):
+    def handle_request(
+        self,
+        request: httpx.Request,
+    ) -> httpx.Response:
+        if "images/generations" in request.url.path and request.url.params[
+            "api-version"
+        ] in [
+            "2023-06-01-preview",
+            "2023-07-01-preview",
+            "2023-08-01-preview",
+            "2023-09-01-preview",
+            "2023-10-01-preview",
+        ]:
+            request.url = request.url.copy_with(path="/openai/images/generations:submit")
+            response = super().handle_request(request)
+            operation_location_url = response.headers["operation-location"]
+            request.url = httpx.URL(operation_location_url)
+            request.method = "GET"
+            response = super().handle_request(request)
+            response.read()
+
+            timeout_secs: int = 120
+            start_time = time.time()
+            while response.json()["status"] not in ["succeeded", "failed"]:
+                if time.time() - start_time > timeout_secs:
+                    timeout = {"error": {"code": "Timeout", "message": "Operation polling timed out."}}
+                    return httpx.Response(
+                        status_code=400,
+                        headers=response.headers,
+                        content=json.dumps(timeout).encode("utf-8"),
+                        request=request,
+                    )
+
+                time.sleep(int(response.headers.get("retry-after")) or 10)
+                response = super().handle_request(request)
+                response.read()
+
+            if response.json()["status"] == "failed":
+                error_data = response.json()
+                return httpx.Response(
+                    status_code=400,
+                    headers=response.headers,
+                    content=json.dumps(error_data).encode("utf-8"),
+                    request=request,
+                )
+
+            result = response.json()["result"]
+            return httpx.Response(
+                status_code=200,
+                headers=response.headers,
+                content=json.dumps(result).encode("utf-8"),
+                request=request,
+            )
+        return super().handle_request(request)
+
+
+client = openai.AzureOpenAI(
+    azure_endpoint="<azure_endpoint>",
+    api_key="<api_key>",
+    api_version="<api_version>",
+    http_client=httpx.Client(
+        transport=CustomHTTPTransport(),
+    ),
+)
+image = client.images.generate(prompt="a cute baby seal")
+
+print(image.data[0].url)
+```
+
+# [DALLE-Fix Async](#tab/dalle-fix-async)
+
+```python
+import time
+import asyncio
+import json
+import httpx
+import openai
+
+
+class AsyncCustomHTTPTransport(httpx.AsyncHTTPTransport):
+    async def handle_async_request(
+        self,
+        request: httpx.Request,
+    ) -> httpx.Response:
+        if "images/generations" in request.url.path and request.url.params[
+            "api-version"
+        ] in [
+            "2023-06-01-preview",
+            "2023-07-01-preview",
+            "2023-08-01-preview",
+            "2023-09-01-preview",
+            "2023-10-01-preview",
+        ]:
+            request.url = request.url.copy_with(path="/openai/images/generations:submit")
+            response = await super().handle_async_request(request)
+            operation_location_url = response.headers["operation-location"]
+            request.url = httpx.URL(operation_location_url)
+            request.method = "GET"
+            response = await super().handle_async_request(request)
+            await response.aread()
+
+            timeout_secs: int = 120
+            start_time = time.time()
+            while response.json()["status"] not in ["succeeded", "failed"]:
+                if time.time() - start_time > timeout_secs:
+                    timeout = {"error": {"code": "Timeout", "message": "Operation polling timed out."}}
+                    return httpx.Response(
+                        status_code=400,
+                        headers=response.headers,
+                        content=json.dumps(timeout).encode("utf-8"),
+                        request=request,
+                    )
+
+                await asyncio.sleep(int(response.headers.get("retry-after")) or 10)
+                response = await super().handle_async_request(request)
+                await response.aread()
+
+            if response.json()["status"] == "failed":
+                error_data = response.json()
+                return httpx.Response(
+                    status_code=400,
+                    headers=response.headers,
+                    content=json.dumps(error_data).encode("utf-8"),
+                    request=request,
+                )
+
+            result = response.json()["result"]
+            return httpx.Response(
+                status_code=200,
+                headers=response.headers,
+                content=json.dumps(result).encode("utf-8"),
+                request=request,
+            )
+        return await super().handle_async_request(request)
+
+
+async def dall_e():
+    client = openai.AsyncAzureOpenAI(
+        azure_endpoint="<azure_endpoint>",
+        api_key="<api_key>",
+        api_version="<api_version>",
+        http_client=httpx.AsyncClient(
+            transport=AsyncCustomHTTPTransport(),
+        ),
+    )
+    image = await client.images.generate(prompt="a cute baby seal")
+
+    print(image.data[0].url)
+
+asyncio.run(dall_e())
+```
+
+---
 ## Name changes
 
 > [!NOTE]
@@ -260,7 +425,7 @@ print(completion.model_dump_json(indent=2))
 | OpenAI Python 0.28.1 | OpenAI Python 1.x |
 | --------------- | --------------- |
 | `openai.api_base` | `openai.base_url` |
-| `openai.proxy` | `openai.proxies (docs)` |
+| `openai.proxy` | `openai.proxies` |
 | `openai.InvalidRequestError` | `openai.BadRequestError` |
 | `openai.Audio.transcribe()` | `client.audio.transcriptions.create()` |
 | `openai.Audio.translate()` | `client.audio.translations.create()` |
@@ -296,22 +461,22 @@ print(completion.model_dump_json(indent=2))
 
 ### Removed
 
-`openai.api_key_path`
-`openai.app_info`
-`openai.debug`
-`openai.log`
-`openai.OpenAIError`
-`openai.Audio.transcribe_raw()`
-`openai.Audio.translate_raw()`
-`openai.ErrorObject`
-`openai.Customer`
-`openai.api_version`
-`openai.verify_ssl_certs`
-`openai.api_type`
-`openai.enable_telemetry`
-`openai.ca_bundle_path`
-`openai.requestssession` (OpenAI now uses `httpx`)
-`openai.aiosession` (OpenAI now uses `httpx`)
-`openai.Deployment` (Previously used for Azure OpenAI)
-`openai.Engine`
-`openai.File.find_matching_files()`
+- `openai.api_key_path`
+- `openai.app_info`
+- `openai.debug`
+- `openai.log`
+- `openai.OpenAIError`
+- `openai.Audio.transcribe_raw()`
+- `openai.Audio.translate_raw()`
+- `openai.ErrorObject`
+- `openai.Customer`
+- `openai.api_version`
+- `openai.verify_ssl_certs`
+- `openai.api_type`
+- `openai.enable_telemetry`
+- `openai.ca_bundle_path`
+- `openai.requestssession` (OpenAI now uses `httpx`)
+- `openai.aiosession` (OpenAI now uses `httpx`)
+- `openai.Deployment` (Previously used for Azure OpenAI)
+- `openai.Engine`
+- `openai.File.find_matching_files()`

articles/ai-services/openai/how-to/working-with-models.md

@@ -17,7 +17,7 @@ keywords:
 
 Azure OpenAI Service is powered by a diverse set of models with different capabilities and price points. [Model availability varies by region](../concepts/models.md).
 
-You can get a list of models that are available for both inference and fine-tuning by your Azure OpenAI resource by using the [Models List API](/rest/api/cognitiveservices/azureopenaistable/models/list).
+You can get a list of models that are available for both inference and fine-tuning by your Azure OpenAI resource by using the [Models List API](/rest/api/azureopenai/models/list).
 
 ## Model updates
 

articles/ai-services/openai/includes/fine-tuning-python.md

@@ -130,8 +130,8 @@ After it guides you through the process of implementing suggested changes, the t
 
 The next step is to either choose existing prepared training data or upload new prepared training data to use when customizing your model. After you prepare your training data, you can upload your files to the service. There are two ways to upload training data:
 
-- [From a local file](/rest/api/cognitiveservices/azureopenaistable/files/upload)
-- [Import from an Azure Blob store or other web location](/rest/api/cognitiveservices/azureopenaistable/files/import)
+- [From a local file](/rest/api/azureopenai/files/upload)
+- [Import from an Azure Blob store or other web location](/rest/api/azureopenai/files/import)
 
 For large data files, we recommend that you import from an Azure Blob  store. Large files can become unstable when uploaded through multipart forms because the requests are atomic and can't be retried or resumed. For more information about Azure Blob storage, see [What is Azure Blob storage?](../../../storage/blobs/storage-blobs-overview.md)
 
@@ -209,7 +209,7 @@ print(response)
 
 ## Deploy a customized model
 
-When the fine-tune job succeeds, the value of the `fine_tuned_model` variable in the response body is set to the name of your customized model. Your model is now also available for discovery from the [list Models API](/rest/api/cognitiveservices/azureopenaistable/models/list). However, you can't issue completion calls to your customized model until your customized model is deployed. You must deploy your customized model to make it available for use with completion calls.
+When the fine-tune job succeeds, the value of the `fine_tuned_model` variable in the response body is set to the name of your customized model. Your model is now also available for discovery from the [list Models API](/rest/api/azureopenai/models/list). However, you can't issue completion calls to your customized model until your customized model is deployed. You must deploy your customized model to make it available for use with completion calls.
 
 [!INCLUDE [Fine-tuning deletion](fine-tune.md)]
 
@@ -384,7 +384,7 @@ Similarly, you can use various methods to delete your customized model:
 You can optionally delete training and validation files that you uploaded for training, and result files generated during training, from your Azure OpenAI subscription. You can use the following methods to delete your training, validation, and result files:
 
 - [Azure OpenAI Studio](../how-to/fine-tuning.md?pivots=programming-language-studio#delete-your-training-files)
-- The [REST APIs](/rest/api/cognitiveservices/azureopenaistable/files/delete)
+- The [REST APIs](/rest/api/azureopenai/files/delete)
 - The Python SDK
 
 The following Python example uses the Python SDK to delete the training, validation, and result files for your customized model:

articles/ai-services/openai/reference.md

@@ -791,5 +791,5 @@ Azure OpenAI is deployed as a part of the Azure AI services. All Azure AI servic
 
 ## Next steps
 
-Learn about [ Models, and fine-tuning with the REST API](/rest/api/cognitiveservices/azureopenaistable/files).
+Learn about [ Models, and fine-tuning with the REST API](/rest/api/azureopenai/fine-tuning?view=rest-azureopenai-2023-10-01-preview).
 Learn more about the [underlying models that power Azure OpenAI](./concepts/models.md).

articles/ai-services/speech-service/batch-transcription-create.md

@@ -8,7 +8,7 @@ author: eric-urban
 ms.author: eur
 ms.service: azure-ai-speech
 ms.topic: how-to
-ms.date: 10/3/2023
+ms.date: 11/7/2023
 zone_pivot_groups: speech-cli-rest
 ms.custom: devx-track-csharp
 ---
@@ -171,7 +171,7 @@ Here are some property options that you can use to configure a transcription whe
 |`contentContainerUrl`| You can submit individual audio files, or a whole storage container.<br/><br/>You must specify the audio data location via either the `contentContainerUrl` or `contentUrls` property. For more information about Azure blob storage for batch transcription, see [Locate audio files for batch transcription](batch-transcription-audio-data.md).<br/><br/>This property won't be returned in the response.|
 |`contentUrls`| You can submit individual audio files, or a whole storage container.<br/><br/>You must specify the audio data location via either the `contentContainerUrl` or `contentUrls` property. For more information, see [Locate audio files for batch transcription](batch-transcription-audio-data.md).<br/><br/>This property won't be returned in the response.|
 |`destinationContainerUrl`|The result can be stored in an Azure container. If you don't specify a container, the Speech service stores the results in a container managed by Microsoft. When the transcription job is deleted, the transcription result data is also deleted. For more information such as the supported security scenarios, see [Destination container URL](#destination-container-url).|
-|`diarization`|Indicates that diarization analysis should be carried out on the input, which is expected to be a mono channel that contains multiple voices. Specify the minimum and maximum number of people who might be speaking. You must also set the `diarizationEnabled` property to `true`. The [transcription file](batch-transcription-get.md#transcription-result-file) will contain a `speaker` entry for each transcribed phrase.<br/><br/>You need to use this property when you expect three or more speakers. For two speakers setting `diarizationEnabled` property to `true` is enough. See an example of the property usage in [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create) operation description.<br/><br/>Diarization is the process of separating speakers in audio data. The batch pipeline can recognize and separate multiple speakers on mono channel recordings. The maximum number of speakers for diarization must be less than 36 and more or equal to the `minSpeakers` property (see [example](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create)). The feature isn't available with stereo recordings.<br/><br/>When this property is selected, source audio length can't exceed 240 minutes per file.<br/><br/>**Note**: This property is only available with Speech to text REST API version 3.1 and later.|
+|`diarization`|Indicates that diarization analysis should be carried out on the input, which is expected to be a mono channel that contains multiple voices. Specify the minimum and maximum number of people who might be speaking. You must also set the `diarizationEnabled` property to `true`. The [transcription file](batch-transcription-get.md#transcription-result-file) will contain a `speaker` entry for each transcribed phrase.<br/><br/>You need to use this property when you expect three or more speakers. For two speakers setting `diarizationEnabled` property to `true` is enough. See an example of the property usage in [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create) operation description.<br/><br/>Diarization is the process of separating speakers in audio data. The batch pipeline can recognize and separate multiple speakers on mono channel recordings. The maximum number of speakers for diarization must be less than 36 and more or equal to the `minSpeakers` property (see [example](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create)). The feature isn't available with stereo recordings.<br/><br/>When this property is selected, source audio length can't exceed 240 minutes per file.<br/><br/>**Note**: This property is only available with Speech to text REST API version 3.1 and later. If you set this property with any previous version (such as version 3.0) then it will be ignored and only 2 speakers will be identified.|
 |`diarizationEnabled`|Specifies that diarization analysis should be carried out on the input, which is expected to be a mono channel that contains two voices. The default value is `false`.<br/><br/>For three or more voices you also need to use property `diarization` (only with Speech to text REST API version 3.1 and later).<br/><br/>When this property is selected, source audio length can't exceed 240 minutes per file.|
 |`displayName`|The name of the batch transcription. Choose a name that you can refer to later. The display name doesn't have to be unique.<br/><br/>This property is required.|
 |`displayFormWordLevelTimestampsEnabled`|Specifies whether to include word-level timestamps on the display form of the transcription results. The results are returned in the displayWords property of the transcription file. The default value is `false`.<br/><br/>**Note**: This property is only available with Speech to text REST API version 3.1 and later.|

articles/aks/TOC.yml

@@ -637,8 +637,11 @@
     - name: Upgrade
       items:
         - name: Upgrade options
-          href: upgrade-cluster.md
           items:
+          - name: Upgrade options
+            href: upgrade-cluster.md
+          - name: Stop cluster upgrades automatically on API breaking changes
+            href: stop-cluster-upgrade-api-breaking-changes.md
           - name: Perform manual upgrades
             items:
             - name: Upgrade an AKS cluster
@@ -655,8 +658,6 @@
                 href: auto-upgrade-cluster.md
               - name: Use Planned Maintenance to schedule and control upgrades
                 href: planned-maintenance.md
-              - name: Stop cluster upgrades automatically on API breaking changes
-                href: stop-cluster-upgrade-api-breaking-changes.md
               - name: Automatically upgrade AKS cluster node operating system images
                 href: auto-upgrade-node-image.md
               - name: Upgrade the node image automatically with GitHub Actions