Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into postRel

Author: roygara

articles/ai-services/openai/concepts/content-filter.md

@@ -6,7 +6,7 @@ author: mrbullwinkle
 ms.author: mbullwin
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 09/15/2023
+ms.date: 11/06/2023
 ms.custom: template-concept
 manager: nitinme
 keywords: 
@@ -294,11 +294,10 @@ When annotations are enabled as shown in the code snippet below, the following i
 
 Annotations are currently in preview for Completions and Chat Completions (GPT models); the following code snippet shows how to use annotations in preview:
 
-# [Python](#tab/python)
+# [OpenAI Python 0.28.1](#tab/python)
 
 
 ```python
-# Note: The openai-python library support for Azure OpenAI is in preview.
 # os.getenv() for the endpoint and key assumes that you are using environment variables.
 
 import os
@@ -387,7 +386,6 @@ print(response)
 The following code snippet shows how to retrieve annotations when content was filtered:
 
 ```python
-# Note: The openai-python library support for Azure OpenAI is in preview.
 # os.getenv() for the endpoint and key assumes that you are using environment variables.
 
 import os
@@ -416,6 +414,29 @@ except openai.error.InvalidRequestError as e:
 
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+# os.getenv() for the endpoint and key assumes that you are using environment variables.
+
+import os
+from openai import AzureOpenAI
+client = AzureOpenAI(
+    api_key=os.getenv("AZURE_OPENAI_KEY"),  
+    api_version="2023-10-01-preview",
+    azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") 
+    )
+
+response = client.completions.create(
+    model="gpt-35-turbo-instruct", # model = "deployment_name".
+    prompt="{Example prompt where a severity level of low is detected}" 
+    # Content that is detected at severity level medium or high is filtered, 
+    # while content detected at severity level low isn't filtered by the content filters.
+)
+
+print(response.model_dump_json(indent=2))
+```
+
 # [JavaScript](#tab/javascrit)
 
 [Azure OpenAI JavaScript SDK source code & samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai)

articles/ai-services/openai/tutorials/embeddings.md

@@ -6,7 +6,7 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: tutorial
-ms.date: 09/12/2023
+ms.date: 11/06/2023
 author: mrbullwinkle #noabenefraim
 ms.author: mbullwin
 recommendations: false
@@ -46,10 +46,20 @@ In this tutorial, you learn how to:
 
 If you haven't already, you need to install the following libraries:
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```cmd
 pip install "openai==0.28.1" num2words matplotlib plotly scipy scikit-learn pandas tiktoken
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```console
+pip install openai num2words matplotlib plotly scipy scikit-learn pandas tiktoken
+```
+
+---
+
 <!--Alternatively, you can use our [requirements.txt file](https://github.com/Azure-Samples/Azure-OpenAI-Docs-Samples/blob/main/Samples/Tutorials/Embeddings/requirements.txt).-->
 
 ### Download the BillSum dataset
@@ -105,7 +115,9 @@ Run the following code in your preferred Python IDE:
 
 <!--If you wish to view the Jupyter notebook that corresponds to this tutorial you can download the tutorial from our [samples repo](https://github.com/Azure-Samples/Azure-OpenAI-Docs-Samples/blob/main/Samples/Tutorials/Embeddings/embedding_billsum.ipynb).-->
 
-## Import libraries and list models
+## Import libraries
+
+# [OpenAI Python 0.28.1](#tab/python)
 
 ```python
 import openai
@@ -193,6 +205,23 @@ print(r.text)
 
 The output of this command will vary based on the number and type of models you've deployed. In this case, we need to confirm that we have an entry for **text-embedding-ada-002**. If you find that you're missing this model, you'll need to [deploy the model](../how-to/create-resource.md#deploy-a-model) to your resource before proceeding.
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+import os
+import re
+import requests
+import sys
+from num2words import num2words
+import os
+import pandas as pd
+import numpy as np
+import tiktoken
+from openai import AzureOpenAI
+```
+
+---
+
 Now we need to read our csv file and create a pandas DataFrame. After the initial DataFrame is created, we can view the contents of the table by running `df`.
 
 ```python
@@ -334,10 +363,29 @@ len(decode)
 
 Now that we understand more about how tokenization works we can move on to embedding. It is important to note, that we haven't actually tokenized the documents yet. The `n_tokens` column is simply a way of making sure none of the data we pass to the model for tokenization and embedding exceeds the input token limit of 8,192. When we pass the documents to the embeddings model, it will break the documents into tokens similar (though not necessarily identical) to the examples above and then convert the tokens to a series of floating point numbers that will be accessible via vector search. These embeddings can be stored locally or in an [Azure Database to support Vector Search](../../../cosmos-db/mongodb/vcore/vector-search.md). As a result, each bill will have its own corresponding embedding vector in the new `ada_v2` column on the right side of the DataFrame.
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
 df_bills['ada_v2'] = df_bills["text"].apply(lambda x : get_embedding(x, engine = 'text-embedding-ada-002')) # engine should be set to the deployment name you chose when you deployed the text-embedding-ada-002 (Version 2) model
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+client = AzureOpenAI(
+  api_key = os.getenv("AZURE_OPENAI_API_KEY"),  
+  api_version = "2023-05-15",
+  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
+)
+
+def generate_embeddings(text, model="text-embedding-ada-002"): # model = "deployment_name"
+    return client.embeddings.create(input = [text], model=model).data[0].embedding
+
+df_bills['ada_v2'] = df_bills["text"].apply(lambda x : generate_embeddings (x, model = 'text-embedding-ada-002')) # model should be set to the deployment name you chose when you deployed the text-embedding-ada-002 (Version 2) model
+```
+
+---
+
 ```python
 df_bills
 ```
@@ -348,6 +396,8 @@ df_bills
 
 As we run the search code block below, we'll embed the search query *"Can I get information on cable company tax revenue?"* with the same **text-embedding-ada-002 (Version 2)** model. Next we'll find the closest bill embedding to the newly embedded text from our query ranked by [cosine similarity](../concepts/understand-embeddings.md).
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
 # search through the reviews for a specific product
 def search_docs(df, user_query, top_n=3, to_print=True):
@@ -369,6 +419,36 @@ def search_docs(df, user_query, top_n=3, to_print=True):
 res = search_docs(df_bills, "Can I get information on cable company tax revenue?", top_n=4)
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+def cosine_similarity(a, b):
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+
+def get_embedding(text, model="text-embedding-ada-002"): # model = "deployment_name"
+    return client.embeddings.create(input = [text], model=model).data[0].embedding
+
+def search_docs(df, user_query, top_n=4, to_print=True):
+    embedding = get_embedding(
+        user_query,
+        model="text-embedding-ada-002" # model should be set to the deployment name you chose when you deployed the text-embedding-ada-002 (Version 2) model
+    )
+    df["similarities"] = df.ada_v2.apply(lambda x: cosine_similarity(x, embedding))
+
+    res = (
+        df.sort_values("similarities", ascending=False)
+        .head(top_n)
+    )
+    if to_print:
+        display(res)
+    return res
+
+
+res = search_docs(df_bills, "Can I get information on cable company tax revenue?", top_n=4)
+```
+
+---
+
 **Output**:
 
 :::image type="content" source="../media/tutorials/query-result.png" alt-text="Screenshot of the formatted results of res once the search query has been run." lightbox="../media/tutorials/query-result.png":::

articles/ai-services/speech-service/includes/release-notes/release-notes-tts.md

@@ -6,6 +6,12 @@ ms.date: 02/28/2023
 ms.author: eur
 ---
 
+### November 2023 release
+
+#### Custom neural voice
+
+- Added support for the 24 new locales for cross-lingual voice. See the [full language list](../../language-support.md?tabs=tts#custom-neural-voice) for more information.
+
 ### October 2023 release
 
 #### Custom neural voice

articles/ai-services/speech-service/speech-services-quotas-and-limits.md

@@ -48,8 +48,6 @@ You can use real-time speech to text with the [Speech SDK](speech-sdk.md) or the
 |--|--|--|
 | [Speech to text REST API](rest-speech-to-text.md) limit | Not available for F0 | 300 requests per minute |
 | Max audio input file size | N/A | 1 GB |
-| Max input blob size (for example, can contain more than one file in a zip archive). Note the file size limit from the preceding row. | N/A | 2.5 GB |
-| Max blob container size | N/A | 5 GB |
 | Max number of blobs per container | N/A | 10000 |
 | Max number of files per transcription request (when you're using multiple content URLs as input). | N/A | 1000  |
 | Max audio length for transcriptions with diarizaion enabled. | N/A | 240 minutes per file  |

articles/api-management/api-management-features.md

@@ -49,6 +49,7 @@ Each API Management [pricing tier](https://aka.ms/apimpricing) offers a distinct
 | [Pass-through WebSocket APIs](websocket-api.md)                                                                                    | No          | Yes       | Yes   | Yes      | Yes     |
 | [Pass-through GraphQL APIs](graphql-apis-overview.md)                                                                               | Yes          | Yes       | Yes   | Yes      | Yes     |
 | [Synthetic GraphQL APIs](graphql-apis-overview.md)                                            | Yes           | Yes       | Yes   | Yes      | Yes     |
+| [Pass-through gRPC APIs](grpc-api.md) (preview)                                                                              | No          | Yes       | No   | No      | Yes     |
 
 <sup>1</sup> Enables the use of Microsoft Entra ID (and Azure AD B2C) as an identity provider for user sign in on the developer portal.<br/>
 <sup>2</sup> Including related functionality such as users, groups, issues, applications, and email templates and notifications.<br/>

articles/api-management/api-management-gateways-overview.md

@@ -7,7 +7,7 @@ author: dlepow
 
 ms.service: api-management
 ms.topic: conceptual
-ms.date: 09/18/2023
+ms.date: 11/6/2023
 ms.author: danlep
 ---
 
@@ -100,6 +100,7 @@ The following table compares features available in the managed gateway versus th
 | [Pass-through GraphQL](graphql-apis-overview.md) |  ✔️ | ✔️ | ✔️ |
 | [Synthetic GraphQL](graphql-apis-overview.md)|  ✔️ |  ✔️<sup>1</sup> | ✔️<sup>1</sup> |
 | [Pass-through WebSocket](websocket-api.md) |  ✔️ |  ❌ | ✔️ |
+| [Pass-through gRPC](grpc-api.md) |  ❌ |  ❌ | ✔️ |
 
 <sup>1</sup> Synthetic GraphQL subscriptions (preview) aren't supported.
 

articles/api-management/grpc-api.md

@@ -0,0 +1,76 @@
+---
+title: Import a gRPC API to Azure API Management (preview) | Microsoft Docs
+description: Learn how to import a gRPC service definition as an API to an API Management instance using the Azure portal, ARM template, or bicep template.
+services: api-management
+author: dlepow
+
+ms.service: api-management
+ms.topic: how-to
+ms.date: 10/04/2023
+ms.author: danlep
+ms.custom: 
+---
+# Import a gRPC API (preview)
+
+This article shows how to import a gRPC service definition as an API in API Management. You can then manage the API in API Management, secure access and apply other polices, and pass gRPC API requests through the gateway to the gRPC backend. 
+
+To add a gRPC API to API Management, you need to:
+
+* Upload the API's Protobuf (protocol buffer) definition file to API Management
+* Specify the location of your gRPC service
+* Configure the API in API Management
+
+API Management supports pass-through with the following types of gRPC service methods: unary, server streaming, client streaming, and bidirectional streaming. For background about gRPC, see [Introduction to gRPC](https://grpc.io/docs/what-is-grpc/introduction/).
+
+
+> [!NOTE]
+> * Importing a gRPC API is in preview. Currently, gRPC APIs are only supported in the self-hosted gateway, not the managed gateway for your API Management instance.
+> * Currently, testing gRPC APIs isn't supported in the test console of the Azure portal or in the API Management developer portal.
+
+[!INCLUDE [api-management-availability-premium-dev](../../includes/api-management-availability-premium-dev.md)]
+
+## Prerequisites
+
+* An API Management instance. If you don't already have one, complete the following quickstart: [Create an Azure API Management instance](get-started-create-service-instance.md).
+
+* A gateway resource provisioned in your instance. If you don't already have one, see [Provision a self-hosted gateway in Azure API Management](api-management-howto-provision-self-hosted-gateway.md).
+
+* A gRPC Protobuff (.proto) file available locally and gRPC service that's accessible over HTTPS.
+
+## Add a gRPC API
+
+#### [Portal](#tab/portal)
+
+1. In the [Azure portal](https://portal.azure.com), navigate to your API Management instance.
+
+1. In the left menu, select **APIs** > **+ Add API**.
+
+1. Under **Define a new API**, select **gRPC**.
+
+    :::image type="content" source="./media/grpc-api/grpc-api.png" alt-text="Screenshot of creating a gRPC API in the portal." :::
+
+1. In the **Create a gRPC API window**, select **Full**.
+
+1. For a gRPC API, you must specify the following settings:
+
+    1. In **Upload schema**, select a local .proto file associated with the API to import.
+
+    1. In **gRPC server URL**, enter the address of the gRPC service. The address must be accessible over HTTPS.
+
+    1. In **Gateways**, select the gateway resource that you want to use to expose the API. 
+
+        > [!IMPORTANT]
+        > In public preview, you can only select a self-hosted gateway. The **Managed** gateway isn't supported. 
+
+1. Enter remaining settings to configure your API. These settings are explained in the [Import and publish your first API](import-and-publish.md#import-and-publish-a-backend-api) tutorial.
+
+1. Select **Create**.
+
+    The API is added to the **APIs** list. You can view update your settings by going to the **Settings** tab of the API. 
+
+---
+
+
+[!INCLUDE [api-management-append-apis.md](../../includes/api-management-append-apis.md)]
+
+[!INCLUDE [api-management-define-api-topics.md](../../includes/api-management-define-api-topics.md)]

articles/api-management/media/grpc-api/grpc-api.png

@@ -6,7 +6,7 @@ author: greglin
 ms.service: application-gateway
 ms.subservice: appgw-for-containers
 ms.topic: article
-ms.date: 10/23/2023
+ms.date: 11/07/2023
 ms.author: greglin
 ---
 
@@ -23,10 +23,13 @@ Instructions for new or existing deployments of ALB Controller are found in the
 - [Upgrade existing ALB Controller](quickstart-deploy-application-gateway-for-containers-alb-controller.md#for-existing-deployments)
 
 ## Latest Release (Recommended)
-September 25, 2023 - 0.5.024542 - Custom Health Probes, Controller HA, Multi-site support for Ingress, [helm_release via Terraform fix](https://github.com/Azure/AKS/issues/3857), Path rewrite for Gateway API, status for Ingress resources, quality improvements
+November 6, 2023 - 0.6.1 - Gateway / Ingress API - Header rewrite support, Ingress API - URL rewrite support, Ingress multiple-TLS listener bug fix,
+two certificates maximum per host, adopting [semantic versioning (semver)](https://semver.org/), quality improvements
 
 ## Release history
-July 25, 2023 - 0.4.023971 - Ingress + Gateway co-existence improvements
+September 25, 2023 - 0.5.024542 - Custom Health Probes, Controller HA, Multi-site support for Ingress, [helm_release via Terraform fix](https://github.com/Azure/AKS/issues/3857), Path rewrite for Gateway API, status for Ingress resources, quality improvements
+
+July 25, 2023 - 0.4.023971 - Ingress + Gateway coexistence improvements
 
 July 24, 2023 - 0.4.023961 - Improved Ingress support