Merge pull request #2385 from MicrosoftDocs/main

ttorble · web-flow · commit 06d2c74c9117 · 2025-01-19T12:10:35.000Z
Publish to live, Sunday 4 AM PST, 1/19
diff --git a/articles/ai-studio/faq.yml b/articles/ai-studio/faq.yml
@@ -8,7 +8,7 @@ metadata:
   ms.custom:
     - build-2024
   ms.topic: faq
-  ms.date: 5/21/2024
+  ms.date: 01/17/2025
   ms.reviewer: sgilley
   ms.author: sgilley
   author: sdgilley
@@ -25,7 +25,7 @@ sections:
       - question: |
           How can customers access Azure AI Foundry? 
         answer: |
-          Customers can explore Azure AI Foundry unauthenticated - including its cutting-edge AI capabilities. When they're ready to begin using templates, tools, and the robust model catalog to stitch together their own AI solutions, they'll be prompted to register or sign in to their Azure account. During preview, there's no extra charge for using Azure AI Foundry. When deploying solutions, Azure AI services, Azure Machine Learning, and other Azure resources used inside of Azure AI Foundry will be billed at their existing rates. Pricing is subject to change when Azure AI Foundry is generally available.
+          Customers can explore Azure AI Foundry unauthenticated - including its cutting-edge AI capabilities. When you're ready to begin using templates, tools, and the robust model catalog to stitch together your own AI solutions, you're prompted to register or sign in to your Azure account. Currently, there's no extra charge for using Azure AI Foundry. When deploying solutions, you're billed for the Azure AI services, Azure Machine Learning, and other Azure resources used inside of Azure AI Foundry at their existing rates.
       - question: |
           What regions is Azure AI Foundry available in?
         answer: | 
@@ -37,23 +37,23 @@ sections:
       - question: |
           Can I use models other than ChatGPT in Azure AI Foundry portal? 
         answer: |
-          Yes. Azure AI Foundry includes a robust and growing catalog of frontier and open-source models from OpenAI, Hugging Face, Meta and more that can be applied over your data. You can even compare models by task using open-source datasets and evaluate the model with your own test data to see how the pre-trained model would perform to fit your own use case.
+          Yes. Azure AI Foundry includes a robust and growing catalog of frontier and open-source models from OpenAI, Hugging Face, Meta, and more that can be applied over your data. You can even compare models by task using open-source datasets and evaluate the model with your own test data to see how the pretrained model would perform to fit your own use case.
       - question: |
           Will there be multiple varying model benchmarks in Azure AI Foundry portal based on individual projects and data sources? 
         answer: |
           In the model benchmarks view, customers can view varying model benchmarks published by Azure AI. 
       - question: |
           Is prompt flow Microsoft's equivalent to LangChain? 
         answer: |
-          Prompt flow is complementary to LangChain and Semantic Kernel and it can work with either. Prompt flow supports LLMOps for generative AI solutions, providing evaluation, connection management, and flow logic to help debug applications, manage deployment and monitor at scale.
+          Prompt flow is complementary to LangChain and Semantic Kernel and it can work with either. Prompt flow supports LLMOps for generative AI solutions, providing evaluation, connection management, and flow logic to help debug applications, manage deployment, and monitor at scale.
       - question: |
           How is prompt injection handled, and how do we ensure no malicious code is running from prompt injection? 
         answer: |
           Prompt templates in prompt flow provide robust examples and instructions for avoiding prompt injection attacks in the application. Azure AI Content Safety helps detect offensive or inappropriate content in text and images. Content moderation also checks for jailbreaks.
       - question: |
           What is the billing model for Model-as-a-Service (MaaS)? 
         answer: |
-          Azure AI Foundry offers paygo inference APIs and hosted fine-tuning for [Llama 2 family models](how-to/deploy-models-llama.md). During preview, there's no extra charge for Azure AI Foundry outside of typical AI services and other Azure resource charges.
+          Azure AI Foundry offers pay-as-you-go inference APIs and hosted fine-tuning for [Llama 2 family models](how-to/deploy-models-llama.md). Currently, there's no extra charge for Azure AI Foundry outside of typical AI services and other Azure resource charges.
       - question: |
           Can all models be secured with content filtering? 
         answer: |
diff --git a/articles/ai-studio/reference/reference-model-inference-api.md b/articles/ai-studio/reference/reference-model-inference-api.md
@@ -202,7 +202,7 @@ Use the reference section to explore the API design and which parameters are ava
 __Request__
 
 ```HTTP/1.1
-POST /chat/completions?api-version=2024-04-01-preview
+POST /chat/completions?api-version=2024-05-01-preview
 Authorization: Bearer <bearer-token>
 Content-Type: application/json
 ```
@@ -277,7 +277,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}");
 __Request__
 
 ```HTTP/1.1
-POST /chat/completions?api-version=2024-04-01-preview
+POST /chat/completions?api-version=2024-05-01-preview
 Authorization: Bearer <bearer-token>
 Content-Type: application/json
 extra-parameters: pass-through
@@ -411,7 +411,7 @@ catch (RequestFailedException ex)
 __Request__
 
 ```HTTP/1.1
-POST /chat/completions?api-version=2024-04-01-preview
+POST /chat/completions?api-version=2024-05-01-preview
 Authorization: Bearer <bearer-token>
 Content-Type: application/json
 ```
@@ -552,7 +552,7 @@ catch (RequestFailedException ex)
 __Request__
 
 ```HTTP/1.1
-POST /chat/completions?api-version=2024-04-01-preview
+POST /chat/completions?api-version=2024-05-01-preview
 Authorization: Bearer <bearer-token>
 Content-Type: application/json
 ```
diff --git a/articles/ai-studio/reference/reference-model-inference-chat-completions.md b/articles/ai-studio/reference/reference-model-inference-chat-completions.md
@@ -21,7 +21,7 @@ ms.custom:
 Creates a model response for the given chat conversation.
 
 ```http
-POST /chat/completions?api-version=2024-04-01-preview
+POST /chat/completions?api-version=2024-05-01-preview
 ```
 
 ## URI Parameters
diff --git a/articles/ai-studio/reference/reference-model-inference-embeddings.md b/articles/ai-studio/reference/reference-model-inference-embeddings.md
@@ -21,7 +21,7 @@ ms.custom:
 Creates an embedding vector representing the input text.
 
 ```http
-POST /embeddings?api-version=2024-04-01-preview
+POST /embeddings?api-version=2024-05-01-preview
 ```
 
 ## URI Parameters
diff --git a/articles/ai-studio/reference/reference-model-inference-images-embeddings.md b/articles/ai-studio/reference/reference-model-inference-images-embeddings.md
@@ -21,7 +21,7 @@ ms.custom:
 Creates an embedding vector representing the input image and text pair.
 
 ```http
-POST /images/embeddings?api-version=2024-04-01-preview
+POST /images/embeddings?api-version=2024-05-01-preview
 ```
 
 ## URI Parameters
diff --git a/articles/ai-studio/reference/reference-model-inference-info.md b/articles/ai-studio/reference/reference-model-inference-info.md
@@ -21,7 +21,7 @@ ms.custom:
 Returns the information about the model deployed under the endpoint.
 
 ```http
-GET /info?api-version=2024-04-01-preview
+GET /info?api-version=2024-05-01-preview
 ```
 
 ## URI Parameters
diff --git a/articles/search/cognitive-search-create-custom-skill-example.md b/articles/search/cognitive-search-create-custom-skill-example.md
@@ -6,7 +6,7 @@ author: gmndrg
 ms.author: gimondra
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 03/18/2024
+ms.date: 01/18/2025
 ms.custom:
   - devx-track-csharp
   - ignite-2023
@@ -308,7 +308,7 @@ namespace SampleSkills
 }
 ```
 
-Make sure to enter your own *key* value in the `key` constant based on the key you got when signing up for the Bing entity search API.
+Make sure to enter your own *key* value in the `key` constant based on the key you got when signing up for the Bing Entity search API.
 
 ## Test the function from Visual Studio
 
diff --git a/articles/search/cognitive-search-custom-skill-scale.md b/articles/search/cognitive-search-custom-skill-scale.md
@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 03/18/2024
+ms.date: 01/18/2025
 ---
 
 # Efficiently scale out a custom skill
diff --git a/articles/search/cognitive-search-incremental-indexing-conceptual.md b/articles/search/cognitive-search-incremental-indexing-conceptual.md
@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 06/25/2024
+ms.date: 01/17/2025
 ---
 
 # Incremental enrichment and caching in Azure AI Search
@@ -40,7 +40,7 @@ The following example illustrates an indexer with caching enabled. See [Enable e
 To use the cache property, you can use 2020-06-30-preview or later when you [create or update an indexer](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). We recommend the latest preview API.
 
 ```json
-POST https://[search service name].search.windows.net/indexers?api-version=2024-05-01-rreview
+POST https://[YOUR-SEARCH-SERVICE-NAME].search.windows.net/indexers?api-version=2024-05-01-preview
     {
         "name": "myIndexerName",
         "targetIndexName": "myIndex",
diff --git a/articles/search/cognitive-search-tutorial-blob-dotnet.md b/articles/search/cognitive-search-tutorial-blob-dotnet.md
@@ -9,7 +9,7 @@ manager: nitinme
 
 ms.service: azure-ai-search
 ms.topic: tutorial
-ms.date: 03/06/2024
+ms.date: 01/17/2025
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet
diff --git a/articles/search/cognitive-search-tutorial-blob.md b/articles/search/cognitive-search-tutorial-blob.md
@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: tutorial
-ms.date: 03/06/2024
+ms.date: 01/17/2025
 ---
 
 # REST Tutorial: Use skillsets to generate searchable content in Azure AI Search
diff --git a/articles/search/index-sql-relational-data.md b/articles/search/index-sql-relational-data.md
@@ -9,16 +9,16 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 03/18/2024
+ms.date: 01/18/2025
 ---
 
 # How to model relational SQL data for import and indexing in Azure AI Search
 
-Azure AI Search accepts a flat rowset as input to the [indexing pipeline](search-what-is-an-index.md). If your source data originates from joined tables in a SQL Server relational database, this article explains how to construct the result set, and how to model a parent-child relationship in an Azure AI Search index.
+Azure AI Search accepts a flat rowset as input to the [indexing pipeline](search-what-is-an-index.md). If your source data originates from joined tables in a SQL Server relational database, this article explains how to construct the rowset, and how to model a parent-child relationship in an Azure AI Search index.
 
 As an illustration, we refer to a hypothetical hotels database, based on [demo data](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/hotels). Assume the database consists of a `Hotels$` table with 50 hotels, and a `Rooms$` table with rooms of varying types, rates, and amenities, for a total of 750 rooms. There's a one-to-many relationship between the tables. In our approach, a view provides the query that returns 50 rows, one row per hotel, with associated room detail embedded into each row.
 
-   ![Tables and view in the Hotels database](media/index-sql-relational-data/hotels-database-tables-view.png "Tables and view in the Hotels database")
+![Tables and view in the Hotels database](media/index-sql-relational-data/hotels-database-tables-view.png "Screenshot of tables and view in the Hotels database.")
 
 ## The problem of denormalized data
 
@@ -32,9 +32,9 @@ ON Rooms$.HotelID = Hotels$.HotelID
 
 Results from this query return all of the Hotel fields, followed by all Room fields, with preliminary hotel information repeating for each room value.
 
-   ![Denormalized data, redundant hotel data when room fields are added](media/index-sql-relational-data/denormalize-data-query.png "Denormalized data, redundant hotel data when room fields are added")
+![Denormalized data, redundant hotel data when room fields are added](media/index-sql-relational-data/denormalize-data-query.png "Screenshot of denormalized data, redundant hotel data when room fields are added.")
 
-While this query succeeds on the surface (providing all of the data in a flat row set), it fails in delivering the right document structure for the expected search experience. During indexing, Azure AI Search creates one search document for each row ingested. If your search documents looked like the above results, you would have perceived duplicates - seven separate documents for the Old Century Hotel alone. A query on "hotels in Florida" would return seven results for just the Old Century Hotel, pushing other relevant hotels deep into the search results.
+While this query succeeds on the surface (providing all of the data in a flat rowset), it fails in delivering the right document structure for the expected search experience. During indexing, Azure AI Search creates one search document for each row ingested. If your search documents looked like the above results, you would have perceived duplicates - seven separate documents for the Old Century Hotel alone. A query on "hotels in Florida" would return seven results for just the Old Century Hotel, pushing other relevant hotels deep into the search results.
 
 To get the expected experience of one document per hotel, you should provide a rowset at the right granularity, but with complete information. This article explains how.
 
@@ -94,11 +94,11 @@ The solution is to capture the room detail as nested JSON, and then insert the J
 
    The following screenshot shows the resulting view, with the *Rooms* nvarchar field at the bottom. The *Rooms* field exists only in the HotelRooms view.
 
-   ![HotelRooms view](media/index-sql-relational-data/hotelsrooms-view.png "HoteRooms view")
+   ![HotelRooms view](media/index-sql-relational-data/hotelsrooms-view.png "Screenshot of the HotelRooms view.")
 
 1. Run `SELECT * FROM dbo.HotelRooms` to retrieve the row set. This query returns 50 rows, one per hotel, with associated room information as a JSON collection. 
 
-   ![Rowset from HotelRooms view](media/index-sql-relational-data/hotelrooms-rowset.png "Rowset from HotelRooms view")
+   ![Rowset from HotelRooms view](media/index-sql-relational-data/hotelrooms-rowset.png "Screenshot of the rowset from the HotelRooms view.")
 
 This rowset is now ready for import into Azure AI Search.
 
@@ -159,7 +159,7 @@ As noted in [Model complex types](search-howto-complex-data-types.md): "the docu
 
 Using your own data set, you can use the [Import data wizard](search-import-data-portal.md) to create and load the index. The wizard detects the embedded JSON collection, such as the one contained in *Rooms*, and infers an index schema that includes a complex type collection. 
 
-  ![Index inferred by Import data wizard](media/index-sql-relational-data/search-index-rooms-complex-collection.png "Index inferred by Import data wizard")
+  ![Index inferred by Import data wizard](media/index-sql-relational-data/search-index-rooms-complex-collection.png "Screenshot of the an index inferred by Import data wizard.")
 
 Try the following quickstart to learn the basic steps of the Import data wizard.
 
diff --git a/articles/search/search-get-started-arm.md b/articles/search/search-get-started-arm.md
@@ -12,7 +12,7 @@ ms.custom:
   - mode-arm
   - devx-track-arm-template
   - ignite-2023
-ms.date: 04/24/2024
+ms.date: 01/17/2025
 ---
 
 # Quickstart: Deploy Azure AI Search using an Azure Resource Manager template
diff --git a/articles/search/search-howto-index-one-to-many-blobs.md b/articles/search/search-howto-index-one-to-many-blobs.md
@@ -11,7 +11,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 01/18/2024
+ms.date: 01/18/2025
 ---
 
 # Indexing blobs and files to produce multiple search documents
@@ -105,12 +105,12 @@ When you create an indexer with `delimitedText` **parsingMode**, it might feel n
 }
 ```
 
-However, this mapping won't result in four documents showing up in the index because the `recordid` field isn't unique _across blobs_. Hence, we recommend you to make use of the implicit field mapping applied from the `AzureSearch_DocumentKey` property to the key index field for "one-to-many" parsing modes.
+However, this mapping doesn't result in four documents showing up in the index because the `recordid` field isn't unique _across blobs_. Hence, we recommend you to make use of the implicit field mapping applied from the `AzureSearch_DocumentKey` property to the key index field for "one-to-many" parsing modes.
 
 If you do want to set up an explicit field mapping, make sure that the _sourceField_ is distinct for each individual entity **across all blobs**.
 
 > [!NOTE]
-> The approach used by `AzureSearch_DocumentKey` of ensuring uniqueness per extracted entity is subject to change and therefore you should not rely on it's value for your application's needs.
+> The approach used by `AzureSearch_DocumentKey` of ensuring uniqueness per extracted entity is subject to change and therefore you shouldn't rely on its value for your application's needs.
 
 ## Specify the index key field in your data
 
@@ -132,9 +132,9 @@ id, temperature, pressure, timestamp
 2, 120, 3,"2022-05-11T00:00:00Z" 
 ```
 
-Notice that each document contains the `id` field, which is defined as the `key` field in the index. In such a case, even though a document-unique `AzureSearch_DocumentKey` will be generated, it won't be used as the "key" for the document. Rather, the value of the `id` field will be mapped to the `key` field
+Notice that each document contains the `id` field, which is defined as the `key` field in the index. In such a case, even though a document-unique `AzureSearch_DocumentKey` is generated, it isn't used as the "key" for the document. Rather, the value of the `id` field is mapped to the `key` field
 
-Similar to the previous example, this mapping won't result in four documents showing up in the index because the `id` field isn't unique _across blobs_. When this is the case, any json entry that specifies an `id` will result in a merge on the existing document instead of an upload of a new document, and the state of the index will reflect the latest read entry with the specified `id`.
+Similar to the previous example, this mapping doesn't result in four documents showing up in the index because the `id` field isn't unique _across blobs_. When this is the case, any json entry that specifies an `id` results in a merge on the existing document instead of an upload of a new document, and the state of the index reflects the latest read entry with the specified `id`.
 
 ## Next steps
 
diff --git a/articles/search/search-howto-monitor-indexers.md b/articles/search/search-howto-monitor-indexers.md
@@ -11,7 +11,7 @@ ms.custom:
   - devx-track-dotnet
   - ignite-2023
 ms.topic: conceptual
-ms.date: 06/25/2024
+ms.date: 01/17/2025
 ---
 
 # Monitor indexer status and results in Azure AI Search
diff --git a/articles/search/tutorial-create-custom-analyzer.md b/articles/search/tutorial-create-custom-analyzer.md
@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: tutorial
-ms.date: 03/07/2024
+ms.date: 01/17/2025
 ---
 
 # Tutorial: Create a custom analyzer for phone numbers
diff --git a/articles/search/tutorial-csharp-create-mvc-app.md b/articles/search/tutorial-csharp-create-mvc-app.md
@@ -11,7 +11,7 @@ ms.devlang: csharp
 ms.custom:
   - ignite-2023
 ms.topic: tutorial
-ms.date: 04/22/2024
+ms.date: 01/17/2025
 ---
 
 # Create a search app in ASP.NET Core
