Merge pull request #121 from HeidiSteen/heidist-rag

fixed link in data source gallery

Author: prmerger-automator[bot]

articles/search/index.yml

@@ -50,10 +50,10 @@ landingContent:
             url: retrieval-augmented-generation-overview.md
       - linkListType: quickstart
         links:
-          - text: Create a vector index
-            url: search-get-started-vector.md
           - text: Retrieve data using an LLM
             url: search-get-started-rag.md
+          - text: Create a vector index
+            url: search-get-started-vector.md
           - text: Query a vector index
             url: vector-search-how-to-query.md
       - linkListType: sample

articles/search/search-data-sources-gallery.md

@@ -9,8 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-layout: LandingPage
-ms.date: 06/18/2024
+ms.date: 09/04/2024
 ---
 
 # Data sources gallery
@@ -153,7 +152,7 @@ By [Azure AI Search](search-how-to-index-onelake-files.md)
 
 Connect to a OneLake lakehouse to extract supported files content from a hierarchy of directories and nested subdirectories.
 
-[More details](search-howto-index-cosmosdb.md)
+[More details](search-how-to-index-onelake-files.md)
 
 :::image type="icon" source="media/search-data-sources-gallery/fabric_onelake_logo.png":::
 

articles/search/search-region-support.md

@@ -21,7 +21,7 @@ This article identifies the cloud regions in which Azure AI Search is available.
 
 | Feature | Availability |
 |---------|--------------|
-| [Extra capacity](search-limits-quotas-capacity.md#service-limits) | Higher capacity partitions became available in selected regions starting in April 2024 with a second wave following in May 2024. Currently, there are just a few regions that *don't* offer higher capacity partitions. If you're using an older search service, create a new search service to benefit from more capacity at the same billing rate. To check existing capacity, [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) and select the **Properties** tab in the middle of the Overview page. To check search service age, follow [these instructions](vector-search-index-size.md#how-to-check-service-creation-date). Regional support for extra capacity is noted in the footnotes of this article.|
+| [Extra capacity](search-limits-quotas-capacity.md#service-limits) | Higher capacity partitions became available in selected regions starting in April 2024 with a second wave following in May 2024. If you're using an older search service, create a new search service to benefit from more capacity at the same billing rate. To check existing capacity, [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) and select the **Properties** tab in the middle of the Overview page. To check search service age, follow [these instructions](vector-search-index-size.md#how-to-check-service-creation-date).  Currently, there are just a few regions that *don't* offer higher capacity partitions. Regional support for extra capacity is noted in the footnotes of this article.|
 | [Availability zones](search-reliability.md#availability-zone-support) | Divides a region's data centers into distinct physical location groups, providing high-availability within the same geo. Regional support is noted in this article. |
 | [Azure AI enrichment](cognitive-search-concept-intro.md) | Refers to skills that make internal calls to Azure AI for enrichment and transformation during indexing. Integration requires that Azure AI Search coexists with an [Azure AI multi-service account](/azure/ai-services/multi-service-resource) in the same physical region. Regional support is noted in this article. |
 | [Azure OpenAI integration](vector-search-integrated-vectorization.md)  | Refers to skills and vectorizers that make internal calls to deployed embedding and chat models on Azure OpenAI. Check [Azure OpenAI model region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability) for the most current list of regions for each embedding and chat model. Specific Azure OpenAI models are in fewer regions, so be sure to check for joint regional availability before installing.|

articles/search/search-what-is-azure-search.md

@@ -41,13 +41,13 @@ On the search service itself, the two primary workloads are *indexing* and *quer
 
 + [**Indexing**](search-what-is-an-index.md) is an intake process that loads content into your search service and makes it searchable. Internally, inbound text is processed into tokens and stored in inverted indexes, and inbound vectors are stored in vector indexes. The document format that Azure AI Search can index is JSON. You can upload JSON documents that you've assembled, or use an indexer to retrieve and serialize your data into JSON. 
 
-  [Applied AI](cognitive-search-concept-intro.md) through a [skillset](cognitive-search-working-with-skillsets.md) extends indexing with image and language models. If you have images or large unstructured text in source document, you can attach skills that perform OCR, analyze and escribe images, infer structure, translate text and more. 
+  [Applied AI](cognitive-search-concept-intro.md) through a [skillset](cognitive-search-working-with-skillsets.md) extends indexing with image and language models. If you have images or large unstructured text in source document, you can attach skills that perform OCR, analyze and describe images, infer structure, translate text and more. Output is text that can be serialized into JSON and ingested into a search index.
 
-  Skillsets can also perform [data chunking and vectorization during indexing](vector-search-integrated-vectorization.md). Skills that attach to Azure OpenAI, the model catalog in Azure AI Studio, or custom skills that attach to any external chunking and embedding model can be used during indexing to create vector data.
+  Skillsets can also perform [data chunking and vectorization during indexing](vector-search-integrated-vectorization.md). Skills that attach to Azure OpenAI, the model catalog in Azure AI Studio, or custom skills that attach to any external chunking and embedding model can be used during indexing to create vector data. Output is chunked vector content that can be ingested into a search index.
 
 + [**Querying**](search-query-overview.md) can happen once an index is populated with searchable content, when your client app sends query requests to a search service and handles responses. All query execution is over a search index that you control.
 
-  [Semantic ranking](semantic-search-overview.md) is an extension of query execution. It adds secondary ranking, using language understanding to reevalute a result set, promoting the most semantically relevant results to the top.
+  [Semantic ranking](semantic-search-overview.md) is an extension of query execution. It adds secondary ranking, using language understanding to reevaluate a result set, promoting the most semantically relevant results to the top.
 
   [Integrated vectorization](vector-search-integrated-vectorization.md) is also an extension of query execution. If you have vector fields in your search index, you can submit raw vector queries or text that's vectorized at query time.
 
@@ -99,7 +99,7 @@ Or, try solution accelerators:
 
 + [**Chat with your data** solution accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator) helps you create a custom RAG solution over your content.
 
-+ [**Conversational Knowledge Mining** solution accelerator](https://github.com/microsoft/Customer-Service-Conversational-Insights-with-Azure-OpenAI-Services), helps you create an interactive solution to extract actionable insights from post-contact center transcripts.
++ [**Conversational Knowledge Mining** solution accelerator](https://github.com/microsoft/Customer-Service-Conversational-Insights-with-Azure-OpenAI-Services) helps you create an interactive solution to extract actionable insights from post-contact center transcripts.
 
 + [**Build your own copilot** solution accelerator](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator), leverages Azure OpenAI Service, Azure AI Search and Microsoft Fabric, to create custom copilot solutions.