checkpoint

Author: HeidiSteen

articles/search/tutorial-rag-build-solution-index-schema.md

@@ -45,7 +45,7 @@ Chunks are the focus of the schema, and each chunk is the defining element of a
 
 In addition to structural considerations, like chunked content, you also want to consider the substance of your content because it also informs what fields are indexed.
 
-In this tutorial, sample data consists of PDFs and content from the NASA Earth Book. This content is descriptive and informative, with numerous references to geographies, countries, and areas across the world. To capture this information in our index and potentially use it in queries, we include skills in our indexing pipeline that recognize and extract this information, loading it into a searchable and filterable `locations` field.
+In this tutorial, sample data consists of PDFs and content from the NASA Earth Book. This content is descriptive and informative, with numerous references to geographies, countries, and areas across the world. To capture this information in our index and potentially use it in queries, we include skills in our indexing pipeline that recognize and extract this information, loading it into a searchable and filterable `locations` field. Adding structured content to your index gives you more options for filtering, relevance tuning, and richer answers.
 
 The original ebook is large, over 100 pages and 35 MB in size. We broke it up into smaller PDFs, one per page of text, to stay under the REST API payload limit of 16 MB per API call.
 
@@ -57,7 +57,7 @@ Chunked content typically derives from a larger document. And although the schem
 
 An inflection point in schema design is whether to have two indexes for parent and child/chunked content, or a single index that repeats parent elements for each chunk.
 
-In this tutorial, because all of the chunks of text originate from a single parent (NASA Earth Book), you don't need a separate index dedicated to up level parent fields. However, if you index from multiple parent PDFs, you might want a parent-child index pair to capture level-specific fields and then send lookup queries to the parent index to retrieve those fields relevant to each chunk.
+In this tutorial, because all of the chunks of text originate from a single parent (NASA Earth Book), you don't need a separate index dedicated to up level the parent fields. However, if you're indexing from multiple parent PDFs, you might want a parent-child index pair to capture level-specific fields and then send [lookup queries](https://learn.microsoft.com/rest/api/searchservice/documents/get) to the parent index to retrieve those fields relevant to each chunk.
 
 ### Checklist of schema considerations
 

articles/search/tutorial-rag-build-solution-models.md

@@ -102,13 +102,21 @@ This tutorial series uses the following models and model providers:
 You must have [**Cognitive Services OpenAI Contributor**]( /azure/ai-services/openai/how-to/role-based-access-control#cognitive-services-openai-contributor) or higher to deploy models in Azure OpenAI.
 
 1. Go to [Azure OpenAI Studio](https://oai.azure.com/).
+
 1. Select **Deployments** on the left menu.
+
 1. Select **Deploy model** > **Deploy base model**.
+
 1. Select **text-embedding-ada-02** from the dropdown list and confirm the selection.
+
 1. Specify a deployment name. We recommend "text-embedding-ada-002".
+
 1. Accept the defaults.
+
 1. Select **Deploy**.
+
 1. Repeat the previous steps for **gpt-35-turbo**.
+
 1. Make a note of the model names and endpoint. Embedding skills and vectorizers assemble the full endpoint internally, so you only need the resource URI. For example, given `https://MY-FAKE-ACCOUNT.openai.azure.com/openai/deployments/text-embedding-ada-002/embeddings?api-version=2023-05-15`, the endpoint you should provide in skill and vectorizer definitions is `https://MY-FAKE-ACCOUNT.openai.azure.com`.
 
 ## Configure search engine access to Azure models
@@ -118,13 +126,20 @@ For pipeline and query execution, this tutorial uses Microsoft Entra ID for auth
 Assign yourself and the search service identity permissions on Azure OpenAI. The code for this tutorial runs locally. Requests to Azure OpenAI originate from your system. Also, search results from the search engine are passed to Azure OpenAI. For these reasons, both you and the search service need permissions on Azure OpenAI.
 
 1. Sign in to the [Azure portal](https://portal.azure.com) and [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
+
 1. Configure Azure AI Search to [use a system-managed identity](search-howto-managed-identities-data-sources.md).
+
 1. Find your Azure OpenAI resource.
+
 1. Select **Access control (IAM)** on the left menu. 
+
 1. Select **Add role assignment**.
+
 1. Select [**Cognitive Services OpenAI User**](/azure/ai-services/openai/how-to/role-based-access-control#cognitive-services-openai-userpermissions).
 1. Select **Managed identity** and then select **Members**. Find the system-managed identity for your search service in the dropdown list.
+
 1. Next, select **User, group, or service principal** and then select **Members**. Search for your user account and then select it from the dropdown list.
+
 1. Select **Review and Assign** to create the role assignments.
 
 For access to models on Azure AI Vision, assign **Cognitive Services OpenAI User**. For Azure AI Studio, assign **Azure AI Developer**.

articles/search/tutorial-rag-build-solution.md

@@ -7,7 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
-ms.topic: conceptual
+ms.topic: overview
 ms.date: 09/12/2024
 
 ---