Merge pull request #2221 from HeidiSteen/heidist-freshness

clarification on where to insert the semantic config code in the hotels index

Author: prmerger-automator[bot]

articles/search/search-get-started-rag.md

@@ -8,20 +8,20 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2024
 ms.topic: quickstart
-ms.date: 10/14/2024
+ms.date: 01/07/2025
 ---
 
 # Quickstart: Generative search (RAG) with grounding data from Azure AI Search
 
-This quickstart shows you how to send basic and complex queries to a Large Language Model (LLM) for a conversational search experience over your indexed content on Azure AI Search. You use the Azure portal to set up the resources, and then run Python code to call the APIs. 
+This quickstart shows you how to send queries to a chat completion model for a conversational search experience over your indexed content on Azure AI Search. You use the Azure portal to set up the resources, and then run Python code to call the APIs. 
 
 ## Prerequisites
 
 - An Azure subscription. [Create one for free](https://azure.microsoft.com/free/).
 
 - [Azure AI Search](search-create-service-portal.md), Basic tier or higher so that you can [enable semantic ranker](semantic-how-to-enable-disable.md). Region must be the same one used for Azure OpenAI.
 
-- [Azure OpenAI](https://aka.ms/oai/access) resource with a deployment of `gpt-4o`, `gpt-4o-mini`, or equivalent LLM, in the same region as Azure AI Search.
+- [Azure OpenAI](https://aka.ms/oai/access) resource with a deployment of `gpt-4o`, `gpt-4o-mini`, or equivalent chat completion model, in the same region as Azure AI Search.
 
 - [Visual Studio Code](https://code.visualstudio.com/download) with the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and the [Jupyter package](https://pypi.org/project/jupyter/). For more information, see [Python in Visual Studio Code](https://code.visualstudio.com/docs/languages/python).
 
@@ -90,7 +90,17 @@ We recommend the hotels-sample-index, which can be created in minutes and runs o
 
 1. Select **Edit JSON**. 
 
-1. Search for "semantic" to find the section in the index for a semantic configuration. Replace the empty `"semantic": {}` line with the following semantic configuration. This example specifies a `"defaultConfiguration"`, which is important to the running of this quickstart.
+1. Scroll to the end of the index, where you can find placeholders for constructs that can be added to an index.
+
+   ```json
+   "analyzers": [],
+   "tokenizers": [],
+   "tokenFilters": [],
+   "charFilters": [],
+   "normalizers": [],
+   ```
+
+1. On a new line after "normalizers", paste in the following semantic configuration. This example specifies a `"defaultConfiguration"`, which is important to the running of this quickstart.
 
     ```json
     "semantic":{

articles/search/search-limits-quotas-capacity.md

@@ -69,8 +69,6 @@ Maximum number of documents per index are:
 + 288 billion on L1
 + 576 billion on L2
 
-You can check the number of documents in the Azure portal and through REST calls that include `search=*` and `count=true`.
-
 Maximum size of each document is approximately 16 megabytes. Document size is actually a limit on the size of the indexing API request payload, which is 16 megabytes. That payload can be a single document, or a batch of documents. For a batch with a single document, the maximum document size is 16 MB of JSON. 
 
 Document size applies to *push mode* indexing that uploads documents to a search service. If you're using an indexer for *pull mode* indexing, your source files can be any file size, subject to [indexer limits](#indexer-limits). For the blob indexer, file size limits are larger for higher tiers. For example, the S1 limit is 128 megabytes, S2 limit is 256 megabytes, and so forth.