@@ -17,7 +17,7 @@ ms.date: 09/19/2022
 
 The **Text Translation** skill evaluates text and, for each record, returns the text translated to the specified target language. This skill uses the [Translator Text API v3.0](/azure/ai-services/translator/reference/v3-0-translate) available in Azure AI services.
 
-This capability is useful if you expect that your documents may not all be in one language, in which case you can normalize the text to a single language before indexing for search by translating it.  It's also useful for localization use cases, where you may want to have copies of the same text available in multiple languages.
+This capability is useful if you expect that your documents may not all be in one language, in which case you can normalize the text to a single language before indexing for search by translating it.  It's also useful for localization use cases, where you might want to have copies of the same text available in multiple languages.
 
 The [Translator Text API v3.0](/azure/ai-services/translator/reference/v3-0-reference) is a non-regional Azure AI service, meaning that your data isn't guaranteed to stay in the same region as your Azure AI Search or attached Azure AI services resource.
 

@@ -175,7 +175,7 @@ Suffix matching requires the regular expression forward slash `/` delimiters. Ge
 
 During query parsing, queries that are formulated as prefix, suffix, wildcard, or regular expressions are passed as-is to the query tree, bypassing [lexical analysis](search-lucene-query-architecture.md#stage-2-lexical-analysis). Matches will only be found if the index contains the strings in the format your query specifies. In most cases, you need an analyzer during indexing that preserves string integrity so that partial term and pattern matching succeeds. For more information, see [Partial term search in Azure AI Search queries](search-query-partial-matching.md).
 
-Consider a situation where you may want the search query `terminal*` to return results that contain terms such as `terminate`, `termination`, and `terminates`.
+Consider a situation where you might want the search query `terminal*` to return results that contain terms such as `terminate`, `termination`, and `terminates`.
 
 If you were to use the en.lucene (English Lucene) analyzer, it would apply aggressive stemming of each term. For example, `terminate`, `termination`, `terminates` will all be tokenized down to the token `termi` in your index. On the other side, terms in queries using wildcards or fuzzy search aren't analyzed at all, so there would be no results that would match the `terminat*` query.
 
@@ -187,7 +187,7 @@ Azure AI Search uses frequency-based scoring ([BM25](https://en.wikipedia.org/wi
 
 ## Special characters
 
-In some circumstances, you may want to search for a special character, like an '❤' emoji or the '€' sign. In such cases, make sure that the analyzer you use doesn't filter those characters out. The standard analyzer bypasses many special characters, excluding them from your index.
+In some circumstances, you might want to search for a special character, like an '❤' emoji or the '€' sign. In such cases, make sure that the analyzer you use doesn't filter those characters out. The standard analyzer bypasses many special characters, excluding them from your index.
 
 Analyzers that tokenize special characters include the whitespace analyzer, which takes into consideration any character sequences separated by whitespaces as tokens (so the `❤` string would be considered a token). Also, a language analyzer like the Microsoft English analyzer ("en.microsoft"), would take the "€" string as a token. You can [test an analyzer](/rest/api/searchservice/indexes/analyze) to see what tokens it generates for a given query.
 

@@ -137,7 +137,7 @@ A connector in Power Apps is a data source connection. In this step, create a cu
     ```
 
     > [!TIP] 
-    > There is a character limit to the JSON response you can enter, so you may want to simplify the JSON before pasting it. The schema and format of the response is more important than the values themselves. For example, the Description field could be simplified to just the first sentence.
+    > There is a character limit to the JSON response you can enter, so you might want to simplify the JSON before pasting it. The schema and format of the response is more important than the values themselves. For example, the Description field could be simplified to just the first sentence.
 
 1. Select **Import** to add the default response.
 

@@ -167,7 +167,7 @@ Within the perimeter, all resources have mutual access at the network level. You
 
 For resources outside of the network security perimeter, you must specify inbound and outbound access rules. Inbound rules specify which connections to allow in, and outbound rules specify which requests are allowed out.
 
-A search service accepts inbound requests from apps like Azure AI Foundry, Azure OpenAI Studio, Azure Machine Learning prompt flow, and any app that sends indexing or query requests. A search service sends outbound requests during indexer-based indexing and skillset execution. This section explains how to set up inbound and outbound access rules for Azure AI Search scenarios.
+A search service accepts inbound requests from apps like Azure AI Foundry portal, Azure Machine Learning prompt flow, and any app that sends indexing or query requests. A search service sends outbound requests during indexer-based indexing and skillset execution. This section explains how to set up inbound and outbound access rules for Azure AI Search scenarios.
 
    > [!NOTE]
    > Any service associated with a network security perimeter implicitly allows inbound and outbound access to any other service associated with the same network security perimeter when that access is authenticated using [managed identities and role assignments](/entra/identity/managed-identities-azure-resources/overview). Access rules only need to be created when allowing access outside of the network security perimeter, or for access authenticated using API keys.

@@ -196,7 +196,7 @@ embedding_skill = AzureOpenAIEmbeddingSkill(
     resource_url=AZURE_OPENAI_ACCOUNT,  
     deployment_name="text-embedding-3-large",  
     model_name="text-embedding-3-large",
-    dimensions=1536,
+    dimensions=1024,
     inputs=[  
         InputFieldMappingEntry(name="text", source="/document/pages/*"),  
     ],