Merge pull request #5515 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-ai-docs (branch main)

Author: Taojunshen

articles/ai-services/speech-service/includes/quickstarts/voice-live-api/realtime-python.md

@@ -353,7 +353,7 @@ For the recommended keyless authentication with Microsoft Entra ID, you need to:
 
                     if event.get("type") == "session.created":
                         session = event.get("session")
-                        logger.info(f"Session created: {session.get("id")}")
+                        logger.info(f"Session created: {session.get('id')}")
 
                     elif event.get("type") == "response.audio.delta":
                         if event.get("item_id") != last_audio_item_id:

articles/search/index-add-scoring-profiles.md

@@ -248,7 +248,7 @@ See this [blog post](https://farzzy.hashnode.dev/enhance-azure-ai-search-documen
 
 The following example shows the schema of an index with two scoring profiles (`boostGenre`, `newAndHighlyRated`). Any query against this index that includes either profile as a query parameter will use the profile to score the result set. 
 
-The `boostGenre` profile uses weighted text fields, boosting matches found in albumTitle, genre, and artistName fields. The fields are boosted 1.5, 5, and 2 respectively. Why is genre boosted so much higher than the others? If search is conducted over data that is somewhat homogenous (as is the case with 'genre' in the musicstoreindex), you might need a larger variance in the relative weights. For example, in the musicstoreindex, 'rock' appears as both a genre and in identically phrased genre descriptions. If you want genre to outweigh genre description, the genre field will need a much higher relative weight.
+The `boostGenre` profile uses weighted text fields, boosting matches found in albumTitle, genre, and artistName fields. The fields are boosted 1.5, 5, and 2 respectively. Why is genre boosted so much higher than the others? If search is conducted over data that is somewhat homogeneous (as is the case with 'genre' in the musicstoreindex), you might need a larger variance in the relative weights. For example, in the musicstoreindex, 'rock' appears as both a genre and in identically phrased genre descriptions. If you want genre to outweigh genre description, the genre field will need a much higher relative weight.
 
 ```json
 {  

articles/search/vector-search-how-to-assign-narrow-data-types.md

@@ -45,7 +45,7 @@ Data types are assigned to fields in an index definition. You can use the Azure
 
    You can use other narrow data types if your model emits embeddings in the smaller data format or if you have custom quantization that converts vectors to a smaller format.
 
-1. Make sure you understand the tradeoffs of a narrow data type. `Collection(Edm.Half)` has less information, which results in lower resolution. If your data is homogenous or dense, losing extra detail or nuance could lead to unacceptable results at query time because there's less detail that can be used to distinguish nearby vectors apart.
+1. Make sure you understand the tradeoffs of a narrow data type. `Collection(Edm.Half)` has less information, which results in lower resolution. If your data is homogeneous or dense, losing extra detail or nuance could lead to unacceptable results at query time because there's less detail that can be used to distinguish nearby vectors apart.
 
 ## Assign the data type