@@ -7,7 +7,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 05/08/2025
+ms.date: 01/31/2026
 ms.update-cycle: 365-days
 ms.custom:
   - ignite-2023
@@ -38,15 +38,15 @@ Debug sessions work with all generally available [indexer data sources](search-d
 
 + Azure Cosmos DB for MongoDB indexer.
 
-+ For the Azure Cosmos DB for NoSQL, if a row fails during index and there's no corresponding metadata, the debug session might not pick the correct row.
++ For Azure Cosmos DB for NoSQL, if a row fails during index and there's no corresponding metadata, the debug session might not pick the correct row.
 
-+ For the SQL API of Azure Cosmos DB, if a partitioned collection was previously non-partitioned, the debug session won't find the document.
++ For the NoSQL API of Azure Cosmos DB, if a partitioned collection was previously non-partitioned, the debug session won't find the document.
 
 + For custom skills, a user-assigned managed identity isn't supported for a debug session connection to Azure Storage. As stated in the prerequisites, you can use a system managed identity, or specify a full access connection string that includes a key. For more information, see [Connect a search service to other Azure resources using a managed identity](search-how-to-managed-identities.md).
 
 ## Security and permissions
 
-+ To save a debug session to Azure storage, the search service identity must have **Storage Blob Data Contributor** permissions on Azure Storage. Otherwise, plan on choosing a full access connection string for the debug session connection to Azure Storage.
++ To save a debug session to Azure Storage, the search service identity must have **Storage Blob Data Contributor** permissions on Azure Storage. Otherwise, plan on choosing a full access connection string for the debug session connection to Azure Storage.
 
 + If the Azure Storage account is behind a firewall, configure it to [allow search service access](search-indexer-howto-access-ip-restricted.md).
 
@@ -66,10 +66,18 @@ Debug sessions work with all generally available [indexer data sources](search-d
 
 1. In **Document to debug**, choose the first document in the index or select a specific document. If you select a specific document, depending on the data source, you're asked for a URI or a row ID.
 
-   If your specific document is a blob, provide the blob URI. You can find the URI in the blob property page in the Azure portal.
+   For a specific document in Blob storage: provide the blob URI. You can find the URI in the blob property page in the Azure portal.
 
    :::image type="content" source="media/cognitive-search-debug/copy-blob-url.png" lightbox="media/cognitive-search-debug/copy-blob-url.png" alt-text="Screenshot of the URI property in blob storage." border="true":::
 
+   For a specific document in Azure CosmosDB for NoSQL: provide the document key in the search index for the value, and the ID of the entity in Cosmos DB for the partition key.
+
+   :::image type="content" source="media/cognitive-search-debug/cosmos-db-source.png" lightbox="media/cognitive-search-debug/cosmos-db-source.png" alt-text="Screenshot of the properties in Cosmos DB." border="true":::
+
+   Here's the debug session configuration for the Cosmos DB example.
+
+   :::image type="content" source="media/cognitive-search-debug/cosmos-db-document.png" lightbox="media/cognitive-search-debug/cosmos-db-document.png" alt-text="Screenshot of the Cosmos DB properties in debug session configuration." border="true":::
+
 1. In **Storage account**, choose a general-purpose storage account for caching the debug session.
 
 1. Select **Authenticate using managed identity** if you previously assigned **Storage Blob Data Contributor** permissions to the search service system-managed identity. If you don't check this box, the search service connects using a full access connection string.

@@ -520,7 +520,7 @@ console.log(`Index named ${index.name} has been created.`);
 
 In Azure AI Search, documents are data structures that are both inputs to indexing and outputs from queries. You can push such data to the index or use an [indexer](/azure/search/search-indexer-overview). In this case, we'll programmatically push the documents to the index.
 
-Document inputs might be rows in a database, blobs in Blob storage, or, as in this sample, JSON documents on disk. You can either download [hotels.json](https://github.com/Azure-Samples/azure-search-javascript-samples/blob/main/quickstart/hotels.json) or create your own *hotels.json* file with the following content:
+Document inputs might be rows in a database, blobs in Blob storage, or, as in this sample, JSON documents on disk. You can either download [hotels.json](https://github.com/Azure-Samples/azure-search-javascript-samples/blob/main/quickstart-keyword-search/hotels.json) or create your own *hotels.json* file with the following content:
 
 
 Similar to what we did with the indexDefinition, we also need to import `hotels.json` at the top of *index.ts* so that the data can be accessed in our main function.

@@ -210,7 +210,7 @@ For Azure AI Search, the Azure SDKs implement generally available features. As s
 | .NET | [SearchIndexerClient](/dotnet/api/azure.search.documents.indexes.searchindexerclient) | [DotNetHowToIndexers](https://github.com/Azure-Samples/search-dotnet-getting-started/tree/master/DotNetHowToIndexers) |
 | Java | [SearchIndexerClient](/java/api/com.azure.search.documents.indexes.searchindexerclient) | [CreateIndexerExample.java](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents/src/samples/java/com/azure/search/documents/indexes/CreateIndexerExample.java) |
 | JavaScript | [SearchIndexerClient](/javascript/api/@azure/search-documents/searchindexerclient) | [Indexers](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/search/search-documents/samples/v11/javascript) |
-| Python | [SearchIndexerClient](/python/api/azure-search-documents/azure.search.documents.indexes.searchindexerclient) | [sample_indexers_operations.py](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/search/azure-search-documents/samples/sample_indexers_operations.py) |
+| Python | [SearchIndexerClient](/python/api/azure-search-documents/azure.search.documents.indexes.searchindexerclient) | [sample_indexers_operations.py](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_indexer_crud.py) |
 
 ---
 

@@ -167,7 +167,7 @@ The Azure SDK for Python provides the following APIs for simple and bulk documen
 
 Code samples include:
 
-+ [sample_crud_operations.py](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_crud_operations.py)
++ [sample_crud_operations.py](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_documents_crud.py)
 
 + Be sure to check the [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples) repo for code examples showing how to index vector fields.
 

@@ -258,6 +258,7 @@ General:
 Indexing APIs:
 
 + Supported maximum 1,000 documents per batch of index uploads, merges, or deletes.
++ Each request supports between 1 and 32,000 indexing actions.
 
 Query APIs:
 

@@ -9,7 +9,7 @@ ms.update-cycle: 180-days
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 10/08/2025
+ms.date: 01/31/2026
 ---
 
 # Truncate dimensions using MRL compression
@@ -75,10 +75,10 @@ The following example illustrates a vector search configuration that meets the r
     "profiles": [ 
       { 
         "name": "use-bq-with-mrl", 
-        "compression": "use-mrl,use-bq", 
-        "algorithm": "use-hnsw" 
-      } 
-    ],
+        "compression": "use-bq-with-truncation", 
+        "algorithm": "use-hnsw" 
+      } 
+    ],
     "algorithms": [
        {
           "name": "use-hnsw",
@@ -91,38 +91,29 @@ The following example illustrates a vector search configuration that meets the r
           }
        }
     ],
-    "compressions": [ 
-      { 
-        "name": "use-mrl", 
-        "kind": "binaryQuantization", 
+    "compressions": [ 
+      { 
+        "name": "use-bq-with-truncation", 
+        "kind": "binaryQuantization", 
         "rescoringOptions": {
             "enableRescoring": true,
             "defaultOversampling": 10,
             "rescoreStorageMethod": "preserveOriginals"
         },
-        "truncationDimension": 1024
-      }, 
-      { 
-        "name": "use-bq", 
-        "kind": "binaryQuantization", 
-        "rescoringOptions": {
-            "enableRescoring": true,
-            "defaultOversampling": 10,
-            "rescoreStorageMethod": "discardOriginals"
-        }
-       } 
-    ] 
-  } 
-} 
+        "truncationDimension": 1024
+      }
+    ]
+  }
+}
 ```
 
-Here's an example of a [fully specified vector field definition](/rest/api/searchservice/indexes/create-or-update#searchfield) that satisfies the requirements for MRL. Recall that vector fields must:
+The following JSON shows the vector field definition that uses this profile:
 
-- Be of type `Edm.Half` or `Edm.Single`.
+- Data type is either `Collection(Edm.Half)` or `Collection(Edm.Single)`.
 
-- Have a `vectorSearchProfile` property that specifies the algorithm and compression settings.
+- The field has a `vectorSearchProfile` property that specifies the algorithm and compression settings.
 
-- Have a `dimensions` property that specifies the number of dimensions for scoring and ranking results. Its value should be the dimensions limit of the model you're using (1,536 for text-embedding-3-small).
+- The field has a `dimensions` property that specifies the number of dimensions for scoring and ranking results. Its value should be the dimensions limit of the model you're using (1,536 for text-embedding-3-small).
 
 ```json
 {