Merge pull request #280211 from MicrosoftDocs/main

Publish to Live Monday 4AM PST 7/8

Author: PMEds28

.openpublishing.redirection.defender-for-cloud.json

@@ -1,5 +1,25 @@
 {
     "redirections": [
+        {
+            "source_path_from_root": "/articles/defender-for-cloud/recommendations-reference.md",
+            "redirect_url": "/azure/defender-for-cloud/security-policy-concept",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/defender-for-cloud/recommendations-reference-gcp.md",
+            "redirect_url": "/azure/defender-for-cloud/security-policy-concept",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/defender-for-cloud/recommendations-reference-aws.md",
+            "redirect_url": "/azure/defender-for-cloud/security-policy-concept",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/defender-for-cloud/upcoming-changes.md",
+            "redirect_url": "/azure/defender-for-cloud/release-notes",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/defender-for-cloud/secret-scanning.md",
             "redirect_url": "/azure/defender-for-cloud/secrets-scanning-servers",

articles/backup/encryption-at-rest-with-cmk-for-backup-vault.md

@@ -5,6 +5,11 @@
       "redirect_url": "/azure/cosmos-db/container-copy",
       "redirect_document_id": true
     },
+    {
+      "source_path_from_root": "/articles/cosmos-db/nosql/how-to-javascript-vector-index-query.md",
+      "redirect_url": "/azure/cosmos-db/nosql",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/cosmos-db/account-databases-containers-items.md",
       "redirect_url": "/azure/cosmos-db/resource-model",
@@ -5801,4 +5806,4 @@
       "redirect_document_id": true
     }
   ]
-}
+}

articles/backup/media/encryption-at-rest-with-cmk-for-backup-vault/add-key-uri.png

@@ -10,11 +10,13 @@ ms.date: 06/01/2022
 ms.reviewer: thweiss
 ---
 
+
 # Access Azure Key Vault from Azure Cosmos DB using a managed identity
 [!INCLUDE[NoSQL, MongoDB, Cassandra, Gremlin, Table](includes/appliesto-nosql-mongodb-cassandra-gremlin-table.md)]
 
 Azure Cosmos DB may need to read secret/key data from Azure Key Vault. For example, your Azure Cosmos DB may require a customer-managed key stored in Azure Key Vault. To do this, Azure Cosmos DB should be configured with a managed identity, and then an Azure Key Vault access policy should grant the managed identity access.
 
+
 ## Prerequisites
 
 - An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).

articles/backup/media/encryption-at-rest-with-cmk-for-backup-vault/backup-vault-properties.png

@@ -490,8 +490,6 @@
         href: how-to-dotnet-vector-index-query.md
       - name: Python
         href: how-to-python-vector-index-query.md
-      - name: JavaScript
-        href: how-to-javascript-vector-index-query.md
       - name: Java
         href: how-to-java-vector-index-query.md
     - name: Access preview features

articles/backup/media/encryption-at-rest-with-cmk-for-backup-vault/update-encryption-settings.png

@@ -18,7 +18,7 @@ ms.date: 5/7/2024
 
 [!INCLUDE[NoSQL](../includes/appliesto-nosql.md)]
 
-Azure Cosmos DB for NoSQL now offers vector indexing and search in preview. This feature is designed to handle high-dimensional vectors, enabling efficient and accurate vector search at any scale. You can now store vectors directly in the documents alongside your data. This means that each document in your database can contain not only traditional schema-free data, but also high-dimensional vectors as other properties of the documents. This colocation of data and vectors allows for efficient indexing and searching, as the vectors are stored in the same logical unit as the data they represent. This simplifies data management, AI application architectures, and the efficiency of vector-based operations. 
+Azure Cosmos DB for NoSQL now offers vector indexing and search in preview. This feature is designed to handle high-dimensional vectors, enabling efficient and accurate vector search at any scale. You can now store vectors directly in the documents alongside your data. Each document in your database can contain not only traditional schema-free data, but also high-dimensional vectors as other properties of the documents. This colocation of data and vectors allows for efficient indexing and searching, as the vectors are stored in the same logical unit as the data they represent. Keeping vectors and data together simplifies data management, AI application architectures, and the efficiency of vector-based operations. 
 
 Azure Cosmos DB for NoSQL offers the flexibility it offers in choosing the vector indexing method: 
 - A "flat" or k-nearest neighbors exact search (sometimes called brute-force) can provide 100% retrieval recall for smaller, focused vector searches. especially when combined with query filters and partition-keys.
@@ -43,7 +43,7 @@ In a vector store, vector search algorithms are used to index and query embeddin
 In the Integrated Vector Database in Azure Cosmos DB for NoSQL, embeddings can be stored, indexed, and queried alongside the original data. This approach eliminates the extra cost of replicating data in a separate pure vector database. Moreover, this architecture keeps the vector embeddings and original data together, which better facilitates multi-modal data operations, and enables greater data consistency, scale, and performance.
 
 ## Enroll in the Vector Search Preview Feature
-Vector search for Azure Cosmos DB for NoSQL requires preview feature registration on the Features page of your Azure Cosmos DB . Follow the below steps to register: 
+Vector search for Azure Cosmos DB for NoSQL requires preview feature registration on the Features page of your Azure Cosmos DB. Follow the below steps to register: 
 
 1. Navigate to your Azure Cosmos DB for NoSQL resource page.
 
@@ -83,7 +83,7 @@ Performing vector search with Azure Cosmos DB for NoSQL requires you to define a
    * “dimensions”: The dimensionality or length of each vector in the path. All vectors in a path should have the same number of dimensions. (default 1536).
    * “distanceFunction”: The metric used to compute distance/similarity. Supported metrics are:   
      *  [cosine](https://en.wikipedia.org/wiki/Cosine_similarity), which has values from -1 (least similar) to +1 (most similar). 
-     *  [dotproduct](https://en.wikipedia.org/wiki/Dot_product), which has values from -inf (least simialr) to +inf (most similar).
+     *  [dot product](https://en.wikipedia.org/wiki/Dot_product), which has values from -inf (least similar) to +inf (most similar).
      *  [euclidean](https://en.wikipedia.org/wiki/Euclidean_distance), which has values from 0 (most similar) to +inf) (least similar). 
 
 
@@ -203,7 +203,7 @@ Here are examples of valid vector index policies:
 
 ## Perform vector search with queries using VectorDistance()
 
-Once you have created a container with the desired vector policy, and inserted vector data into the container, you can conduct a vector search using the [Vector Distance](query/vectordistance.md) system function in a query. An example of a NoSQL query that projects the similarity score as the alias `SimilarityScore`, and sorts in order of most-similar to least-similar is shown below:
+Once you created a container with the desired vector policy, and inserted vector data into the container, you can conduct a vector search using the [Vector Distance](query/vectordistance.md) system function in a query. An example of a NoSQL query that projects the similarity score as the alias `SimilarityScore`, and sorts in order of most-similar to least-similar:
 
 ```sql
 SELECT c.title, VectorDistance(c.contentVector, [1,2,3]) AS SimilarityScore   
@@ -217,15 +217,14 @@ Vector indexing and search in Azure Cosmos DB for NoSQL has some limitations whi
 - You can specify, at most, one DiskANN index type per container
 - Vector indexing is only supported on new containers.
 - Vectors indexed with the `flat` index type can be at most 505 dimensions. Vectors indexed with the `quantizedFlat` or `DiskANN` index type can be at most 4,096 dimensions.
-- `quantizedFlat` utilizes the same quantization method as DiskANN and is not configurable at this time. 
+- `quantizedFlat` utilizes the same quantization method as DiskANN and isn't configurable at this time. 
 - Shared throughput databases can't use the vector search preview feature at this time.
 - Ingestion rate should be limited while using an early preview of DiskANN.
 
 ## Next step
 - [DiskANN + Azure Cosmos DB - Microsoft Mechanics Video](https://www.youtube.com/watch?v=MlMPIYONvfQ)
 - [.NET - How-to Index and query vector data](how-to-dotnet-vector-index-query.md)
 - [Python - How-to Index and query vector data](how-to-python-vector-index-query.md)
-- [JavaScript - How-to Index and query vector data](how-to-javascript-vector-index-query.md)
 - [Java - How-to Index and query vector data](how-to-java-vector-index-query.md)
 - [VectorDistance system function](query/vectordistance.md)
 - [Vector index overview](../index-overview.md#vector-indexes)