Merge pull request #268118 from MicrosoftDocs/main

3/5/2024 AM Publish

Author: Taojunshen

.openpublishing.redirection.azure-monitor.json

@@ -15,7 +15,7 @@ ms.custom:
 
 # Understand embeddings in Azure OpenAI Service
 
-An embedding is a special format of data representation that machine learning models and algorithms can easily use. The embedding is an information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating-point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar. Embeddings power vector similarity search in Azure Databases such as [Azure Cosmos DB for MongoDB vCore](../../../cosmos-db/mongodb/vcore/vector-search.md) or [Azure Database for PostgreSQL - Flexible Server](../../../postgresql/flexible-server/how-to-use-pgvector.md).
+An embedding is a special format of data representation that machine learning models and algorithms can easily use. The embedding is an information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating-point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar. Embeddings power vector similarity search in Azure Databases such as [Azure Cosmos DB for MongoDB vCore](../../../cosmos-db/mongodb/vcore/vector-search.md) ,  [Azure SQL Database](/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql&preserve-view=true#vector-search) or [Azure Database for PostgreSQL - Flexible Server](../../../postgresql/flexible-server/how-to-use-pgvector.md).
 
 ## Embedding models
 
@@ -38,4 +38,4 @@ An alternative method of identifying similar documents is to count the number of
 ## Next steps
 
 * Learn more about using Azure OpenAI and embeddings to perform document search with our [embeddings tutorial](../tutorials/embeddings.md).
-* Store your embeddings and perform vector (similarity) search using [Azure Cosmos DB for MongoDB vCore](../../../cosmos-db/mongodb/vcore/vector-search.md), [Azure Cosmos DB for NoSQL](../../../cosmos-db/rag-data-openai.md) or [Azure Database for PostgreSQL - Flexible Server](../../../postgresql/flexible-server/how-to-use-pgvector.md).
+* Store your embeddings and perform vector (similarity) search using [Azure Cosmos DB for MongoDB vCore](../../../cosmos-db/mongodb/vcore/vector-search.md), [Azure Cosmos DB for NoSQL](../../../cosmos-db/rag-data-openai.md) ,  [Azure SQL Database](/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql&preserve-view=true#vector-search) or [Azure Database for PostgreSQL - Flexible Server](../../../postgresql/flexible-server/how-to-use-pgvector.md).

.whatsnew/.application-management.json

@@ -52,6 +52,7 @@ Learn more about Azure OpenAI's models:
 * Store your embeddings and perform vector (similarity) search using your choice of Azure service:
   * [Azure AI Search](../../../search/vector-search-overview.md)
   * [Azure Cosmos DB for MongoDB vCore](../../../cosmos-db/mongodb/vcore/vector-search.md)
+  * [Azure SQL Database](/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql&preserve-view=true#vector-search)
   * [Azure Cosmos DB for NoSQL](../../../cosmos-db/vector-search.md)
   * [Azure Cosmos DB for PostgreSQL](../../../cosmos-db/postgresql/howto-use-pgvector.md)
   * [Azure Cache for Redis](../../../azure-cache-for-redis/cache-tutorial-vector-similarity.md)

.whatsnew/.application-provisioning.json

@@ -35,7 +35,9 @@ For more information on Kubernetes volumes, see [Storage options for application
 
 This section provides guidance for cluster administrators who want to provision one or more persistent volumes that include details of Blob storage for use by a workload. A persistent volume claim (PVC) uses the storage class object to dynamically provision an Azure Blob storage container.
 
-### Dynamic provisioning parameters
+### Storage class parameters for dynamic PersistentVolumes
+
+The following table includes parameters you can use to define a custom storage class for your PersistentVolumeClaim.
 
 |Name | Description | Example | Mandatory | Default value|
 |--- | --- | --- | --- | --- |
@@ -240,7 +242,9 @@ In this example, the following manifest configures using blobfuse and mounts a B
 
 This section provides guidance for cluster administrators who want to create one or more persistent volumes that include details of Blob storage for use by a workload.
 
-### Static provisioning parameters
+### Static provisioning parameters for PersistentVolume
+
+The following table includes parameters you can use to define a PersistentVolume.
 
 |Name | Description | Example | Mandatory | Default value|
 |--- | --- | --- | --- | ---|