Merge pull request #3780 from mrbullwinkle/mrb_03_27_2025_pm_pr

[Azure OpenAI] PM updates

Author: prmerger-automator[bot]

articles/ai-services/openai/how-to/embeddings.md

@@ -6,14 +6,14 @@ manager: nitinme
 ms.service: azure-ai-openai
 ms.custom: devx-track-python
 ms.topic: how-to
-ms.date: 03/26/2025
+ms.date: 03/27/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
 ---
 # Learn how to generate embeddings with Azure OpenAI
 
-An embedding is a special format of data representation that can be easily utilized by machine learning models and algorithms. 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](/azure/cosmos-db/mongodb/vcore/vector-search) , [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](/azure/postgresql/flexible-server/how-to-use-pgvector).
+An embedding is a special format of data representation that can be easily utilized by machine learning models and algorithms. 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 NoSQL](/azure/cosmos-db/nosql/vector-search), [Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/vector-search), [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](/azure/postgresql/flexible-server/how-to-use-pgvector).
 
 ## How to get embeddings