Merge pull request #272459 from wmwxwa/patch-22

"vector store" language for MongoDB API

Author: prmerger-automator[bot]

articles/cosmos-db/mongodb/vcore/vector-search.md

@@ -13,15 +13,15 @@ ms.topic: conceptual
 ms.date: 11/1/2023
 ---
 
-# Vector Database in Azure Cosmos DB for MongoDB vCore
+# Vector Store in Azure Cosmos DB for MongoDB vCore
 
 [!INCLUDE[MongoDB vCore](../../includes/appliesto-mongodb-vcore.md)]
 
 Use the Integrated Vector Database in Azure Cosmos DB for MongoDB vCore to seamlessly connect your AI-based applications with your data that's stored in Azure Cosmos DB. This integration can include apps that you built by using [Azure OpenAI embeddings](../../../ai-services/openai/tutorials/embeddings.md). The natively integrated vector database enables you to efficiently store, index, and query high-dimensional vector data that's stored directly in Azure Cosmos DB for MongoDB vCore, along with the original data from which the vector data is created. It eliminates the need to transfer your data to alternative vector stores and incur additional costs.
 
-## What is a vector store or vector database?
+## What is a vector store?
 
-A [vector database](../../vector-database.md) is a database designed to store and manage vector embeddings, which are mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, and tens of thousands of dimensions might be used to represent sophisticated data. A vector's position in this space represents its characteristics. Words, phrases, or entire documents, and images, audio, and other types of data can all be vectorized. 
+A vector store or [vector database](../../vector-database.md) is a database designed to store and manage vector embeddings, which are mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, and tens of thousands of dimensions might be used to represent sophisticated data. A vector's position in this space represents its characteristics. Words, phrases, or entire documents, and images, audio, and other types of data can all be vectorized. 
 
 ## How does a vector store work?