Update vector-search.md

Author: wmwxwa

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

@@ -17,15 +17,17 @@ ms.date: 11/1/2023
 
 [!INCLUDE[MongoDB vCore](../../includes/appliesto-mongodb-vcore.md)]
 
-Use the 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 are created. It eliminates the need to transfer your data to alternative vector stores and incur additional costs.
+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 database or vector store?
+## What is a vector store or vector database?
 
-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. Vector search algorithms are used to index and query these embeddings.
+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. 
 
-## What is vector search?
+## How does a vector store work?
 
-Vector search is a method that helps you find similar items based on their data characteristics rather than by exact matches on a property field. This technique is useful in applications such as searching for similar text, finding related images, making recommendations, or even detecting anomalies. It is used to query the [vector embeddings](../../../ai-services/openai/concepts/understand-embeddings.md) (lists of numbers) of your data that you created by using a machine learning model by using an embeddings API. Examples of embeddings APIs are [Azure OpenAI Embeddings](/azure/ai-services/openai/how-to/embeddings) or [Hugging Face on Azure](https://azure.microsoft.com/solutions/hugging-face-on-azure/). Vector search measures the distance between the data vectors and your query vector. The data vectors that are closest to your query vector are the ones that are found to be most similar semantically.
+In a vector store, vector search algorithms are used to index and query embeddings. Some well-known vector search algorithms include Hierarchical Navigable Small World (HNSW), Inverted File (IVF), DiskANN, etc. Vector search is a method that helps you find similar items based on their data characteristics rather than by exact matches on a property field. This technique is useful in applications such as searching for similar text, finding related images, making recommendations, or even detecting anomalies. It is used to query the [vector embeddings](../../../ai-services/openai/concepts/understand-embeddings.md) (lists of numbers) of your data that you created by using a machine learning model by using an embeddings API. Examples of embeddings APIs are [Azure OpenAI Embeddings](/azure/ai-services/openai/how-to/embeddings) or [Hugging Face on Azure](https://azure.microsoft.com/solutions/hugging-face-on-azure/). Vector search measures the distance between the data vectors and your query vector. The data vectors that are closest to your query vector are the ones that are found to be most similar semantically.
+
+In the Integrated Vector Database in Azure Cosmos DB for MongoDB vCore, 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.
 
 ## Create a vector index
 To perform vector similiarity search over vector properties in your documents, you'll have to first create a _vector index_.