Merge pull request #115211 from KimiaTahvildari/patch-11

 Fixing typo

Author: Jak-MS

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

@@ -19,7 +19,7 @@ Use vector search in Azure Cosmos DB for MongoDB vCore to seamlessly integrate y
 
 ## What is vector search?
 
-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 works by taking the [vector representations](../../../ai-services/openai/concepts/understand-embeddings.md) (lists of numbers) of your data that you created by using a machine learning model by using or 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/). It then 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.
+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 works by taking the [vector representations](../../../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/). It then 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.
 
 By integrating vector search capabilities natively, you can unlock the full potential of your data in applications that are built on top of the [OpenAI API](../../../ai-services/openai/concepts/understand-embeddings.md). You can also create custom-built solutions that use vector embeddings.