Update vector-search-overview.md

Author: wmwxwa

articles/cosmos-db/gen-ai/vector-search-overview.md

@@ -10,7 +10,7 @@ ms.date: 07/01/2024
 
 # 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 embeddings](vector-embeddings.md) of your data that you created by using an embedding generation model, such as [Azure OpenAI Embeddings](../../ai-services/openai/how-to/embeddings.md) or [Hugging Face on Azure](https://azure.microsoft.com/solutions/hugging-face-on-azure). It then measures the [distance](distance-functions.md) 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. Some well-known vector search algorithms include Hierarchical Navigable Small World (HNSW), Inverted File (IVF), and the state-of-the-art DiskANN.
+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 embeddings](vector-embeddings.md) of your data and query, and then measuring the [distance](distance-functions.md) 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. Some well-known vector search algorithms include Hierarchical Navigable Small World (HNSW), Inverted File (IVF), and the state-of-the-art DiskANN.
 
 This [interactive visualization](https://openai.com/index/introducing-text-and-code-embeddings/#_1Vr7cWWEATucFxVXbW465e) shows some examples of closeness and distance between vectors.