Tie together embeddings and vector similarity search

Author: gahl-levy

articles/ai-services/openai/concepts/understand-embeddings.md

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: openai
 ms.topic: tutorial
-ms.date: 03/22/2023
+ms.date: 09/12/2023
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -16,7 +16,7 @@ ms.custom:
 
 # Understanding embeddings in Azure OpenAI Service
 
-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.
+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](../../../cosmos-db/mongodb/vcore/vector-search.md). 
 
 ## Embedding models
 
@@ -34,4 +34,6 @@ An alternative method of identifying similar documents is to count the number of
 
 ## Next steps
 
-Learn more about using Azure OpenAI and embeddings to perform document search with our [embeddings tutorial](../tutorials/embeddings.md).
+* Learn more about using Azure OpenAI and embeddings to perform document search with our [embeddings tutorial](../tutorials/embeddings.md).
+* Store your embeddings and perform vector (similarity) search using [Azure Cosmos DB for MongoDB vCore](../../../cosmos-db/mongodb/vcore/vector-search.md) or [Azure Cosmos DB for NoSQL](../../../cosmos-db/rag-data-openai.md)
+

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

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: openai
 ms.topic: how-to
-ms.date: 5/9/2023
+ms.date: 9/12/2023
 author: ChrisHMSFT
 ms.author: chrhoder
 recommendations: false
@@ -16,7 +16,8 @@ keywords:
 ---
 # 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.
+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](../../../cosmos-db/mongodb/vcore/vector-search.md). 
+
 
 ## How to get embeddings
 
@@ -93,3 +94,5 @@ Our embedding models may be unreliable or pose social risks in certain cases, an
 
 * Learn more about using Azure OpenAI and embeddings to perform document search with our [embeddings tutorial](../tutorials/embeddings.md).
 * Learn more about the [underlying models that power Azure OpenAI](../concepts/models.md).
+* Store your embeddings and perform vector (similarity) search using [Azure Cosmos DB for MongoDB vCore](../../../cosmos-db/mongodb/vcore/vector-search.md) or [Azure Cosmos DB for NoSQL](../../../cosmos-db/rag-data-openai.md)
+

articles/ai-services/openai/tutorials/embeddings.md

@@ -398,4 +398,4 @@ If you created an OpenAI resource solely for completing this tutorial and want t
 Learn more about Azure OpenAI's models:
 > [!div class="nextstepaction"]
 > [Azure OpenAI Service models](../concepts/models.md)
-- Perform Vector (similarity) search with your embeddings using [Azure Cosmos DB for MongoDB vCore](../../../cosmos-db/mongodb/vcore/vector-search.md) or [Azure Cosmos DB for NoSQL](../../../cosmos-db/rag-data-openai.md)
+* Store your embeddings and perform vector (similarity) search using [Azure Cosmos DB for MongoDB vCore](../../../cosmos-db/mongodb/vcore/vector-search.md) or [Azure Cosmos DB for NoSQL](../../../cosmos-db/rag-data-openai.md)