Link Vector Search with Embedding Generation as a next step

Author: gahl-levy

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

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: openai
 ms.topic: tutorial
-ms.date: 06/14/2023
+ms.date: 09/12/2023
 author: mrbullwinkle #noabenefraim
 ms.author: mbullwin
 recommendations: false
@@ -333,7 +333,7 @@ len(decode)
 1466
 ```
 
-Now that we understand more about how tokenization works we can move on to embedding. It is important to note, that we haven't actually tokenized the documents yet. The `n_tokens` column is simply a way of making sure none of the data we pass to the model for tokenization and embedding exceeds the input token limit of 8,192. When we pass the documents to the embeddings model, it will break the documents into tokens similar (though not necessarily identical) to the examples above and then convert the tokens to a series of floating point numbers that will be accessible via vector search. These embeddings can be stored locally or in an Azure Database. As a result, each bill will have its own corresponding embedding vector in the new `ada_v2` column on the right side of the DataFrame.
+Now that we understand more about how tokenization works we can move on to embedding. It is important to note, that we haven't actually tokenized the documents yet. The `n_tokens` column is simply a way of making sure none of the data we pass to the model for tokenization and embedding exceeds the input token limit of 8,192. When we pass the documents to the embeddings model, it will break the documents into tokens similar (though not necessarily identical) to the examples above and then convert the tokens to a series of floating point numbers that will be accessible via vector search. These embeddings can be stored locally or in an [Azure Database to support Vector Search](../../../cosmos-db/mongodb/vcore/vector-search.md). As a result, each bill will have its own corresponding embedding vector in the new `ada_v2` column on the right side of the DataFrame.
 
 ```python
 df_bills['ada_v2'] = df_bills["text"].apply(lambda x : get_embedding(x, engine = 'text-embedding-ada-002')) # engine should be set to the deployment name you chose when you deployed the text-embedding-ada-002 (Version 2) model
@@ -398,3 +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)