Update articles/cosmos-db/mongodb/vcore/AI-ad-gen.md

Co-authored-by: Gahl Levy <[email protected]>

Author: khelanmodi

articles/cosmos-db/mongodb/vcore/AI-ad-gen.md

@@ -114,7 +114,7 @@ if embeddings is not None:
 The function takes a text input — like a product description — and uses the `client.embeddings.create` method from the OpenAI API to generate a vector embedding for that text. We're using the `text-embedding-ada-002` model here, but you can choose other models based on your requirements. If the process is successful, it prints the generated embeddings; otherwise, it handles exceptions by printing an error message.
 
 ## 3. Connect and setup Cosmos DB for MongoDB vCore
-With our embeddings ready, the next step is to store and index them in a database that supports vector similarity search. Azure Cosmos DB for MongoDB vCore is a perfect fit for this task.
+With our embeddings ready, the next step is to store and index them in a database that supports vector similarity search. Azure Cosmos DB for MongoDB vCore is a perfect fit for this task because it's purpose built to store your transactional data and perform vector search all in one place. 
 
 ### 3.1 Set up the connection
 To connect to Cosmos DB, we use the pymongo library, which allows us to interact with MongoDB easily. The following code snippet establishes a connection with our Cosmos DB for MongoDB vCore instance: