Merge pull request #270395 from wmwxwa/patch-2

Update vector-database.md

Author: prmerger-automator[bot]

articles/cosmos-db/vector-database.md

@@ -28,8 +28,8 @@ Here's how to implement our integrated vector database:
 
 | | Description |
 | --- | --- |
-| **[Azure Cosmos DB for Mongo DB vCore](#implement-vector-database-functionalities-using-our-api-for-mongodb-vcore)** | Store your application data and vector embeddings together in a single MongoDB-compatible service featuring native support for vector search. |
-| **[Azure Cosmos DB for PostgreSQL](#implement-vector-database-functionalities-using-our-api-for-postgresql)** | Store your data and vectors together in a scalable PostgreSQL offering with native support for vector search. |
+| **[Azure Cosmos DB for Mongo DB vCore](#implement-vector-database-functionalities-using-our-api-for-mongodb-vcore)** | Store your application data and vector embeddings together in a single MongoDB-compatible service featuring natively integrated vector database. |
+| **[Azure Cosmos DB for PostgreSQL](#implement-vector-database-functionalities-using-our-api-for-postgresql)** | Store your data and vectors together in a scalable PostgreSQL offering with natively integrated vector database. |
 | **[Azure Cosmos DB for NoSQL with Azure AI Search](#implement-vector-database-functionalities-using-our-nosql-api-and-ai-search)** | Augment your Azure Cosmos DB data with semantic and vector search capabilities of Azure AI Search. |
 
 ## What is a vector database?
@@ -38,15 +38,15 @@ A vector database is a database designed to store and manage [vector embeddings]
 
 It's increasingly popular to use the [vector search](#vector-search) feature in a vector database to enable [retrieval-augmented generation](#retrieval-augmented-generation) that harnesses LLMs and custom data or domain-specific information. This process involves extracting pertinent information from a custom data source and integrating it into the model request through prompt engineering.
 
-A robust mechanism is necessary to identify the most relevant data from the custom source that can be passed to the LLM. Our vector search features convert the data in your database into embeddings and store them as vectors for future use. The vector search feature captures the semantic meaning of the text and going beyond mere keywords to comprehend the context. Moreover, this mechanism allows you to optimize for the LLM’s limit on the number of [tokens](#tokens) per request.
+A robust mechanism is necessary to identify the most relevant data from the custom source that can be passed to the LLM. Our integrated vector database converts the data in your database into embeddings and store them as vectors for future use. The vector search captures the semantic meaning of the text and going beyond mere keywords to comprehend the context. Moreover, this mechanism allows you to optimize for the LLM’s limit on the number of [tokens](#tokens) per request.
 
 Prior to sending a request to the LLM, the user input/query/request is also transformed into an embedding, and vector search techniques are employed to locate the most similar embeddings within the database. This technique enables the identification of the most relevant data records in the database. These retrieved records are then supplied as input to the LLM request using [prompt engineering](#prompts-and-prompt-engineering).
 
 Here are multiple ways to implement RAG on your data by using our vector database functionalities.
 
 ## Implement vector database functionalities using our API for MongoDB vCore
 
-Use the native vector search feature in [Azure Cosmos DB for MongoDB vCore](mongodb/vcore/vector-search.md), which offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications.
+Use the natively integrated vector database in [Azure Cosmos DB for MongoDB vCore](mongodb/vcore/vector-search.md), which offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications.
 
 ### Vector database implementation code samples
 
@@ -60,15 +60,15 @@ Use the native vector search feature in [Azure Cosmos DB for MongoDB vCore](mong
 
 ## Implement vector database functionalities using our API for PostgreSQL
 
-Use the native vector search feature in [Azure Cosmos DB for PostgreSQL](postgresql/howto-use-pgvector.md), which offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications.
+Use the natively integrated vector database in [Azure Cosmos DB for PostgreSQL](postgresql/howto-use-pgvector.md), which offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications.
 
 ### Vector database implementation code samples
 
 - Python: [Python notebook tutorial - food review chatbot](https://github.com/microsoft/AzureDataRetrievalAugmentedGenerationSamples/tree/main/Python/CosmosDB-PostgreSQL_CognitiveSearch)
 
 ## Implement vector database functionalities using our NoSQL API and AI Search
 
-The native vector search feature in our NoSQL API is under development. In the meantime, you may implement RAG patterns with Azure Cosmos DB for NoSQL and [Azure AI Search](../search/vector-search-overview.md). This approach enables powerful integration of your data residing in the NoSQL API into your AI-oriented applications.
+The natively integrated vector database in our NoSQL API will become available in mid-2024. In the meantime, you may implement RAG patterns with Azure Cosmos DB for NoSQL and [Azure AI Search](../search/vector-search-overview.md). This approach enables powerful integration of your data residing in the NoSQL API into your AI-oriented applications.
 
 ### Vector database implementation code samples