Merge pull request #280579 from GitHubber17/259443-d

Freshness - Azure Machine Learning

Author: AnnaMHuff

articles/machine-learning/concept-vector-stores.md

@@ -1,7 +1,7 @@
 ---
-title: Concept Vector Stores in Azure Machine Learning (preview)
+title: Concept vector stores in Azure Machine Learning (preview)
 titleSuffix: Azure Machine Learning embedding vectors in AzureML
-description: This concept article helps you use a vector index in Azure Machine Learning for performing Retrieval Augmented Generation.
+description: Explore concepts about vector indexes in Azure Machine Learning and how you can use them for retrieval-augmented generation.
 services: machine-learning
 ms.author: ssalgado
 author: ssalgadodev
@@ -10,46 +10,56 @@ ms.service: machine-learning
 ms.subservice: core
 ms.custom:
   - ignite-2023
-ms.date: 07/27/2023
-ms.topic: conceptual
+ms.date: 07/17/2024
+ms.topic: concept-article
+
+#customer intent: As a developer, I want to use vector indexes in Azure Machine Learning so that I can perform retrieval-augmented generation. 
 ---
 
 # Vector stores in Azure Machine Learning (preview)
 
 [!INCLUDE [machine-learning-preview-generic-disclaimer](includes/machine-learning-preview-generic-disclaimer.md)]
 
-This concept article helps you use a vector index in Azure Machine Learning for performing Retrieval Augmented Generation (RAG). A vector index stores embeddings, which are numerical representations of concepts (data) converted to number sequences, which enable LLMs to understand the relationships between those concepts. Creating vector stores helps you to hook up your data with a large language model (LLM) like GPT-4 and retrieve the data efficiently.
+This article describes vector indexes in Azure Machine Learning that you can use to perform retrieval-augmented generation (RAG). A vector index stores embeddings that are numerical representations of _concepts_ (data) converted to number sequences. Embeddings enable large language models (LLMs) to understand the relationships between the concepts. You can create vector stores to connect your data with LLMs like GPT-4, and retrieve the data efficiently.
+
+Azure Machine Learning supports two vector stores that contain your supplemental data used in a RAG workflow:
+
+| Vector store | Description | Features and usage |
+| --- | --- | --- |
+| **Faiss** | Open source library | - Use local file-based store <br> - Incur minimal costs <br> - Support vector-only data <br> - Support development and testing |
+| **Azure AI Search** | Azure PaaS resource | - Store text data in search index <br> - Host large number of indexes with single service <br> - Support enterprise-level business requirements <br> - Access hybrid information retrieval |
 
-Azure Machine Learning supports two types of vector stores that contain your supplemental data used in a RAG workflow:
+The following sections explore considerations for working with these vector stores.
 
-+ [Faiss](https://github.com/facebookresearch/faiss) is an open source library that provides a local file-based store. The vector index is stored in the storage account of your Azure Machine Learning workspace. Since it's stored locally, the costs are minimal making it ideal for development and testing.
+## Faiss library
 
-+ [Azure AI Search](/azure/search/search-what-is-azure-search) (formerly Cognitive Search) is an Azure resource that supports information retrieval over your vector and textual data stored in search indexes. A prompt flow can create, populate, and query your vector data stored in Azure AI Search.
+[Faiss](https://github.com/facebookresearch/faiss) is an open source library that provides a local file-based store. The vector index is stored in the Azure storage account of your Azure Machine Learning workspace. To work with Faiss, you download the library and use it as a component of your solution. Because the index is stored locally, the costs are minimal. 
 
-## Choose a vector store
+You can use the Faiss library as your vector store and perform the following actions:
 
-You can use either store in prompt flow, so which one should you use?
+- Store vector data locally, with no costs for creating an index (only storage cost)
 
-**Faiss** is an open source library that you download and use a component of your solution. This library might be the best place to start if you have vector-only data. Some key points about working with Faiss:
+- Build and query an index in memory
 
-+ Local storage, with no costs for creating an index (only storage cost).
+- Share copies for individual use, and configure hosting of the index for an application
 
-+ You can build and query an index in memory.
+- Scale with underlying compute loading index
 
-+ You can share copies for individual use. If you want to host the index for an application, you need to set that up.
+## Azure AI Search
 
-+ Faiss scales with underlying compute loading index.
+[Azure AI Search](/azure/search/search-what-is-azure-search) (formerly Cognitive Search) is a dedicated Azure PaaS resource that you create in an Azure subscription. The resource supports information retrieval over your vector and textual data stored in search indexes. A prompt flow can create, populate, and query your vector data stored in Azure AI Search. A single search service can host a large number of indexes, which can be queried and used in a RAG pattern.
 
-**Azure AI Search** is a dedicated PaaS resource that you create in an Azure subscription. A single search service can host a large number of indexes, which can be queried and used in a RAG pattern. Some key points about using Azure AI Search for your vector store:
+Here are some key points about using Azure AI Search for your vector store:
 
-+ Supports enterprise level business requirements for scale, security, and availability.
+- Support enterprise level business requirements for scale, security, and availability.
 
-+ Supports hybrid information retrieval. Vector data can coexist with non-vector data, which means you can use any of the [features of Azure AI Search](/azure/search/search-features-list) for indexing and queries, including [hybrid search](/azure/search/vector-search-how-to-query) and [semantic reranking](/azure/search/semantic-ranking).
+- Access hybrid information retrieval. Vector data can coexist with nonvector data, which means you can use any of the [features of Azure AI Search](/azure/search/search-features-list) for indexing and queries, including [hybrid search](/azure/search/vector-search-how-to-query) and [semantic reranking](/azure/search/semantic-search-overview).
 
-+ [Vector support is in public preview](/azure/search/vector-search-overview). Currently, vectors must be generated externally and then passed to Azure AI Search for indexing and query encoding. The prompt flow handles these transitions for you.
+- Keep in mind that [vector support is in preview](/azure/search/vector-search-overview). Currently, vectors must be generated externally and then passed to Azure AI Search for indexing and query encoding. The prompt flow handles these transitions for you.
 
-To use AI Search as a vector store for Azure Machine Learning, [you must have a search service](/azure/search/search-create-service-portal). Once the service exists and you've granted access to developers, you can choose **Azure AI Search** as a vector index in a prompt flow. The prompt flow creates the index on Azure AI Search, generates vectors from your source data, sends the vectors to the index, invokes similarity search on AI Search, and returns the response.
+To use AI Search as a vector store for Azure Machine Learning, [you must have a search service](/azure/search/search-create-service-portal). After the service exists, and you grant access to developers, you can choose **Azure AI Search** as a vector index in a prompt flow. The prompt flow creates the index on Azure AI Search, generates vectors from your source data, sends the vectors to the index, invokes similarity search on AI Search, and returns the response.
 
-## Next steps
+## Related content
 
-[How to create vector index in Azure Machine Learning prompt flow (preview)](how-to-create-vector-index.md)
+- [Create vector index in Azure Machine Learning prompt flow (preview)](how-to-create-vector-index.md)
+- [Vectors in Azure AI Search](/azure/search/vector-search-overview)