Merge pull request #274337 from MicrosoftDocs/main

5/6 11:00 AM IST Publish

Author: Saisang

articles/defender-for-cloud/auto-deploy-vulnerability-assessment.md

@@ -12,7 +12,6 @@ Defender for Cloud collects data from your machines using agents and extensions.
 To assess your machines for vulnerabilities, you can use one of the following solutions:
 
 - Microsoft Defender Vulnerability Management solution (included with Microsoft Defender for Servers)
-- Built-in Qualys agent (included with Microsoft Defender for Servers)
 - A Qualys or Rapid7 scanner that you've licensed separately and configured within Defender for Cloud (this scenario is called the Bring Your Own License, or BYOL, scenario)
 
 > [!NOTE]

articles/healthcare-apis/fhir/import-data.md

@@ -70,11 +70,8 @@ To achieve the best performance with the `import` operation, consider these fact
 
 - Configure the FHIR server. The FHIR data must be stored in resource-specific files in FHIR NDJSON format on the Azure blob store. For more information, see [Configure import settings](configure-import-data.md).
 
-- All the resources in a file must be the same type. You can have multiple files for each resource type.
-
 - The data must be in the same tenant as the FHIR service.
 
-- The maximum number of files allowed for each `import` operation is 10,000.
 
 ### Make a call
 
@@ -313,6 +310,10 @@ Here are the error messages that occur if the `import` operation fails, along wi
 
 **Solution:** Reduce the size of your data or consider Azure API for FHIR, which has a higher storage limit.
 
+## Limitations
+- The maximum number of files allowed for each `import` operation is 10,000.
+- The number of files ingested in the FHIR server with same lastUpdated field value upto milliseconds, cannot exceed beyond 10,000.
+
 ## Next steps
 
 [Convert your data to FHIR](convert-data.md)

articles/search/search-get-started-portal-import-vectors.md

@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: quickstart
-ms.date: 01/02/2024
+ms.date: 05/05/2024
 ---
 
 # Quickstart: Integrated vectorization (preview)
@@ -22,8 +22,8 @@ Get started with [integrated vectorization (preview)](vector-search-integrated-v
 In this preview version of the wizard:
 
 + Source data is blob only, using the default parsing mode (one search document per blob).
-+ Index schema is nonconfigurable. Source fields include `content` (chunked and vectorized), `metadata_storage_name` for title, and a `metadata_storage_path` for the document key which is populated as `parent_id` in the Index.
-+ Vectorization is Azure OpenAI only (text-embedding-ada-002), using the [HNSW](vector-search-ranking.md) algorithm with defaults.
++ Index schema is nonconfigurable. Source fields include `content` (chunked and vectorized), `metadata_storage_name` for title, and a `metadata_storage_path` for the document key, represented as `parent_id` in the Index.
++ Vectorization is Azure OpenAI only (text-embedding-ada-002), using the [Hierarchical Navigable Small Worlds (HNSW)](vector-search-ranking.md) algorithm with defaults.
 + Chunking is nonconfigurable. The effective settings are:
 
   ```json
@@ -32,21 +32,22 @@ In this preview version of the wizard:
   pageOverlapLength: 500
   ```
 
-## Prerequisites
+For more configuration and data source options, try Python or the REST APIs. See [integrated vectorization sample](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb) for details.
+
 
 + An Azure subscription. [Create one for free](https://azure.microsoft.com/free/).
 
-+ Azure AI Search, in any region and on any tier. Most existing services support vector search. For a small subset of services created prior to January 2019, an index containing vector fields fails on creation. In this situation, a new service must be created.
++ Azure AI Search, in any region and on any tier. Most existing services support vector search. For a small subset of services created before January 2019, an index containing vector fields fails on creation. In this situation, a new service must be created.
 
 + [Azure OpenAI](https://aka.ms/oai/access) endpoint with a deployment of **text-embedding-ada-002** and an API key or [**Cognitive Services OpenAI User**](/azure/ai-services/openai/how-to/role-based-access-control#azure-openai-roles) permissions to upload data. You can only choose one vectorizer in this preview, and the vectorizer must be Azure OpenAI.
 
-+ [Azure Storage account](/azure/storage/common/storage-account-overview), standard performance (general-purpose v2), Hot and Cool access tiers.
++ [Azure Storage account](/azure/storage/common/storage-account-overview), standard performance (general-purpose v2), hot, cool, and cold access tiers.
 
 + Blobs providing text content, unstructured docs only, and metadata. In this preview, your data source must be Azure blobs.
 
 + Read permissions in Azure Storage. A storage connection string that includes an access key gives you read access to storage content. If instead you're using Microsoft Entra logins and roles, make sure the [search service's managed identity](search-howto-managed-identities-data-sources.md) has [**Storage Blob Data Reader**](/azure/storage/blobs/assign-azure-role-data-access) permissions.
 
-+ All components (data source and embedding endpoint) must have public access enabled for the portal nodes to be able to access them. Otherwise, the wizard will fail. After the wizard runs, firewalls and private endpoints can be enabled in the different integration components for security. If private endpoints are already present and can't be disabled, the alternative option is to run the respective end-to-end flow from a script or program from a Virtual Machine within the same VNET as the private endpoint. Here is a [Python code sample](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-python/code/integrated-vectorization) for integrated vectorization. In the same [GitHub repo](https://github.com/Azure/azure-search-vector-samples/tree/main) are samples in other programming languages. 
++ All components (data source and embedding endpoint) must have public access enabled for the portal nodes to be able to access them. Otherwise, the wizard fails. After the wizard runs, firewalls and private endpoints can be enabled in the different integration components for security. If private endpoints are already present and can't be disabled, the alternative option is to run the respective end-to-end flow from a script or program from a virtual machine within the same virtual network as the private endpoint. Here is a [Python code sample](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-python/code/integrated-vectorization) for integrated vectorization. In the same [GitHub repo](https://github.com/Azure/azure-search-vector-samples/tree/main) are samples in other programming languages. 
 
 ## Check for space
 
@@ -202,4 +203,4 @@ Azure AI Search is a billable resource. If it's no longer needed, delete it from
 
 ## Next steps
 
-This quickstart introduced you to the **Import and vectorize data** wizard that creates all of the objects necessary for integrated vectorization. If you want to explore each step in detail, try an [integrated vectorization sample](https://github.com/HeidiSteen/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb).
+This quickstart introduced you to the **Import and vectorize data** wizard that creates all of the objects necessary for integrated vectorization. If you want to explore each step in detail, try an [integrated vectorization sample](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb).

articles/search/vector-search-how-to-configure-vectorizer.md

@@ -1,5 +1,5 @@
 ---
-title: Configure vectorizer
+title: Configure a vectorizer
 titleSuffix: Azure AI Search
 description: Steps for adding a vectorizer to a search index in Azure AI Search. A vectorizer calls an embedding model that generates embeddings from text.
 
@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 03/28/2024
+ms.date: 05/05/2024
 ---
 
 # Configure a vectorizer in a search index

articles/search/vector-search-integrated-vectorization.md

@@ -9,27 +9,28 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 03/27/2024
+ms.date: 05/05/2024
 ---
 
 # Integrated data chunking and embedding in Azure AI Search
 
 > [!IMPORTANT] 
-> This feature is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [2023-10-01-Preview REST API](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2023-10-01-preview&preserve-view=true) supports this feature.
+> Integrated data chunking and vectorization is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [2023-10-01-Preview REST API](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2023-10-01-preview&preserve-view=true) provides this feature.
 
-*Integrated vectorization* adds data chunking and text-to-vector embedding to skills in indexer-based indexing. It also adds text-to-vector conversions to queries. 
+*Integrated vectorization* adds data chunking and text-to-vector conversions during indexing and at query time. 
 
-This capability is preview-only. In the generally available version of [vector search](vector-search-overview.md) and in previous preview versions, data chunking and vectorization rely on external components for chunking and vectors, and your application code must handle and coordinate each step. In this preview, chunking and vectorization are built into indexing through skills and indexers. You can set up a skillset that chunks data using the Text Split skill, and then call an embedding model using either the AzureOpenAIEmbedding skill or a custom skill. Any vectorizers used during indexing can also be called on queries to convert text to vectors.
+For data chunking and text-to-vector conversions during indexing, you need:
 
-For indexing, integrated vectorization requires:
++ [An indexer](search-indexer-overview.md) to retrieve data from a supported data source.
++ [A skillset](cognitive-search-working-with-skillsets.md) to call the [Text Split skill](cognitive-search-skill-textsplit.md) to chunk the data.
++ The same skillset, calling an embedding model. The embedding model is accessed through the [AzureOpenAIEmbedding skill](cognitive-search-skill-azure-openai-embedding.md), attached to text-embedding-ada-002 on Azure OpenAI, or a [custom skill](cognitive-search-custom-skill-web-api.md) that points to another embedding model, for example any supported embedding model on OpenAI.
++ You also need a [vector index](search-what-is-an-index.md) to receive the chunked and vectorized content.
 
-+ [An indexer](search-indexer-overview.md) retrieving data from a supported data source.
-+ [A skillset](cognitive-search-working-with-skillsets.md)  that calls the [Text Split skill](cognitive-search-skill-textsplit.md) to chunk the data, and either [AzureOpenAIEmbedding skill](cognitive-search-skill-azure-openai-embedding.md) or a [custom skill](cognitive-search-custom-skill-web-api.md) to vectorize the data.
-+ [One or more indexes](search-what-is-an-index.md) to receive the chunked and vectorized content.
-
-For queries:
+For text-to-vector queries:
 
 + [A vectorizer](vector-search-how-to-configure-vectorizer.md) defined in the index schema, assigned to a vector field, and used automatically at query time to convert a text query to a vector.
++ A query that specifies one or more vector fields.
++ A text string that's converted to a vector at query time.
 
 Vector conversions are one-way: text-to-vector. There's no vector-to-text conversion for queries or results (for example, you can't convert a vector result to a human-readable string).
 
@@ -44,15 +45,15 @@ Here's a checklist of the components responsible for integrated vectorization:
 + A supported data source for indexer-based indexing.
 + An index that specifies vector fields, and a vectorizer definition assigned to vector fields.
 + A skillset providing a Text Split skill for data chunking, and a skill for vectorization (either the AzureOpenAiEmbedding skill or a custom skill pointing to an external embedding model).
-+ Optionally, index projections (also defined in a skillset) to push chunked data to a secondary index
++ Optionally, index projections (also defined in a skillset) to push chunked data to a secondary index.
 + An embedding model, deployed on Azure OpenAI or available through an HTTP endpoint.
 + An indexer for driving the process end-to-end. An indexer also specifies a schedule, field mappings, and properties for change detection.
 
 This checklist focuses on integrated vectorization, but your solution isn't limited to this list. You can add more skills for AI enrichment, create a knowledge store, add semantic ranking, add relevance tuning, and other query features.
 
 ## Availability and pricing
 
-Integrated vectorization availability is based on the embedding model. If you're using Azure OpenAI, check [regional availability]( https://azure.microsoft.com/explore/global-infrastructure/products-by-region/?products=cognitive-services).
+Integrated vectorization is available in all regions and tiers. However, if you're using Azure OpenAI and the AzureOpenAIEmbedding skill, check [regional availability]( https://azure.microsoft.com/explore/global-infrastructure/products-by-region/?products=cognitive-services) of that service.
 
 If you're using a custom skill and an Azure hosting mechanism (such as an Azure function app, Azure Web App, and Azure Kubernetes), check the [product by region page](https://azure.microsoft.com/explore/global-infrastructure/products-by-region/) for feature availability. 
 
@@ -119,15 +120,7 @@ Here are some of the key benefits of the integrated vectorization:
 
 + Projecting chunked content to secondary indexes. Secondary indexes are created as you would any search index (a schema with fields and other constructs), but they're populated in tandem with a primary index by an indexer. Content from each source document flows to fields in primary and secondary indexes during the same indexing run. 
 
-  Secondary indexes are intended for data chunking and Retrieval Augmented Generation (RAG) apps. Assuming a large PDF as a source document, the primary index might have basic information (title, date, author, description), and a secondary index has the chunks of content. Vectorization at the data chunk level makes it easier to find relevant information (each chunk is searchable) and return a relevant response, especially in a chat-style search app.
-
-## Chunked indexes
-
-Chunking is a process of dividing content into smaller manageable parts (chunks) that can be processed independently. Chunking is required if source documents are too large for the maximum input size of embedding or large language models, but you might find it gives you a better index structure for [RAG patterns](retrieval-augmented-generation-overview.md) and chat-style search.
-
-The following diagram shows the components of chunked indexing.
-
-:::image type="content" source="media/vector-search-integrated-vectorization/integrated-vectorization-chunked-indexes.png" alt-text="Diagram of chunking and vectorization workflow." border="false" lightbox="media/vector-search-integrated-vectorization/integrated-vectorization-chunked-indexes.png":::
+  Secondary indexes are intended for question and answer or chat style apps. The secondary index contains granular information for more specific matches, but the parent index has more information and can often produce a more complete answer. When a match is found in the secondary index, the query returns the parent document from the primary index. For example, assuming a large PDF as a source document, the primary index might have basic information (title, date, author, description), while a secondary index has chunks of searchable content. 
 
 ## Next steps