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Author: HeidiSteen

articles/search/search-features-list.md

@@ -35,15 +35,15 @@ The following table summarizes features by category. There's feature parity in a
 | Category                             | Features |
 |-------------------|----------|
 | Chat completion models used during indexing | [**GenAI prompt skill (preview)**](cognitive-search-skill-genai-prompt.md) is a skill that calls a large language model during indexing and provides a prompt that determines the task. You decide what the task is. It might describing an image, summarizing or manipulating content, or any task the model can perform. Output is added as a new field in a searchable index. |
-| Chat completion models used at query time | [**Agentic retrieval (preview)**](search-agentic-retrieval-concept.md) uses a large language model for query planning, decomposing and paraphrasing complex queries for better query coverage over your index. Responses from agentic retrieval are designed for agent-to-agent workflows. You can pass search results as single large string, which simplifies agent consumption of your proprietary content. The response also includes citations and query execution information. </br></br>[**RAG patterns**](retrieval-augmented-generation-overview.md) can be implemented using existing capabilities. The ability to [tune for relevance](search-relevance-overview.md) and construct hybrid queries are advantages for RAG solutions that send content to chat bots for answer generation. |
+| Chat completion models used at query time | [**Agentic retrieval (preview)**](search-agentic-retrieval-concept.md) uses a large language model for query planning, decomposing and paraphrasing complex queries for better query coverage over your index. Responses from agentic retrieval are designed for agent-to-agent workflows. You can pass search results as single large string, which simplifies agent consumption of your proprietary content. The response also includes citations and query execution information. </br></br>[**RAG patterns**](retrieval-augmented-generation-overview.md) can be implemented using existing capabilities. The ability to [tune for relevance](search-relevance-overview.md) and construct hybrid queries improve the quality of the content sent to chat bots for answer generation. |
 
 ## Applied AI and AI integration
 
 | Category&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  | Features |
 |-------------------|----------|
 | AI processing during indexing | [**AI enrichment**](cognitive-search-concept-intro.md) refers to embedded image and natural language processing in an indexer pipeline that extracts text and information from content that can't otherwise be indexed for full text search. AI processing is achieved by adding and combining skills in a skillset, which is then attached to an indexer. AI can be either [built-in skills](cognitive-search-predefined-skills.md) from Microsoft, such as text translation or Optical Character Recognition (OCR), or [custom skills](cognitive-search-create-custom-skill-example.md) that you provide. </p>[**Integrated data chunking and vectorization**](vector-search-integrated-vectorization.md) splits up larger passages into smaller chunks that can be vectorized, with vectors routed to dedicated fields in an index for vector and hybrid search.|
-| AI processing during query execution | [**Vectorizers**](vector-search-how-to-configure-vectorizer/md) are used to encode user query strings into vectors for vector search. You can use the same embedding models for queries that you used for indexing. </p>|
-| Storing enriched content for analysis and consumption in non-search scenarios | [**Knowledge store**](knowledge-store-concept-intro.md) is persistent storage of enriched content, intended for non-search scenarios like knowledge mining and data science processing. A knowledge store is defined in a skillset, but created in Azure Storage as objects or tabular rowsets.|
+| AI processing during query execution | [**Vectorizers**](vector-search-how-to-configure-vectorizer.md) are used to encode user query strings into vectors for vector search. You can use the same embedding models for queries that you used for indexing. </p>|
+| Storing enriched content for analysis and consumption in non-search scenarios | [**Knowledge store**](knowledge-store-concept-intro.md) is persistent storage of AI enriched or AI generated content, intended for non-search scenarios like knowledge mining and data science workloads. A knowledge store is defined in a skillset, but created in Azure Storage as objects or tabular rowsets.|
 | Cached enrichments | [**Enrichment caching (preview)**](enrichment-cache-how-to-configure.md) refers to cached enrichments that can be reused during skillset execution. Caching is valuable in skillsets that include OCR and image analysis, which are expensive to process. |
 
 ## Vector and hybrid retrieval
@@ -72,11 +72,10 @@ The following table summarizes features by category. There's feature parity in a
 
 | Category&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  | Features |
 |-------------------|----------|
-| Network security | [**Network security perimeter**](search-security-network-security-perimeter.md) support allows you to join Azure AI Search to a network security perimeter that includes other Azure resources so that you can manage network access holistically. |
-| Data encryption | [**Microsoft-managed encryption-at-rest**](search-security-overview.md#encryption) is built into the internal storage layer and is irrevocable. </br></br>[**Customer-managed encryption keys**](search-security-manage-encryption-keys.md) that you create and manage in Azure Key Vault can be used for supplemental encryption of indexes and synonym maps. For services created after August 1 2020, CMK encryption extends to data on temporary disks, for full double encryption of indexed content.|
-| Endpoint protection | [**IP rules for inbound firewall support**](service-configure-firewall.md) allows you to set up IP ranges over which the search service accepts requests.</br></br>[**Create a private endpoint**](service-create-private-endpoint.md) using Azure Private Link to force all requests through a virtual network. |
-| Inbound access | [**Role-based access control**](search-security-rbac.md) assigns roles to users and groups in Microsoft Entra ID for controlled access to search content and operations. You can also use [**key-based authentication**](search-security-api-keys.md) if you don't want to use role assignments. You can implement [document-level access control (preview)](search-document-level-access-overview.md) to filter out results that a user isn't authorized to see.  |
-| Outbound security (indexers) | [**Data access through private endpoints**](search-indexer-howto-access-private.md) allows an indexer to connect to Azure resources that are protected through Azure Private Link.</br></br>[**Data access using a trusted identity**](search-how-to-managed-identities.md) means that connection strings to external data sources can omit user names and passwords. When an indexer connects to the data source, the resource allows the connection if the search service was previously registered as a trusted service. |
+| Network security | [**IP rules for inbound firewall support**](service-configure-firewall.md) allows you to set up IP ranges over which the search service accepts requests. </br></br>[**Create a private endpoint**](service-create-private-endpoint.md) using Azure Private Link to force all requests through a virtual network. </br></br>[**Network security perimeter**](search-security-network-security-perimeter.md) support allows you to join Azure AI Search to a network security perimeter that includes other Azure resources so that you can manage network access holistically. |
+| Data encryption | [**Microsoft-managed encryption-at-rest**](search-security-overview.md#encryption) is built into the internal storage layer and is irrevocable. </br></br>[**Customer-managed encryption keys (CMK)**](search-security-manage-encryption-keys.md) that you create and manage in Azure Key Vault can be used for supplemental encryption of indexes and synonym maps. For services created after August 1 2020, CMK encryption extends to data on temporary disks, for full double encryption of indexed content.|
+| Inbound access | [**Role-based access control**](search-security-rbac.md) assigns roles to users and groups in Microsoft Entra ID for controlled access to search content and operations. You can also use [**key-based authentication**](search-security-api-keys.md) if you don't want to use role assignments. </br></br>[**Document-level access control (preview)**](search-document-level-access-overview.md) filters out search results that a user isn't authorized to see. For several data sources, if the data source provides an access control model, you can configure an index to inherit the user permission metadata. |
+| Outbound security (indexers) | [**Data connections through private endpoints**](search-indexer-howto-access-private.md) allows an indexer to connect to Azure resources that are protected through Azure Private Link. </br></br>[**Data connections through managed identities**](search-how-to-managed-identities.md) authenticates connections to Azure resources using a Microsoft Entra security principal, which eliminates storage and passing of hardcoded API keys.</br></br>[**Data access using a trusted identity**](search-how-to-managed-identities.md) means that connection strings to external data sources can omit user names and passwords. When an indexer connects to the data source, the resource allows the connection if the search service was previously registered as a trusted service (applies to Azure Storage only). |
 
 ## Portal features