Merge pull request #272931 from HeidiSteen/heidist-vectors

[azure search] GH issue, add a semantic kernel refernce to RAG doc

Author: prmerger-automator[bot]

articles/search/retrieval-augmented-generation-overview.md

@@ -10,7 +10,7 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 11/20/2023
+ms.date: 04/22/2024
 ---
 
 # Retrieval Augmented Generation (RAG) in Azure AI Search
@@ -71,7 +71,7 @@ RAG patterns that include Azure AI Search have the elements indicated in the fol
 
 The web app provides the user experience, providing the presentation, context, and user interaction. Questions or prompts from a user start here. Inputs pass through the integration layer, going first to information retrieval to get the search results, but also go to the LLM to set the context and intent. 
 
-The app server or orchestrator is the integration code that coordinates the handoffs between information retrieval and the LLM. One option is to use [LangChain](https://python.langchain.com/docs/get_started/introduction) to coordinate the workflow. LangChain [integrates with Azure AI Search](https://python.langchain.com/docs/integrations/retrievers/azure_ai_search/), making it easier to include Azure AI Search as a [retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/) in your workflow.
+The app server or orchestrator is the integration code that coordinates the handoffs between information retrieval and the LLM. One option is to use [LangChain](https://python.langchain.com/docs/get_started/introduction) to coordinate the workflow. LangChain [integrates with Azure AI Search](https://python.langchain.com/docs/integrations/retrievers/azure_ai_search/), making it easier to include Azure AI Search as a [retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/) in your workflow. [Semantic Kernel](https://devblogs.microsoft.com/semantic-kernel/announcing-semantic-kernel-integration-with-azure-cognitive-search/) is another option.
 
 The information retrieval system provides the searchable index, query logic, and the payload (query response). The search index can contain vectors or nonvector content. Although most samples and demos include vector fields, it's not a requirement. The query is executed using the existing search engine in Azure AI Search, which can handle keyword (or term) and vector queries. The index is created in advance, based on a schema you define, and loaded with your content that's sourced from files, databases, or storage.
 

articles/search/search-what-is-azure-search.md

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: overview
-ms.date: 11/22/2023
+ms.date: 04/22/2024
 ms.custom:
   - build-2023
   - build-2023-dataai
@@ -43,7 +43,7 @@ On the search service itself, the two primary workloads are *indexing* and *quer
 
 + [**Indexing**](search-what-is-an-index.md) is an intake process that loads content into your search service and makes it searchable. Internally, inbound text is processed into tokens and stored in inverted indexes, and inbound vectors are stored in vector indexes. The document format that Azure AI Search can index is JSON. You can upload JSON documents that you've assembled, or use an indexer to retrieve and serialize your data into JSON. 
 
-  [AI enrichment](cognitive-search-concept-intro.md) through [cognitive skills](cognitive-search-working-with-skillsets.md) is an extension of indexing. If you have images or large unstructured text in source document, you can attach skills that perform OCR, describe images, infer structure, translate text and more. You can also attach skills that perform [data chunking and vectorization](vector-search-integrated-vectorization.md).
+  [Applied AI](cognitive-search-concept-intro.md) through a [skillset](cognitive-search-working-with-skillsets.md) extends indexing with image and language models. If you have images or large unstructured text in source document, you can attach skills that perform OCR, describe images, infer structure, translate text and more. You can also attach skills that perform [data chunking and vectorization](vector-search-integrated-vectorization.md).
 
 + [**Querying**](search-query-overview.md) can happen once an index is populated with searchable content, when your client app sends query requests to a search service and handles responses. All query execution is over a search index that you control.
 
@@ -104,7 +104,7 @@ Customers often ask how Azure AI Search compares with other search-related solut
 |-------------|-----------------|
 | Microsoft Search | [Microsoft Search](/microsoftsearch/overview-microsoft-search) is for Microsoft 365 authenticated users who need to query over content in SharePoint. Azure AI Search pulls in content across Azure and any JSON dataset. |
 |Bing | [Bing APIs](/bing/search-apis/bing-web-search/bing-api-comparison) query the indexes on Bing.com for matching terms. Azure AI Search searches over indexes populated with your content. You control data ingestion and the schema. |
-|Database search | SQL Server has [full text search](/sql/relational-databases/search/full-text-search) and Azure Cosmos DB and similar technologies have queryable indexes. Azure AI Search becomes an attractive alternative when you need features like lexical analyzers and relevance tuning, or content from heterogeneous sources. Resource utilization is another inflection point. Indexing and queries are computationally intensive. Offloading search from the DBMS preserves system resources for transaction processing. |
+|Database search | Azure SQL has [full text search](/sql/relational-databases/search/full-text-search) and [vector search](/samples/azure-samples/azure-sql-db-openai/azure-sql-db-openai/). Azure Cosmos DB also has [text search](/azure/cosmos-db/nosql/query/) and [vector search](/azure/cosmos-db/vector-database). Azure AI Search becomes an attractive alternative when you need features like relevance tuning, or content from heterogeneous sources. Resource utilization is another inflection point. Indexing and queries are computationally intensive. Offloading search from the DBMS preserves system resources for transaction processing. |
 |Dedicated search solution | Assuming you've decided on dedicated search with full spectrum functionality, a final categorical comparison is between search technologies. Among cloud providers, Azure AI Search is strongest for vector, keyword, and hybrid workloads over content on Azure, for apps that rely primarily on search for both information retrieval and content navigation. |
 
 Key strengths include: