Merge pull request #247718 from HeidiSteen/heidist-freshness

more vector demo links

Author: prmerger-automator[bot]

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: 07/10/2023
+ms.date: 08/10/2023
 ms.custom: contperf-fy21q1, build-2023, build-2023-dataai
 ---
 # What's Azure Cognitive Search?
@@ -94,7 +94,7 @@ Customers often ask how Azure Cognitive Search compares with other search-relate
 | Compared to | Key differences |
 |-------------|-----------------|
 | Microsoft Search | [Microsoft Search](/microsoftsearch/overview-microsoft-search) is for Microsoft 365 authenticated users who need to query over content in SharePoint. It's offered as a ready-to-use search experience, enabled and configured by administrators, with the ability to accept external content through connectors from Microsoft and other sources. If this describes your scenario, then Microsoft Search with Microsoft 365 is an attractive option to explore.<br/><br/>In contrast, Azure Cognitive Search executes queries over an index that you define, populated with data and documents you own, often from diverse sources. Azure Cognitive Search has crawler capabilities for some Azure data sources through [indexers](search-indexer-overview.md), but you can push any JSON document that conforms to your index schema into a single, consolidated searchable resource. You can also customize the indexing pipeline to include machine learning and lexical analyzers. Because Cognitive Search is built to be a plug-in component in larger solutions, you can integrate search into almost any app, on any platform.|
-|Bing | [Bing Web Search API](../cognitive-services/bing-web-search/index.yml) searches the indexes on Bing.com for matching terms you submit. Indexes are built from HTML, XML, and other web content on public sites. Built on the same foundation, [Bing Custom Search](/azure/cognitive-services/bing-custom-search/) offers the same crawler technology for web content types, scoped to individual web sites.<br/><br/>In Cognitive Search, you can define and populate the index. You can use [indexers](search-indexer-overview.md) to crawl data on Azure data sources, or push any index-conforming JSON document to your search service. |
+|Bing | [Bing family of search APIs](/bing/search-apis/bing-web-search/bing-api-comparison) search the indexes on Bing.com for matching terms you submit. Indexes are built from HTML, XML, and other web content on public sites. Built on the same foundation, [Bing Custom Search](/bing/search-apis/bing-custom-search/overview) offers the same crawler technology for web content types, scoped to individual web sites.<br/><br/>In Cognitive Search, you define and populate the search index with your content. You control data ingestion. One way is to use [indexers](search-indexer-overview.md) to crawl Azure data sources. You can also push any index-conforming JSON document to your search service. |
 |Database search | Many database platforms include a built-in search experience. SQL Server has [full text search](/sql/relational-databases/search/full-text-search). Azure Cosmos DB and similar technologies have queryable indexes. When evaluating products that combine search and storage, it can be challenging to determine which way to go. Many solutions use both: DBMS for storage, and Azure Cognitive Search for specialized search features.<br/><br/>Compared to DBMS search, Azure Cognitive Search stores content from heterogeneous sources and offers specialized text processing features such as linguistic-aware text processing (stemming, lemmatization, word forms) in [56 languages](/rest/api/searchservice/language-support). It also supports autocorrection of misspelled words, [synonyms](/rest/api/searchservice/synonym-map-operations), [suggestions](/rest/api/searchservice/suggestions), [scoring controls](/rest/api/searchservice/add-scoring-profiles-to-a-search-index), [facets](search-faceted-navigation.md),  and [custom tokenization](/rest/api/searchservice/custom-analyzers-in-azure-search). The [full text search engine](search-lucene-query-architecture.md) in Azure Cognitive Search is built on Apache Lucene, an industry standard in information retrieval. However, while Azure Cognitive Search persists data in the form of an inverted index, it isn't a replacement for true data storage and we don't recommend using it in that capacity. For more information, see this [forum post](https://stackoverflow.com/questions/40101159/can-azure-search-be-used-as-a-primary-database-for-some-data). <br/><br/>Resource utilization is another inflection point in this category. Indexing and some query operations are often computationally intensive. Offloading search from the DBMS to a dedicated solution in the cloud preserves system resources for transaction processing. Furthermore, by externalizing search, you can easily adjust scale to match query volume.|
 |Dedicated search solution | Assuming you've decided on dedicated search with full spectrum functionality, a final categorical comparison is between on premises solutions or a cloud service. Many search technologies offer controls over indexing and query pipelines, access to richer query and filtering syntax, control over rank and relevance, and features for self-directed and intelligent search. <br/><br/>A cloud service is the right choice if you want a turn-key solution with minimal overhead and maintenance, and adjustable scale. <br/><br/>Within the cloud paradigm, several providers offer comparable baseline features, with full-text search, geospatial search, and the ability to handle a certain level of ambiguity in search inputs. Typically, it's a [specialized feature](search-features-list.md), or the ease and overall simplicity of APIs, tools, and management that determines the best fit. |
 

articles/search/whats-new.md

@@ -22,9 +22,9 @@ Learn about the latest updates to Azure Cognitive Search functionality, docs, an
 
 | Item                         | Type |  Description |
 |-----------------------------|------|--------------|
-| [**Vector demo (Azure SDK for JavaScript)**](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-javascript/code/azure-search-vector-sample.js) | Sample | Uses Node.js and the **@azure/search-documents 12.0.0-beta.2** library in the Azure SDK for JavaScript to generate embeddings, create and load an index, and run several vector queries. |
-| [**Vector demo (Azure SDK for .NET)**](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-dotnet/readme.md) | Sample | Uses the **Azure.Search.Documents 11.5.0-beta.3** library to generate embeddings, create and load an index, and run several vector queries. |
-| [**Vector demo (Azure SDK for Python)**](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-python/code/azure-search-vector-image-python-sample.ipynb) | Sample | Uses the latest beta release of the **azure.search.documents** library in the Azure SDK for Python to generate embeddings, create and load an index, and run several vector queries. For more vector search Python demos, see [cognitive-search-vector-pr/demo-python](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-python). |
+| [**Vector demo (Azure SDK for JavaScript)**](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-javascript/code/azure-search-vector-sample.js) | Sample | Uses Node.js and the **@azure/search-documents 12.0.0-beta.2** library to generate embeddings, create and load an index, and run several vector queries. |
+| [**Vector demo (Azure SDK for .NET)**](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-dotnet/readme.md) | Sample | Uses the **Azure.Search.Documents 11.5.0-beta.3** library to generate embeddings, create and load an index, and run several vector queries. You can also try [this sample](https://github.com/Azure/azure-sdk-for-net/blob/master/sdk/search/Azure.Search.Documents/samples/Sample07_VectorSearch.md) from the Azure SDK team.|
+| [**Vector demo (Azure SDK for Python)**](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-python/code/azure-search-vector-image-python-sample.ipynb) | Sample | Uses the latest beta release of the **azure.search.documents** to generate embeddings, create and load an index, and run several vector queries. Visit the [cognitive-search-vector-pr/demo-python](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-python) repo for more vector search demos. |
 
 ## June 2023