Merge pull request #268623 from HeidiSteen/heidist-fix

[azure search] fixed broken links (9 articles)

Author: AnnaMHuff

articles/search/samples-javascript.md

@@ -70,5 +70,5 @@ The following samples are also published by the Azure AI Search team, but aren't
 
 | Samples | Description |
 |---------|-------------|
-| [azure-search-vector-sample.js](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-javascript/JavaScriptVectorDemo)  | Vector search sample using the Azure SDK for JavaScript |
+| [azure-search-vector-sample.js](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-javascript/readme.md)  | Vector search sample using the Azure SDK for JavaScript |
 | [azure-search-react-template](https://github.com/dereklegenzoff/azure-search-react-template) | React template for Azure AI Search (github.com) |

articles/search/samples-python.md

@@ -61,13 +61,13 @@ A demo repo provides proof-of-concept source code for examples or scenarios show
 
 | Repository | Description |
 |------------|-------------|
-| [azure-search-vector-python-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-vector-image-python-sample.ipynb) | Uses the **azure.search.documents** library in the Azure SDK for Python to create, load, and query a vector store. |
-| [azure-search-integrated-vectorization-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-integrated-vectorization-sample.ipynb) | Extends the vector store workflow to include integrated data chunking and embedding. |
-| [azure-search-vector-image-index-creation-python-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-vector-image-index-creation-python-sample.ipynb) | Demonstrates image embedding. |
-| [azure-search-vector-image-python-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-vector-image-python-sample.ipynb) | Demonstrates image retrieval. |
-| [azure-search-vector-python-huggingface-model-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-vector-python-huggingface-model-sample.ipynb) | Hugging Face integration. |
-| [azure-search-vector-python-langchain-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-vector-python-langchain-sample.ipynb) | LangChain integration. |
-| [azure-search-vector-python-llamaindex-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-vector-python-llamaindex-sample.ipynb) | Llamaindex integration. |
+| [azure-search-vector-python-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/basic-vector-workflow/azure-search-vector-python-sample.ipynb) | Uses the **azure.search.documents** library in the Azure SDK for Python to create, load, and query a vector store. |
+| [azure-search-integrated-vectorization-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb) | Extends the vector store workflow to include integrated data chunking and embedding. |
+| [azure-search-vector-image-index-creation-python-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/multimodal/azure-search-vector-image-index-creation-python-sample.ipynb) | Demonstrates multimodal search over text and images. |
+| [azure-search-custom-vectorization-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/custom-vectorizer/azure-search-custom-vectorization-sample.ipynb) | Demonstrates custom vectorization. |
+| [azure-search-vector-python-huggingface-model-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/community-integration/hugging-face/azure-search-vector-python-huggingface-model-sample.ipynb) | Hugging Face integration. |
+| [azure-search-vector-python-langchain-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/community-integration/langchain/azure-search-vector-python-langchain-sample.ipynb) | LangChain integration. |
+| [azure-search-vector-python-llamaindex-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/community-integration/llamaindex/azure-search-vector-python-llamaindex-sample.ipynb) | Llamaindex integration. |
 | [azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/README.md) | ChatGPT + Enterprise data with Azure OpenAI Python code showing how to use Azure AI Search with the large language models in Azure OpenAI. For background, see this Tech Community blog post: [Revolutionize your Enterprise Data with ChatGPT](https://techcommunity.microsoft.com/t5/ai-applied-ai-blog/revolutionize-your-enterprise-data-with-chatgpt-next-gen-apps-w/ba-p/3762087). |
 
 ## Other samples
@@ -76,7 +76,7 @@ The following samples are also published by the Azure AI Search team, but aren't
 
 | Repository | Description |
 |------------|-------------|
-| [azure-search-backup-and-restore.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-backup-and-restore.ipynb) | Uses the **azure.search.documents** library in the Azure SDK for Python to make a local copy of the retrievable fields of a search index, and then push those fields to a new search index. |
+| [azure-search-backup-and-restore.ipynb](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-python/code/index-backup-restore) | Uses the **azure.search.documents** library in the Azure SDK for Python to make a local copy of the retrievable fields of a search index, and then push those fields to a new search index. |
 
 > [!TIP]
 > Try the [Samples browser](/samples/browse/?languages=python&products=azure-cognitive-search) to search for Microsoft code samples in GitHub, filtered by product, service, and language.

articles/search/samples-rest.md

@@ -27,23 +27,14 @@ Code samples from the Azure AI Search team demonstrate features and workflows. M
 
 | Samples | Description |
 |---------|---------|
-| [Quickstart](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart) | Source code for [Quickstart: Text search using REST](search-get-started-rest.md). This sample  covers the basic workflow for creating, loading, and querying a search index using sample data. |
-| [Quickstart-vectors](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-vectors) | Source code for [Quickstart: Vector search using REST APIs](search-get-started-vector.md). This sample  covers the basic workflow for indexing and querying vector data. |
-| [Tutorial](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Tutorial) | Source code for [Tutorial: Use REST and AI to generate searchable content from Azure blobs](cognitive-search-tutorial-blob.md). This sample  shows you how to create a skillset that iterates over Azure blobs to extract information and infer structure.|
-| [Debug-sessions](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Debug-sessions) | Source code for [Tutorial: Diagnose, repair, and commit changes to your skillset](cognitive-search-tutorial-debug-sessions.md). This sample  shows you how to use a skillset debug session in the Azure portal. REST is used to create the objects used during debug.|
+| [quickstart](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart) | Source code for [Quickstart: Text search using REST](search-get-started-rest.md). This sample  covers the basic workflow for creating, loading, and querying a search index using sample data. |
+| [quickstart-vectors](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-vectors) | Source code for [Quickstart: Vector search using REST APIs](search-get-started-vector.md). This sample  covers the basic workflow for indexing and querying vector data. |
+| [skillset-tutorial](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/skillset-tutorial) | Source code for [Tutorial: Use REST and AI to generate searchable content from Azure blobs](cognitive-search-tutorial-blob.md). This sample  shows you how to create a skillset that iterates over Azure blobs to extract information and infer structure.|
+| [debug-sessions](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Debug-sessions) | Source code for [Tutorial: Diagnose, repair, and commit changes to your skillset](cognitive-search-tutorial-debug-sessions.md). This sample  shows you how to use a skillset debug session in the Azure portal. REST is used to create the objects used during debug.|
 | [custom-analyzers](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/custom-analyzers) | Source code for [Tutorial: Create a custom analyzer for phone numbers](tutorial-create-custom-analyzer.md). This sample  explains how to use analyzers to preserve patterns and special characters in searchable content.|
 | [knowledge-store](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/knowledge-store) | Source code for [Create a knowledge store using REST](knowledge-store-create-rest.md). This sample  explains the necessary steps for populating a knowledge store used for knowledge mining workflows. |
-| [semantic ranker](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/semantic-search) | Source code for [Define projections in a knowledge store](knowledge-store-projections-examples.md). This sample creates a basic search index with a semantic configuration, loads data into the index, and then creates a semantic query.|
 | [projections](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/projections) | Source code for [Define projections in a knowledge store](knowledge-store-projections-examples.md). This sample explains how to specify the physical data structures in a knowledge store.|
 | [index-encrypted-blobs](https://github.com/Azure-Samples/azure-search-rest-samples/commit/f5ebb141f1ff98f571ab84ac59dcd6fd06a46718) | Source code for [How to index encrypted blobs using blob indexers and skillsets](search-howto-index-encrypted-blobs.md). This article shows how to index documents in Azure Blob Storage that have been previously encrypted using Azure Key Vault. |
 
 > [!TIP]
-> Try the [Samples browser](/samples/browse/?expanded=azure&languages=http&products=azure-cognitive-search) to search for Microsoft code samples in GitHub, filtered by product, service, and language.
-
-## Other samples
-
-The following samples are also published by the Azure AI Search team, but aren't referenced in documentation. Associated readme files provide usage instructions.
-
-| Samples | Description |
-|---------|-------------|
-| [Query-examples](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Query-examples) | Postman collections demonstrating the various query techniques, including fuzzy search, RegEx and wildcard search, autocomplete, and so on. |
+> Try the [Samples browser](/samples/browse/?expanded=azure&languages=http&products=azure-cognitive-search) to search for Microsoft code samples in GitHub, filtered by product, service, and language.

articles/search/search-faq-frequently-asked-questions.yml

@@ -77,7 +77,7 @@ sections:
         answer: |
           There's no native support for porting indexes. Search indexes are considered downstream data structures, accepting content from other data sources that collect operational data. As such, there's no built-in support for backing up and restoring indexes because the expectation is that you would rebuild an index from source data if you deleted it, or wanted to move it.
           
-          However, if you want to move an index between search services, you can try the **index-backup-restore** sample code in this [Azure AI Search .NET sample repo](https://github.com/Azure-Samples/azure-search-dotnet-utilities). There's also a [Python version of backup and restore](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-backup-and-restore.ipynb).
+          However, if you want to move an index between search services, you can try the **index-backup-restore** sample code in this [Azure AI Search .NET sample repo](https://github.com/Azure-Samples/azure-search-dotnet-utilities). There's also a [Python version of backup and restore](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/index-backup-restore/azure-search-backup-and-restore.ipynb).
                    
       - question: |
           Can I restore my index or service once it's deleted?

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

@@ -200,4 +200,4 @@ 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/Azure/azure-search-vector-samples/blob/main/demo-python/code/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/HeidiSteen/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb).

articles/search/search-get-started-rest.md

@@ -19,7 +19,7 @@ ms.custom:
 
 Learn how to use the [Search REST APIs](/rest/api/searchservice) to create, load, and query a search index in Azure AI Search. 
 
-[Download a REST sample](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/quickstart) to send the requests in this quickstart, or create requests manually using the instructions in this article.
+[Download a REST sample](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart) to send the requests in this quickstart, or create requests manually using the instructions in this article.
 
 If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/?WT.mc_id=A261C142F) before you begin.
 

articles/search/search-get-started-vector.md

@@ -17,7 +17,7 @@ Learn how to use the [Search REST APIs](/rest/api/searchservice) to create, load
 
 In Azure AI Search, a [*vector store*](vector-store.md) has an index schema that defines vector and nonvector fields, a vector configuration for algorithms that create the embedding space, and settings on vector field definitions that are used in query requests. The [Create Index](/rest/api/searchservice/indexes/create-or-update) API creates the vector store.
 
-[Download a REST sample](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/quickstart-vectors) to send the requests in this quickstart, or create requests manually to adapt the instructions for your own data.
+[Download a REST sample](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-vectors) to send the requests in this quickstart, or create requests manually to adapt the instructions for your own data.
 
 If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/?WT.mc_id=A261C142F) before you begin.
 
@@ -240,7 +240,7 @@ Creating and loading the index are separate steps. In Azure AI Search, the index
 The URI is extended to include the `docs` collection and `index` operation.
 
 > [!IMPORTANT]
-> The following example isn't runnable code. For readability, we excluded vector values because each one contains 1536 embeddings, which is too long for this article. Copy runnable code from the [sample on GitHub](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/quickstart-vectors) if you want to try this step.
+> The following example isn't runnable code. For readability, we excluded vector values because each one contains 1536 embeddings, which is too long for this article. Copy runnable code from the [sample on GitHub](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-vectors) if you want to try this step.
 
 ```http
 ### Upload documents
@@ -399,7 +399,7 @@ The vector queries in this section are based on two strings:
 The vector query string is semantically similar to the search string, but includes terms that don't exist in the search index. If you do a keyword search for "classic lodging near running trails, eateries, retail", results are zero. We use this example to show how you can get relevant results even if there are no matching terms.
 
 > [!IMPORTANT]
-> The following examples aren't runnable code. For readability, we excluded vector values because each array contains 1536 embeddings, which is too long for this article. Copy runnable code from the [sample on GitHub](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/quickstart-vectors) if you want to try these queries.
+> The following examples aren't runnable code. For readability, we excluded vector values because each array contains 1536 embeddings, which is too long for this article. Copy runnable code from the [sample on GitHub](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-vectors) if you want to try these queries.
 
 ### Single vector search
 

articles/search/vector-search-how-to-query.md

@@ -238,7 +238,7 @@ Use Search explorer to formulate a vector query. Search explorer has a **Query v
 
 + See the [JavaScript SDK samples for vector search](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/search/search-documents/samples/v12/javascript)
 
-+ See the [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-javascript/JavaScriptVectorDemo) GitHub repository for JavaScript code samples.
++ See the [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-javascript) GitHub repository for JavaScript code samples.
 
 ---
 
@@ -610,4 +610,4 @@ During query execution, a vector query can only target one internal vector index
 
 ## Next steps
 
-As a next step, review vector query code examples in [Python](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-python), [C#](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-dotnet) or [JavaScript](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-javascript/JavaScriptVectorDemo).
+As a next step, review vector query code examples in [Python](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-python), [C#](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-dotnet) or [JavaScript](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-javascript).