@@ -9,8 +9,8 @@ ms.author: heidist
 ms.service: cognitive-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 03/27/2024
+ms.topic: reference
+ms.date: 09/19/2024
 ---
 
 # Troubleshooting common indexer errors and warnings in Azure AI Search
@@ -32,29 +32,28 @@ Warnings don't stop indexing, but they do indicate conditions that could result
 
 To verify an indexer status and identify errors in the Azure portal, follow the steps below:
 
-1. Navigate to the Azure portal and locate your AI Search service.
-1. Once you're in the AI Search service, click on the 'Indexers' tab.
-1. From the list of indexers, identify the specific indexer you wish to verify.
-1. Under the 'Execution History' column, click on the 'Status' hyperlink associated with the selected indexer.
-1. If there's an error, hover over the error message. A pane will appear on the right side of your screen displaying detailed information about the error.
+1. Sign in to the [Azure portal](https://portal.azure.com/) and [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
 
-## Transient errors 
+1. On the left, expand **Search Management** > **Indexers** and select an indexer.
 
-For various reasons, such as transient network communication interruptions, timeouts from long-running processes, or specific document nuances, it's common to encounter transient errors or warnings during indexer runs. However, these errors are temporary and should be resolved in subsequent indexer runs. 
+1. Under **Execution History**, select the status. All statuses, including Success, have details about the execution.
 
-To manage these errors effectively, it is recommended [putting your indexer on a schedule](search-howto-schedule-indexers.md), for instance, to run every five minutes. This means the next run will commence five minutes after the completion of the first run, adhering to the [maximum runtime limit](search-limits-quotas-capacity.md#indexer-limits). Regularly scheduled runs help to rectify any transient errors or warnings swiftly. 
+1. If there's an error, hover over the error message. A pane appears on the right side of your screen displaying detailed information about the error.
 
-If you notice an error persisting over multiple indexer runs, it's likely not a transient issue. In such cases, refer to the list below for potential solutions. Please note, always ensure your indexing schedule aligns with the limitations outlined in our indexer limits guide.
+## Transient errors
 
+For various reasons, such as transient network communication interruptions, timeouts from long-running processes, or specific document nuances, it's common to encounter transient errors or warnings during indexer runs. However, these errors are temporary and should be resolved in subsequent indexer runs. 
 
-## Error properties
+To manage these errors effectively, we recommend [putting your indexer on a schedule](search-howto-schedule-indexers.md), for instance, to run every five minutes, where the next run commences five minutes after completing the first run, adhering to the [maximum runtime limit](search-limits-quotas-capacity.md#indexer-limits) on your service. Regularly scheduled runs help rectify transient errors or warnings. 
 
-Beginning with API version `2019-05-06`, item-level Indexer errors and warnings are structured to provide increased clarity around causes and next steps. They contain the following properties:
+If an error persists over multiple indexer runs, it's likely not a transient issue. In such cases, refer to the list below for potential solutions.
+
+## Error properties
 
 | Property | Description | Example |
 | --- | --- | --- |
-| Key | The document ID of the document impacted by the error or warning. | `https://<storageaccount>.blob.core.windows.net/jfk-1k/docid-32112954.pdf`|
-| Name | The operation name describing where the error or warning occurred. This is generated by the following structure: `[category]`.`[subcategory]`.`[resourceType]`.`[resourceName]` | `DocumentExtraction.azureblob.myBlobContainerName` `Enrichment.WebApiSkill.mySkillName` `Projection.SearchIndex.OutputFieldMapping.myOutputFieldName` `Projection.SearchIndex.MergeOrUpload.myIndexName` `Projection.KnowledgeStore.Table.myTableName` |
+| Key | The ID of the document impacted by the error or warning. | Azure Storage example, where the default ID is the metadata storage path: `https://<storageaccount>.blob.core.windows.net/jfk-1k/docid-32112954.pdf`|
+| Name | The operation causing the error or warning. This is generated by the following structure: `[category]`.`[subcategory]`.`[resourceType]`.`[resourceName]` | `DocumentExtraction.azureblob.myBlobContainerName` `Enrichment.WebApiSkill.mySkillName` `Projection.SearchIndex.OutputFieldMapping.myOutputFieldName` `Projection.SearchIndex.MergeOrUpload.myIndexName` `Projection.KnowledgeStore.Table.myTableName` |
 | Message | A high-level description of the error or warning. | `Could not execute skill because the Web Api request failed.` |
 | Details | Specific information that might be helpful in diagnosing the issue, such as the WebApi response if executing a custom skill failed. | `link-cryptonyms-list - Error processing the request record : System.ArgumentNullException: Value cannot be null. Parameter name: source at System.Linq.Enumerable.All[TSource](IEnumerable 1 source, Func 2 predicate) at Microsoft.CognitiveSearch.WebApiSkills.JfkWebApiSkills. ...rest of stack trace...` |
 | DocumentationLink | A link to relevant documentation with detailed information to debug and resolve the issue. This link will often point to one of the below sections on this page. | `https://go.microsoft.com/fwlink/?linkid=2106475` |
@@ -95,8 +94,8 @@ Indexer read the document from the data source, but there was an issue convertin
 | The document key is missing | `Document key cannot be missing or empty` | Ensure all documents have valid document keys. The document key is determined by setting the 'key' property as part of the [index definition](/rest/api/searchservice/indexes/create#request-body). Indexers emit this error when the property flagged as the 'key' can't be found on a particular document. |
 | The document key is invalid | `Invalid document key. Keys can only contain letters, digits, underscore (_), dash (-), or equal sign (=). ` | Ensure all documents have valid document keys. Review [Indexing Blob Storage](search-howto-indexing-azure-blob-storage.md) for more details. If you're using the blob indexer, and your document key is the `metadata_storage_path` field, make sure that the indexer definition has a [base64Encode mapping function](search-indexer-field-mappings.md?tabs=rest#base64encode-function) with `parameters` equal to `null`, instead of the path in plain text. |
 | The document key is invalid | `Document key cannot be longer than 1024 characters` | Modify the document key to meet the validation requirements. |
-| Could not apply field mapping to a field | `Could not apply mapping function 'functionName' to field 'fieldName'. Array cannot be null. Parameter name: bytes` | Double check the [field mappings](search-indexer-field-mappings.md) defined on the indexer, and compare with the data of the specified field of the failed document. It might be necessary to modify the field mappings or the document data. |
-| Could not read field value | `Could not read the value of column 'fieldName' at index 'fieldIndex'. A transport-level error has occurred when receiving results from the server. (provider: TCP Provider, error: 0 - An existing connection was forcibly closed by the remote host.)` | These errors are typically due to unexpected connectivity issues with the data source's underlying service. Try running the document through your indexer again later. |
+| Couldn't apply field mapping to a field | `Could not apply mapping function 'functionName' to field 'fieldName'. Array cannot be null. Parameter name: bytes` | Double check the [field mappings](search-indexer-field-mappings.md) defined on the indexer, and compare with the data of the specified field of the failed document. It might be necessary to modify the field mappings or the document data. |
+| Couldn't read field value | `Could not read the value of column 'fieldName' at index 'fieldIndex'. A transport-level error has occurred when receiving results from the server. (provider: TCP Provider, error: 0 - An existing connection was forcibly closed by the remote host.)` | These errors are typically due to unexpected connectivity issues with the data source's underlying service. Try running the document through your indexer again later. |
 
 <a name="Could not map output field '`xyz`' to search index due to deserialization problem while applying mapping function '`abc`'"></a>
 
@@ -189,7 +188,7 @@ The document was read and processed, but the indexer couldn't add it to the sear
 | Trouble connecting to the target index (that persists after retries) because the service is under other load, such as querying or indexing. | Failed to establish connection to update index. Search service is under heavy load. | [Scale up your search service](search-capacity-planning.md)
 | Search service is being patched for service update, or is in the middle of a topology reconfiguration. | Failed to establish connection to update index. Search service is currently down/Search service is undergoing a transition. | Configure service with at least three replicas for 99.9% availability per [SLA documentation](https://azure.microsoft.com/support/legal/sla/search/v1_0/)
 | Failure in the underlying compute/networking resource (rare) | Failed to establish connection to update index. An unknown failure occurred. | Configure indexers to [run on a schedule](search-howto-schedule-indexers.md) to pick up from a failed state.
-| An indexing request made to the target index wasn't acknowledged within a timeout period due to network issues. | Could not establish connection to the search index in a timely manner. | Configure indexers to [run on a schedule](search-howto-schedule-indexers.md) to pick up from a failed state. Additionally, try lowering the indexer [batch size](/rest/api/searchservice/indexers/create#indexingparameters) if this error condition persists.
+| An indexing request made to the target index wasn't acknowledged within a timeout period due to network issues. | Couldn't establish connection to the search index in a timely manner. | Configure indexers to [run on a schedule](search-howto-schedule-indexers.md) to pick up from a failed state. Additionally, try lowering the indexer [batch size](/rest/api/searchservice/indexers/create#indexingparameters) if this error condition persists.
 
 <a name="could-not-index-document-because-the-indexer-data-to-index-was-invalid"></a>
 
@@ -225,9 +224,9 @@ This error occurs when the indexer is attempting to [project data into a knowled
 
 | Reason | Details/Example | Resolution |
 | --- | --- | --- |
-| Could not update projection blob `'blobUri'` in container `'containerName'` |The specified container doesn't exist. | The indexer checks if the specified container has been previously created and will create it if necessary, but it only performs this check once per indexer run. This error means that something deleted the container after this step.  To resolve this error, try this: leave your storage account information alone, wait for the indexer to finish, and then rerun the indexer. |
-| Could not update projection blob `'blobUri'` in container `'containerName'` |Unable to write data to the transport connection: An existing connection was forcibly closed by the remote host. | This is expected to be a transient failure with Azure Storage and thus should be resolved by rerunning the indexer. If you encounter this error consistently, file a [support ticket](https://portal.azure.com/#create/Microsoft.Support) so it can be investigated further.  |
-| Could not update row `'projectionRow'` in table `'tableName'` | The server is busy. | This is expected to be a transient failure with Azure Storage and thus should be resolved by rerunning the indexer. If you encounter this error consistently, file a [support ticket](https://portal.azure.com/#create/Microsoft.Support) so it can be investigated further.  |
+| Couldn't update projection blob `'blobUri'` in container `'containerName'` |The specified container doesn't exist. | The indexer checks if the specified container has been previously created and will create it if necessary, but it only performs this check once per indexer run. This error means that something deleted the container after this step.  To resolve this error, try this: leave your storage account information alone, wait for the indexer to finish, and then rerun the indexer. |
+| Couldn't update projection blob `'blobUri'` in container `'containerName'` |Unable to write data to the transport connection: An existing connection was forcibly closed by the remote host. | This is expected to be a transient failure with Azure Storage and thus should be resolved by rerunning the indexer. If you encounter this error consistently, file a [support ticket](https://portal.azure.com/#create/Microsoft.Support) so it can be investigated further.  |
+| Couldn't update row `'projectionRow'` in table `'tableName'` | The server is busy. | This is expected to be a transient failure with Azure Storage and thus should be resolved by rerunning the indexer. If you encounter this error consistently, file a [support ticket](https://portal.azure.com/#create/Microsoft.Support) so it can be investigated further.  |
 
 <a name="skill-throttled"></a>
 
@@ -237,7 +236,7 @@ Skill execution failed because the call to Azure AI services was throttled. Typi
 
 ## `Error: Expected IndexAction metadata`
 
-An 'Expected IndexAction metadata' error means when the indexer attempted to read the document to identify what action should be taken, it did not find any corresponding metadata on the document. Typically, this error occurs when the indexer has an annotation cache added or removed without resetting the indexer. To address this, you should [reset and rerun the indexer](search-howto-run-reset-indexers.md).  
+An 'Expected IndexAction metadata' error means when the indexer attempted to read the document to identify what action should be taken, it didn't find any corresponding metadata on the document. Typically, this error occurs when the indexer has an annotation cache added or removed without resetting the indexer. To address this, you should [reset and rerun the indexer](search-howto-run-reset-indexers.md).  
 
 <a name="could-not-execute-skill-because-a-skill-input-was-invalid"></a>
 
@@ -379,7 +378,7 @@ Output field mappings that reference non-existent/null data will produce warning
 
 | Reason | Details/Example | Resolution |
 | --- | --- | --- |
-| Cannot iterate over non-array | "Cannot iterate over non-array `/document/normalized_images/0/imageCelebrities/0/detail/celebrities`." | This error occurs when the output isn't an array. If you think the output should be an array, check the indicated output source field path for errors. For example, you might have a missing or extra `*` in the source field name. It's also possible that the input to this skill is null, resulting in an empty array. Find similar details in [Skill Input was Invalid](cognitive-search-common-errors-warnings.md#warning-skill-input-was-invalid) section.    |
+| Can't iterate over non-array | "Cannot iterate over non-array `/document/normalized_images/0/imageCelebrities/0/detail/celebrities`." | This error occurs when the output isn't an array. If you think the output should be an array, check the indicated output source field path for errors. For example, you might have a missing or extra `*` in the source field name. It's also possible that the input to this skill is null, resulting in an empty array. Find similar details in [Skill Input was Invalid](cognitive-search-common-errors-warnings.md#warning-skill-input-was-invalid) section.    |
 | Unable to select `0` in non-array | "Unable to select `0` in non-array `/document/pages`." | This could happen if the skills output doesn't produce an array and the output source field name has array index or `*` in its path. Double check the paths provided in the output source field names and the field value for the indicated field name. Find similar details in [Skill Input was Invalid](cognitive-search-common-errors-warnings.md#warning-skill-input-was-invalid) section.  |
 
 <a name="the-data-change-detection-policy-is-configured-to-use-key-column-x"></a>

@@ -8,19 +8,19 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
   - build-2024
-ms.topic: conceptual
-ms.date: 10/28/2023
+ms.topic: concept-article
+ms.date: 09/19/2024
 ---
 
 # Skills for extra processing during indexing (Azure AI Search)
 
-This article describes the skills provided with Azure AI Search that you can include in a [skillset](cognitive-search-working-with-skillsets.md) to access external processing. 
+This article describes the skills in Azure AI Search that you can include in a [skillset](cognitive-search-working-with-skillsets.md) to access external processing. 
 
 A *skill* provides an atomic operation that transforms content in some way. Often, it's an operation that recognizes or extracts text, but it can also be a utility skill that reshapes the enrichments that are already created. Typically, the output is text-based so that it can be used in [full text search](search-lucene-query-architecture.md) or vectors used in [vector search](vector-search-overview.md).
 
 Skills are organized into categories:
 
-* A *built-in skill* wraps API calls to an Azure resource, where the inputs, outputs, and processing steps are well understood. For skills that call an Azure AI resource, the connection is made over the internal network. For skills that call Azure OpenAI, you provide the connection information that the search service uses to connect to the resource. A small quantity of processing is non-billable, but at larger volumes, processing is billable. Built-in skills are based on pretrained models from Microsoft, which means you can't train the model using your own training data.
+* A *built-in skill* wraps API calls to an Azure AI resource, where the inputs, outputs, and processing steps are well understood. For skills that call an Azure AI resource, the connection is made over the internal network. For skills that call Azure OpenAI, you provide the connection information that the search service uses to connect to the resource. A small quantity of processing is non-billable, but at larger volumes, processing is billable. Built-in skills are based on pretrained models from Microsoft, which means you can't train the model using your own training data.
 
 * A *custom skill* provides custom code that executes externally to the search service. It's accessed through a URI. Custom code is often made available through an Azure function app. To attach an open-source or third-party vectorization model, use a custom skill.
 

@@ -9,13 +9,13 @@ ms.author: beloh
 ms.service: cognitive-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 02/22/2024
+ms.topic: concept-article
+ms.date: 09/19/2024
 ---
 
 # Lucene query syntax in Azure AI Search
 
-When creating queries in Azure AI Search, you can opt for the full [Lucene Query Parser](https://lucene.apache.org/core/6_6_1/queryparser/org/apache/lucene/queryparser/classic/package-summary.html) syntax for specialized query forms: wildcard, fuzzy search, proximity search, regular expressions. Much of the Lucene Query Parser syntax is [implemented intact in Azure AI Search](search-lucene-query-architecture.md), except for *range searches, which are constructed through **`$filter`** expressions. 
+When creating queries in Azure AI Search, you can opt for the full [Lucene Query Parser](https://lucene.apache.org/core/6_6_1/queryparser/org/apache/lucene/queryparser/classic/package-summary.html) syntax for specialized query forms: wildcard, fuzzy search, proximity search, regular expressions. Much of the Lucene Query Parser syntax is [implemented intact in Azure AI Search](search-lucene-query-architecture.md), except for *range searches*, which are constructed through **`$filter`** expressions. 
 
 To use full Lucene syntax, set the queryType to `full` and pass in a query expression patterned for wildcard, fuzzy search, or one of the other query forms supported by the full syntax. In REST, query expressions are provided in the **`search`** parameter of a [Search Documents (REST API)](/rest/api/searchservice/documents/search-post) request.
 

@@ -12,7 +12,7 @@ ms.custom:
   - devx-track-python
   - ignite-2023
 ms.topic: conceptual
-ms.date: 08/16/2024
+ms.date: 09/19/2024
 ---
 
 # Python samples for Azure AI Search
@@ -28,46 +28,26 @@ Learn about the Python code samples that demonstrate the functionality and workf
 
 ## SDK samples
 
-Code samples from the Azure SDK development team demonstrate API usage. You can find these samples in [**azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples**](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples) on GitHub.
-
-| Samples | Description |
-|---------|-------------|
-| [Authenticate](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_authentication.py) | Demonstrates how to configure a client and authenticate to the service. | 
-| [Index Create-Read-Update-Delete operations](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_index_crud_operations.py) | Demonstrates how to create, update, get, list, and delete [search indexes](search-what-is-an-index.md). |
-| [Indexer Create-Read-Update-Delete operations](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_indexers_operations.py) | Demonstrates how to create, update, get, list, reset, and delete [indexers](search-indexer-overview.md). |
-| [Search indexer data sources](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_indexer_datasource_skillset.py) | Demonstrates how to create, update, get, list, and delete indexer data sources, required for indexer-based indexing of [supported Azure data sources](search-indexer-overview.md#supported-data-sources). |
-| [Synonyms](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_synonym_map_operations.py) | Demonstrates how to create, update, get, list, and delete [synonym maps](search-synonyms.md).  |
-| [Load documents](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_crud_operations.py) | Demonstrates how to upload or merge documents into an index in a [data import](search-what-is-data-import.md) operation. |
-| [Simple query](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_simple_query.py) | Demonstrates how to set up a [basic query](search-query-overview.md). |
-| [Filter query](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_filter_query.py) | Demonstrates setting up a [filter expression](search-filters.md). |
-| [Facet query](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_facet_query.py) | Demonstrates working with [facets](search-faceted-navigation.md). |
-| [Semantic ranking sample](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_semantic_search.py) | Shows you how to configure semantic ranking in an index and invoke semantic queries. |
-| [Vector search](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_vector_search.py) | Demonstrates how to get embeddings from a description field and then send vector queries against the data. |
+[**azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples**](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples) on GitHub provides code samples from the Azure SDK development team, demonstrating API usage.
 
 ## Doc samples
 
 Code samples from the Azure AI Search team demonstrate features and workflows. Many of these samples are referenced in tutorials, quickstarts, and how-to articles. You can find these samples in [**Azure-Samples/azure-search-python-samples**](https://github.com/Azure-Samples/azure-search-python-samples) on GitHub.
 
 | Samples | Article |
 |---------|---------|
-| [quickstart](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart) | Source code for the Python portion of [Quickstart: Full text search using the Azure SDKs](search-get-started-text.md). This article covers the basic workflow for creating, loading, and querying a search index using sample data. |
-| [quickstart-semantic-search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search) | Source code for the Python portion of [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md). It shows the index schema and query request for invoking semantic ranking. |
+| [Tutorial-RAG](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Tutorial-RAG) | Source code for the Python portion of [How to build a RAG solution using Azure AI Search](tutorial-rag-build-solution.md).|
+| [Quickstart](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart) | Source code for the Python portion of [Quickstart: Full text search using the Azure SDKs](search-get-started-text.md). This article covers the basic workflow for creating, loading, and querying a search index using sample data. |
+| [Quickstart-RAG](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-RAG) | Source code for the Python portion of [Quickstart: Generative search (RAG) with grounding data from Azure AI Search](search-get-started-rag.md). |
+| [Quickstart-Semantic-Search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search) | Source code for the Python portion of [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md). It shows the index schema and query request for invoking semantic ranking. |
 | [bulk-insert](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/bulk-insert) | Source code for the Python example of how to [use the push APIs](search-how-to-load-search-index.md) to upload and index documents. |
-| [azure-functions](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/azure-function-search) | Source code for the Python example of an Azure function that sends queries to a search service. You can substitute this Python version of the `api` code used in the [Add search to web sites](tutorial-csharp-overview.md) C# sample. |
+| [azure-function-search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/azure-function-search) | Source code for the Python example of an Azure function that sends queries to a search service. You can substitute this Python version of the `api` code used in the [Add search to web sites](tutorial-csharp-overview.md) C# sample. |
 
 ## Demos
 
-A demo repo provides proof-of-concept source code for examples or scenarios shown in demonstrations. Demo solutions aren't designed for adaptation by customers.
+[**azure-search-vector-samples**](https://github.com/Azure/azure-search-vector-samples/blob/main/README.md) on GitHub provides a comprehensive collection of samples for vector search scenarios, organized by scenario or technology.
 
-| Repository | Description |
-|------------|-------------|
-| [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 index. |
-| [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 indexing workflow to include integrated data chunking and embedding. |
-| [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). |
+We also recommend [**azure-search-openai-demo**](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/README.md). This is a ChatGPT-like experience over 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
 

@@ -11,7 +11,7 @@ ms.custom:
   - references_regions
   - build-2024
 ms.topic: conceptual
-ms.date: 08/22/2024
+ms.date: 09/19/2024
 ---
 
 # Create an Azure AI Search service in the portal
@@ -87,25 +87,25 @@ Service name requirements:
 ## Choose a region
 
 > [!IMPORTANT]
-> Due to high demand, Azure AI Search is currently unavailable for new instances in West Europe. If you don't immediately need semantic ranker or skillsets, choose Sweden Central because it has the most data center capacity. Otherwise, North Europe is another option. Currently, there are also capacity constraints for Basic and Standard (S1) tiers within a given region.
+> Due to high demand, Azure AI Search is currently unavailable for new instances in some regions. 
 
 If you use multiple Azure services, putting all of them in the same region minimizes or voids bandwidth charges. There are no charges for data egress among same-region services.
 
 Generally, choose a region near you, unless the following considerations apply:
 
-+ Your nearest region is capacity constrained. West Europe is at capacity and unavailable for new instances. Other regions are [at capacity for specific tiers](search-sku-tier.md#region-availability-by-tier). One advantage to using the Azure portal for resource setup is that it provides only those regions and tiers that are available. You can't select regions or tiers that are unavailable.
++ Your nearest region is capacity constrained. For example, West Europe is at capacity and unavailable for new instances. Other regions are [at capacity for specific tiers](search-sku-tier.md#region-availability-by-tier). One advantage to using the Azure portal for resource setup is that it provides only those regions and tiers that are available.
 
 + You want to use integrated data chunking and vectorization or built-in skills for AI enrichment. Azure OpenAI and Azure AI services multiservice accounts must be in the same region as Azure AI Search for integration purposes. [Choose a region](search-region-support.md) that provides all necessary resources.
 
 + You want to use Azure Storage for indexer-based indexing or you need to store application data that isn't in an index. Debug session state, enrichment caches, and knowledge stores are Azure AI Search features that have a dependency on Azure Storage. The region you choose for Azure Storage has implications for network security. Specifically, if you're setting up a firewall, you should place the resources in separate regions. For more information, see [Outbound connections from Azure AI Search to Azure Storage](search-indexer-securing-resources.md).
 
 Here's a checklist for choosing a region:
 
-1. Is Azure AI Search available in a nearby region? Check the [supported regions list](search-region-support.md). Capacity-constrained regions are indicated in the footnotes.
+1. Is Azure AI Search available in a nearby region? Check the [supported regions list](search-region-support.md).
 
 1. Do you know which tier you want to use? Tiers are covered in the next step. Check [region availability by tier](search-sku-tier.md#region-availability-by-tier) to determine if you can create a search service at the desired tier in your region of choice.
 
-1. Do you need [AI enrichment](cognitive-search-concept-intro.md) or [integrated data chunking and vectorization](vector-search-integrated-vectorization.md)? Verify that Azure OpenAI and Azure AI services are [offered in the same region](search-region-support.md) as Azure AI Search. 
+1. Do you need [AI enrichment](cognitive-search-concept-intro.md) or [integrated data chunking and vectorization](vector-search-integrated-vectorization.md)? Verify that Azure OpenAI and Azure AI multiservice are [offered in the same region](search-region-support.md) as Azure AI Search. 
 
    Be aware that Azure AI Vision multimodal embeddings API, used for [integrated image vectorization](search-get-started-portal-image-search.md), must be accessed through an Azure AI multiservice account, but is available in a [smaller subset of regions](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
 

@@ -7,18 +7,20 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
-ms.topic: conceptual
-ms.date: 02/22/2024
+ms.topic: concept-article
+ms.date: 09/19/2024
 ms.custom:
   - devx-track-csharp
   - ignite-2023
 ---
 
-# Filters in text queries
+# Filters in keyword search
 
-A *filter* provides value-based criteria for including or excluding content before query execution. For example, including or excluding documents based on dates, locations, or language. Filters are specified on individual fields. A field definition must be attributed as "filterable" if you want to use it in filter expressions.
+A *filter* provides value-based criteria for including or excluding content before query execution for keyword search, or before or after query execution for vector search. Filters are applied to nonvector fields, but can be used in vector search if documents include nonvector fields. For example, for indexes organized around chunked content, you might have parent-level fields or metadata fields that can be filtered.
 
-A filter is specified using [OData filter expression syntax](search-query-odata-filter.md). In contrast with full text search, a filter succeeds only if the match is exact.
+This article explains filtering for keyword search. For more information about vectors, see [Add a filter in a vector query](vector-search-filters.md).
+
+A filter is specified using [OData filter expression syntax](search-query-odata-filter.md). In contrast with keyword and vector search, a filter succeeds only if the match is exact.
 
 ## When to use a filter
 
@@ -42,7 +44,9 @@ At query time, a filter parser accepts criteria as input, converts the expressio
 
 Filtering occurs in tandem with search, qualifying which documents to include in downstream processing for document retrieval and relevance scoring. When paired with a search string, the filter effectively reduces the recall set of the subsequent search operation. When used alone (for example, when the query string is empty where `search=*`), the filter criteria is the sole input. 
 
-## Defining filters
+## How filters are defined
+
+Filters apply to alphanumeric content on fields that are attributed as `filterable`.
 
 Filters are OData expressions, articulated in the [filter syntax](search-query-odata-filter.md) supported by Azure AI Search.
 
@@ -112,7 +116,7 @@ The following examples illustrate several usage patterns for filter scenarios. F
 
 In the REST API, filterable is *on* by default for simple fields. Filterable fields increase index size; be sure to set `"filterable": false` for fields that you don't plan to actually use in a filter. For more information about settings for field definitions, see [Create Index](/rest/api/searchservice/indexes/create).
 
-In the .NET SDK, the filterable is *off* by default. You can make a field filterable by setting the [IsFilterable property](/dotnet/api/azure.search.documents.indexes.models.searchfield.isfilterable) of the corresponding [SearchField](/dotnet/api/azure.search.documents.indexes.models.searchfield) object to `true`. In the next example, the attribute is set on the `Rating` property of a model class that maps to the index definition.
+In the Azure SDKs, filterable is *off* by default. You can make a field filterable by setting the [IsFilterable property](/dotnet/api/azure.search.documents.indexes.models.searchfield.isfilterable) of the corresponding [SearchField](/dotnet/api/azure.search.documents.indexes.models.searchfield) object to `true`. In the next example, the attribute is set on the `Rating` property of a model class that maps to the index definition.
 
 ```csharp
 [SearchField(IsFilterable = true, IsSortable = true, IsFacetable = true)]

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: conceptual
-ms.date: 09/04/2024
+ms.date: 09/19/2024
 ms.custom:
   - references_regions
   - build-2024
@@ -78,55 +78,36 @@ When estimating document size, remember to consider only those fields that add v
 
 ## Vector index size limits
 
-When you index documents with vector fields, Azure AI Search constructs internal vector indexes using the algorithm parameters you provide. The size of these vector indexes is restricted by the memory reserved for vector search for your service's tier (or `SKU`).
+When you index documents with vector fields, Azure AI Search constructs internal vector indexes using the algorithm parameters you provide. The size of these vector indexes is restricted by the memory reserved for vector search for your service's tier (or `SKU`). For guidance on managing and maximizing vector storage, see [Vector index size and staying under limits](vector-search-index-size.md).
 
-The service enforces a vector index size quota **for every partition** in your search service. Each extra partition increases the available vector index size quota. This quota is a hard limit to ensure your service remains healthy, which means that further indexing attempts once the limit is exceeded results in failure. You can resume indexing once you free up available quota by either deleting some vector documents or by scaling up in partitions.
+Vector limits vary by:
 
-Vector limits vary by service creation date and tier.
++ [service creation date](vector-search-index-size.md#how-to-check-service-creation-date)
++ [region](search-region-support.md)
 
-+ To check the age of your search service or learn more about vector indexes, see [Vector index size and staying under limits](vector-search-index-size.md).
-
-+ To view the vector quota in effect for your search service, use [GET Service Statistics](/rest/api/searchservice/get-service-statistics/get-service-statistics), or check the **Properties** and **Usage** tabs for your search service in the Azure portal.
-
-#### Vector quota per partition (GB)
+Higher vector limits from April 2024 onwards exist on *new search services* in regions providing the extra capacity, which is most of them.
 
 This table shows the progression of vector quota increases in GB over time. The quota is per partition, so if you scale a new Standard (S1) service to 6 partitions, total vector quota is 35 multiplied by 6.
 
 | Service creation date |Basic | S1| S2 | S3/HD | L1 | L2 |
 |-----------------------|------|---|----|----|----|----|
 |**Before July 1, 2023** <sup>1</sup> | 0.5 | 1 | 6 | 12 | 12 | 36 |
 | **July 1, 2023 through April 3, 2024** <sup>2</sup>| 1  | 3 | 12 | 36 | 12 | 36 |
-|**April 3, 2024 through May 17, 2024** <sup>3</sup> | 5  | 35 | 100 | 200 | 12 | 36 |
-|**After May 17, 2024** <sup>4</sup> | 5  | 35 | 150 | 300 | 150 | 300 |
+|**April 3, 2024 through May 17, 2024** <sup>3</sup> | **5**  | **35** | **150** | **300** | 12 | 36 |
+|**After May 17, 2024** <sup>4</sup> | 5  | 35 | 150 | 300 | **150** | **300** |
 
 <sup>1</sup> Initial vector limits during early preview.
 
 <sup>2</sup> Vector limits during the later preview period. Three regions didn't have the higher limits: Germany West Central, West India, Qatar Central.
 
-<sup>3</sup> Higher vector quota based on the larger partitions for supported tiers and regions.
+<sup>3</sup> Higher vector quota based on the larger partitions for supported tiers and regions. 
 
 <sup>4</sup> Higher vector quota for more tiers and regions based on partition size updates.
 
-#### Partition limits (GB)
-
-This table repeats [partition storage limits](#service-limits) for context. The table shows the progression of storage quota increases in GB over time. Vector quota is per partition, so the more significant increases in vector quota that occurred starting in April 2024 correspond to the increases in per-partition storage occuring at the same time. 
-
-Higher capacity partitions were brought online starting in April 2024.
-
-| Service creation date |Basic | S1| S2 | S3/HD | L1 | L2 |
-|-----------------------|------|---|----|----|----|----|
-|**Before July 1, 2023** <sup>1</sup> | 2  | 25 | 100 | 200 | 1,024 | 2,048 |
-|**July 1, 2023 through April 3, 2024** <sup>2</sup>| 2  | 25 | 100 | 200 | 1,024 | 2,048 |
-|**April 3, 2024 through May 17, 2024** <sup>3</sup> | 15  | 160 | 512 | 1,024 | 1,024 | 2,048 |
-|**After May 17, 2024** <sup>4</sup> | 15  | 160 | 512 | 1,024 | 2,048 | 4,096 |
-
-<sup>1</sup> Partition sizes during early preview.
-
-<sup>2</sup> No change during the later preview period.
-
-<sup>3</sup> Higher capacity storage for Basic, S1, S2, S3 in these regions. **Americas**: Brazil South​, Canada Central​, Canada East​​, East US​, East US 2, ​Central US​, North Central US​, South Central US​, West US​, West US 2​, West US 3​, West Central US. **Europe**: France Central​. Italy North​​, North Europe​​, Norway East, Poland Central​​, Switzerland North​, Sweden Central​, UK South​, UK West​. **Middle East**:  UAE North. **Africa**: South Africa North. **Asia Pacific**: Australia East​, Australia Southeast​​, Central India, Jio India West​, East Asia, Southeast Asia​, Japan East, Japan West​, Korea Central, Korea South​.
+The service enforces a vector index size quota *for every partition* in your search service. Each extra partition increases the available vector index size quota. This quota is a hard limit to ensure your service remains healthy, which means that further indexing attempts once the limit is exceeded results in failure. You can resume indexing once you free up available quota by either deleting some vector documents or by scaling up in partitions.
 
-<sup>4</sup> Higher capacity storage for more tiers and more regions. **Europe**: Germany North​, Germany West Central, Switzerland West​. **Azure Government**: Texas, Arizona, Virginia. **Africa**: South Africa North​. **Asia Pacific**: China North 3, China East 3.
+> [!IMPORTANT]
+> Higher vector limits are tied to larger partition sizes. Regions that run on older infrastructure are subject to the July-April limits. Review the [regions list](search-region-support.md) for status on partition storage limits.
 
 ## Indexer limits
 

@@ -1,5 +1,5 @@
 ---
-title: Feature availability across clouds regions
+title: Supported regions
 titleSuffix: Azure AI Search
 description: Shows supported regions and feature availability across regions for Azure AI Search.
 
@@ -9,13 +9,13 @@ ms.author: heidist
 ms.service: cognitive-search
 ms.topic: conceptual
 ms.custom: references_regions
-ms.date: 09/03/2024
+ms.date: 09/19/2024
 
 ---
 
-# Azure AI Search feature availability across cloud regions
+# Azure AI Search regions list
 
-This article identifies the cloud regions in which Azure AI Search is available. It also lists which premium features are available in each region.
+This article identifies the cloud regions in which Azure AI Search is available. It also lists which premium features are available in each region. 
 
 ## Features subject to regional availability
 
@@ -57,37 +57,39 @@ You can create an Azure AI Search resource in any of the following Azure public
 | Canada East​​ ​ |  | ✅ | |
 | East US​ | ✅ | ✅ | ✅ |
 | East US 2 ​ | ✅ | ✅ | ✅ |
-| ​Central US​ <sup>1</sup>​ | ✅ | ✅ | ✅ |
+| ​Central US​​ <sup>1</sup> | ✅ | ✅ | ✅ |
 | North Central US​ ​ | ✅ | ✅ | |
-| South Central US​ <sup>1</sup>​ | ✅ | ✅ | ✅ |
+| South Central US​ <sup>2</sup>​ | ✅ | ✅ | ✅ |
 | West US​ ​ | ✅ | ✅ | |
-| West US 2​ <sup>1</sup>​ | ✅ | ✅ | ✅ |
-| West US 3​ <sup>1</sup>​ | ✅ | ✅ |✅ |
+| West US 2​ ​ | ✅ | ✅ | ✅ |
+| West US 3​ <sup>2</sup>​ | ✅ | ✅ |✅ |
 | West Central US​ ​ | ✅ | ✅ | |
 
-<sup>1</sup> Currently, this region is at capacity for Basic and Standard (S1) tiers. Choose a higher tier or a different region.
+<sup>1</sup> This region has capacity, but some tiers are [not available](search-sku-tier.md#region-availability-by-tier). 
+
+<sup>2</sup> Currently, this region is at full capacity and not accepting new search services. 
 
 ### Europe
 
 | Region | AI integration | Semantic ranking | Availability zones |
 |--|--|--|--|
-| North Europe​​ <sup>1</sup>| ✅ | ✅ | ✅ |
-| West Europe​​ <sup>2</sup>| ✅ | ✅ | ✅ |
+| North Europe​​ | ✅ | ✅ | ✅ |
+| West Europe​​ <sup>1, 2</sup>| ✅ | ✅ | ✅ |
 | France Central​​ | ✅ | ✅ | ✅ |
 | Germany West Central​ ​| ✅ |  | ✅ |
 | Italy North​​ |  |  | ✅ |
 | Norway East​​ | ✅ |  | ✅ |
 | Poland Central​​ |  |  |  |
-| Spain Central |  |  | ✅  |
+| Spain Central <sup>2</sup> |  |  | ✅  |
 | Sweden Central​​ | ✅ |  | ✅ |
 | Switzerland North​ | ✅ | ✅ | ✅ |
 | Switzerland West​ | ✅ | ✅ | ✅ |
 | UK South​ | ✅ | ✅ | ✅ |
 | UK West​ ​|  | ✅ | |
 
-<sup>1</sup> Currently, this region is at capacity for Basic and Standard (S1) tiers. Choose a higher tier or a different region.
+<sup>1</sup> Currently, this region is at full capacity and not accepting new search services.
 
-<sup>2</sup> West Europe is at capacity for all tiers and isn't accepting any new search services. Additionally, the clusters used to run Azure AI Search don't have the [higher capacity partitions](search-limits-quotas-capacity.md#service-limits) that were brought online in April 2024. This means that search services deployed in this region have lower storage and computing capability.
+<sup>2</sup> This region runs on older infrastructure that has lower storage limits per partition at every tier. Choose a different region if you want [higher limits](search-limits-quotas-capacity.md#service-limits).
 
 ### Middle East
 
@@ -97,9 +99,9 @@ You can create an Azure AI Search resource in any of the following Azure public
 | Qatar Central​ <sup>1, 2</sup> |  |  | ✅ |
 | UAE North​​ | ✅ |  | ✅ |
 
-<sup>1</sup> Currently, this region is at capacity for Basic and Standard (S1) tiers. Choose a higher tier or a different region.
+<sup>1</sup> Currently, this region is at full capacity and not accepting new search services.
 
-<sup>2</sup> These regions run on older infrastructure that has lower capacity per partition at every tier. Choose a different region if you want [higher capacity](search-limits-quotas-capacity.md#service-limits).
+<sup>2</sup> This region runs on older infrastructure that has lower storage limits per partition at every tier. Choose a different region if you want [higher limits](search-limits-quotas-capacity.md#service-limits).
 
 ### Africa
 
@@ -123,51 +125,37 @@ You can create an Azure AI Search resource in any of the following Azure public
 | Korea Central | ✅ | ✅ | ✅ |
 | Korea South​ ​ |  | ✅ |  |
 
-<sup>1</sup> Currently, this region is at capacity for Basic and Standard (S1) tiers. Choose a higher tier or a different region.
+<sup>1</sup> This region has capacity, but some tiers are [not available](search-sku-tier.md#region-availability-by-tier). 
 
-<sup>2</sup> These regions run on older infrastructure that has lower capacity per partition at every tier. Choose a different region if you want [higher capacity](search-limits-quotas-capacity.md#service-limits).
+<sup>2</sup> This region runs on older infrastructure that has lower storage limits per partition at every tier. Choose a different region if you want [higher limits](search-limits-quotas-capacity.md#service-limits).
 
 ## Azure Government regions
 
-All of these regions support [higher capacity tiers](search-limits-quotas-capacity.md#service-limits). 
-
-None of these regions support Azure [role-based access for data plane operations](search-security-rbac.md). You must use key-based authentication for indexing and query workloads.
-
 | Region | AI integration | Semantic ranking | Availability zones |
 |--|--|--|--|
 | Arizona | ✅ | ✅  | |
 | Texas |  |  |  |
-| Virginia | ✅ | ✅  | ✅ |
+| Virginia <sup>1</sup> | ✅ | ✅  | ✅ |
 
-## Azure operated by 21Vianet
+<sup>1</sup> Currently, this region is at full capacity and not accepting new search services.
 
-You can install Azure AI Search in any of the following regions. If you need semantic ranking or AI enrichment, choose a region that provides the feature.
+## Azure operated by 21Vianet
 
 | Region | AI integration | Semantic ranking | Availability zones |
 |--|--|--|--|
-| China East <sup>1</sup> |  |  |  |
+| China East |  |  |  |
 | China East 2 <sup>1</sup> | ✅  | | |
 | China East 3 |  |  |  |
-| China North <sup>1</sup> |  |  | |
+| China North |  |  | |
 | China North 2 <sup>1</sup> |  |  | |
 | China North 3 | | ✅ | ✅ |
 
-<sup>1</sup> These regions run on older infrastructure that has lower capacity per partition at every tier. Choose a different region if you want [higher capacity](search-limits-quotas-capacity.md#service-limits).
-
-<!-- ## Early Update Access Program (EUAP)
-
-These regions
-
-| Region | AI enrichment | Semantic ranking | Availability zones |
-|--|--|--|--|
-| Central US EUAP​ <sup>1</sup> | | ✅ | |
-| East US 2 EUAP ​ | | ✅ | |
-
-<sup>1</sup> This region runs on older infrastructure that has lower capacity per partition at every tier. You can't create a search service with [higher capacity](search-limits-quotas-capacity.md#service-limits) in this region. -->
+<sup>1</sup> This region runs on older infrastructure that has lower storage limits per partition at every tier. Choose a different region if you want [higher limits](search-limits-quotas-capacity.md#service-limits).
 
 ## See also
 
 - [Azure AI Studio region availability](/azure/ai-studio/reference/region-support)
 - [Azure OpenAI model region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)
+- [Azure AI Vision region list](/azure/ai-services/computer-vision/overview-image-analysis#region-availability)
 - [Availability zone region availability](/azure/reliability/availability-zones-service-support#azure-regions-with-availability-zone-support)
 - [Azure product by region page](https://azure.microsoft.com/explore/global-infrastructure/products-by-region/?products=search)
\ No newline at end of file

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: conceptual
-ms.date: 08/22/2024
+ms.date: 09/19/2024
 
 ---
 
@@ -53,7 +53,9 @@ You can find out more about the various tiers on the [pricing page](https://azur
 
 ## Region availability by tier
 
-Currently, several regions are at capacity for Basic and Standard (S1) tiers and can't be used for new search services. If you use the Azure portal to create a search service, the portal excludes any region-tier combinations that aren't available.
+The supported [regions list](search-region-support.md) provides the locations where Azure AI Search is offered.
+
+Currently, several regions are at capacity for specific tiers and can't be used for new search services. If you use the Azure portal to create a search service, the portal excludes any region-tier combinations that aren't available.
 
 | Region | Disabled tier (SKU) due to over-capacity |
 |--------|------------------------------------------|
@@ -93,7 +95,7 @@ Tiers determine the  maximum storage of the service itself, plus the maximum num
 Tier pricing includes details about per-partition storage that ranges from 15 GB for Basic, up to 2 TB for Storage Optimized (L2) tiers. Other hardware characteristics, such as speed of operations, latency, and transfer rates, aren't published, but tiers that are designed for specific solution architectures are built on hardware that has the features to support those scenarios. For more information about partitions, see [Estimate and manage capacity](search-capacity-planning.md) and [Reliability in Azure AI Search](search-reliability.md).
 
 > [!NOTE]
-> Higher capacity partitions became available in selected regions starting in April 2024. A second wave of higher capacity partitions released in May 2024. If you're using an older search service, consider creating a new search service to benefit from more capacity at the same billing rate. For more information, see [Service limits](search-limits-quotas-capacity.md#service-limits)
+> Higher capacity partitions became available in selected regions starting in April 2024. A second wave of higher capacity partitions released in May 2024. If you're using an older search service, consider creating a new search service to benefit from more capacity at the same billing rate. For more information, see [Service limits](search-limits-quotas-capacity.md#service-limits). To check the age of your search service, see [How to check service creation date](vector-search-index-size.md#how-to-check-service-creation-date).
 
 ## Billing rates
 

@@ -9,17 +9,18 @@ ms.author: heidist
 ms.service: cognitive-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 01/17/2024
+ms.topic: concept-article
+ms.date: 09/17/2024
 ---
+
 # Data import in Azure AI Search
 
 In Azure AI Search, queries execute over user-owned content that's loaded into a [search index](search-what-is-an-index.md). This article describes the two basic workflows for populating an index: *push* your data into the index programmatically, or *pull* in the data using a [search indexer](search-indexer-overview.md).
 
 Both approaches load documents from an external data source. Although you can create an empty index, it's not queryable until you add the content.
 
 > [!NOTE]
-> If [AI enrichment](cognitive-search-concept-intro.md) is a solution requirement, you must use the pull model (indexers) to load an index. Skillsets are attached to an indexer and don't run independently.
+> If [AI enrichment](cognitive-search-concept-intro.md) or [integrated vectorization](vector-search-integrated-vectorization.md) are solution requirements, you must use the pull model (indexers) to load an index. Skillsets are attached to indexers and don't run independently.
 
 ## Pushing data to an index
 
@@ -77,7 +78,8 @@ The pull model uses *indexers* connecting to a supported data source, automatica
 + [Azure Files (preview)](search-file-storage-integration.md)
 + [Azure Cosmos DB](search-howto-index-cosmosdb.md)
 + [Azure SQL Database, SQL Managed Instance, and SQL Server on Azure VMs](search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md)
-+ [SharePoint in Microsoft 365 (preview)](search-howto-index-sharepoint-online.md)
++ [OneLake files and shortcuts](search-how-to-index-onelake-files.md)
++ [SharePoint Online (preview)](search-howto-index-sharepoint-online.md)
 
 You can use third-party connectors, developed and maintained by Microsoft partners. For more information and links, see [Data source gallery](search-data-sources-gallery.md).
 
@@ -87,7 +89,7 @@ Indexers connect an index to a data source (usually a table, view, or equivalent
 
 Use the following tools and APIs for indexer-based indexing:
 
-+ [Import data wizard in the Azure portal](search-import-data-portal.md)
++ [Import data wizard or Import and vectorize data wizard](search-import-data-portal.md)
 + REST APIs: [Create Indexer (REST)](/rest/api/searchservice/indexers/create), [Create Data Source (REST)](/rest/api/searchservice/data-sources/create), [Create Index (REST)](/rest/api/searchservice/indexes/create)
 + Azure SDK for .NET: [SearchIndexer](/dotnet/api/azure.search.documents.indexes.models.searchindexer), [SearchIndexerDataSourceConnection](/dotnet/api/azure.search.documents.indexes.models.searchindexerdatasourceconnection), [SearchIndex](/dotnet/api/azure.search.documents.indexes.models.searchindex),
 + Azure SDK for Python: [SearchIndexer](/python/api/azure-search-documents/azure.search.documents.indexes.models.searchindexer), [SearchIndexerDataSourceConnection](/python/api/azure-search-documents/azure.search.documents.indexes.models.searchindexerdatasourceconnection), [SearchIndex](/python/api/azure-search-documents/azure.search.documents.indexes.models.searchindex),

@@ -9,12 +9,12 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 08/05/2024
+ms.date: 09/19/2024
 ---
 
 # Generate embeddings for search queries and documents
 
-Azure AI Search doesn't host vectorization models, so one of your challenges is creating embeddings for query inputs and outputs. You can use any embedding model, but this article assumes Azure OpenAI embedding models.
+Azure AI Search doesn't host vectorization models, so one of your challenges is creating embeddings for query inputs and outputs. You can use any supported embedding model, but this article assumes Azure OpenAI embedding models for the steps.
 
 We recommend [integrated vectorization](vector-search-integrated-vectorization.md), which provides built-in data chunking and vectorization. Integrated vectorization takes a dependency on indexers, skillsets, and built-in or custom skills that point to a model that executes externally from Azure AI Search.
 
@@ -30,13 +30,13 @@ If you want to handle data chunking and vectorization yourself, we provide demos
 
 ## Create resources in the same region
 
-If you want resources in the same region, start with:
+Integrated vectorization requires resources to be in the same region:
 
-1. [Check regions for a text embedding model](/azure/ai-services/openai/concepts/models#standard-and-global-standard-deployment-model-quota).
+1. [Check regions for a text embedding model](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability).
 
 1. [Find the same region for Azure AI Search](search-region-support.md). 
 
-1. To support hybrid queries that include [semantic ranking](semantic-how-to-query-request.md), or if you want to try machine learning model integration using a [custom skill](cognitive-search-custom-skill-interface.md) in an [AI enrichment pipeline](cognitive-search-concept-intro.md), note the Azure AI Search regions that provide those features.
+1. To support hybrid queries that include [semantic ranking](semantic-how-to-query-request.md), or if you want to try machine learning model integration using a [custom skill](cognitive-search-custom-skill-interface.md) in an [AI enrichment pipeline](cognitive-search-concept-intro.md), select an Azure AI Search region that provides those features.
 
 ## Generate an embedding for an improvised query
 
@@ -64,16 +64,23 @@ Output is a vector array of 1,536 dimensions.
 
 ## Tips and recommendations for embedding model integration
 
-+ **Identify use cases**: Evaluate the specific use cases where embedding model integration for vector search features can add value to your search solution. This can include matching image content with text content, cross-lingual searches, or finding similar documents.
++ **Identify use cases**: Evaluate the specific use cases where embedding model integration for vector search features can add value to your search solution. This can include multimodal or matching image content with text content, multilingual search, or similarity search.
+
 + **Design a chunking strategy**: Embedding models have limits on the number of tokens they can accept, which introduces a data chunking requirement for large files. For more information, see [Chunk large documents for vector search solutions](vector-search-how-to-chunk-documents.md).
-+ **Optimize cost and performance**: Vector search can be resource-intensive and is subject to maximum limits, so consider only vectorizing the fields that contain semantic meaning. [Reduce vector size]() so that you can store more vectors for the same price.
+
++ **Optimize cost and performance**: Vector search can be resource-intensive and is subject to maximum limits, so consider only vectorizing the fields that contain semantic meaning. [Reduce vector size](vector-search-how-to-configure-compression-storage.md) so that you can store more vectors for the same price.
+
 + **Choose the right embedding model:** Select an appropriate model for your specific use case, such as word embeddings for text-based searches or image embeddings for visual searches. Consider using pretrained models like **text-embedding-ada-002** from OpenAI or **Image Retrieval** REST API from [Azure AI Computer Vision](/azure/ai-services/computer-vision/how-to/image-retrieval).
+
 + **Normalize Vector lengths**: Ensure that the vector lengths are normalized before storing them in the search index to improve the accuracy and performance of similarity search. Most pretrained models already are normalized but not all. 
+
 + **Fine-tune the model**: If needed, fine-tune the selected model on your domain-specific data to improve its performance and relevance to your search application.
+
 + **Test and iterate**: Continuously test and refine your embedding model integration to achieve the desired search performance and user satisfaction.
 
 ## Next steps
 
 + [Understanding embeddings in Azure OpenAI Service](/azure/ai-services/openai/concepts/understand-embeddings)
 + [Learn how to generate embeddings](/azure/ai-services/openai/how-to/embeddings?tabs=console)
 + [Tutorial: Explore Azure OpenAI Service embeddings and document search](/azure/ai-services/openai/tutorials/embeddings?tabs=command-line)
++ [Tutorial: Choose a model (RAG solutions in Azure AI Search)](tutorial-rag-build-solution-models.md)

@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - build-2024
 ms.topic: conceptual
-ms.date: 08/05/2024
+ms.date: 09/19/2024
 ---
 
 # Vector index size and staying under limits

@@ -8,8 +8,8 @@ ms.author: jlembicz
 ms.service: cognitive-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 08/05/2024
+ms.topic: concept-article
+ms.date: 09/19/2024
 ---
 
 # Relevance in vector search

@@ -8,15 +8,15 @@ ms.author: robertlee
 ms.service: cognitive-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 02/14/2024
+ms.topic: concept-article
+ms.date: 09/19/2024
 ---
 
 # Vector storage in Azure AI Search
 
 Azure AI Search provides vector storage and configurations for [vector search](vector-search-overview.md) and [hybrid search](hybrid-search-overview.md). Support is implemented at the field level, which means you can combine vector and nonvector fields in the same search corpus.
 
-Vectors are stored in a search index. Use the [Create Index REST API](/rest/api/searchservice/indexes/create-or-update) or an equivalent Azure SDK method to [create the vector store](vector-search-how-to-create-index.md).
+Vectors are stored in a search index. Use the [Create Index REST API](/rest/api/searchservice/indexes/create) or an equivalent Azure SDK method to [create the vector store](vector-search-how-to-create-index.md).
 
 Considerations for vector storage include the following points:
 
@@ -54,7 +54,7 @@ Vector fields are distinguished by their data type and vector-specific propertie
 }
 ```
 
-Vector fields are of type `Collection(Edm.Single)`. 
+Vector fields have [specific data types](/rest/api/searchservice/supported-data-types#edm-data-types-for-vector-fields). Currently, `Collection(Edm.Single)` is the most common, but using narrow data types can save on storage.
 
 Vector fields must be searchable and retrievable, but they can't be filterable, facetable, or sortable, or have analyzers, normalizers, or synonym map assignments. 
 
@@ -146,7 +146,7 @@ Here's a screenshot showing search results in [Search Explorer](search-explorer.
 
 ## Physical structure and size
 
-In Azure AI Search, the physical structure of an index is largely an internal implementation. You can access its schema, load and query its content, monitor its size, and manage capacity, but the clusters themselves (inverted and vector indexes), and other files and folders) are managed internally by Microsoft.
+In Azure AI Search, the physical structure of an index is largely an internal implementation. You can access its schema, load and query its content, monitor its size, and manage capacity, but the clusters themselves (inverted and vector indexes), and other files and folders are managed internally by Microsoft.
 
 The size and substance of an index is determined by: