@@ -7,20 +7,16 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 11/14/2025
+ms.date: 11/19/2025
 zone_pivot_groups: agentic-retrieval-pivots
 ---
 
 # Create a blob knowledge source from Azure Blob Storage and ADLS Gen2
 
-<!--
 ::: zone pivot="csharp"
-[!INCLUDE [C#](includes/how-tos/file-name.md)]
+[!INCLUDE [C#](includes/how-tos/agentic-knowledge-source-how-to-blob-csharp.md)]
 ::: zone-end
 
-Add C# to agentic-retrieval-pivots in zone-pivot-groups.yml, and then uncomment this section.
--->
-
 ::: zone pivot="python"
 [!INCLUDE [Python](includes/how-tos/agentic-knowledge-source-how-to-blob-python.md)]
 ::: zone-end

@@ -9,20 +9,17 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2025
 ms.topic: how-to
-ms.date: 11/14/2025
+ms.date: 11/20/2025
 zone_pivot_groups: agentic-retrieval-pivots
 ---
 
 # Create a OneLake knowledge source
 
-<!--
+
 ::: zone pivot="csharp"
-[!INCLUDE [C#](includes/how-tos/file-name.md)]
+[!INCLUDE [C#](includes/how-tos/agentic-knowledge-source-how-to-onelake-csharp.md)]
 ::: zone-end
 
-Add C# to agentic-retrieval-pivots in zone-pivot-groups.yml, and then uncomment this section.
--->
-
 ::: zone pivot="python"
 [!INCLUDE [Python](includes/how-tos/agentic-knowledge-source-how-to-onelake-python.md)]
 ::: zone-end

@@ -7,19 +7,16 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
+ms.date: 11/19/2025
 zone_pivot_groups: agentic-retrieval-pivots
 ---
 
 # Create a search index knowledge source
 
-<!--
 ::: zone pivot="csharp"
-[!INCLUDE [C#](includes/how-tos/file-name.md)]
+[!INCLUDE [C#](includes/how-tos/agentic-knowledge-source-how-to-search-index-csharp.md)]
 ::: zone-end
 
-Add C# to agentic-retrieval-pivots in zone-pivot-groups.yml, and then uncomment this section.
--->
-
 ::: zone pivot="python"
 [!INCLUDE [Python](includes/how-tos/agentic-knowledge-source-how-to-search-index-python.md)]
 ::: zone-end

@@ -7,19 +7,16 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
+ms.date: 11/20/2025
 zone_pivot_groups: agentic-retrieval-pivots
 ---
 
 # Create an indexed SharePoint knowledge source
 
-<!--
 ::: zone pivot="csharp"
-[!INCLUDE [C#](includes/how-tos/file-name.md)]
+[!INCLUDE [C#](includes/how-tos/agentic-knowledge-source-how-to-sharepoint-indexed-csharp.md)]
 ::: zone-end
 
-Add C# to agentic-retrieval-pivots in zone-pivot-groups.yml, and then uncomment this section.
--->
-
 ::: zone pivot="python"
 [!INCLUDE [Python](includes/how-tos/agentic-knowledge-source-how-to-sharepoint-indexed-python.md)]
 ::: zone-end

@@ -7,19 +7,16 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
+ms.date: 11/19/2025
 zone_pivot_groups: agentic-retrieval-pivots
 ---
 
 # Create a remote SharePoint knowledge source
 
-<!--
 ::: zone pivot="csharp"
-[!INCLUDE [C#](includes/how-tos/file-name.md)]
+[!INCLUDE [C#](includes/how-tos/agentic-knowledge-source-how-to-sharepoint-remote-csharp.md)]
 ::: zone-end
 
-Add C# to agentic-retrieval-pivots in zone-pivot-groups.yml, and then uncomment this section.
--->
-
 ::: zone pivot="python"
 [!INCLUDE [Python](includes/how-tos/agentic-knowledge-source-how-to-sharepoint-remote-python.md)]
 ::: zone-end

@@ -9,6 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2025
 ms.topic: how-to
+ms.date: 11/19/2025
 zone_pivot_groups: agentic-retrieval-pivots
 ---
 

@@ -9,19 +9,16 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2025
 ms.topic: how-to
+ms.date: 11/19/2025
 zone_pivot_groups: agentic-retrieval-pivots
 ---
 
 # Create a Web Knowledge Source resource
 
-<!--
 ::: zone pivot="csharp"
-[!INCLUDE [C#](includes/how-tos/file-name.md)]
+[!INCLUDE [C#](includes/how-tos/agentic-knowledge-source-how-to-web-csharp.md)]
 ::: zone-end
 
-Add C# to agentic-retrieval-pivots in zone-pivot-groups.yml, and then uncomment this section.
--->
-
 ::: zone pivot="python"
 [!INCLUDE [Python](includes/how-tos/agentic-knowledge-source-how-to-web-python.md)]
 ::: zone-end

@@ -6,20 +6,16 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 11/13/2025
+ms.date: 11/19/2025
 zone_pivot_groups: agentic-retrieval-pivots
 ---
 
 # Create a knowledge base in Azure AI Search
 
-<!--
 ::: zone pivot="csharp"
-[!INCLUDE [C#](includes/how-tos/file-name.md)]
+[!INCLUDE [C#](includes/how-tos/agentic-knowledge-source-how-to-search-index-csharp.md)]
 ::: zone-end
 
-Add C# to agentic-retrieval-pivots in zone-pivot-groups.yml, and then uncomment this section.
--->
-
 ::: zone pivot="python"
 [!INCLUDE [Python](includes/how-tos/agentic-retrieval-how-to-create-knowledge-base-python.md)]
 ::: zone-end

@@ -31,7 +31,7 @@ Here's what it does:
 
 This high-performance pipeline helps you generate high quality grounding data (or an answer) for your chat application, with the ability to answer complex questions quickly.
 
-Programmatically, agentic retrieval is supported through a new [Knowledge Base object](/rest/api/searchservice/knowledgebases?view=rest-searchservice-2025-11-01-preview&preserve-view=true) in the 2025-11-01-preview and in Azure SDK preview packages that provide the feature. A knowledge base's retrieval response is designed for downstream consumption by other agents and chat apps.
+Programmatically, agentic retrieval is supported through a new [Knowledge Base object](/rest/api/searchservice/knowledge-bases?view=rest-searchservice-2025-11-01-preview&preserve-view=true) in the 2025-11-01-preview and in Azure SDK preview packages that provide the feature. A knowledge base's retrieval response is designed for downstream consumption by other agents and chat apps.
 
 ## Why use agentic retrieval
 
@@ -115,6 +115,7 @@ Currently, Azure portal support for agentic retrieval is limited to the 2025-08-
   + [Blob](agentic-knowledge-source-how-to-blob.md)
   + [OneLake](agentic-knowledge-source-how-to-onelake.md)
   + [Remote SharePoint](agentic-knowledge-source-how-to-sharepoint-remote.md)
+  + [Indexed SharePoint](agentic-knowledge-source-how-to-sharepoint-indexed.md)
   + [Search index](agentic-knowledge-source-how-to-search-index.md)
   + [Web](agentic-knowledge-source-how-to-web.md)
 + [Create a knowledge base](agentic-retrieval-how-to-create-knowledge-base.md)
@@ -130,12 +131,12 @@ Currently, Azure portal support for agentic retrieval is limited to the 2025-08-
 + [Quickstart-Agentic-Retrieval: Python](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Agentic-Retrieval)
 + [Quickstart-Agentic-Retrieval: .NET](https://github.com/Azure-Samples/azure-search-dotnet-samples/blob/main/quickstart-agentic-retrieval)
 + [Quickstart-Agentic-Retrieval: REST](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-agentic-retrieval)
-+ [End-to-end with Azure AI Search and Azure AI Agent Service](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example)
++ [End-to-end with Azure AI Search and Foundry Agent Service](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example)
 
 ### [**REST API references**](#tab/rest-api-references)
 
 + [Knowledge Sources](/rest/api/searchservice/knowledge-sources?view=rest-searchservice-2025-11-01-preview&preserve-view=true)
-+ [Knowledge Bases](/rest/api/searchservice/knowledgebases?view=rest-searchservice-2025-11-01-preview&preserve-view=true)
++ [Knowledge Bases](/rest/api/searchservice/knowledge-bases?view=rest-searchservice-2025-11-01-preview&preserve-view=true)
 + [Knowledge Retrieval](/rest/api/searchservice/knowledge-retrieval/retrieve?view=rest-searchservice-2025-11-01-preview&preserve-view=true)
 
 ### [**Demos**](#tab/demos)

@@ -6,7 +6,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 05/01/2025
+ms.date: 11/21/2025
 ms.update-cycle: 180-days
 ms.custom:
   - devx-track-csharp
@@ -15,16 +15,16 @@ ms.custom:
 
 # Extract text and information from images by using AI enrichment
 
-Images often contain useful information that's relevant in search scenarios. You can [vectorize images](search-get-started-portal-image-search.md) to represent visual content in your search index. Or, you can use [AI enrichment and skillsets](cognitive-search-concept-intro.md) to create and extract searchable *text* from images, including:
+Images often contain useful information that's relevant in search scenarios. Azure AI Search doesn't query image content in real time, but you can extract information about an image during indexing and make that content searchable. To represent images in a search index, you can use these approaches:
 
- + [GenAI Prompt](cognitive-search-skill-genai-prompt.md) to pass a prompt to a chat completion skill, requesting a description of image content.
-+ [OCR](cognitive-search-skill-ocr.md) for optical character recognition of text and digits
-+ [Image Analysis](cognitive-search-skill-image-analysis.md) that describes images through visual features
-+ [Custom skills](#passing-images-to-custom-skills) to invoke any external image processing that you want to provide
++ [Vectorize images](search-get-started-portal-image-search.md) to represent visual content as a searchable vector.
++ [Verbalize images](cognitive-search-skill-genai-prompt.md) using the GenAI Prompt skill that sends a verbalization request to a chat completion model to describe the image.
++ [Analyze images](cognitive-search-skill-image-analysis.md) using an image analysis skill to generate a text representation of an image, such as *dandelion* for a photo of a dandelion, or the color *yellow*. You can also extract metadata about the image, such as its size.
++ [Use OCR](cognitive-search-skill-ocr.md) to extract text and from photos or pictures, such as the word *STOP* in a stop sign.
 
-By using OCR, you can extract text and from photos or pictures, such as the word *STOP* in a stop sign. Through image analysis, you can generate a text representation of an image, such as *dandelion* for a photo of a dandelion, or the color *yellow*. You can also extract metadata about the image, such as its size.
+You can also create a [custom skill](#passing-images-to-custom-skills) to invoke any external image processing that you want to provide.
 
-This article covers the fundamentals of working with images in skillsets, and also describes several common scenarios, such as working with embedded images, custom skills, and overlaying visualizations on original images.
+This article focuses on image analysis and OCR, custom skills that provide external processing, working with embedded images, and overlaying visualizations on original images. If verbalization or vectorization is your preferred approach, see [Multimodal search](multimodal-search-overview.md) instead.
 
 To work with image content in a skillset, you need:
 
@@ -102,7 +102,7 @@ Metadata adjustments are captured in a complex type created for each image. You
 
    The default of 2,000 pixels for the normalized images maximum width and height is based on the maximum sizes supported by the [OCR skill](cognitive-search-skill-ocr.md) and the [image analysis skill](cognitive-search-skill-image-analysis.md). The [OCR skill](cognitive-search-skill-ocr.md) supports a maximum width and height of 4,200 for non-English languages, and 10,000 for English. If you increase the maximum limits, processing could fail on larger images depending on your skillset definition and the language of the documents. 
 
-1.  Optionally, [set file type criteria](search-blob-storage-integration.md#PartsOfBlobToIndex) if the workload targets a specific file type. Blob indexer configuration includes file inclusion and exclusion settings. You can filter out files you don't want.
+1. Optionally, [set file type criteria](search-blob-storage-integration.md#PartsOfBlobToIndex) if the workload targets a specific file type. Blob indexer configuration includes file inclusion and exclusion settings. You can filter out files you don't want.
 
   ```json
   {

@@ -85,7 +85,7 @@ In Azure AI Search, an indexer saves the output it creates. A single indexer run
 | Data store | Required | Location | Description |
 |------------|----------|----------|-------------|
 | [searchable index](search-what-is-an-index.md) | Required | Search service | Used for full-text search and other query forms. Specifying an index is an indexer requirement. Index content is populated from skill outputs, plus any source fields that are mapped directly to fields in the index. |
-| [knowledge store](knowledge-store-concept-intro.md) | Optional | Azure Storage | Used for downstream apps like knowledge mining, data science, and multimodal search. A knowledge store is defined within a skillset. Its definition determines whether your enriched documents are projected as tables or objects (files or blobs) in Azure Storage. For [multimodal search scenarios](multimodal-search-overview.md#how-multimodal-search-works-in-azure-ai-search), you can save extracted images to the knowledge store and reference them at query time, allowing the images to be returned directly to client apps. |
+| [knowledge store](knowledge-store-concept-intro.md) | Optional | Azure Storage | Used for downstream apps like knowledge mining, data science, and multimodal search. A knowledge store is defined within a skillset. Its definition determines whether your enriched documents are projected as tables or objects (files or blobs) in Azure Storage. For [multimodal search scenarios](multimodal-search-overview.md#how-does-multimodal-search-work), you can save extracted images to the knowledge store and reference them at query time, allowing the images to be returned directly to client apps. |
 | [enrichment cache](enrichment-cache-how-to-configure.md) | Optional | Azure Storage | Used for caching enrichments for reuse in subsequent skillset executions. The cache stores imported, unprocessed content (cracked documents). It also stores the enriched documents created during skillset execution. Caching is helpful if you're using image analysis or OCR, and you want to avoid the time and expense of reprocessing image files. |
 
 Indexes and knowledge stores are fully independent of each other. While you must attach an index to satisfy indexer requirements, if your sole objective is a knowledge store, you can ignore the index after it's populated.

@@ -1,41 +1,52 @@
 ---
-title: Hybrid search
+title: Hybrid Search
 titleSuffix: Azure AI Search
-description: Describes concepts and architecture of hybrid query processing and document retrieval. Hybrid queries combine vector search and full text search.
+description: Describes concepts and architecture of hybrid query processing and document retrieval. Hybrid queries combine vector search and full-text search.
 author: robertklee
 ms.author: robertlee
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 07/21/2025
+ms.date: 11/21/2025
 ---
 
 # Hybrid search using vectors and full text in Azure AI Search
 
-Hybrid search is a single query request, configured for full text and vector queries, that executes against a search index containing both searchable plain text content and generated embeddings. For query purposes, hybrid search is:
+A hybrid search is a single query request configured for both full-text and vector queries. It executes against a search index that contains searchable, plain-text content and generated embeddings. For query purposes, hybrid search:
 
-+ A single query request that includes both `search` and `vectors` query parameters
-+ Executing in parallel
-+ Merging results from each query using [Reciprocal Rank Fusion (RRF)](hybrid-search-ranking.md)
++ Is a single query request that includes both `search` and `vectors` query parameters.
++ Executes full-text search and vector search in parallel.
++ Merges results from each query using [Reciprocal Rank Fusion (RRF)](hybrid-search-ranking.md).
 
-This article explains the concepts, benefits, and limitations of hybrid search. Links at the end provide instructions and next steps. You can also watch this [embedded video](#why-choose-hybrid-search) for an explanation of how hybrid retrieval contributes to high quality generative search applications.
+This article explains the concepts, benefits, and limitations of hybrid search. Links at the end provide usage instructions and next steps. You can also watch the [embedded video](#why-use-hybrid-search) for an explanation of how hybrid retrieval contributes to high-quality generative search applications.
+
+## Why use hybrid search?
+
+Hybrid search combines the strengths of vector search and keyword search. The advantage of vector search is finding information that's conceptually similar to your search query, even if there are no keyword matches in the inverted index. The advantage of keyword or full-text search is precision, with the ability to apply optional semantic ranking that improves the quality of the initial results. Some scenarios, such as querying over product codes, highly specialized jargon, dates, and people's names, can perform better with keyword search because it can identify exact matches.
+
+Benchmark testing on real-world and benchmark datasets indicates that hybrid retrieval with semantic ranker offers significant benefits in search relevance.
+
+The following video explains how hybrid retrieval gives you optimal grounding data for generating useful AI responses.
+
+> [!VIDEO https://www.youtube.com/embed/Xwx1DJ0OqCk]
 
 ## How does hybrid search work?
 
-In a search index, vector fields containing embeddings coexist with textual and numerical fields, allowing you to formulate hybrid queries that execute in parallel. Hybrid queries can take advantage of existing text-based functionality like filtering, faceting, sorting, scoring profiles, and [semantic ranking](semantic-search-overview.md) on your text fields, while executing a similarity search against vectors, all in a single search request.
+In a search index, vector fields containing embeddings coexist with textual and numerical fields, allowing you to formulate hybrid queries that execute simultaneously. Hybrid queries take advantage of existing text-based functionality like filtering, faceting, sorting, scoring profiles, and [semantic ranking](semantic-search-overview.md) on your text fields, while executing a similarity search against vectors in a single search request.
 
-Hybrid search combines results from both full text and vector queries, which use different ranking functions such as BM25 for text, and Hierarchical Navigable Small World (HNSW) and exhaustive K Nearest Neighbors (eKNN) for vectors. A [Reciprocal Rank Fusion (RRF)](hybrid-search-ranking.md) algorithm merges the results. The query response provides just one result set, using RRF to rank the unified results.
+Hybrid search combines results from both full-text and vector queries, which use different ranking functions such as BM25 for text, and Hierarchical Navigable Small World (HNSW) and exhaustive K Nearest Neighbors (eKNN) for vectors. An [RRF](hybrid-search-ranking.md) algorithm merges the results. The query response provides just one result set, using RRF to rank the unified results.
 
 ## Structure of a hybrid query
 
-Hybrid search is predicated on having a search index that contains fields of various [data types](/rest/api/searchservice/supported-data-types), including plain text and numbers, geo coordinates if you want geospatial search, and vectors for a mathematical representation of a chunk of text. You can use almost all query capabilities in Azure AI Search with a vector query, except for pure text client-side interactions such as autocomplete and suggestions.
+Hybrid search relies on a search index that contains fields of various [data types](/rest/api/searchservice/supported-data-types), including plain text and numbers, geo coordinates if you want geospatial search, and vectors to mathematically represent a chunk of text. You can use almost all query capabilities in Azure AI Search with a vector query, except for pure text client-side interactions, such as autocomplete and suggestions.
 
-A representative hybrid query might be as follows (notice that the vector queries have placeholder values for brevity):
+A representative hybrid query might look like the following. For brevity, the vector queries have placeholder values.
 
 ```http
 POST https://{{searchServiceName}}.search.windows.net/indexes/hotels-vector-quickstart/docs/search?api-version=2025-09-01
-  content-type: application/JSON
+content-type: application/JSON
+
 {
     "count": true,
     "search": "historic hotel walk to restaurants and shopping",
@@ -69,22 +80,22 @@ POST https://{{searchServiceName}}.search.windows.net/indexes/hotels-vector-quic
 }
 ```
 
-Key points include:
+**Key points:**
 
-+ `search` specifies a single full text search query.
-+ `vectorQueries` for vector queries, which can be multiple, targeting multiple vector fields. If the embedding space includes multi-lingual content, vector queries can find the match with no language analyzers or translation required. If you're also using the semantic ranker, set `k` to 50 to maximize its inputs.
-+ `select` specifies which fields to return in results, which should be text fields that are human readable if you're showing them to users or sending them to an LLM.
-+ `filters` can specify geospatial search or other include and exclude criteria, such as whether parking is included. The geospatial query in this example finds hotels within a 300-kilometer radius of Washington D.C. You can apply the filter at the beginning or end of query processing. If you use the semantic ranker, you probably want post-filtering as the last step but you should test to confirm which behavior is best for your queries.
++ `search` specifies a single full-text search query.
++ `vectorQueries` specifies vector queries, which can be multiple, targeting multiple vector fields. If the embedding space includes multi-lingual content, vector queries can find the match with no language analyzers or translation required. If you're using semantic ranker, set `k` to 50 to maximize its inputs.
++ `select` specifies which fields to return in results, which should be human-readable text fields if you're showing them to users or sending them to a large language model (LLM).
++ `filters` can specify geospatial search or other inclusion and exclusion criteria, such as whether parking is included. The geospatial query in this example finds hotels within a 300-kilometer radius of Washington D.C. You can apply the filter at the beginning or end of query processing. If you're using semantic ranker, you probably want post-filtering as the last step, but you should test to confirm which behavior is best for your queries.
 + `facets` can be used to compute facet buckets over results that are returned from hybrid queries.
-+ `queryType=semantic` invokes semantic ranker, applying machine reading comprehension to surface more relevant search results. Semantic ranking is optional. If you aren't using that feature, remove the last three lines of the hybrid query.
++ `queryType=semantic` invokes [semantic ranker](semantic-search-overview.md), applying machine reading comprehension to surface more relevant search results. Semantic ranking is optional. If you aren't using this feature, remove the last three lines of the hybrid query.
 
-Filters and facets target data structures within the index that are distinct from the inverted indexes used for full text search and the vector indexes used for vector search. As such, when filters and faceted operations execute, the search engine can apply the operational result to the hybrid search results in the response.
+Filters and facets target data structures within the index that are distinct from the inverted indexes used for full-text search and the vector indexes used for vector search. As such, when filters and faceted operations execute, the search engine can apply the operational result to the hybrid search results in the response.
 
 Notice how there's no `orderby` in the query. Explicit sort orders override relevanced-ranked results, so if you want similarity and BM25 relevance, omit sorting in your query.
 
-A response from the above query might look like this:
+A response from the query might look like the following JSON.
 
-```http
+```json
 {
     "@odata.count": 3,
     "@search.facets": {
@@ -128,17 +139,7 @@ A response from the above query might look like this:
 }
 ```
 
-## Why choose hybrid search?
-
-Hybrid search combines the strengths of vector search and keyword search. The advantage of vector search is finding information that's conceptually similar to your search query, even if there are no keyword matches in the inverted index. The advantage of keyword or full text search is precision, with the ability to apply optional semantic ranking that improves the quality of the initial results. Some scenarios - such as querying over product codes, highly specialized jargon, dates, and people's names - can perform better with keyword search because it can identify exact matches.
-
-Benchmark testing on real-world and benchmark datasets indicates that hybrid retrieval with semantic ranker offers significant benefits in search relevance.
-
-The following video explains how hybrid retrieval gives you optimal grounding data for generating useful AI responses.
-
-> [!VIDEO https://www.youtube.com/embed/Xwx1DJ0OqCk]
-
-## See also
+## Related content
 
 + [Create a hybrid query](hybrid-search-how-to-query.md)
 + [Relevance scoring in hybrid search](hybrid-search-ranking.md)

@@ -0,0 +1,205 @@
+---
+manager: nitinme
+author: heidisteen
+ms.author: heidist
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 11/19/2025
+---
+
+[!INCLUDE [Feature preview](../previews/preview-generic.md)]
+
+Use a *blob knowledge source* to index and query Azure blob content in an agentic retrieval pipeline. [Knowledge sources](../../agentic-knowledge-source-overview.md) are created independently, referenced in a [knowledge base](../../agentic-retrieval-how-to-create-knowledge-base.md), and used as grounding data when an agent or chatbot calls a [retrieve action](../../agentic-retrieval-how-to-retrieve.md) at query time.
+
+Unlike a [search index knowledge source](../../agentic-knowledge-source-how-to-search-index.md), which specifies an existing and qualified index, a blob knowledge source specifies an external data source, models, and properties to automatically generate the following Azure AI Search objects:
+
++ A data source that represents a blob container.
++ A skillset that chunks and optionally vectorizes multimodal content from the container.
++ An index that stores enriched content and meets the criteria for agentic retrieval.
++ An indexer that uses the previous objects to drive the indexing and enrichment pipeline.
+
+> [!NOTE]
+> If user access is specified at the document (blob) level in Azure Storage, a knowledge source can carry permission metadata forward to indexed content in Azure AI Search. For more information, see [ADLS Gen2 permission metadata](/azure/search/search-indexer-access-control-lists-and-role-based-access) or [Blob RBAC scopes](/azure/search/search-blob-indexer-role-based-access).
+
+## Prerequisites
+
++ Azure AI Search in any [region that provides agentic retrieval](../../search-region-support.md). You must have [semantic ranker enabled](../../semantic-how-to-enable-disable.md).
+
++ An [Azure Blob Storage](/azure/storage/common/storage-account-create) or [Azure Data Lake Storage (ADLS) Gen2](/azure/storage/blobs/create-data-lake-storage-account) account. 
+
++ A blob container with [supported content types](../../search-how-to-index-azure-blob-storage.md#supported-document-formats) for text content. For optional image verbalization, the supported content type depends on whether your chat completion model can analyze and describe the image file.
+
++ The latest preview version of the [`Azure.Search.Documents` client library](https://www.nuget.org/packages/Azure.Search.Documents/11.8.0-beta.1) for the .NET SDK.
+
++ Permission to create and use objects on Azure AI Search. We recommend [role-based access](../../search-security-rbac.md), but you can use [API keys](../../search-security-api-keys.md) if a role assignment isn't feasible. For more information, see [Connect to a search service](../../search-get-started-rbac.md).
+
+> [!NOTE]
+> Although you can use the Azure portal to create blob knowledge sources, the portal uses the 2025-08-01-preview, which uses the previous "knowledge agent" terminology and doesn't support all 2025-11-01-preview features. For help with breaking changes, see [Migrate your agentic retrieval code](../../agentic-retrieval-how-to-migrate.md).
+
+## Check for existing knowledge sources
+
+[!INCLUDE [Check for existing knowledge sources using C#](knowledge-source-check-csharp.md)]
+
+The following JSON is an example response for a blob knowledge source.
+
+```json
+{
+  "name": "my-blob-ks",
+  "kind": "azureBlob",
+  "description": "A sample blob knowledge source.",
+  "encryptionKey": null,
+  "azureBlobParameters": {
+    "connectionString": "<REDACTED>",
+    "containerName": "blobcontainer",
+    "folderPath": null,
+    "isADLSGen2": false,
+    "ingestionParameters": {
+      "disableImageVerbalization": false,
+      "ingestionPermissionOptions": [],
+      "contentExtractionMode": "standard",
+      "identity": null,
+      "embeddingModel": {
+        "kind": "azureOpenAI",
+        "azureOpenAIParameters": {
+          "resourceUri": "<REDACTED>",
+          "deploymentId": "text-embedding-3-large",
+          "apiKey": "<REDACTED>",
+          "modelName": "text-embedding-3-large",
+          "authIdentity": null
+        }
+      },
+      "chatCompletionModel": {
+        "kind": "azureOpenAI",
+        "azureOpenAIParameters": {
+          "resourceUri": "<REDACTED>",
+          "deploymentId": "gpt-5-mini",
+          "apiKey": "<REDACTED>",
+          "modelName": "gpt-5-mini",
+          "authIdentity": null
+        }
+      },
+      "ingestionSchedule": null,
+      "assetStore": null,
+      "aiServices": {
+        "uri": "<REDACTED>",
+        "apiKey": "<REDACTED>"
+      }
+    },
+    "createdResources": {
+      "datasource": "my-blob-ks-datasource",
+      "indexer": "my-blob-ks-indexer",
+      "skillset": "my-blob-ks-skillset",
+      "index": "my-blob-ks-index"
+    }
+  }
+}
+```
+
+> [!NOTE]
+> Sensitive information is redacted. The generated resources appear at the end of the response.
+
+## Create a knowledge source
+
+Run the following code to [create a blob knowledge source](/dotnet/api/azure.search.documents.indexes.models.azureblobknowledgesource?view=azure-dotnet-preview&preserve-view=true).
+
+```csharp
+// Create a blob knowledge source
+using Azure.Search.Documents.Indexes;
+using Azure.Search.Documents.Indexes.Models;
+using Azure.Search.Documents.KnowledgeBases.Models;
+using Azure;
+
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), new AzureKeyCredential(apiKey));
+
+var chatCompletionParams = new AzureOpenAIVectorizerParameters
+{
+    ResourceUri = new Uri(aoaiEndpoint),
+    DeploymentName = aoaiGptDeployment,
+    ModelName = aoaiGptModel
+};
+
+var embeddingParams = new AzureOpenAIVectorizerParameters
+{
+    ResourceUri = new Uri(aoaiEndpoint),
+    DeploymentName = aoaiEmbeddingDeployment,
+    ModelName = aoaiEmbeddingModel
+};
+
+var ingestionParams = new KnowledgeSourceIngestionParameters
+{
+    DisableImageVerbalization = false,
+    ChatCompletionModel = new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: chatCompletionParams),
+    EmbeddingModel = new KnowledgeSourceAzureOpenAIVectorizer
+    {
+        AzureOpenAIParameters = embeddingParams
+    }
+};
+
+var blobParams = new AzureBlobKnowledgeSourceParameters(
+    connectionString: connectionString,
+    containerName: containerName
+)
+{
+    IsAdlsGen2 = false,
+    IngestionParameters = ingestionParams
+};
+
+var knowledgeSource = new AzureBlobKnowledgeSource(
+    name: "my-blob-ks",
+    azureBlobParameters: blobParams
+)
+{
+    Description = "This knowledge source pulls from a blob storage container."
+};
+
+await indexClient.CreateOrUpdateKnowledgeSourceAsync(knowledgeSource);
+Console.WriteLine($"Knowledge source '{knowledgeSource.Name}' created or updated successfully.");
+```
+
+### Source-specific properties
+
+You can pass the following properties to create a blob knowledge source.
+
+| Name | Description | Type | Editable | Required |
+|--|--|--|--|--|
+| `name` | The name of the knowledge source, which must be unique within the knowledge sources collection and follow the [naming guidelines](/rest/api/searchservice/naming-rules) for objects in Azure AI Search. | String | No | Yes |
+| `Description` | A description of the knowledge source. | String | Yes | No |
+| `encryptionKey` | A [customer-managed key](../../search-security-manage-encryption-keys.md) to encrypt sensitive information in both the knowledge source and the generated objects. | Object | Yes | No |
+| `chatCompletionParams` | Parameters specific to chat completion models used for query planning and optional answer synthesis when the retrieval reasoning effort is low or medium. | Object |  | No |
+| `embeddingParams` | Parameters specific to embedding models used if you want to vectorize chunks of content.  | Object |  | No |
+| `azureBlobParameters` | Parameters specific to blob knowledge sources: `connectionString`, `containerName`, `folderPath`, and `isAdlsGen2`. | Object |  | No |
+| `connectionString` | A key-based [connection string](../../search-how-to-index-azure-blob-storage.md#supported-credentials-and-connection-strings) or, if you're using a managed identity, the resource ID. | String | No | Yes |
+| `containerName` | The name of the blob storage container. | String | No | Yes |
+| `folderPath` | A folder within the container. | String | No | No |
+| `isAdlsGen2` | The default is `False`. Set to `True` if you're using an ADLS Gen2 storage account. | Boolean | No | No |
+
+### `ingestionParameters` properties
+
+[!INCLUDE [C# ingestionParameters properties](knowledge-source-ingestion-parameters-csharp.md)]
+
+## Check ingestion status
+
+[!INCLUDE [C# knowledge source status](knowledge-source-status-csharp.md)]
+
+## Review the created objects
+
+When you create a blob knowledge source, your search service also creates an indexer, index, skillset, and data source. We don't recommend that you edit these objects, as introducing an error or incompatibility can break the pipeline.
+
+After you create a knowledge source, the response lists the created objects. These objects are created according to a fixed template, and their names are based on the name of the knowledge source. You can't change the object names.
+
+We recommend using the Azure portal to validate output creation. The workflow is:
+
+1. Check the indexer for success or failure messages. Connection or quota errors appear here.
+1. Check the index for searchable content. Use Search Explorer to run queries.
+1. Check the skillset to learn how your content is chunked and optionally vectorized.
+1. Check the data source for connection details. Our example uses API keys for simplicity, but you can use Microsoft Entra ID for authentication and role-based access control for authorization.
+
+## Assign to a knowledge base
+
+If you're satisfied with the knowledge source, continue to the next step: specify the knowledge source in a [knowledge base](../../agentic-retrieval-how-to-create-knowledge-base.md).
+
+After the knowledge base is configured, use the [retrieve action](../../agentic-retrieval-how-to-retrieve.md) to query the knowledge source.
+
+## Delete a knowledge source
+
+[!INCLUDE [Delete knowledge source using C#](knowledge-source-delete-csharp.md)]

@@ -0,0 +1,193 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 11/20/2025
+---
+
+[!INCLUDE [Feature preview](../previews/preview-generic.md)]
+
+Use a *OneLake knowledge source* to index and query Microsoft OneLake files in an agentic retrieval pipeline. [Knowledge sources](../../agentic-knowledge-source-overview.md) are created independently, referenced in a [knowledge base](../../agentic-retrieval-how-to-create-knowledge-base.md), and used as grounding data when an agent or chatbot calls a [retrieve action](../../agentic-retrieval-how-to-retrieve.md) at query time.
+
+When you create a OneLake knowledge source, you specify an external data source, models, and properties to automatically generate the following Azure AI Search objects:
+
++ A data source that represents a lakehouse.
++ A skillset that chunks and optionally vectorizes multimodal content from the lakehouse.
++ An index that stores enriched content and meets the criteria for agentic retrieval.
++ An indexer that uses the previous objects to drive the indexing and enrichment pipeline.
+
+The generated indexer conforms to the *OneLake indexer*, whose prerequisites, supported tasks, supported document formats, supported shortcuts, and limitations also apply to OneLake knowledge sources. For more information, see the [OneLake indexer documentation](../../search-how-to-index-onelake-files.md).
+
+## Prerequisites
+
++ Azure AI Search in any [region that provides agentic retrieval](../../search-region-support.md). You must have [semantic ranker enabled](../../semantic-how-to-enable-disable.md).
+
++ Completion of the [OneLake indexer prerequisites](../../search-how-to-index-onelake-files.md#prerequisites).
+
++ Completion of the [OneLake indexer data preparation](../../search-how-to-index-onelake-files.md#prepare-data-for-indexing).
+
++ The latest preview version of the [`azure-search-documents` client library](https://pypi.org/project/azure-search-documents/11.7.0b2/) for C#.
+
++ Permission to create and use objects on Azure AI Search. We recommend [role-based access](../../search-security-rbac.md), but you can use [API keys](../../search-security-api-keys.md) if a role assignment isn't feasible. For more information, see [Connect to a search service](../../search-get-started-rbac.md).
+
+## Check for existing knowledge sources
+
+[!INCLUDE [Check for existing knowledge sources using C#](knowledge-source-check-csharp.md)]
+
+The following JSON is an example response for a OneLake knowledge source.
+
+```json
+{
+  "name": "my-onelake-ks",
+  "kind": "indexedOneLake",
+  "description": "A sample indexed OneLake knowledge source.",
+  "encryptionKey": null,
+  "indexedOneLakeParameters": {
+    "fabricWorkspaceId": "<REDACTED>",
+    "lakehouseId": "<REDACTED>",
+    "targetPath": null,
+    "ingestionParameters": {
+      "disableImageVerbalization": false,
+      "ingestionPermissionOptions": [],
+      "contentExtractionMode": "standard",
+      "identity": null,
+      "embeddingModel": {
+        "kind": "azureOpenAI",
+        "azureOpenAIParameters": {
+          "resourceUri": "<REDACTED>",
+          "deploymentId": "text-embedding-3-large",
+          "apiKey": "<REDACTED>",
+          "modelName": "text-embedding-3-large"
+        }
+      },
+      "chatCompletionModel": {
+        "kind": "azureOpenAI",
+        "azureOpenAIParameters": {
+          "resourceUri": "<REDACTED>",
+          "deploymentId": "gpt-5-mini",
+          "apiKey": "<REDACTED>",
+          "modelName": "gpt-5-mini"
+        }
+      },
+      "ingestionSchedule": null,
+      "aiServices": {
+        "uri": "<REDACTED>",
+        "apiKey": "<REDACTED>"
+      }
+    },
+    "createdResources": {
+    "datasource": "my-onelake-ks-datasource",
+    "indexer": "my-onelake-ks-indexer",
+    "skillset": "my-onelake-ks-skillset",
+    "index": "my-onelake-ks-index"
+    }
+  }
+}
+```
+
+> [!NOTE]
+> Sensitive information is redacted. The generated resources appear at the end of the response.
+
+## Create a knowledge source
+
+Run the following code to create a OneLake knowledge source.
+
+```csharp
+// Create an IndexedOneLake knowledge source
+using Azure.Search.Documents.Indexes;
+using Azure.Search.Documents.Indexes.Models;
+using Azure.Search.Documents.KnowledgeBases.Models;
+using Azure;
+
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), new AzureKeyCredential(apiKey));
+
+var chatCompletionParams = new AzureOpenAIVectorizerParameters
+{
+    ResourceUri = new Uri(aoaiEndpoint),
+    DeploymentName = aoaiGptDeployment,
+    ModelName = aoaiGptModel
+};
+
+var embeddingParams = new AzureOpenAIVectorizerParameters
+{
+    ResourceUri = new Uri(aoaiEndpoint),
+    DeploymentName = aoaiEmbeddingDeployment,
+    ModelName = aoaiEmbeddingModel
+};
+
+var ingestionParams = new KnowledgeSourceIngestionParameters
+{
+    DisableImageVerbalization = false,
+    ChatCompletionModel = new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: chatCompletionParams),
+    EmbeddingModel = new KnowledgeSourceAzureOpenAIVectorizer
+    {
+        AzureOpenAIParameters = embeddingParams
+    }
+};
+
+var oneLakeParams = new IndexedOneLakeKnowledgeSourceParameters(
+    fabricWorkspaceId: fabricWorkspaceId,
+    lakehouseId: lakehouseId)
+{
+    IngestionParameters = ingestionParams
+};
+
+var knowledgeSource = new IndexedOneLakeKnowledgeSource(
+    name: "my-onelake-ks",
+    indexedOneLakeParameters: oneLakeParams)
+{
+    Description = "This knowledge source pulls content from a lakehouse."
+};
+
+await indexClient.CreateOrUpdateKnowledgeSourceAsync(knowledgeSource);
+Console.WriteLine($"Knowledge source '{knowledgeSource.Name}' created or updated successfully.");
+```
+
+### Source-specific properties
+
+You can pass the following properties to create a OneLake knowledge source.
+
+| Name | Description | Type | Editable | Required |
+|--|--|--|--|--|
+| `Name` | The name of the knowledge source, which must be unique within the knowledge sources collection and follow the [naming guidelines](/rest/api/searchservice/naming-rules) for objects in Azure AI Search. | String | Yes | Yes |
+| `Description` | A description of the knowledge source. | String | Yes | No |
+| `EncryptionKey` | A [customer-managed key](../../search-security-manage-encryption-keys.md) to encrypt sensitive information in both the knowledge source and the generated objects. | Object | Yes | No |
+| `IndexedOneLakeKnowledgeSourceParameters` | Parameters specific to OneLake knowledge sources: `fabricWorkspaceId`, `lakehouseId`, and `targetPath`. | Object |  | Yes |
+| `fabricWorkspaceId` | The GUID of the workspace that contains the lakehouse. | String | No | Yes |
+| `lakehouseId` | The GUID of the lakehouse. | String | No | Yes |
+| `targetPath` | A folder or shortcut within the lakehouse. When unspecified, the entire lakehouse is indexed. | String | No | No |
+
+### `ingestionParameters` properties
+
+[!INCLUDE [C# ingestionParameters properties](knowledge-source-ingestion-parameters-csharp.md)]
+
+## Check ingestion status
+
+[!INCLUDE [C# knowledge source status](knowledge-source-status-csharp.md)]
+
+## Review the created objects
+
+When you create a OneLake knowledge source, your search service also creates an indexer, index, skillset, and data source. We don't recommend that you edit these objects, as introducing an error or incompatibility can break the pipeline.
+
+After you create a knowledge source, the response lists the created objects. These objects are created according to a fixed template, and their names are based on the name of the knowledge source. You can't change the object names.
+
+We recommend using the Azure portal to validate output creation. The workflow is:
+
+1. Check the indexer for success or failure messages. Connection or quota errors appear here.
+1. Check the index for searchable content. Use Search Explorer to run queries.
+1. Check the skillset to learn how your content is chunked and optionally vectorized.
+1. Check the data source for connection details. Our example uses API keys for simplicity, but you can use Microsoft Entra ID for authentication and role-based access control for authorization.
+
+## Assign to a knowledge base
+
+If you're satisfied with the knowledge source, continue to the next step: specify the knowledge source in a [knowledge base](../../search-agentic-retrieval-how-to-create.md).
+
+For any knowledge base that specifies a OneLake knowledge source, be sure to set `includeReferenceSourceData` to `true`. This step is necessary for pulling the source document URL into the citation.
+
+After the knowledge base is configured, use the [retrieve action](../../agentic-retrieval-how-to-retrieve.md) to query the knowledge source.
+
+## Delete a knowledge source
+
+[!INCLUDE [Delete knowledge source using C#](knowledge-source-delete-csharp.md)]

@@ -0,0 +1,101 @@
+---
+manager: nitinme
+author: heidisteen
+ms.author: heidist
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 11/19/2025
+---
+
+[!INCLUDE [Feature preview](../previews/preview-generic.md)]
+
+A *search index knowledge source* specifies a connection to an Azure AI Search index that provides searchable content in an agentic retrieval pipeline. [Knowledge sources](../../agentic-knowledge-source-overview.md) are created independently, referenced in a [knowledge base](../../agentic-retrieval-how-to-create-knowledge-base.md), and used as grounding data when an agent or chatbot calls a [retrieve action](../../agentic-retrieval-how-to-retrieve.md) at query time.
+
+## Prerequisites
+
++ Azure AI Search in any [region that provides agentic retrieval](../../search-region-support.md). You must have [semantic ranker enabled](../../semantic-how-to-enable-disable.md). 
+
++ A search index containing plain text or vector content with a semantic configuration. [Review the index criteria for agentic retrieval](../../agentic-retrieval-how-to-create-index.md#criteria-for-agentic-retrieval). The index must be on the same search service as the knowledge base.
+
++ The latest preview version of the [`Azure.Search.Documents` client library](https://www.nuget.org/packages/Azure.Search.Documents/11.8.0-beta.1) for the .NET SDK.
+
++ Permission to create and use objects on Azure AI Search. We recommend [role-based access](../../search-security-rbac.md), but you can use [API keys](../../search-security-api-keys.md) if a role assignment isn't feasible. For more information, see [Connect to a search service](../../search-get-started-rbac.md).
+
+> [!NOTE]
+> Although you can use the Azure portal to create search index knowledge sources, the portal uses the 2025-08-01-preview, which uses the previous "knowledge agent" terminology and doesn't support all 2025-11-01-preview features. For help with breaking changes, see [Migrate your agentic retrieval code](../../agentic-retrieval-how-to-migrate.md).
+
+## Check for existing knowledge sources
+
+[!INCLUDE [Check for existing knowledge sources using C#](knowledge-source-check-csharp.md)]
+
+The following JSON is an example response for a search index knowledge source. Notice that the knowledge source specifies a single index name and which fields in the index to include in the query.
+
+```json
+{
+  "SearchIndexParameters": {
+    "SearchIndexName": "earth-at-night",
+    "SourceDataFields": [
+      {
+        "Name": "id"
+      },
+      {
+        "Name": "page_chunk"
+      },
+      {
+        "Name": "page_number"
+      }
+    ],
+    "SearchFields": [],
+    "SemanticConfigurationName": "semantic-config"
+  },
+  "Name": "earth-knowledge-source",
+  "Description": null,
+  "EncryptionKey": null,
+  "ETag": "<redacted>"
+}
+```
+
+## Create a knowledge source
+
+Run the following code to create a search index knowledge source.
+
+```csharp
+using Azure.Search.Documents.Indexes.Models;
+
+// Create the knowledge source
+var indexKnowledgeSource = new SearchIndexKnowledgeSource(
+    name: knowledgeSourceName,
+    searchIndexParameters: new SearchIndexKnowledgeSourceParameters(searchIndexName: indexName)
+    {
+        SourceDataFields = { new SearchIndexFieldReference(name: "id"), new SearchIndexFieldReference(name: "page_chunk"), new SearchIndexFieldReference(name: "page_number") }
+    }
+);
+
+await indexClient.CreateOrUpdateKnowledgeSourceAsync(indexKnowledgeSource);
+Console.WriteLine($"Knowledge source '{knowledgeSourceName}' created or updated successfully.");
+```
+
+### Source-specific properties
+
+You can pass the following properties to create a search index knowledge source.
+
+| Name | Description | Type | Editable | Required |
+|--|--|--|--|--|
+| `Name` | The name of the knowledge source, which must be unique within the knowledge sources collection and follow the [naming guidelines](/rest/api/searchservice/naming-rules) for objects in Azure AI Search. | String | No | Yes |
+| `Description` | A description of the knowledge source. | String | Yes | No |
+| `EncryptionKey` | A [customer-managed key](../../search-security-manage-encryption-keys.md) to encrypt sensitive information in both the knowledge source and the generated objects. | Object | Yes | No |
+| `SearchIndexParameters` | Parameters specific to search index knowledge sources: `search_index_name`, `SemanticConfigurationName`, `SourceDataFields`, and `SearchFields`. | Object | Yes | Yes |
+| `SearchIndexName` | The name of the existing search index. | String | Yes | Yes |
+| `SemanticConfigurationName` | Overrides the default semantic configuration for the search index. | String | Yes | No |
+| `SourceDataFields` | The index fields returned when you specify `include_reference_source_data` in the knowledge base definition. These fields are used for citations and should be `retrievable`. Examples include the document name, file name, page numbers, or chapter numbers. | Array | Yes | No |
+| `SearchFields` | The index fields to specifically search against. When unspecified, all fields are searched. | Array | Yes | No |
+
+## Assign to a knowledge base
+
+If you're satisfied with the knowledge source, continue to the next step: specify the knowledge source in a [knowledge base](../../agentic-retrieval-how-to-create-knowledge-base.md).
+
+After the knowledge base is configured, use the [retrieve action](../../agentic-retrieval-how-to-retrieve.md) to query the knowledge source.
+
+## Delete a knowledge source
+
+[!INCLUDE [Delete knowledge source using Python](knowledge-source-delete-python.md)]

@@ -0,0 +1,187 @@
+---
+manager: nitinme
+author: heidisteen
+ms.author: heidist
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 11/20/2025
+---
+
+[!INCLUDE [Feature preview](../previews/preview-generic.md)]
+
+Use an *indexed SharePoint knowledge source* to index and query SharePoint content in an agentic retrieval pipeline. [Knowledge sources](../../agentic-knowledge-source-overview.md) are created independently, referenced in a [knowledge base](../../agentic-retrieval-how-to-create-knowledge-base.md), and used as grounding data when an agent or chatbot calls a [retrieve action](../../agentic-retrieval-how-to-retrieve.md) at query time.
+
+When you create an indexed SharePoint knowledge source, you specify a SharePoint connection string, models, and properties to automatically generate the following Azure AI Search objects:
+
++ A data source that points to SharePoint sites.
++ A skillset that chunks and optionally vectorizes multimodal content.
++ An index that stores enriched content and meets the criteria for agentic retrieval.
++ An indexer that uses the previous objects to drive the indexing and enrichment pipeline.
+
+## Prerequisites
+
++ Azure AI Search in any [region that provides agentic retrieval](../../search-region-support.md). You must have [semantic ranker enabled](../../semantic-how-to-enable-disable.md).
+
++ Completion of the [SharePoint indexer prerequisites](../../search-how-to-index-sharepoint-online.md#prerequisites).
+
++ Completion of three SharePoint indexer configuration steps:
+
+  + [Step 1: Enable a managed identity for Azure AI Search](../../search-how-to-index-sharepoint-online.md#step-1-optional-enable-system-assigned-managed-identity)
+  + [Step 2: Choose between delegated or application permissions](../../search-how-to-index-sharepoint-online.md#step-2-decide-which-permissions-the-indexer-requires)
+  + [Step 3: Application registration step for Microsoft Entra ID authentication](../../search-how-to-index-sharepoint-online.md#step-3-create-a-microsoft-entra-application-registration)
+
++ The latest preview version of the [`Azure.Search.Documents` client library](https://www.nuget.org/packages/Azure.Search.Documents/11.8.0-beta.1) for the .NET SDK.
+
++ Permission to create and use objects on Azure AI Search. We recommend [role-based access](../../search-security-rbac.md), but you can use [API keys](../../search-security-api-keys.md) if a role assignment isn't feasible. For more information, see [Connect to a search service](../../search-get-started-rbac.md).
+
+## Check for existing knowledge sources
+
+[!INCLUDE [Check for existing knowledge sources using C#](knowledge-source-check-csharp.md)]
+
+The following JSON is an example response for an indexed SharePoint knowledge source.
+
+```json
+{
+  "name": "my-indexed-sharepoint-ks",
+  "kind": "indexedSharePoint",
+  "description": "A sample indexed SharePoint knowledge source",
+  "encryptionKey": null,
+  "indexedSharePointParameters": {
+    "connectionString": "<redacted>",
+    "containerName": "defaultSiteLibrary",
+    "query": null,
+    "ingestionParameters": {
+      "disableImageVerbalization": false,
+      "ingestionPermissionOptions": [],
+      "contentExtractionMode": "minimal",
+      "identity": null,
+      "embeddingModel": {
+        "kind": "azureOpenAI",
+        "azureOpenAIParameters": {
+          "resourceUri": "<redacted>",
+          "deploymentId": "text-embedding-3-large",
+          "apiKey": "<redacted>",
+          "modelName": "text-embedding-3-large",
+          "authIdentity": null
+        }
+      },
+      "chatCompletionModel": null,
+      "ingestionSchedule": null,
+      "assetStore": null,
+      "aiServices": null
+    },
+    "createdResources": {
+      "datasource": "my-indexed-sharepoint-ks-datasource",
+      "indexer": "my-indexed-sharepoint-ks-indexer",
+      "skillset": "my-indexed-sharepoint-ks-skillset",
+      "index": "my-indexed-sharepoint-ks-index"
+    }
+  },
+  "indexedOneLakeParameters": null
+}
+```
+
+> [!NOTE]
+> Sensitive information is redacted. The generated resources appear at the end of the response.
+
+## Create a knowledge source
+
+Run the following code to create an indexed SharePoint knowledge source.
+
+```csharp
+// Create an IndexedSharePoint knowledge source
+using Azure.Search.Documents.Indexes;
+using Azure.Search.Documents.Indexes.Models;
+using Azure.Search.Documents.KnowledgeBases.Models;
+using Azure;
+
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), new AzureKeyCredential(apiKey));
+
+var chatCompletionParams = new AzureOpenAIVectorizerParameters
+{
+    ResourceUri = new Uri(aoaiEndpoint),
+    DeploymentName = aoaiGptDeployment,
+    ModelName = aoaiGptModel
+};
+
+var embeddingParams = new AzureOpenAIVectorizerParameters
+{
+    ResourceUri = new Uri(aoaiEndpoint),
+    DeploymentName = aoaiEmbeddingDeployment,
+    ModelName = aoaiEmbeddingModel
+};
+
+var ingestionParams = new KnowledgeSourceIngestionParameters
+{
+    DisableImageVerbalization = false,
+    ChatCompletionModel = new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: chatCompletionParams),
+    EmbeddingModel = new KnowledgeSourceAzureOpenAIVectorizer
+    {
+        AzureOpenAIParameters = embeddingParams
+    }
+};
+
+var sharePointParams = new IndexedSharePointKnowledgeSourceParameters(
+    connectionString: sharePointConnectionString,
+    containerName: "defaultSiteLibrary")
+{
+    IngestionParameters = ingestionParams
+};
+
+var knowledgeSource = new IndexedSharePointKnowledgeSource(
+    name: "my-indexed-sharepoint-ks",
+    indexedSharePointParameters: sharePointParams)
+{
+    Description = "A sample indexed SharePoint knowledge source."
+};
+
+await indexClient.CreateOrUpdateKnowledgeSourceAsync(knowledgeSource);
+Console.WriteLine($"Knowledge source '{knowledgeSource.Name}' created or updated successfully.");
+```
+
+### Source-specific properties
+
+You can pass the following properties to create an indexed SharePoint knowledge source.
+
+| Name | Description | Type | Editable | Required |
+|--|--|--|--|--|
+| `Name` | The name of the knowledge source, which must be unique within the knowledge sources collection and follow the [naming guidelines](/rest/api/searchservice/naming-rules) for objects in Azure AI Search. | String | No | Yes |
+| `Description` | A description of the knowledge source. | String | Yes | No |
+| `EncryptionKey` | A [customer-managed key](../../search-security-manage-encryption-keys.md) to encrypt sensitive information in both the knowledge source and the generated objects. | Object | Yes | No |
+| `IndexedSharePointKnowledgeSourceParameters` | Parameters specific to indexed SharePoint knowledge sources: `connectionString`, `containerName`, and `query`. | Object | No | No |
+| `connectionString` | The connection string to a SharePoint site. For more information, see [Connection string syntax](../../search-how-to-index-sharepoint-online.md#connection-string-format). | String | Yes | Yes |
+| `containerName` | The SharePoint library to access. Use `defaultSiteLibrary` to index content from the site's default document library or `allSiteLibraries` to index content from every document library in the site. Ignore `useQuery` for now. | String | No | Yes |
+| `query` | Ignore for now. | String | Yes | No |
+
+### `ingestion_parameters` properties
+
+[!INCLUDE [C# ingestionParameters properties](knowledge-source-ingestion-parameters-csharp.md)]
+
+## Check ingestion status
+
+[!INCLUDE [C# knowledge source status](knowledge-source-status-csharp.md)]
+
+## Review the created objects
+
+When you create an indexed SharePoint knowledge source, your search service also creates an indexer, index, skillset, and data source. We don't recommend that you edit these objects, as introducing an error or incompatibility can break the pipeline.
+
+After you create a knowledge source, the response lists the created objects. These objects are created according to a fixed template, and their names are based on the name of the knowledge source. You can't change the object names.
+
+We recommend using the Azure portal to validate output creation. The workflow is:
+
+1. Check the indexer for success or failure messages. Connection or quota errors appear here.
+1. Check the index for searchable content. Use Search Explorer to run queries.
+1. Check the skillset to learn how your content is chunked and optionally vectorized.
+1. Check the data source for connection details. Our example uses API keys for simplicity, but you can use Microsoft Entra ID for authentication and role-based access control for authorization.
+
+## Assign to a knowledge base
+
+If you're satisfied with the knowledge source, continue to the next step: specify the knowledge source in a [knowledge base](../../search-agentic-retrieval-how-to-create.md).
+
+For any knowledge base that specifies an indexed SharePoint knowledge source, be sure to set `includeReferenceSourceData` to `true`. This step is necessary for pulling the source document URL into the citation.
+
+After the knowledge base is configured, use the [retrieve action](../../agentic-retrieval-how-to-retrieve.md) to query the knowledge source.
+
+## Delete a knowledge source
+
+[!INCLUDE [Delete knowledge source using C#](knowledge-source-delete-csharp.md)]

@@ -0,0 +1,225 @@
+---
+manager: nitinme
+author: heidisteen
+ms.author: heidist
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 11/20/2025
+---
+
+[!INCLUDE [Feature preview](../previews/preview-generic.md)]
+
+A *remote SharePoint knowledge source* uses the [Copilot Retrieval API](/microsoft-365-copilot/extensibility/api/ai-services/retrieval/overview) to query textual content directly from SharePoint in Microsoft 365, returning results to the agentic retrieval engine for merging, ranking, and response formulation. There's no search index used by this knowledge source, and only textual content is queried.
+
+At query time, the remote SharePoint knowledge source calls the Copilot Retrieval API on behalf of the user identity, so no connection strings are needed in the knowledge source definition. All content to which a user has access is in-scope for knowledge retrieval. To limit sites or constrain search, set a [filter expression](/sharepoint/dev/general-development/keyword-query-language-kql-syntax-reference). Your Azure tenant and the Microsoft 365 tenant must use the same Microsoft Entra ID tenant, and the caller's identity must be recognized by both tenants.
+
++ You can use filters to scope search by URLs, date ranges, file types, and other metadata.
+
++ SharePoint permissions and Purview labels are honored in requests for content.
+
++ Usage is billed through Microsoft 365 and a Copilot license.
+
+Like any other knowledge source, you specify a remote SharePoint knowledge source in a [knowledge base](../../agentic-retrieval-how-to-create-knowledge-base.md) and use the results as grounding data when an agent or chatbot calls a [retrieve action](../../agentic-retrieval-how-to-retrieve.md) at query time.
+
+## Prerequisites
+
++ Azure AI Search in any [region that provides agentic retrieval](../../search-region-support.md). You must have [semantic ranker enabled](../../semantic-how-to-enable-disable.md). 
+
++ SharePoint in a Microsoft 365 tenant that's under the same Microsoft Entra ID tenant as Azure.
+
++ A personal access token for local development or a user's identity from a client application.
+
++ The latest preview version of the [`Azure.Search.Documents` client library](https://www.nuget.org/packages/Azure.Search.Documents/11.8.0-beta.1) for the .NET SDK.
+
++ Permission to create and use objects on Azure AI Search. We recommend [role-based access](../../search-security-rbac.md), but you can use [API keys](../../search-security-api-keys.md) if a role assignment isn't feasible.
+
+For local development, the agentic retrieval engine uses your access token to call SharePoint on your behalf. For more information about using a personal access token on requests, see [Connect to Azure AI Search](../../search-get-started-rbac.md).
+
+## Limitations
+
+The following limitations in the [Copilot Retrieval API](/microsoft-365-copilot/extensibility/api/ai-services/retrieval/overview) apply to remote SharePoint knowledge sources.
+
++ There's no support for Copilot connectors or OneDrive content. Content is retrieved from SharePoint sites only.
+
++ Limit of 200 requests per user per hour.
+
++ Query character limit of 1,500 characters.
+
++ Hybrid queries are only supported for the following file extensions: .doc, .docx, .pptx, .pdf, .aspx, and .one.
+
++ Multimodal retrieval (nontextual content, including tables, images, and charts) isn't supported.
+
++ Maximum of 25 results from a query.
+
++ Results are returned by Copilot Retrieval API as unordered.
+
++ Invalid Keyword Query Language (KQL) filter expressions are ignored and the query continues to execute without the filter.
+
+## Check for existing knowledge sources
+
+[!INCLUDE [Check for existing knowledge sources using C#](knowledge-source-check-csharp.md)]
+
+The following JSON is an example response for a remote SharePoint knowledge source.
+
+```json
+{
+  "name": "my-sharepoint-ks",
+  "kind": "remoteSharePoint",
+  "description": "A sample remote SharePoint knowledge source",
+  "encryptionKey": null,
+  "remoteSharePointParameters": {
+    "filterExpression": "filetype:docx",
+    "containerTypeId": null,
+    "resourceMetadata": [
+      "Author",
+      "Title"
+    ]
+  }
+}
+```
+
+## Create a knowledge source
+
+Run the following code to create a remote SharePoint knowledge source.
+
+[API keys](../../search-security-api-keys.md) are used for your client connection to Azure AI Search and Azure OpenAI. Your access token is used by Azure AI Search to connect to SharePoint in Microsoft 365 on your behalf. You can only retrieve content that you're permitted to access. For more information about getting a personal access token and other values, see [Connect to Azure AI Search](../../search-get-started-rbac.md).
+
+> [!NOTE]
+> You can also use your personal access token to access Azure AI Search and Azure OpenAI if you [set up role assignments on each resource](../../search-security-rbac.md). Using API keys allows you to omit this step in this example.
+
+```csharp
+// Create a remote SharePoint knowledge source
+using Azure.Search.Documents.Indexes;
+using Azure.Search.Documents.Indexes.Models;
+using Azure.Search.Documents.KnowledgeBases.Models;
+using Azure;
+
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
+
+var knowledgeSource = new RemoteSharePointKnowledgeSource(name: "my-remote-sharepoint-ks")
+{
+    Description = "This knowledge source queries .docx files in a trusted Microsoft 365 tenant.",
+    RemoteSharePointParameters = new RemoteSharePointKnowledgeSourceParameters()
+    {
+        FilterExpression = "filetype:docx",
+        ResourceMetadata = { "Author", "Title" }
+    }
+};
+
+await indexClient.CreateOrUpdateKnowledgeSourceAsync(knowledgeSource);
+Console.WriteLine($"Knowledge source '{knowledgeSource.Name}' created or updated successfully.");
+```
+
+### Source-specific properties
+
+You can pass the following properties to create a remote SharePoint knowledge source.
+
+| Name | Description | Type | Editable | Required |
+|--|--|--|--|--|
+| `name` | The name of the knowledge source, which must be unique within the knowledge sources collection and follow the [naming guidelines](/rest/api/searchservice/naming-rules) for objects in Azure AI Search. | String | No | Yes |
+| `description` | A description of the knowledge source. | String | Yes | No |
+| `encryptionKey` | A [customer-managed key](../../search-security-manage-encryption-keys.md) to encrypt sensitive information in the knowledge source. | Object | Yes | No |
+| `remoteSharePointParameters` | Parameters specific to remote SharePoint knowledge sources: `filterExpression`, `resourceMetadata`, and `containerTypeId`. | Object | No | No |
+| `filterExpression` | An expression written in the SharePoint [KQL](/sharepoint/dev/general-development/keyword-query-language-kql-syntax-reference), which is used to specify sites and paths to content. | String | Yes |No |
+| `resourceMetadata` | A comma-delimited list of standard metadata fields: author, file name, creation date, content type, and file type. | Array | Yes | No |
+| `containerTypeId` | Container ID for the SharePoint Embedded connection. When unspecified, SharePoint Online is used. | String | Yes | No |
+
+### Filter expression examples
+
+Not all SharePoint properties are supported in the `filterExpression`. For a list of supported properties, see the [API reference](/microsoft-365-copilot/extensibility/api/ai-services/retrieval/copilotroot-retrieval). Here's some more information about queryable properties that you can use in filter: [queryable properties](/graph/connecting-external-content-manage-schema#queryable).
+
+Learn more about [KQL filters](/microsoft-365-copilot/extensibility/api/ai-services/retrieval/copilotroot-retrieval?pivots=graph-v1#example-7-use-filter-expressions) in the syntax reference.
+
+| Example | Filter expression |
+|---------|-------------------|
+| Filter to a single site by ID | `"filterExpression": "SiteID:\"00aa00aa-bb11-cc22-dd33-44ee44ee44ee\""` |
+| Filter to multiple sites by ID | `"filterExpression": "SiteID:\"00aa00aa-bb11-cc22-dd33-44ee44ee44ee\" OR SiteID:\"11bb11bb-cc22-dd33-ee44-55ff55ff55ff\""` |
+| Filter to files under a specific path | `"filterExpression": "Path:\"https://my-demo.sharepoint.com/sites/mysite/Shared Documents/en/mydocs\""` |
+| Filter to a specific date range | `"filterExpression": "LastModifiedTime >= 2024-07-22 AND LastModifiedTime <= 2025-01-08"` |
+| Filter to files of a specific file type | `"filterExpression": "FileExtension:\"docx\" OR FileExtension:\"pdf\" OR FileExtension:\"pptx\""` |
+| Filter to files of a specific information protection label | `"filterExpression": "InformationProtectionLabelId:\"f0ddcc93-d3c0-4993-b5cc-76b0a283e252\""` |
+
+## Assign to a knowledge base
+
+If you're satisfied with the knowledge source, continue to the next step: specify the knowledge source in a [knowledge base](../../search-agentic-retrieval-how-to-create.md).
+
+After the knowledge base is configured, use the [retrieve action](../../agentic-retrieval-how-to-retrieve.md) to query the knowledge source.
+
+## Query a knowledge base
+
+The [retrieve action](../../agentic-retrieval-how-to-retrieve.md) on the knowledge base provides the user identity that authorizes access to content in Microsoft 365.
+
+Azure AI Search uses the access token to call the Copilot Retrieval API on behalf of the user identity. The access token is provided in the retrieve endpoint as a `xMsQuerySourceAuthorization` HTTP header.
+
+```csharp
+using Azure;
+using Azure.Search.Documents.KnowledgeBases;
+using Azure.Search.Documents.KnowledgeBases.Models;
+
+// Get access token
+var credential = new DefaultAzureCredential();
+var tokenRequestContext = new Azure.Core.TokenRequestContext(new[] { "https://search.azure.com/.default" });
+var accessToken = await credential.GetTokenAsync(tokenRequestContext);
+string token = accessToken.Token;
+
+// Create knowledge base retrieval client
+var baseClient = new KnowledgeBaseRetrievalClient(
+    endpoint: new Uri(searchEndpoint),
+    knowledgeBaseName: knowledgeBaseName,
+    credential: new AzureKeyCredential()
+);
+
+var spMessages = new List<Dictionary<string, string>>
+{
+    new Dictionary<string, string>
+    {
+        { "role", "user" },
+        { "content", @"contoso product planning" }
+    }
+};
+
+// Create retrieval request
+var retrievalRequest = new KnowledgeBaseRetrievalRequest();
+foreach (Dictionary<string, string> message in spMessages) {
+    if (message["role"] != "system") {
+        retrievalRequest.Messages.Add(new KnowledgeBaseMessage(content: new[] { new KnowledgeBaseMessageTextContent(message["content"]) }) { Role = message["role"] });
+    }
+}
+retrievalRequest.RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort();
+var retrievalResult = await baseClient.RetrieveAsync(retrievalRequest, xMsQuerySourceAuthorization: token);
+
+Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent).Text);
+```
+
+The response might look like the following:
+
+`Contoso's product planning for the NextGen Camera includes a 2019 launch with a core package design and minor modifications for three product versions, featuring Wi-Fi enabled technology and a new mobile app for photo organization and sharing, aiming for 100,000 users within six months [ref_id:0][ref_id:1]. Research and forecasting are central to their planning, with phase two research focusing on feedback from a diverse user group to shape deliverables and milestones [ref_id:0][ref_id:1].`
+
+The retrieve request also takes a [KQL filter](/microsoft-365-copilot/extensibility/api/ai-services/retrieval/copilotroot-retrieval?pivots=graph-v1#example-7-use-filter-expressions) (`filterExpressionAddOn`) if you want to apply constraints at query time. If you specify `filterExpressionAddOn` on both the knowledge source and knowledge base retrieve action, the filters are AND'd together.
+
+Queries asking questions about the content itself are more effective than questions about where a file is located or when it was last updated. For example, if you ask, "Where is the keynote doc for Ignite 2024", you might get "No relevant content was found for your query" because the content itself doesn't disclose its location. A filter on metadata is a better solution for file location or date-specific queries.
+
+A better question to ask is, "What is the keynote doc for Ignite 2024". The response includes the synthesized answer, query activity and token counts, plus the URL and other metadata.
+
+```json
+{
+    "resourceMetadata": {
+        "Author": "Nuwan Amarathunga;Nurul Izzati",
+        "Title": "Ignite 2024 Keynote Address"
+    }
+},
+"rerankerScore": 2.489522,
+"webUrl": "https://contoso-my.sharepoint.com/keynotes/nuamarth_contoso_com/Documents/Keynote-Ignite-2024.docx",
+"searchSensitivityLabelInfo": {
+        "displayName": "Confidential\\Contoso Extended",
+        "sensitivityLabelId": "aaaaaaaa-0b0b-1c1c-2d2d-333333333333",
+        "tooltip": "Data is classified and protected. Contoso Full Time Employees (FTE) and non-employees can edit, reply, forward and print. Recipient can unprotect content with the right justification.",
+        "priority": 5,
+        "color": "#FF8C00",
+        "isEncrypted": true
+    }
+```
+
+## Delete a knowledge source
+
+[!INCLUDE [Delete knowledge source using C#](knowledge-source-delete-csharp.md)]

@@ -0,0 +1,118 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 11/20/2025
+---
+
+> [!IMPORTANT]
+> + Web Knowledge Source, which uses Grounding with Bing Search and/or Grounding with Bing Custom Search, is a [First Party Consumption Service](https://www.microsoft.com/licensing/terms/product/ForOnlineServices/EAEAS) governed by the [Grounding with Bing terms of use](https://www.microsoft.com/en-us/bing/apis/grounding-legal-enterprise) and the [Microsoft Privacy Statement](https://www.microsoft.com/en-us/privacy/privacystatement).
+>
+> + The [Microsoft Data Protection Addendum](https://www.microsoft.com/licensing/docs/view/Microsoft-Products-and-Services-Data-Protection-Addendum-DPA) doesn't apply to data sent to Web Knowledge Source. When Customer uses Web Knowledge Source, Customer Data flows outside the Azure compliance and Geo boundary. This also means use of Web Knowledge Source waives all elevated Government Community Cloud security and compliance commitments to include data sovereignty and screened/citizenship-based support, as applicable.
+>
+> + Use of Web Knowledge Source incurs costs; learn more about [pricing](https://www.microsoft.com/en-us/bing/apis/grounding-pricing).
+>
+> + Learn more about how Azure admins can [manage access to use of Web Knowledge Source](../../agentic-knowledge-source-how-to-web-manage.md).
+
+[!INCLUDE [Feature preview](../previews/preview-generic.md)]
+
+*Web Knowledge Source* enables retrieval of real-time web data from Microsoft Bing in an agentic retrieval pipeline. [Knowledge sources](../../agentic-knowledge-source-overview.md) are created independently, referenced in a [knowledge base](../../agentic-retrieval-how-to-create-knowledge-base.md), and used as grounding data when an agent or chatbot calls a [retrieve action](../../agentic-retrieval-how-to-retrieve.md) at query time.
+
+Bing Custom Search is always the search provider for Web Knowledge Source. Although you can't specify alternative search providers or engines, you can include or exclude specific *domains*, such as https://learn.microsoft.com. When no domains are specified, Web Knowledge Source has unrestricted access to the entire public internet.
+
+Web Knowledge Source works best alongside other knowledge sources. Use Web Knowledge Source when your proprietary content doesn't provide complete, up-to-date answers or when you want to supplement results with information from a commercial search engine.
+
+When you use Web Knowledge Source, keep the following in mind:
+
++ The response is always a single, formulated answer to the query instead of raw search results from the web.
+
++ Because Web Knowledge Source doesn't support extractive data, your knowledge base must use [answer synthesis](../../agentic-retrieval-how-to-answer-synthesis.md) and [low or medium reasoning effort](../../agentic-retrieval-how-to-create-knowledge-base.md#create-a-knowledge-base). You also can't define answer instructions.
+
+## Prerequisites
+
++ An Azure subscription with [access to Web Knowledge Source](../../agentic-knowledge-source-how-to-web-manage.md). By default, access is enabled. Contact your admin if access is disabled.
+
++ An Azure AI Search service in any [region that provides agentic retrieval](../../search-region-support.md). You must have [semantic ranker enabled](../../semantic-how-to-enable-disable.md). The service must also be in an [Azure public region](../../search-region-support.md#azure-public-regions), as Web Knowledge Source isn't supported in private or sovereign clouds.
+
++ The latest preview version of the [`Azure.Search.Documents` client library](https://www.nuget.org/packages/Azure.Search.Documents/11.8.0-beta.1) for the .NET SDK.
+
++ Permission to create and use objects on Azure AI Search. We recommend [role-based access](../../search-security-rbac.md), but you can use [API keys](../../search-security-api-keys.md) if a role assignment isn't feasible. For more information, see [Connect to a search service](../../search-get-started-rbac.md).
+
+## Check for existing knowledge sources
+
+[!INCLUDE [Check for existing knowledge sources using C#](knowledge-source-check-csharp.md)]
+
+The following JSON is an example response for a Web Knowledge Source resource.
+
+```json
+{
+  "WebParameters": {
+    "Domains": null
+  },
+  "Name": "my-web-ks",
+  "Description": "A sample Web Knowledge Source.",
+  "EncryptionKey": null,
+}
+```
+
+## Create a knowledge source
+
+Run the following code to create a Web Knowledge Source resource.
+
+```csharp
+// Create Web Knowledge Source
+// Create a Web knowledge source
+using Azure.Search.Documents.Indexes;
+using Azure.Search.Documents.Indexes.Models;
+using Azure;
+
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), new AzureKeyCredential(apiKey));
+
+var knowledgeSource = new WebKnowledgeSource(name: "my-web-ks")
+{
+    Description = "A sample Web Knowledge Source.",
+    WebParameters = new WebKnowledgeSourceParameters
+    {
+        Domains = new WebKnowledgeSourceDomains
+        {
+            AllowedDomains = 
+            {
+                new WebKnowledgeSourceDomain(address: "learn.microsoft.com") { IncludeSubpages = true }
+            },
+            BlockedDomains = 
+            {
+                new WebKnowledgeSourceDomain(address: "bing.com") { IncludeSubpages = false }
+            }
+        }
+    }
+};
+
+await indexClient.CreateOrUpdateKnowledgeSourceAsync(knowledgeSource);
+Console.WriteLine($"Knowledge source '{knowledgeSource.Name}' created or updated successfully.");
+```
+
+### Source-specific properties
+
+You can pass the following properties to create a Web Knowledge Source resource.
+
+| Name | Description | Type | Editable | Required |
+|--|--|--|--|--|
+| `Name` | The name of the knowledge source, which must be unique within the knowledge sources collection and follow the [naming guidelines](/rest/api/searchservice/naming-rules) for objects in Azure AI Search. | String | Yes | Yes |
+| `Description` | A description of the knowledge source. When unspecified, Azure AI Search applies a default description. | String | Yes | No |
+| `EncryptionKey` | A [customer-managed key](../../search-security-manage-encryption-keys.md) to encrypt sensitive information in the knowledge source. | Object | Yes | No |
+| `WebParameters` | Parameters specific to Web Knowledge Source. Currently, this is only `Domains`. | Object | Yes | No |
+| `Domains` | Domains to allow or block from the search space. By default, the knowledge source uses [Grounding with Bing Search](/azure/ai-foundry/agents/how-to/tools/bing-grounding) to search the entire public internet. When you specify domains, the knowledge source uses [Grounding with Bing Custom Search](/azure/ai-foundry/agents/how-to/tools/bing-custom-search) to restrict results to the specified domains. In both cases, Bing Custom Search is the search provider. | Object | Yes | No |
+| `AllowedDomains` | Domains to include in the search space. For each domain, you must specify its `address` in the `website.com` format. You can also specify whether to include the domain's subpages by setting `IncludeSubpages` to `true` or `false`. | Array | Yes | No |
+| `BlockedDomains` | Domains to exclude from the search space. For each domain, you must specify its `address` in the `website.com` format. You can also specify whether to include the domain's subpages by setting `IncludeSubpages` to `true` or `false`. | Array | Yes | No |
+
+## Assign to a knowledge base
+
+If you're satisfied with the knowledge source, continue to the next step: specify the knowledge source in a [knowledge base](../../agentic-retrieval-how-to-create-knowledge-base.md).
+
+After the knowledge base is configured, use the [retrieve action](../../agentic-retrieval-how-to-retrieve.md) to query the knowledge source.
+
+## Delete a knowledge source
+
+[!INCLUDE [Delete knowledge source using C#](knowledge-source-delete-csharp.md)]
\ No newline at end of file

@@ -0,0 +1,329 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 11/19/2025
+---
+
+[!INCLUDE [Feature preview](../previews/preview-generic.md)]
+
+In Azure AI Search, a *knowledge base* is a top-level object that orchestrates [agentic retrieval](../../agentic-retrieval-overview.md). It defines which knowledge sources to query and the default behavior for retrieval operations. At query time, the [retrieve method](../../agentic-retrieval-how-to-retrieve.md) targets the knowledge base to run the configured retrieval pipeline.
+
+A knowledge base specifies:
+
++ One or more knowledge sources that point to searchable content.
++ An optional LLM that provides reasoning capabilities for query planning and answer formulation.
++ A retrieval reasoning effort that determines whether an LLM is invoked and manages cost, latency, and quality.
++ Custom properties that control routing, source selection, output format, and object encryption.
+
+After you create a knowledge base, you can update its properties at any time. If the knowledge base is in use, updates take effect on the next retrieval.
+
+> [!IMPORTANT]
+> 2025-11-01-preview renames the 2025-08-01-preview *knowledge agent* to *knowledge base*. This is a breaking change. We recommend [migrating existing code](../../agentic-retrieval-how-to-migrate.md) to the new APIs as soon as possible.
+
+## Prerequisites
+
++ Azure AI Search in any [region that provides agentic retrieval](../../search-region-support.md). You must have [semantic ranker enabled](../../semantic-how-to-enable-disable.md). If you're using a [managed identity](../../search-how-to-managed-identities.md) for role-based access to deployed models, your search service must be on the Basic pricing tier or higher.
+
++ Azure OpenAI with a [supported LLM](#supported-models) deployment.
+
++ One or more [knowledge sources](../../agentic-knowledge-source-overview.md#supported-knowledge-sources) on your search service.
+
++ Permission to create and use objects on Azure AI Search. We recommend [role-based access](../../search-security-rbac.md). **Search Service Contributor** can create and manage a knowledge base. **Search Index Data Reader** can run queries. Alternatively, you can use [API keys](../../search-security-api-keys.md) if a role assignment isn't feasible. For more information, see [Connect to a search service](../../search-get-started-rbac.md).
+
++ The latest preview version of the [`azure-search-documents` client library](https://pypi.org/project/azure-search-documents/11.7.0b2/) for Python.
+
+> [!NOTE]
+> Although you can use the Azure portal to create knowledge bases, the portal uses the 2025-08-01-preview, which uses the previous "knowledge agent" terminology and doesn't support all 2025-11-01-preview features. For help with breaking changes, see [Migrate your agentic retrieval code](../../agentic-retrieval-how-to-migrate.md).
+
+### Supported models
+
+Use one of the following LLMs from Azure OpenAI or an equivalent open-source model. For deployment instructions, see [Deploy Azure OpenAI models with Microsoft Foundry](/azure/ai-foundry/how-to/deploy-models-openai).
+
++ `gpt-4o`
++ `gpt-4o-mini`
++ `gpt-4.1`
++ `gpt-4.1-nano`
++ `gpt-4.1-mini`
++ `gpt-5`
++ `gpt-5-nano`
++ `gpt-5-mini`
+
+## Configure access
+
+Azure AI Search needs access to the LLM from Azure OpenAI. We recommend Microsoft Entra ID for authentication and role-based access for authorization. You must be an **Owner or User Access Administrator** to assign roles. If roles aren't feasible, use key-based authentication instead.
+
+### [**Use roles**](#tab/rbac)
+
+1. [Configure Azure AI Search to use a managed identity](../../search-how-to-managed-identities.md).
+
+1. On your model provider, such as Foundry Models, assign **Cognitive Services User** to the managed identity of your search service. If you're testing locally, assign the same role to your user account.
+
+1. For local testing, follow the steps in [Quickstart: Connect without keys](../../search-get-started-rbac.md) to sign in to a specific subscription and tenant. Use `DefaultAzureCredential` instead of `AzureKeyCredential` in each request, which should look similar to the following example:
+
+    ```csharp
+    using Azure.Search.Documents.Indexes;
+    using Azure.Identity;
+    
+    var indexClient = new SearchIndexClient(new Uri(searchEndpoint), new DefaultAzureCredential());
+    ```
+
+### [**Use keys**](#tab/keys)
+
+1. [Copy an Azure AI Search admin API key](../../search-security-api-keys.md#find-existing-keys) from the Azure portal.
+
+1. Use `AzureKeyCredential` to specify the API key in each request, which should look similar to the following example:
+
+    ```csharp
+    using Azure.Search.Documents.Indexes;
+    using Azure;
+    
+    var indexClient = new SearchIndexClient(new Uri(searchEndpoint), new AzureKeyCredential(apiKey));
+    ```
+
+---
+
+> [!IMPORTANT]
+> Code snippets in this article use API keys. If you use role-based authentication, update each request accordingly. In a request that specifies both approaches, the API key takes precedence.
+
+## Check for existing knowledge bases
+
+Knowing about existing knowledge bases is helpful for either reuse or naming new objects. Any 2025-08-01-preview knowledge agents are returned in the knowledge bases collection.
+
+Run the following code to list existing knowledge bases by name.
+
+```csharp
+// List knowledge bases by name
+  using Azure.Search.Documents.Indexes;
+  
+  var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
+  var knowledgeBases = indexClient.GetKnowledgeBasesAsync();
+  
+  Console.WriteLine("Knowledge Bases:");
+  
+  await foreach (var kb in knowledgeBases)
+  {
+      Console.WriteLine($"  - {kb.Name}");
+  }
+```
+
+You can also return a single knowledge base by name to review its JSON definition.
+
+```csharp
+using Azure.Search.Documents.Indexes;
+using System.Text.Json;
+
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
+
+// Specify the knowledge base name to retrieve
+string kbNameToGet = "earth-knowledge-base";
+
+// Get a specific knowledge base definition
+var knowledgeBaseResponse = await indexClient.GetKnowledgeBaseAsync(kbNameToGet);
+var kb = knowledgeBaseResponse.Value;
+
+// Serialize to JSON for display
+string json = JsonSerializer.Serialize(kb, new JsonSerializerOptions { WriteIndented = true });
+Console.WriteLine(json);
+```
+
+The following JSON is an example of a knowledge base.
+
+```json
+{
+  "Name": "earth-knowledge-base",
+  "KnowledgeSources": [
+    {
+      "Name": "earth-knowledge-source"
+    }
+  ],
+  "Models": [
+    {}
+  ],
+  "RetrievalReasoningEffort": {},
+  "OutputMode": {},
+  "ETag": "\u00220x8DE278629D782B3\u0022",
+  "EncryptionKey": null,
+  "Description": null,
+  "RetrievalInstructions": null,
+  "AnswerInstructions": null
+}
+```
+
+## Create a knowledge base
+
+A knowledge base drives the agentic retrieval pipeline. In application code, it's called by other agents or chatbots.
+
+A knowledge base connects knowledge sources (searchable content) to an LLM deployment from Azure OpenAI. Properties on the LLM establish the connection, while properties on the knowledge source establish defaults that inform query execution and the response.
+
+Run the following code to create a knowledge base.
+
+```csharp
+using Azure.Search.Documents.Indexes.Models;
+using Azure.Search.Documents.KnowledgeBases.Models;
+
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
+
+// Create a knowledge base
+var knowledgeBase = new KnowledgeBase(
+    name: knowledgeBaseName,
+    knowledgeSources: new KnowledgeSourceReference[] { new KnowledgeSourceReference(knowledgeSourceName) }
+)
+{
+    RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort(),
+    OutputMode = KnowledgeRetrievalOutputMode.AnswerSynthesis,
+    Models = { model }
+};
+await indexClient.CreateOrUpdateKnowledgeBaseAsync(knowledgeBase);
+Console.WriteLine($"Knowledge base '{knowledgeBaseName}' created or updated successfully.");
+```
+
+```csharp
+# Create a knowledge base
+using Azure.Search.Documents.Indexes;
+using Azure.Search.Documents.Indexes.Models;
+using Azure.Search.Documents.KnowledgeBases.Models;
+using Azure;
+
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), new AzureKeyCredential(apiKey));
+
+var aoaiParams = new AzureOpenAIVectorizerParameters
+{
+    ResourceUri = new Uri(aoaiEndpoint),
+    DeploymentName = aoaiGptDeployment,
+    ModelName = aoaiGptModel
+};
+
+var knowledgeBase = new KnowledgeBase(
+    name: "my-kb",
+    knowledgeSources: new KnowledgeSourceReference[] 
+    { 
+        new KnowledgeSourceReference("hotels-sample-knowledge-source"),
+        new KnowledgeSourceReference("earth-knowledge-source")
+    }
+)
+{
+    Description = "This knowledge base handles questions directed at two unrelated sample indexes.",
+    RetrievalInstructions = "Use the hotels knowledge source for queries about where to stay, otherwise use the earth at night knowledge source.",
+    AnswerInstructions = "Provide a two sentence concise and informative answer based on the retrieved documents.",
+    OutputMode = KnowledgeRetrievalOutputMode.AnswerSynthesis,
+    Models = { new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: aoaiParams) },
+    RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort()
+};
+
+await indexClient.CreateOrUpdateKnowledgeBaseAsync(knowledgeBase);
+Console.WriteLine($"Knowledge base '{knowledgeBase.Name}' created or updated successfully.");
+```
+
+### Knowledge base properties
+
+You can pass the following properties to create a knowledge base.
+
+| Name | Description | Type | Required |
+|--|--|--|--|
+| `name` | The name of the knowledge base, which must be unique within the knowledge bases collection and follow the [naming guidelines](/rest/api/searchservice/naming-rules) for objects in Azure AI Search. | String | Yes |
+| `knowledgeSources` | One or more [supported knowledge sources](../../agentic-knowledge-source-overview.md#supported-knowledge-sources). | Array | Yes |
+| `Description` | A description of the knowledge base. The LLM uses the description to inform query planning. | String | No |
+| `RetrievalInstructions` | A prompt for the LLM to determine whether a knowledge source should be in scope for a query, which is recommended when you have multiple knowledge sources. This field influences both knowledge source selection and query formulation. For example, instructions could append information or prioritize a knowledge source. Instructions are passed directly to the LLM, which means it's possible to provide instructions that break query planning, such as instructions that result in bypassing an essential knowledge source. | String | Yes |
+| `AnswerInstructions` | Custom instructions to shape synthesized answers. The default is null. For more information, see [Use answer synthesis for citation-backed responses](../../agentic-retrieval-how-to-answer-synthesis.md). | String | Yes |
+| `OutputMode` | Valid values are `AnswerSynthesis` for an LLM-formulated answer or `ExtractedData` for full search results that you can pass to an LLM as a downstream step. | String | Yes |
+| `Models` | A connection to a [supported LLM](#supported-models) used for answer formulation or query planning. In this preview, `Models` can contain just one model, and the model provider must be Azure OpenAI. Obtain model information from the Foundry portal or a command-line request. Provide the parameters using the [KnowledgeBaseAzureOpenAIModel class](/dotnet/api/azure.search.documents.indexes.models.knowledgebaseazureopenaimodel?view=azure-dotnet-preview). You can use role-based access control instead of API keys for the Azure AI Search connection to the model. For more information, see [How to deploy Azure OpenAI models with Foundry](/azure/ai-foundry/how-to/deploy-models-openai). | Object | No |
+| `RetrievalReasoningEffort` | Determines the level of LLM-related query processing. Valid values are `minimal`, `low` (default), and `medium`. For more information, see [Set the retrieval reasoning effort](../../agentic-retrieval-how-to-set-retrieval-reasoning-effort.md). | Object | No |
+
+## Query a knowledge base
+
+Set up the instructions and messages to send to the LLM.
+
+```csharp
+string instructions = @"
+Use the earth at night index to answer the question. If you can't find relevant content, say you don't know.
+";
+
+var messages = new List<Dictionary<string, string>>
+{
+    new Dictionary<string, string>
+    {
+        { "role", "system" },
+        { "content", instructions }
+    }
+};
+```
+
+Call the `retrieve` action on the knowledge base to verify the LLM connection and return results. For more information about the `retrieve` request and response schema, see [Retrieve data using a knowledge base in Azure AI Search](../../agentic-retrieval-how-to-retrieve.md).
+
+Replace "Where does the ocean look green?" with a query string that's valid for your knowledge sources.
+
+```csharp
+using Azure.Search.Documents.KnowledgeBases;
+using Azure.Search.Documents.KnowledgeBases.Models;
+
+var baseClient = new KnowledgeBaseRetrievalClient(
+    endpoint: new Uri(searchEndpoint),
+    knowledgeBaseName: knowledgeBaseName,
+    tokenCredential: new DefaultAzureCredential()
+);
+
+messages.Add(new Dictionary<string, string>
+{
+    { "role", "user" },
+    { "content", @"Where does the ocean look green?" }
+});
+
+var retrievalRequest = new KnowledgeBaseRetrievalRequest();
+foreach (Dictionary<string, string> message in messages) {
+    if (message["role"] != "system") {
+        retrievalRequest.Messages.Add(new KnowledgeBaseMessage(content: new[] { new KnowledgeBaseMessageTextContent(message["content"]) }) { Role = message["role"] });
+    }
+}
+retrievalRequest.RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort();
+var retrievalResult = await baseClient.RetrieveAsync(retrievalRequest);
+
+messages.Add(new Dictionary<string, string>
+{
+    { "role", "assistant" },
+    { "content", (retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent).Text }
+});
+
+(retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent).Text 
+
+// Print the response, activity, and references
+Console.WriteLine("Response:");
+Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text);
+```
+
+**Key points:**
+
++ [KnowledgeBaseRetrievalRequest](/dotnet/api/azure.search.documents.knowledgebases.models.knowledgebaseretrievalrequest?view=azure-dotnet-preview&preserve-view=true) is the input contract for the retrieval request.
+
++ [RetrievalReasoningEffort](/dotnet/api/azure.search.documents.knowledgebases.models.knowledgebaseretrievalrequest.retrievalreasoningeffort?view=azure-dotnet-preview#azure-search-documents-knowledgebases-models-knowledgebaseretrievalrequest-retrievalreasoningeffort&preserve-view=true) is required. Setting it to `minimal` excludes LLMs from the query pipeline and only intents are used for the query input. The default is `low` and it supports LLM-based query planning and answer synthesis with messages and context.
+
++ [`knowledgeSourceParams`](/dotnet/api/azure.search.documents.knowledgebases.models.knowledgebaseretrievalrequest.knowledgesourceparams?view=azure-dotnet-preview&preserve-view=true) are used to overwrite default parameters at query time.
+
+The response to the sample query might look like the following example:
+
+```http
+  "response": [
+    {
+      "content": [
+        {
+          "type": "text",
+          "text": "The ocean appears green off the coast of Antarctica due to phytoplankton flourishing in the water, particularly in Granite Harbor near Antarctica’s Ross Sea, where they can grow in large quantities during spring, summer, and even autumn under the right conditions [ref_id:0]. Additionally, off the coast of Namibia, the ocean can also look green due to blooms of phytoplankton and yellow-green patches of sulfur precipitating from bacteria in oxygen-depleted waters [ref_id:1]. In the Strait of Georgia, Canada, the waters turned bright green due to a massive bloom of coccolithophores, a type of phytoplankton [ref_id:5]. Furthermore, a milky green and blue bloom was observed off the coast of Patagonia, Argentina, where nutrient-rich waters from different currents converge [ref_id:6]. Lastly, a large bloom of cyanobacteria was captured in the Baltic Sea, which can also give the water a green appearance [ref_id:9]."
+        }
+      ]
+    }
+  ]
+```
+
+## Delete a knowledge base
+
+If you no longer need the knowledge base or need to rebuild it on your search service, use this request to delete the object.
+
+```csharp
+using Azure.Search.Documents.Indexes;
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
+
+await indexClient.DeleteKnowledgeBaseAsync(knowledgeBaseName);
+System.Console.WriteLine($"Knowledge base '{knowledgeBaseName}' deleted successfully.");
+```

@@ -31,9 +31,9 @@ After you create a knowledge base, you can update its properties at any time. If
 
 + One or more [knowledge sources](../../agentic-knowledge-source-overview.md#supported-knowledge-sources) on your search service.
 
-+ Permissions on your search service. **Search Service Contributor** can create and manage a knowledge base. **Search Index Data Reader** can run queries.
++ Permission to create and use objects on Azure AI Search. We recommend [role-based access](../../search-security-rbac.md). **Search Service Contributor** can create and manage a knowledge base. **Search Index Data Reader** can run queries. Alternatively, you can use [API keys](../../search-security-api-keys.md) if a role assignment isn't feasible. For more information, see [Connect to a search service](../../search-get-started-rbac.md)
 
-+ The [2025-11-01-preview](/rest/api/searchservice/operation-groups?view=rest-searchservice-2025-11-01-preview&preserve-view=true) version of the Search Service REST APIs.
++ The latest preview version of the [`Azure.Search.Documents` client library](https://www.nuget.org/packages/Azure.Search.Documents/11.8.0-beta.1) for the .NET SDK.
 
 > [!NOTE]
 > Although you can use the Azure portal to create knowledge bases, the portal uses the 2025-08-01-preview, which uses the previous "knowledge agent" terminology and doesn't support all 2025-11-01-preview features. For help with breaking changes, see [Migrate your agentic retrieval code](../../agentic-retrieval-how-to-migrate.md).

@@ -0,0 +1,51 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 11/19/2025
+---
+
+A knowledge source is a top-level, reusable object. Knowing about existing knowledge sources is helpful for either reuse or naming new objects.
+
+Run the following code to list knowledge sources by name and type.
+
+```csharp
+// List knowledge sources by name and type
+using Azure.Search.Documents.Indexes;
+
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
+var knowledgeSources = indexClient.GetKnowledgeSourcesAsync();
+
+Console.WriteLine("Knowledge Sources:");
+
+await foreach (var ks in knowledgeSources)
+{
+    Console.WriteLine($"  Name: {ks.Name}, Type: {ks.GetType().Name}");
+}
+```
+
+You can also return a single knowledge source by name to review its JSON definition.
+
+```csharp
+using Azure.Search.Documents.Indexes;
+using System.Text.Json;
+
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
+
+// Specify the knowledge source name to retrieve
+string ksNameToGet = "earth-knowledge-source";
+
+// Get its definition
+var knowledgeSourceResponse = await indexClient.GetKnowledgeSourceAsync(ksNameToGet);
+var ks = knowledgeSourceResponse.Value;
+
+// Serialize to JSON for display
+var jsonOptions = new JsonSerializerOptions 
+{ 
+    WriteIndented = true,
+    DefaultIgnoreCondition = System.Text.Json.Serialization.JsonIgnoreCondition.Never
+};
+Console.WriteLine(JsonSerializer.Serialize(ks, ks.GetType(), jsonOptions));
+```

@@ -0,0 +1,99 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 11/19/2025
+---
+
+Before you can delete a knowledge source, you must delete any knowledge base that references it or update the knowledge base definition to remove the reference. For knowledge sources that generate an index and indexer pipeline, all *generated objects* are also deleted. However, if you used an existing index to create a knowledge source, your index isn't deleted.
+
+If you try to delete a knowledge source that's in use, the action fails and returns a list of affected knowledge bases.
+
+To delete a knowledge source:
+
+1. Get a list of all knowledge bases on your search service.
+
+    ```csharp
+    using Azure.Search.Documents.Indexes;
+    
+    var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
+    var knowledgeBases = indexClient.GetKnowledgeBasesAsync();
+    
+    Console.WriteLine("Knowledge Bases:");
+    
+    await foreach (var kb in knowledgeBases)
+    {
+        Console.WriteLine($"  - {kb.Name}");
+    }
+    ```
+
+   An example response might look like the following:
+
+   ```md
+    Knowledge Bases:
+      - earth-knowledge-base
+      - hotels-sample-knowledge-base
+      - my-demo-knowledge-base
+    ```
+
+1. Get an individual knowledge base definition to check for knowledge source references.
+
+    ```csharp
+    using Azure.Search.Documents.Indexes;
+    using System.Text.Json;
+    
+    var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
+    
+    // Specify the knowledge base name to retrieve
+    string kbNameToGet = "earth-knowledge-base";
+    
+    // Get a specific knowledge base definition
+    var knowledgeBaseResponse = await indexClient.GetKnowledgeBaseAsync(kbNameToGet);
+    var kb = knowledgeBaseResponse.Value;
+    
+    // Serialize to JSON for display
+    string json = JsonSerializer.Serialize(kb, new JsonSerializerOptions { WriteIndented = true });
+    Console.WriteLine(json);
+    ```
+    
+   An example response might look like the following:
+
+   ```json
+    {
+      "Name": "earth-knowledge-base",
+      "KnowledgeSources": [
+        {
+          "Name": "earth-knowledge-source"
+        }
+      ],
+      "Models": [
+        {}
+      ],
+      "RetrievalReasoningEffort": {},
+      "OutputMode": {},
+      "ETag": "\u00220x8DE278629D782B3\u0022",
+      "EncryptionKey": null,
+      "Description": null,
+      "RetrievalInstructions": null,
+      "AnswerInstructions": null
+    }
+   ```
+
+1. Either delete the knowledge base or [update the knowledge base](/dotnet/api/azure.search.documents.indexes.searchindexclient.createorupdateknowledgebaseasync?view=azure-dotnet-preview&preserve-view=true) to remove the knowledge source if you have multiple sources. This example shows deletion.
+
+    ```csharp
+    using Azure.Search.Documents.Indexes;
+    var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
+    
+    await indexClient.DeleteKnowledgeBaseAsync(knowledgeBaseName);
+    System.Console.WriteLine($"Knowledge base '{knowledgeBaseName}' deleted successfully.");
+    ```
+
+1. Delete the knowledge source.
+
+    ```csharp
+    await indexClient.DeleteKnowledgeSourceAsync(knowledgeSourceName);
+    System.Console.WriteLine($"Knowledge source '{knowledgeSourceName}' deleted successfully.");
+    ```
\ No newline at end of file

@@ -0,0 +1,21 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 11/19/2025
+---
+
+For indexed knowledge sources only, you can pass the following `ingestionParameters` properties to control how content is ingested and processed.
+
+| Name | Description | Type | Editable | Required |
+|--|--|--|--|--|
+| `Identity` | A [managed identity](../../search-how-to-managed-identities.md) to use in the generated indexer. | Object | Yes | No |
+| `DisableImageVerbalization` | Enables or disables the use of image verbalization. The default is `False`, which *enables* image verbalization. Set to `True` to *disable* image verbalization. | Boolean | No | No |
+| `ChatCompletionModel` | A chat completion model that verbalizes images or extracts content. Supported models are `gpt-4o`, `gpt-4o-mini`, `gpt-4.1`, `gpt-4.1-mini`, `gpt-4.1-nano`, `gpt-5`, `gpt-5-mini`, and `gpt-5-nano`. The [GenAI Prompt skill](../../cognitive-search-skill-genai-prompt.md) will be included in the generated skillset. Setting this parameter also requires that `disable_image_verbalization` is set to `False`. | Object | Only `api_key` and `deployment_name` are editable | No |
+| `EmbeddingModel` | A text embedding model that vectorizes text and image content during indexing and at query time. Supported models are `text-embedding-ada-002`, `text-embedding-3-small`, and `text-embedding-3-large`. The [Azure OpenAI Embedding skill](../../cognitive-search-skill-azure-openai-embedding.md) will be included in the generated skillset, and the [Azure OpenAI vectorizer](../../vector-search-vectorizer-azure-open-ai.md) will be included in the generated index. | Object | Only `api_key` and `deployment_name` are editable | No |
+| `ContentExtractionMode` | Controls how content is extracted from files. The default is `minimal`, which uses standard content extraction for text and images. Set to `standard` for advanced document cracking and chunking using the [Azure Content Understanding skill](../../cognitive-search-skill-content-understanding.md), which will be included in the generated skillset. For `standard` only, the `AiServices` and `AssetStore` parameters are specifiable. | String | No | No |
+| `AiServices` | A Microsoft Foundry resource to access Azure Content Understanding in Foundry Tools. Setting this parameter requires that `ContentExtractionMode` is set to `standard`. | Object | Only `api_key` is editable | Yes |
+| `IngestionSchedule` | Adds scheduling information to the generated indexer. You can also [add a schedule](../../search-howto-schedule-indexers.md) later to automate data refresh. | Object | Yes | No |
+| `IngestionPermissionOptions` | The document-level permissions to ingest from select knowledge sources: either [ADLS Gen2](../../agentic-knowledge-source-how-to-blob.md) or [indexed SharePoint](../../agentic-knowledge-source-how-to-sharepoint-indexed.md). If you specify `user_ids`, `group_ids`, or `rbac_scope`, the generated [ADLS Gen2 indexer](../../search-indexer-access-control-lists-and-role-based-access.md) or [SharePoint indexer](../../search-indexer-sharepoint-access-control-lists.md) will include the ingested permissions. | Array | No | No |

@@ -0,0 +1,53 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 11/19/2025
+---
+
+Run the following code to monitor ingestion progress and health, including [indexer status](/dotnet/api/azure.search.documents.indexes.models.knowledgesourcestatus?view=azure-dotnet-preview&preserve-view=true) for knowledge sources that generate an indexer pipeline and populate a search index.
+
+```csharp
+// Get knowledge source ingestion status
+using Azure.Search.Documents.Indexes;
+using System.Text.Json;
+
+var indexClient = new SearchIndexClient(new Uri(searchEndpoint), new AzureKeyCredential(apiKey));
+
+// Get the knowledge source status
+var statusResponse = await indexClient.GetKnowledgeSourceStatusAsync(knowledgeSourceName);
+var status = statusResponse.Value;
+
+// Serialize to JSON for display
+var json = JsonSerializer.Serialize(status, new JsonSerializerOptions { WriteIndented = true });
+Console.WriteLine(json);
+```
+
+A response for a request that includes ingestion parameters and is actively ingesting content might look like the following example.
+
+```json
+{ 
+  "synchronizationStatus": "active", // creating, active, deleting 
+  "synchronizationInterval" : "1d", // null if no schedule 
+  "currentSynchronizationState" : { // spans multiple indexer "runs" 
+    "startTime": "2025-10-27T19:30:00Z", 
+    "itemUpdatesProcessed": 1100, 
+    "itemsUpdatesFailed": 100, 
+    "itemsSkipped": 1100, 
+  }, 
+  "lastSynchronizationState" : {  // null on first sync 
+    "startTime": "2025-10-27T19:30:00Z", 
+    "endTime": "2025-10-27T19:40:01Z", // this value appears on the activity record on each /retrieve 
+    "itemUpdatesProcessed": 1100, 
+    "itemsUpdatesFailed": 100, 
+    "itemsSkipped": 1100, 
+  }, 
+  "statistics": {  // null on first sync 
+    "totalSynchronization": 25, 
+    "averageSynchronizationDuration": "00:15:20", 
+    "averageItemsProcessedPerSynchronization" : 500 
+  } 
+} 
+```

@@ -4,7 +4,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: include
-ms.date: 07/11/2025
+ms.date: 11/21/2025
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet
@@ -65,9 +65,7 @@ Once content is extracted, the [skillset](../../cognitive-search-working-with-sk
 
    1. Copy the connection string for either key one or key two. The connection string is similar to the following example:
 
-      ```http
-      DefaultEndpointsProtocol=https;AccountName=<your account name>;AccountKey=<your account key>;EndpointSuffix=core.windows.net
-      ```
+      `DefaultEndpointsProtocol=https;AccountName=<your account name>;AccountKey=<your account key>;EndpointSuffix=core.windows.net`
 
 ### Foundry Tools
 
@@ -77,9 +75,11 @@ Built-in AI enrichment is backed by Foundry Tools, including Azure Language and
 
 For this tutorial, connections to Azure AI Search require an endpoint and an API key. You can get these values from the Azure portal.
 
-1. Sign in to the [Azure portal](https://portal.azure.com), navigate to the search service **Overview** page, and copy the URL. An example endpoint might look like `https://mydemo.search.windows.net`.
+1. Sign in to the [Azure portal](https://portal.azure.com) and select your search service.
 
-1. Under **Settings** > **Keys**, copy an admin key. Admin keys are used to add, modify, and delete objects. There are two interchangeable admin keys. Copy either one.
+1. From the left pane, select **Overview** and copy the endpoint. It should be in this format: `https://my-service.search.windows.net`
+
+1. From the left pane, select **Settings** > **Keys** and copy an admin key for full rights on the service. There are two interchangeable admin keys, provided for business continuity in case you need to roll one over. You can use either key on requests to add, modify, or delete objects.
 
 ## Set up your environment
 

@@ -4,7 +4,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: include
-ms.date: 07/11/2025
+ms.date: 11/21/2025
 ms.custom:
   - ignite-2023
   - sfi-ropc-nochange
@@ -63,9 +63,7 @@ Download a zip file of the sample data repository and extract the contents. [Lea
 
    1. Copy the connection string for either key one or key two. The connection string is similar to the following example:
 
-      ```http
-      DefaultEndpointsProtocol=https;AccountName=<your account name>;AccountKey=<your account key>;EndpointSuffix=core.windows.net
-      ```
+      `DefaultEndpointsProtocol=https;AccountName=<your account name>;AccountKey=<your account key>;EndpointSuffix=core.windows.net`
 
 ### Foundry Tools
 
@@ -75,9 +73,11 @@ Built-in AI enrichment is backed by Foundry Tools, including Azure Language and
 
 For this tutorial, connections to Azure AI Search require an endpoint and an API key. You can get these values from the Azure portal.
 
-1. Sign in to the [Azure portal](https://portal.azure.com), navigate to the search service **Overview** page, and copy the URL. An example endpoint might look like `https://mydemo.search.windows.net`.
+1. Sign in to the [Azure portal](https://portal.azure.com) and select your search service.
 
-1. Under **Settings** > **Keys**, copy an admin key. Admin keys are used to add, modify, and delete objects. There are two interchangeable admin keys. Copy either one.
+1. From the left pane, select **Overview** and copy the endpoint. It should be in this format: `https://my-service.search.windows.net`
+
+1. From the left pane, select **Settings** > **Keys** and copy an admin key for full rights on the service. There are two interchangeable admin keys, provided for business continuity in case you need to roll one over. You can use either key on requests to add, modify, or delete objects.
 
    :::image type="content" source="../../media/search-get-started-rest/get-url-key.png" alt-text="Screenshot of the URL and API keys in the Azure portal.":::
 

@@ -4,7 +4,7 @@ titleSuffix: Azure AI Search
 description: Learn what multimodal search is, how Azure AI Search supports it for text and image content, and where to find detailed concepts, tutorials, and samples.
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 11/04/2025
+ms.date: 11/21/2025
 author: gmndrg
 ms.author: gimondra
 ---
@@ -35,7 +35,7 @@ Azure AI Search addresses these challenges by integrating images into the same r
 
 Multimodal search is ideal for [retrieval-augmented generation (RAG)](retrieval-augmented-generation-overview.md) scenarios. By interpreting the structural logic of images, multimodal search makes your RAG application or AI agent less likely overlook important visual details. It also provides your users with detailed answers that can be traced back to their original sources, regardless of the source's modality.
 
-## How multimodal search works in Azure AI Search
+## How does multimodal search work?
 
 To simplify the creation of a multimodal pipeline, Azure AI Search offers the **Import data (new)** wizard in the Azure portal. The wizard helps you configure a data source, define extraction and enrichment settings, and generate a multimodal index that contains text, embedded image references, and vector embeddings. For more information, see [Quickstart: Multimodal search in the Azure portal](search-get-started-portal-image-search.md).
 

@@ -11,7 +11,7 @@ ms.custom:
   - devx-track-python
   - ignite-2023
 ms.topic: concept-article
-ms.date: 09/23/2025
+ms.date: 11/21/2025
 ---
 
 # Python samples for Azure AI Search
@@ -42,7 +42,7 @@ Code samples from the Azure AI Search team demonstrate features and workflows. T
 | [Quickstart-Semantic-Search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search) | [Quickstart: Semantic ranking](search-get-started-semantic.md) | Add semantic ranking to an index schema and run semantic queries. |
 | [Quickstart-Vector-Search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Vector-Search) | [Quickstart: Vector search](search-get-started-vector.md) | Index and query vector content. |
 | [Tutorial-RAG](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Tutorial-RAG) | [Build a RAG solution using Azure AI Search](tutorial-rag-build-solution.md) | Create an indexing pipeline that loads, chunks, embeds, and ingests searchable content for RAG. |
-| [agentic-retrieval-pipeline-example](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example) | [Tutorial: Build an end-to-end agentic retrieval solution](agentic-retrieval-how-to-create-pipeline.md) | Unlike [Quickstart-Agentic-Retrieval](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Agentic-Retrieval), this sample incorporates Azure AI Agent for request orchestration. |
+| [agentic-retrieval-pipeline-example](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example) | [Tutorial: Build an end-to-end agentic retrieval solution](agentic-retrieval-how-to-create-pipeline.md) | Unlike [Quickstart-Agentic-Retrieval](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Agentic-Retrieval), this sample incorporates Foundry Agent Service for request orchestration. |
 
 ## Accelerators
 
@@ -68,6 +68,8 @@ The following samples are also published by the Azure AI Search team but aren't
 
 | Sample | Description |
 |--|--|
+| [Quickstart-Document-Permissions-Pull-API](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Document-Permissions-Pull-API) | Using an indexer "pull API" approach, flow access control lists from a data source to search results and apply permission filters that restrict access to authorized content. |
+| [Quickstart-Document-Permissions-Push-API](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Document-Permissions-Push-API) | Using the push APIs for indexing a JSON payload, flow embedded permission metadata to indexed documents and search results that are filtered based on user access to authorized content. |
 | [azure-function-search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/azure-function-search) | Use an Azure function to send queries to a search service. You can substitute this Python version for the `api` code used in [Add search to web sites with .NET](tutorial-csharp-overview.md). |
 | [bulk-insert](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/bulk-insert) | [Use the push APIs](search-how-to-load-search-index.md) to upload and index documents. |
 | [index-backup-and-restore.ipynb](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-python/code/utilities/index-backup-restore) | Make a local copy of retrievable fields in an index and push those fields to a new index. |

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 09/23/2025
+ms.date: 11/21/2025
 ---
 
 # REST samples for Azure AI Search
@@ -27,22 +27,29 @@ Code samples from the Azure AI Search team demonstrate features and workflows. T
 | Sample | Article | Description |
 |--|--|--|
 | [quickstart](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart) | [Quickstart: Full-text search](search-get-started-text.md) | Create, load, and query a search index using sample data. |
+| [quickstart-ACL](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-ACL) | [Query-time ACL and RBAC enforcement](search-query-access-control-rbac-enforcement.md) | Implement query-time access control using role-based access control (RBAC) and access control lists (ACLs). |
 | [quickstart-agentic-retrieval](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-agentic-retrieval) | [Quickstart: Agentic retrieval](search-get-started-agentic-retrieval.md) | Integrate semantic ranking with LLM-powered query planning and answer generation. |
+| [quickstart-RAG](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-RAG) | [Quickstart: Classic generative search (RAG)](search-get-started-rag.md) | Use grounding data from Azure AI Search with a chat completion model from Azure OpenAI. |
+| [quickstart-semantic-search](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-semantic-search) | [Quickstart: Semantic ranking](search-get-started-semantic.md) | Add semantic ranking to an index schema and run semantic queries. |
 | [quickstart-vectors](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-vectors) | [Quickstart: Vector search](search-get-started-vector.md) | Index and query vector content. |
-| [skillset-tutorial](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/skillset-tutorial) | [Tutorial: AI-generated searchable content from Azure blobs](tutorial-skillset.md) | 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) | [Tutorial: Fix a skillset using Debug Sessions](cognitive-search-tutorial-debug-sessions.md) | Use REST to create search objects that you later debug in the Azure portal. |
 | [custom-analyzers](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/custom-analyzers) | [Tutorial: Create a custom analyzer for phone numbers](tutorial-create-custom-analyzer.md) | Use an analyzer to preserve patterns and special characters in searchable content. |
+| [debug-sessions](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Debug-sessions) | [Tutorial: Fix a skillset using Debug Sessions](cognitive-search-tutorial-debug-sessions.md) | Create search objects that you later debug in the Azure portal. |
 | [index-json-blobs](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/index-json-blobs) | [Tutorial: Index JSON blobs from Azure Storage](search-semi-structured-data.md) | Create an indexer, data source, and index for nested JSON within a JSON array. Demonstrates the jsonArray parsing model and documentRoot parameters. |
 | [knowledge-store](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/knowledge-store) | [Create a knowledge store using REST](knowledge-store-create-rest.md) | Populate a knowledge store for knowledge mining workflows. |
 | [projections](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/projections) | [Define projections in a knowledge store](knowledge-store-projections-examples.md) | Specify the physical data structures in a knowledge store. |
+| [skillset-tutorial](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/skillset-tutorial) | [Tutorial: AI-generated searchable content from Azure blobs](tutorial-skillset.md) | Create a skillset that iterates over Azure blobs to extract information and infer structure. |
 
 ## Other samples
 
+Currently, there are no other REST samples available.
+
+<!--
 The following samples are also published by the Azure AI Search team but aren't referenced in documentation. Associated README files provide usage instructions.
 
 | Sample | Description |
 |--|--|
-| [skill-examples](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/skill-examples) | Skillset examples in indexer pipelines that include indexes and indexers so you can follow field mappings, output field mappings, and source paths. |
+| | |
+-->
 
 > [!TIP]
 > Use the [samples browser](/samples/browse/?expanded=azure&languages=http&products=azure-cognitive-search) to search for Microsoft code samples on GitHub. You can filter your search by product, service, and language.

@@ -13,7 +13,7 @@ ms.custom:
   - devx-track-python
   - ignite-2023
 ms.topic: conceptual
-ms.date: 07/31/2025
+ms.date: 11/21/2025
 ---
 
 # API versions in Azure AI Search