General freshness update for AI docs

.openpublishing.redirection.ai.json

@@ -1,5 +1,9 @@
 {
     "redirections": [
+        {
+            "source_path_from_root": "/docs/ai/advanced/sample-implementations.md",
+            "redirect_url": "/dotnet/ai/ichatclient"
+        },
         {
             "source_path_from_root": "/docs/ai/azure-ai-for-dotnet-developers.md",
             "redirect_url": "/dotnet/ai/resources/azure-ai"

docs/ai/conceptual/agents.md

@@ -3,7 +3,7 @@ title: Agents
 description: Introduction to agents
 author: luisquintanilla
 ms.author: luquinta
-ms.date: 10/01/2025
+ms.date: 12/10/2025
 ms.topic: concept-article
 ---
 
@@ -62,6 +62,7 @@ Agentic workflows can be orchestrated in a variety of ways. The following are a
 - [Concurrent](#concurrent)
 - [Handoff](#handoff)
 - [Group chat](#group-chat)
+- [Magentic](#magentic)
 
 #### Sequential
 
@@ -87,10 +88,12 @@ Agents collaborate in a shared conversation, exchanging insights in real-time.
 
 ![Group chat orchestration: User and Agents A, B, C collaborate via GroupChat to produce final output](../media/agents/groupchat-workflow.png)
 
+#### Magentic
+
+A lead agent directs other agents.
+
 ## How can I get started building agents in .NET?
 
 The building blocks in <xref:Microsoft.Extensions.AI> and <xref:Microsoft.Extensions.VectorData> supply the foundations for agents by providing modular components for AI models, tools, and data.
 
-These components serve as the foundation for Microsoft Agent Framework.
-
-For more information, see [Microsoft Agent Framework](/agent-framework/overview/agent-framework-overview).
+These components serve as the foundation for Microsoft Agent Framework. For more information, see [Microsoft Agent Framework](/agent-framework/overview/agent-framework-overview).

docs/ai/conceptual/data-ingestion.md

@@ -110,7 +110,7 @@ These processors use [Microsoft.Extensions.AI.Abstractions](https://www.nuget.or
 
 <xref:Microsoft.Extensions.DataIngestion.IngestionChunkWriter`1> stores processed chunks into a data store for later retrieval. Using Microsoft.Extensions.AI and [Microsoft.Extensions.VectorData.Abstractions](https://www.nuget.org/packages/Microsoft.Extensions.VectorData.Abstractions), the library provides the <xref:Microsoft.Extensions.DataIngestion.VectorStoreWriter`1> class that supports storing chunks in any vector store supported by Microsoft.Extensions.VectorData.
 
-Vectore stores include popular options like [Qdrant](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.Qdrant), [SQL Server](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.SqlServer), [CosmosDB](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.CosmosNoSQL), [MongoDB](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.MongoDB), [ElasticSearch](https://www.nuget.org/packages/Elastic.SemanticKernel.Connectors.Elasticsearch), and many more. The writer can also automatically generate embeddings for your chunks using Microsoft.Extensions.AI, readying them for semantic search and retrieval scenarios.
+Vector stores include popular options like [Qdrant](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.Qdrant), [SQL Server](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.SqlServer), [CosmosDB](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.CosmosNoSQL), [MongoDB](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.MongoDB), [ElasticSearch](https://www.nuget.org/packages/Elastic.SemanticKernel.Connectors.Elasticsearch), and many more. The writer can also automatically generate embeddings for your chunks using Microsoft.Extensions.AI, readying them for semantic search and retrieval scenarios.
 
 ```csharp
 OpenAIClient openAIClient = new(

docs/ai/conceptual/embeddings.md

@@ -49,7 +49,7 @@ You generate embeddings for your raw data by using an AI embedding model, which
 
 ### Store and process embeddings in a vector database
 
-After you generate embeddings, you'll need a way to store them so you can later retrieve them with calls to an LLM. Vector databases are designed to store and process vectors, so they're a natural home for embeddings. Different vector databases offer different processing capabilities, so you should choose one based on your raw data and your goals. For information about your options, see [available vector database solutions](vector-databases.md#available-vector-database-solutions).
+After you generate embeddings, you'll need a way to store them so you can later retrieve them with calls to an LLM. Vector databases are designed to store and process vectors, so they're a natural home for embeddings. Different vector databases offer different processing capabilities, so you should choose one based on your raw data and your goals. For information about your options, see [available vector database solutions](vector-databases.md#semantic-kernel-vector-database-solutions).
 
 ### Using embeddings in your LLM solution
 

docs/ai/conceptual/rag.md

@@ -1,11 +1,8 @@
 ---
 title: "Integrate Your Data into AI Apps with Retrieval-Augmented Generation"
 description: "Learn how retrieval-augmented generation lets you use your data with LLMs to generate better completions in .NET."
-ms.topic: concept-article #Don't change.
-ms.date: 05/29/2025
-
-#customer intent: As a .NET developer, I want to understand how retrieval-augmented generation works in .NET so that LLMs can use my data sources to provide more valuable completions.
-
+ms.topic: concept-article
+ms.date: 12/10/2025
 ---
 
 # Retrieval-augmented generation (RAG) provides LLM knowledge
@@ -36,6 +33,6 @@ To perform RAG, you must process each data source that you want to use for retri
 - **Converting the text to vectors**: These are embeddings. Vectors are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships between those concepts.
 - **Links between source data and embeddings**: This information is stored as metadata on the chunks you created, which are then used to help the LLMs generate citations while generating responses.
 
-## Related content
+## See also
 
-- [Prompt engineering](prompt-engineering-dotnet.md)
+- [Data ingestion](data-ingestion.md)

docs/ai/conceptual/vector-databases.md

@@ -1,11 +1,8 @@
 ---
 title: "Using Vector Databases to Extend LLM Capabilities"
 description: "Learn how vector databases extend LLM capabilities by storing and processing embeddings in .NET."
-ms.topic: concept-article #Don't change.
+ms.topic: concept-article
 ms.date: 05/29/2025
-
-#customer intent: As a .NET developer, I want to learn how vector databases store and process embeddings in .NET so I can make more data available to LLMs in my apps.
-
 ---
 
 # Vector databases for .NET + AI
@@ -44,9 +41,9 @@ Other benefits of the RAG pattern include:
 - Overcome LLM tokens limits - the heavy lifting is done through the database vector search.
 - Reduce the costs from frequent fine-tuning on updated data.
 
-## Available vector database solutions
+## Semantic Kernel vector database solutions
 
-[!INCLUDE [vector-databases](../includes/vector-databases.md)]
+[!INCLUDE [sk-connectors](../includes/sk-connectors.md)]
 
 ## Related content
 

docs/ai/dotnet-ai-ecosystem.md

@@ -1,7 +1,7 @@
 ---
 title: .NET + AI ecosystem tools and SDKs
 description: This article provides an overview of the ecosystem of SDKs and tools available to .NET developers integrating AI into their applications.
-ms.date: 11/04/2025
+ms.date: 12/10/2025
 ms.topic: overview
 ---
 
@@ -18,6 +18,12 @@ The .NET ecosystem provides many powerful tools, libraries, and services to deve
 
 `Microsoft.Extensions.AI` provides abstractions that can be implemented by various services, all adhering to the same core concepts. This library is not intended to provide APIs tailored to any specific provider's services. The goal of `Microsoft.Extensions.AI` is to act as a unifying layer within the .NET ecosystem, enabling developers to choose their preferred frameworks and libraries while ensuring seamless integration and collaboration across the ecosystem.
 
+## Other AI-related Microsoft.Extensions libraries
+
+The [📦 Microsoft.Extensions.VectorData.Abstractions package](https://www.nuget.org/packages/Microsoft.Extensions.VectorData.Abstractions/) provides a unified layer of abstractions for interacting with a variety of vector stores. It lets you store processed chunks in vector stores such as Qdrant, Azure SQL, CosmosDB, MongoDB, ElasticSearch, and many more. For more information, see [Build a .NET AI vector search app](quickstarts/build-vector-search-app.md).
+
+The [📦 Microsoft.Extensions.DataIngestion package](https://www.nuget.org/packages/Microsoft.Extensions.DataIngestion) provides foundational .NET building blocks for data ingestion. It enables developers to read, process, and prepare documents for AI and machine learning workflows, especially retrieval-augmented generation (RAG) scenarios. For more information, see [Data ingestion](conceptual/data-ingestion.md).
+
 ## Microsoft Agent Framework
 
 If you want to use low-level services, such as <xref:Microsoft.Extensions.AI.IChatClient> and <xref:Microsoft.Extensions.AI.IEmbeddingGenerator`2>, you can reference the `Microsoft.Extensions.AI.Abstractions` package directly from your app. However, if you want to build agentic AI applications with higher-level orchestration capabilities, you should use [Microsoft Agent Framework](/agent-framework/overview/agent-framework-overview). Agent Framework builds on the `Microsoft.Extensions.AI.Abstractions` package and provides concrete implementations of <xref:Microsoft.Extensions.AI.IChatClient> for different services, including OpenAI, Azure OpenAI, Azure AI Foundry, and more.
@@ -80,14 +86,9 @@ For example, you can use [Ollama](https://ollama.com/) to [connect to local AI m
 > [!NOTE]
 > The preceding SLMs can also be hosted on other services, such as Azure.
 
-## Connect to vector databases and services
-
-[!INCLUDE [vector-databases](includes/vector-databases.md)]
-
 ## Next steps
 
 - [What is Microsoft Agent Framework?](/agent-framework/overview/agent-framework-overview)
-- [What is Semantic Kernel?](/semantic-kernel/overview/)
 - [Quickstart - Summarize text using Azure AI chat app with .NET](quickstarts/prompt-model.md)
 
 [phi3]: https://azure.microsoft.com/products/phi-3

docs/ai/evaluation/evaluate-safety.md

@@ -42,9 +42,9 @@ Complete the following steps to create an MSTest project.
    ```dotnetcli
    dotnet add package Azure.AI.OpenAI
    dotnet add package Azure.Identity
-   dotnet add package Microsoft.Extensions.AI.Abstractions --prerelease
-   dotnet add package Microsoft.Extensions.AI.Evaluation --prerelease
-   dotnet add package Microsoft.Extensions.AI.Evaluation.Reporting --prerelease
+   dotnet add package Microsoft.Extensions.AI.Abstractions
+   dotnet add package Microsoft.Extensions.AI.Evaluation
+   dotnet add package Microsoft.Extensions.AI.Evaluation.Reporting
    dotnet add package Microsoft.Extensions.AI.Evaluation.Safety --prerelease
    dotnet add package Microsoft.Extensions.AI.OpenAI --prerelease
    dotnet add package Microsoft.Extensions.Configuration