Merge main into live

.github/CODEOWNERS

@@ -118,7 +118,7 @@
 # Config settings
 /docs/core/runtime-config/ @gewarren @dotnet/docs
 # Testing
-/docs/core/testing/ @meaghanlewis @Youssef1313
+/docs/core/testing/ @meaghanlewis @nohwnd @Evangelink
 # Tools
 /docs/core/tools/ @meaghanlewis @dotnet/docs
 # Tutorials

.github/agents/whats-new-net.agent.md

@@ -43,6 +43,7 @@ The source files are release notes from the product team. These take the form of
 
 - Remove any 1st person references (we, us, our), and rewrite that information in the 2nd person: tell the reader what the reader can do, using "you" to refer to the reader.
 - Remove any marketing and promotional language. These articles provide technical information, not marketing copy.
+- In feature descriptions, don't call out preview-to-preview or RC-to-RC iteration. For example, if a feature is added in Preview 3 and later improved in Preview 4, describe the feature as it currently stands—don't note the progression across individual previews. The introductory paragraph is the only place to reference the specific current preview or RC version (see the bullets in the next section).
 
 ## Updates for each file
 

.github/workflows/dependency-review.yml

@@ -24,4 +24,4 @@ jobs:
       - name: 'Checkout Repository'
         uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
       - name: 'Dependency Review'
-        uses: actions/dependency-review-action@2031cfc080254a8a887f58cffee85186f0e49e48 # v4.9.0
+        uses: actions/dependency-review-action@a1d282b36b6f3519aa1f3fc636f609c47dddb294 # v5.0.0

.github/workflows/profanity-filter.yml

@@ -25,7 +25,7 @@ jobs:
 
     - name: Profanity filter
       if: ${{ github.actor != 'dependabot[bot]' && github.actor != 'github-actions[bot]'  }}
-      uses: IEvangelist/profanity-filter@7d6e0c79ee3d33ae09b5ed0c6e2fa04b9c512e08 # main
+      uses: IEvangelist/profanity-filter@9d95889a67d0935e5af511e7141cb72c3952abaf # main
       id: profanity-filter
       with:
         token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/scorecards.yml

@@ -71,6 +71,6 @@ jobs:
 
       # Upload the results to GitHub's code scanning dashboard.
       - name: "Upload to code-scanning"
-        uses: github/codeql-action/upload-sarif@e46ed2cbd01164d986452f91f178727624ae40d7 # v3.29.5
+        uses: github/codeql-action/upload-sarif@68bde559dea0fdcac2102bfdf6230c5f70eb485e # v3.29.5
         with:
           sarif_file: results.sarif

.openpublishing.redirection.ai.json

@@ -13,6 +13,10 @@
       "redirect_url": "/dotnet/ai/microsoft-extensions-ai",
       "redirect_document_id": true
     },
+    {
+      "source_path_from_root": "/docs/ai/conceptual/ai-tools.md",
+      "redirect_url": "/dotnet/ai/conceptual/calling-tools"
+    },
     {
       "source_path_from_root": "/docs/ai/conceptual/evaluation-libraries.md",
       "redirect_url": "/dotnet/ai/evaluation/libraries",

.openpublishing.redirection.csharp.json

@@ -8,6 +8,10 @@
       "source_path_from_root": "/docs/csharp/fundamentals/types/namespaces.md",
       "redirect_url": "/dotnet/csharp/fundamentals/program-structure/namespaces"
     },
+    {
+      "source_path_from_root": "/docs/csharp/language-reference/compiler-messages/cs1763.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
     {
       "source_path_from_root": "/docs/csharp/misc/cs0017.md",
       "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/entry-point-errors"
@@ -56,14 +60,38 @@
       "source_path_from_root": "/docs/csharp/misc/cs0276.md",
       "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/property-declaration-errors"
     },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs0406.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs0409.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
     {
       "source_path_from_root": "/docs/csharp/misc/cs0410.md",
       "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/delegate-function-pointer-diagnostics"
     },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs0411.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
     {
       "source_path_from_root": "/docs/csharp/misc/cs0442.md",
       "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/property-declaration-errors"
     },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs0452.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs0453.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs0456.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
     {
       "source_path_from_root": "/docs/csharp/misc/cs0463.md",
       "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/overloaded-operator-errors"
@@ -108,6 +136,26 @@
       "source_path_from_root": "/docs/csharp/misc/cs0659.md",
       "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/overloaded-operator-errors"
     },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs0693.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs0699.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs0701.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs0704.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs0718.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
     {
       "source_path_from_root": "/docs/csharp/misc/cs1021.md",
       "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/overloaded-operator-errors"
@@ -272,6 +320,14 @@
       "source_path_from_root": "/docs/csharp/misc/cs1715.md",
       "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/property-declaration-errors"
     },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs1720.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs1948.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
     {
       "source_path_from_root": "/docs/csharp/misc/cs1958.md",
       "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/delegate-function-pointer-diagnostics"
@@ -280,6 +336,10 @@
       "source_path_from_root": "/docs/csharp/misc/cs2017.md",
       "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/entry-point-errors"
     },
+    {
+      "source_path_from_root": "/docs/csharp/misc/cs3024.md",
+      "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/generic-type-parameters-errors"
+    },
     {
       "source_path_from_root": "/docs/csharp/misc/cs5001.md",
       "redirect_url": "/dotnet/csharp/language-reference/compiler-messages/entry-point-errors"

docs/ai/conceptual/data-ingestion.md

@@ -16,7 +16,7 @@ Data ingestion is the process of collecting, reading, and preparing data from di
 - **Transform** the data by cleaning, chunking, enriching, or converting formats.
 - **Load** the data into a destination like a database, vector store, or AI model for retrieval and analysis.
 
-For AI and machine learning scenarios, especially Retrieval-Augmented Generation (RAG), data ingestion is not just about converting data from one format to another. It is about making data usable for intelligent applications. This means representing documents in a way that preserves their structure and meaning, splitting them into manageable chunks, enriching them with metadata or embeddings, and storing them so they can be retrieved quickly and accurately.
+For AI and machine learning scenarios, especially retrieval-augmented generation (RAG), data ingestion is not just about converting data from one format to another. It is about making data usable for intelligent applications. This means representing documents in a way that preserves their structure and meaning, splitting them into manageable chunks, enriching them with metadata or embeddings, and storing them so they can be retrieved quickly and accurately.
 
 ## Why data ingestion matters for AI applications
 
@@ -26,37 +26,9 @@ Your chatbot needs to understand and search through thousands of documents to pr
 
 This is where data ingestion becomes critical. You need to extract text from different file formats, break large documents into smaller chunks that fit within AI model limits, enrich the content with metadata, generate embeddings for semantic search, and store everything in a way that enables fast retrieval. Each step requires careful consideration of how to preserve the original meaning and context.
 
-## The Microsoft.Extensions.DataIngestion library
-
-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.
-
-With these building blocks, you can create robust, flexible, and intelligent data ingestion pipelines tailored for your application needs:
-
-- **Unified document representation:** Represent any file type (for example, PDF, Image, or Microsoft Word) in a consistent format that works well with large language models.
-- **Flexible data ingestion:** Read documents from both cloud services and local sources using multiple built-in readers, making it easy to bring in data from wherever it lives.
-- **Built-in AI enhancements:** Automatically enrich content with summaries, sentiment analysis, keyword extraction, and classification, preparing your data for intelligent workflows.
-- **Customizable chunking strategies:** Split documents into chunks using token-based, section-based, or semantic-aware approaches, so you can optimize for your retrieval and analysis needs.
-- **Production-ready storage:** Store processed chunks in popular vector databases and document stores, with support for embedding generation, making your pipelines ready for real-world scenarios.
-- **End-to-end pipeline composition:** Chain together readers, processors, chunkers, and writers with the <xref:Microsoft.Extensions.DataIngestion.IngestionPipeline`1> API, reducing boilerplate and making it easy to build, customize, and extend complete workflows.
-- **Performance and scalability:** Designed for scalable data processing, these components can handle large volumes of data efficiently, making them suitable for enterprise-grade applications.
-
-All of these components are open and extensible by design. You can add custom logic and new connectors, and extend the system to support emerging AI scenarios. By standardizing how documents are represented, processed, and stored, .NET developers can build reliable, scalable, and maintainable data pipelines without "reinventing the wheel" for every project.
-
-### Built on stable foundations
-
-![Data Ingestion Architecture Diagram](../media/data-ingestion/dataingestion.png)
-
-These data ingestion building blocks are built on top of proven and extensible components in the .NET ecosystem, ensuring reliability, interoperability, and seamless integration with existing AI workflows:
-
-- **Microsoft.ML.Tokenizers:** Tokenizers provide the foundation for chunking documents based on tokens. This enables precise splitting of content, which is essential for preparing data for large language models and optimizing retrieval strategies.
-- **Microsoft.Extensions.AI:** This set of libraries powers enrichment transformations using large language models. It enables features like summarization, sentiment analysis, keyword extraction, and embedding generation, making it easy to enhance your data with intelligent insights.
-- **Microsoft.Extensions.VectorData:** This set of libraries offers a consistent interface for storing processed chunks in a wide variety of vector stores, including Qdrant, Azure SQL, CosmosDB, MongoDB, ElasticSearch, and many more. This ensures your data pipelines are ready for production and can scale across different storage backends.
-
-In addition to familiar patterns and tools, these abstractions build on already extensible components. Plug-in capability and interoperability are paramount, so as the rest of the .NET AI ecosystem grows, the capabilities of the data ingestion components grow as well. This approach empowers developers to easily integrate new providers, enrichments, and storage options, keeping their pipelines future-ready and adaptable to evolving AI scenarios.
-
 ## Data ingestion building blocks
 
-The [Microsoft.Extensions.DataIngestion](https://www.nuget.org/packages/Microsoft.Extensions.DataIngestion) library is built around several key components that work together to create a complete data processing pipeline. This section explores each component and how they fit together.
+The [Microsoft.Extensions.DataIngestion](medi-library.md) library is built around several key components that work together to create a complete data processing pipeline. This section explores each component and how they fit together.
 
 ### Documents and document readers
 

docs/ai/conceptual/medi-library.md

@@ -0,0 +1,39 @@
+---
+title: "The Microsoft.Extensions.DataIngestion library"
+description: "Learn about the Microsoft.Extensions.DataIngestion library, which provides foundational .NET building blocks for data ingestion."
+ms.topic: concept-article
+ms.date: 04/15/2026
+ai-usage: ai-assisted
+---
+
+# The Microsoft.Extensions.DataIngestion library
+
+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.
+
+With these building blocks, you can create robust, flexible, and intelligent data ingestion pipelines tailored for your application needs:
+
+- **Unified document representation:** Represent any file type (for example, PDF, Image, or Microsoft Word) in a consistent format that works well with large language models.
+- **Flexible data ingestion:** Read documents from both cloud services and local sources using multiple built-in readers, making it easy to bring in data from wherever it lives.
+- **Built-in AI enhancements:** Automatically enrich content with summaries, sentiment analysis, keyword extraction, and classification, preparing your data for intelligent workflows.
+- **Customizable chunking strategies:** Split documents into chunks using token-based, section-based, or semantic-aware approaches, so you can optimize for your retrieval and analysis needs.
+- **Production-ready storage:** Store processed chunks in popular vector databases and document stores, with support for embedding generation, making your pipelines ready for real-world scenarios.
+- **End-to-end pipeline composition:** Chain together readers, processors, chunkers, and writers with the <xref:Microsoft.Extensions.DataIngestion.IngestionPipeline`1> API, reducing boilerplate and making it easy to build, customize, and extend complete workflows.
+- **Performance and scalability:** Designed for scalable data processing, these components can handle large volumes of data efficiently, making them suitable for enterprise-grade applications.
+
+All of these components are open and extensible by design. You can add custom logic and new connectors, and extend the system to support emerging AI scenarios. By standardizing how documents are represented, processed, and stored, .NET developers can build reliable, scalable, and maintainable data pipelines without "reinventing the wheel" for every project.
+
+## Built on stable foundations
+
+![Data Ingestion Architecture Diagram](../media/data-ingestion/dataingestion.png)
+
+These data ingestion building blocks are built on top of proven and extensible components in the .NET ecosystem, ensuring reliability, interoperability, and seamless integration with existing AI workflows:
+
+- **Microsoft.ML.Tokenizers:** Tokenizers provide the foundation for chunking documents based on tokens. This enables precise splitting of content, which is essential for preparing data for large language models and optimizing retrieval strategies.
+- **Microsoft.Extensions.AI:** This set of libraries powers enrichment transformations using large language models. It enables features like summarization, sentiment analysis, keyword extraction, and embedding generation, making it easy to enhance your data with intelligent insights.
+- **Microsoft.Extensions.VectorData:** This set of libraries offers a consistent interface for storing processed chunks in a wide variety of vector stores, including Qdrant, Azure SQL, CosmosDB, MongoDB, ElasticSearch, and many more. This ensures your data pipelines are ready for production and can scale across different storage backends.
+
+In addition to familiar patterns and tools, these abstractions build on already extensible components. Plug-in capability and interoperability are paramount, so as the rest of the .NET AI ecosystem grows, the capabilities of the data ingestion components grow as well. This approach empowers developers to easily integrate new providers, enrichments, and storage options, keeping their pipelines future-ready and adaptable to evolving AI scenarios.
+
+## See also
+
+- [Data ingestion](data-ingestion.md)