Merge pull request #42813 from dotnet/main

Merge main into live

Author: dotnet-policy-service[bot]

.github/workflows/clean-repo.yml

@@ -21,7 +21,7 @@ jobs:
         with:
           egress-policy: audit
 
-      - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+      - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0
 
       # Call clean repo
       - name: Clean repo

.github/workflows/dependabot-bot.yml

@@ -30,7 +30,7 @@ jobs:
         with:
           egress-policy: audit
 
-      - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+      - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0
       # - name: Setup .NET
       #   uses: actions/setup-dotnet@4d4a70f4a5b2a5a5329f13be4ac933f2c9206ac0
       #   with:

.github/workflows/dependency-review.yml

@@ -22,6 +22,6 @@ jobs:
           egress-policy: audit
 
       - name: 'Checkout Repository'
-        uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+        uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0
       - name: 'Dependency Review'
         uses: actions/dependency-review-action@5a2ce3f5b92ee19cbb1541a4984c76d921601d7c # v4.3.4

.github/workflows/docs-verifier.yml

@@ -19,7 +19,7 @@ jobs:
           egress-policy: audit
 
       - name: Checkout the repository
-        uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+        uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0
 
       - name: Validate
         uses: dotnet/docs-actions/actions/docs-verifier@5e8bcc78465d45a7544bba56509a1a69922b6a5a # main

.github/workflows/markdownlint.yml

@@ -26,7 +26,7 @@ jobs:
         with:
           egress-policy: audit
 
-      - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+      - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0
       - uses: DavidAnson/markdownlint-cli2-action@b4c9feab76d8025d1e83c653fa3990936df0e6c8 # v16.0.0
         with:
           config: ".markdownlint-cli2.jsonc"

.github/workflows/scorecards.yml

@@ -36,7 +36,7 @@ jobs:
           egress-policy: audit
 
       - name: "Checkout code"
-        uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+        uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0
         with:
           persist-credentials: false
 
@@ -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@461ef6c76dfe95d5c364de2f431ddbd31a417628 # v3.26.9
+        uses: github/codeql-action/upload-sarif@e2b3eafc8d227b0241d48be5f425d47c2d750a13 # v3.26.10
         with:
           sarif_file: results.sarif

.github/workflows/snippets5000.yml

@@ -36,7 +36,7 @@ jobs:
         egress-policy: audit
 
     - name: Checkout repository
-      uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 #@v4.1.7
+      uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 #@v4.2.0
 
     # Get the latest preview SDK (or sdk not installed by the runner)
     - name: Setup .NET

.github/workflows/whats-new.yml

@@ -27,7 +27,7 @@ jobs:
         with:
           egress-policy: audit
 
-      - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+      - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0
 
       - name: "Print manual run reason"
         if: ${{ github.event_name == 'workflow_dispatch' }}

docs/ai/conceptual/understanding-tokens.md

@@ -81,7 +81,7 @@ As training continues, the model adds any new tokens in the training text to its
 
 The semantic relationships between the tokens can be analyzed by using these token ID sequences. Multi-valued numeric vectors, known as [embeddings](embeddings.md), are used to represent these relationships. An embedding is assigned to each token based on how commonly it's used together with, or in similar contexts to, the other tokens.
 
-After it's trained, a model can calculate an embedding for text that contains multiple tokens. The model tokenizes the text, then calculates an overall embeddings value based on the learned embeddings of the individual tokens. This technique can be used for semantic document searches or adding [memories](/semantic-kernel/memories/) to an AI.
+After it's trained, a model can calculate an embedding for text that contains multiple tokens. The model tokenizes the text, then calculates an overall embeddings value based on the learned embeddings of the individual tokens. This technique can be used for semantic document searches or adding [vector stores](/semantic-kernel/concepts/vector-store-connectors/) to an AI.
 
 During output generation, the model predicts a vector value for the next token in the sequence. The model then selects the next token from it's vocabulary based on this vector value. In practice, the model calculates multiple vectors by using various elements of the previous tokens' embeddings. The model then evaluates all potential tokens from these vectors and selects the most probable one to continue the sequence.
 

docs/ai/how-to/app-service-db-auth.md

@@ -13,7 +13,7 @@ zone_pivot_groups: azure-interface
 
 This article demonstrates how to manage the connection between your App Service .NET application and a [vector database solution](../conceptual/vector-databases.md). It covers using Microsoft Entra managed identities for supported services and securely storing connection strings for others.
 
-By adding a vector database to your application, you can enable [semantic memories](/semantic-kernel/memories/) for your AI. The [Semantic Kernel SDK](/semantic-kernel/overview) for .NET enables you to easily implement memory storage and recall using your preferred vector database solution.
+By adding a vector database to your application, you can enable [semantic memories or *vector stores*]([vector stores](/semantic-kernel/concepts/vector-store-connectors/)) for your AI. The [Semantic Kernel SDK](/semantic-kernel/overview) for .NET enables you to easily implement memory storage and recall using your preferred vector database solution.
 
 ## Prerequisites
 
@@ -22,7 +22,7 @@ By adding a vector database to your application, you can enable [semantic memori
 * [`Microsoft.SemanticKernel` NuGet package](https://www.nuget.org/packages/Microsoft.SemanticKernel)
 * [`Microsoft.SemanticKernel.Plugins.Memory` NuGet package](https://www.nuget.org/packages/Microsoft.SemanticKernel.Plugins.Memory)
 * [Create and deploy a .NET application to App Service](/azure/app-service/quickstart-dotnetcore)
-* [Create and deploy a vector database solution](/semantic-kernel/memories/vector-db)
+* [Create and deploy a vector database solution](/semantic-kernel/concepts/ai-services/integrations#vector-database-solutions)
 
 ## Use Microsoft Entra managed identity for authentication
 
@@ -190,7 +190,7 @@ Before following these steps, retrieve a connection string for your vector datab
 > [!IMPORTANT]
 > Before following these steps, ensure you have [created a Key Vault using the Azure CLI](/azure/key-vault/general/quick-create-cli).
 
-1. Grant your user account permissions to your key vault through Role-Based Access Control (RBAC), assign a role using the Azure CLI command [`az role assignment create`](/cli/azure/role/assignment?view=azure-cli-latest#az-role-assignment-create):
+1. Grant your user account permissions to your key vault through Role-Based Access Control (RBAC), assign a role using the Azure CLI command [`az role assignment create`](/cli/azure/role/assignment#az-role-assignment-create):
 
     ```azurecli
     az role assignment create \
@@ -199,7 +199,7 @@ Before following these steps, retrieve a connection string for your vector datab
     --scope "/subscriptions/<subscriptionId>/resourceGroups/<resourceGroupName>/providers/Microsoft.KeyVault/vaults/<keyVaultName>"
     ```
 
-1. Add the connection string to Key Vault using the Azure CLI command [`az keyvault secret set`](/cli/azure/keyvault/secret?view=azure-cli-latest#az-keyvault-secret-set):
+1. Add the connection string to Key Vault using the Azure CLI command [`az keyvault secret set`](/cli/azure/keyvault/secret#az-keyvault-secret-set):
 
     ```azurecli
     az keyvault secret set \
@@ -271,7 +271,7 @@ Before following these steps, retrieve a connection string for your vector datab
 
 :::zone target="docs" pivot="azure-cli"
 
-Add or edit an app setting with the Azure CLI command [`az webapp config connection-string set`](/cli/azure/webapp/config/connection-string?view=azure-cli-latest#az-webapp-config-connection-string-set):
+Add or edit an app setting with the Azure CLI command [`az webapp config connection-string set`](/cli/azure/webapp/config/connection-string#az-webapp-config-connection-string-set):
 
 ```azurecli
 az webapp config connection-string set \