Migrate vector store docs from Microsoft.SemanticKernel.Connectors.* to CommunityToolkit.VectorData.*

docs/ai/vector-stores/define-your-data-model.md

@@ -127,7 +127,7 @@ Use the <xref:Microsoft.Extensions.VectorData.VectorStoreVectorAttribute> attrib
 It's also possible to use <xref:Microsoft.Extensions.VectorData.VectorStoreVectorAttribute> on properties that don't have a vector type, for example, a property of type `string`. When a property is decorated in this way, you need to provide an <xref:Microsoft.Extensions.AI.IEmbeddingGenerator> instance to the vector store. When upserting the record, the text that's in the `string` property is automatically converted and stored as a vector in the database. (It's not possible to retrieve a vector using this mechanism.)
 
 ```csharp
-[VectorStoreVector(Dimensions: 4, DistanceFunction = DistanceFunction.CosineSimilarity, IndexKind = IndexKind.Hnsw)]
+[VectorStoreVector(dimensions: 4, DistanceFunction = DistanceFunction.CosineSimilarity, IndexKind = IndexKind.Hnsw)]
 public string DescriptionEmbedding { get; set; }
 ```
 
@@ -138,7 +138,7 @@ The following table shows the parameters for `VectorStoreVectorAttribute`.
 
 | Parameter                                                                          | Required | Description |
 |------------------------------------------------------------------------------------|:--------:|-------------|
-| <xref:Microsoft.Extensions.VectorData.VectorStoreVectorAttribute.Dimensions>       | Yes      | The number of dimensions that the vector has. This is required when creating a vector index for a collection. |
+| `dimensions`                                                                       | Yes      | The number of dimensions that the vector has. This is required when creating a vector index for a collection. Pass as a constructor argument: `[VectorStoreVector(dimensions: 1536)]`. |
 | <xref:Microsoft.Extensions.VectorData.VectorStoreVectorAttribute.IndexKind>        | No       | The type of index to index the vector with. Default varies by vector store type. |
 | <xref:Microsoft.Extensions.VectorData.VectorStoreVectorAttribute.DistanceFunction> | No       | The type of function to use when doing vector comparison during vector search over this vector. Default varies by vector store type. |
 | <xref:Microsoft.Extensions.VectorData.VectorStoreVectorAttribute.StorageName>      | No       | Can be used to supply an alternative name for the property in the database. This parameter is not supported by all providers, for example, where alternatives like `JsonPropertyNameAttribute` is supported. |
@@ -174,7 +174,7 @@ Vector databases are all about storing *embeddings* - or numerical representatio
 You can define your vector property as `float[]` or `ReadOnlyMemory<float>`, representing the embedding directly, and generate embeddings yourself before each operation:
 
 ```csharp
-[VectorStoreVector(Dimensions: 1536)]
+[VectorStoreVector(dimensions: 1536)]
 public ReadOnlyMemory<float>? DescriptionEmbedding { get; set; }
 ```
 
@@ -196,7 +196,7 @@ The recommended approach is to configure an <xref:Microsoft.Extensions.AI.IEmbed
 First, define the vector property as `string`:
 
 ```csharp
-[VectorStoreVector(Dimensions: 1536)]
+[VectorStoreVector(dimensions: 1536)]
 public string DescriptionEmbedding { get; set; }
 ```
 

docs/ai/vector-stores/how-to/build-vector-search-app.md

@@ -65,7 +65,7 @@ Complete the following steps to create a .NET console app that can:
     dotnet add package Azure.Identity
     dotnet add package Azure.AI.OpenAI
     dotnet add package Microsoft.Extensions.AI.OpenAI
-    dotnet add package Microsoft.SemanticKernel.Connectors.InMemory --prerelease
+    dotnet add package CommunityToolkit.VectorData.InMemory
     dotnet add package Microsoft.Extensions.Configuration
     dotnet add package Microsoft.Extensions.Configuration.UserSecrets
     dotnet add package System.Linq.AsyncEnumerable
@@ -75,7 +75,7 @@ Complete the following steps to create a .NET console app that can:
 
     - [`Azure.Identity`](https://www.nuget.org/packages/Azure.Identity) provides [`Microsoft Entra ID`](/entra/fundamentals/whatis) token authentication support across the Azure SDK using classes such as `DefaultAzureCredential`.
     - [`Azure.AI.OpenAI`](https://www.nuget.org/packages/Azure.AI.OpenAI) is the official package for using OpenAI's .NET library with the Azure OpenAI Service.
-    - [`Microsoft.SemanticKernel.Connectors.InMemory`](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.InMemory) provides an in-memory vector store class to hold queryable vector data records. It also brings in [`Microsoft.Extensions.VectorData.Abstractions`](https://www.nuget.org/packages/Microsoft.Extensions.VectorData.Abstractions), which enables CRUD and search operations on vector stores.
+    - [`CommunityToolkit.VectorData.InMemory`](https://www.nuget.org/packages/CommunityToolkit.VectorData.InMemory) provides an in-memory vector store class to hold queryable vector data records. It also brings in [`Microsoft.Extensions.VectorData.Abstractions`](https://www.nuget.org/packages/Microsoft.Extensions.VectorData.Abstractions), which enables CRUD and search operations on vector stores.
     - [Microsoft.Extensions.Configuration](https://www.nuget.org/packages/Microsoft.Extensions.Configuration) provides an implementation of key-value pair—based configuration.
     - [`Microsoft.Extensions.Configuration.UserSecrets`](https://www.nuget.org/packages/Microsoft.Extensions.Configuration.UserSecrets) is a user secrets configuration provider implementation for `Microsoft.Extensions.Configuration`.
 
@@ -85,7 +85,7 @@ Complete the following steps to create a .NET console app that can:
 
     ```bash
     dotnet package add Microsoft.Extensions.AI.OpenAI --prerelease
-    dotnet package add Microsoft.SemanticKernel.Connectors.InMemory --prerelease
+    dotnet package add CommunityToolkit.VectorData.InMemory
     dotnet package add Microsoft.Extensions.Configuration
     dotnet package add Microsoft.Extensions.Configuration.UserSecrets
     dotnet package add System.Linq.AsyncEnumerable
@@ -94,7 +94,7 @@ Complete the following steps to create a .NET console app that can:
     The following list describes each package in the `VectorDataAI` app:
 
     - [`Microsoft.Extensions.AI.OpenAI`](https://www.nuget.org/packages/Microsoft.Extensions.AI.OpenAI) provides AI abstractions for OpenAI-compatible models or endpoints. This library also includes the official [`OpenAI`](https://www.nuget.org/packages/OpenAI) library for the OpenAI service API as a dependency.
-    - [`Microsoft.SemanticKernel.Connectors.InMemory`](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.InMemory) provides an in-memory vector store class to hold queryable vector data records. It also brings in [`Microsoft.Extensions.VectorData.Abstractions`](https://www.nuget.org/packages/Microsoft.Extensions.VectorData.Abstractions), which enables CRUD and search operations on vector stores.
+    - [`CommunityToolkit.VectorData.InMemory`](https://www.nuget.org/packages/CommunityToolkit.VectorData.InMemory) provides an in-memory vector store class to hold queryable vector data records. It also brings in [`Microsoft.Extensions.VectorData.Abstractions`](https://www.nuget.org/packages/Microsoft.Extensions.VectorData.Abstractions), which enables CRUD and search operations on vector stores.
     - [Microsoft.Extensions.Configuration](https://www.nuget.org/packages/Microsoft.Extensions.Configuration) provides an implementation of key-value pair—based configuration.
     - [`Microsoft.Extensions.Configuration.UserSecrets`](https://www.nuget.org/packages/Microsoft.Extensions.Configuration.UserSecrets) is a user secrets configuration provider implementation for `Microsoft.Extensions.Configuration`.
 

docs/ai/vector-stores/how-to/use-vector-stores.md

@@ -22,10 +22,10 @@ This article shows you how to use the key features of the library.
 Install a provider package for your vector database. The `Microsoft.Extensions.VectorData.Abstractions` package is brought in automatically as a transitive dependency. The following example uses the in-memory provider for development and testing:
 
 ```dotnetcli
-dotnet package add Microsoft.SemanticKernel.Connectors.InMemory --prerelease
+dotnet package add CommunityToolkit.VectorData.InMemory
 ```
 
-For production scenarios, replace `Microsoft.SemanticKernel.Connectors.InMemory` with the provider for your database. For available providers, see [Out-of-the-box Vector Store providers](/semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/). (Despite the inclusion of "SemanticKernel" in the provider package names, these providers have nothing to do with Semantic Kernel and are usable anywhere in .NET, including Agent Framework.)
+For production scenarios, replace `CommunityToolkit.VectorData.InMemory` with the provider for your database. For available providers, see [Out-of-the-box Vector Store providers](/semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/).
 
 ## Define a data model
 

docs/ai/vector-stores/how-to/vector-store-data-ingestion.md

@@ -36,14 +36,14 @@ The rest of these instructions assume that you're using a container with these s
 
 ## Create your project
 
-Create a new project and add NuGet package references for the [Redis provider](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.Redis), the [OpenXml package](https://www.nuget.org/packages/DocumentFormat.OpenXml) to read the Word document, and the Azure OpenAI packages for generating embeddings.
+Create a new project and add NuGet package references for the [Redis provider](https://www.nuget.org/packages/CommunityToolkit.VectorData.Redis), the [OpenXml package](https://www.nuget.org/packages/DocumentFormat.OpenXml) to read the Word document, and the Azure OpenAI packages for generating embeddings.
 
 ```dotnetcli
 dotnet new console --framework net8.0 --name VectorIngest
 cd VectorIngest
 dotnet package add Azure.AI.OpenAI
 dotnet package add Microsoft.Extensions.AI.OpenAI
-dotnet package add Microsoft.SemanticKernel.Connectors.Redis --prerelease
+dotnet package add CommunityToolkit.VectorData.Redis
 dotnet package add DocumentFormat.OpenXml
 ```
 

docs/ai/vector-stores/manage-data.md

@@ -24,7 +24,7 @@ public class Hotel
     [VectorStoreData(IsFullTextIndexed = true)]
     public required string Description { get; set; }
 
-    [VectorStoreVector(Dimensions: 1536, DistanceFunction = DistanceFunction.CosineSimilarity)]
+    [VectorStoreVector(dimensions: 1536, DistanceFunction = DistanceFunction.CosineSimilarity)]
     public required string DescriptionEmbedding { get; set; }
 
     [VectorStoreData(IsIndexed = true)]

docs/ai/vector-stores/overview.md

@@ -55,7 +55,7 @@ Here's a minimal end-to-end example that creates a collection, upserts records,
 ```csharp
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.VectorData;
-using Microsoft.SemanticKernel.Connectors.InMemory;
+using CommunityToolkit.VectorData.InMemory;
 
 // Configure an embedding generator to transform text to embeddings
 IEmbeddingGenerator<string, Embedding<float>> embeddingGenerator = ...;
@@ -85,22 +85,19 @@ class Movie
     [VectorStoreData]
     public string Title { get; set; }
 
-    [VectorStoreVector(Dimensions: 1536)]
+    [VectorStoreVector(dimensions: 1536)]
     public string Description { get; set; }
 }
 ```
 
 ## Vector store providers
 
-The `Microsoft.Extensions.VectorData.Abstractions` package defines the abstractions, and separate [provider packages](/semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/) provide implementations for specific vector databases. Choose the provider that matches your vector database, for example, [Microsoft.SemanticKernel.Connectors.AzureAISearch](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.AzureAISearch).
-
-> [!NOTE]
-> Despite the inclusion of "SemanticKernel" in the package names, these providers have nothing to do with Semantic Kernel and are usable anywhere in .NET, including Agent Framework.
+The `Microsoft.Extensions.VectorData.Abstractions` package defines the abstractions, and separate provider packages provide implementations for specific vector databases. Choose the provider that matches your vector database, for example, [CommunityToolkit.VectorData.AzureAISearch](https://www.nuget.org/packages/CommunityToolkit.VectorData.AzureAISearch).
 
 All providers implement the same <xref:Microsoft.Extensions.VectorData.VectorStore> and <xref:Microsoft.Extensions.VectorData.VectorStoreCollection`2> abstract classes, so you can switch between them without changing your application logic.
 
 > [!TIP]
-> Use the in-memory provider ([Microsoft.SemanticKernel.Connectors.InMemory](https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.InMemory)) during initial development/prototyping - it doesn't require any external service or configuration, and you can swap it out for a production provider later. Avoid using the InMemory provider for testing, as there can be important differences between this provider and your production database. Consider using [testcontainers](https://dotnet.testcontainers.org/) to run tests against your production database system.
+> Use the in-memory provider ([CommunityToolkit.VectorData.InMemory](https://www.nuget.org/packages/CommunityToolkit.VectorData.InMemory)) during initial development/prototyping - it doesn't require any external service or configuration, and you can swap it out for a production provider later. Avoid using the InMemory provider for testing, as there can be important differences between this provider and your production database. Consider using [testcontainers](https://dotnet.testcontainers.org/) to run tests against your production database system.
 
 ## Related content
 

docs/ai/vector-stores/snippets/chat-with-data/azure-openai/CloudService.cs

@@ -8,13 +8,13 @@
     public int Key { get; set; }
 
     [VectorStoreData]
     public string Name { get; set; }
 
     [VectorStoreData]
     public string Description { get; set; }
 
     [VectorStoreVector(
-        Dimensions: 384,
+        dimensions: 384,
         DistanceFunction = DistanceFunction.CosineSimilarity)]
     public ReadOnlyMemory<float> Vector { get; set; }
 }

docs/ai/vector-stores/snippets/chat-with-data/azure-openai/Program.cs

@@ -3,7 +3,7 @@
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.Configuration;
 using Microsoft.Extensions.VectorData;
-using Microsoft.SemanticKernel.Connectors.InMemory;
+using CommunityToolkit.VectorData.InMemory;
 using System.Linq;
 using VectorDataAI;
 
@@ -46,12 +46,12 @@
 // <SnippetEmbeddingGenerator>
 // Load the configuration values.
 IConfigurationRoot config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
 string endpoint = config["AZURE_OPENAI_ENDPOINT"];
 string model = config["AZURE_OPENAI_GPT_NAME"];
 
 // Create the embedding generator.
 IEmbeddingGenerator<string, Embedding<float>> generator =
     new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
         .GetEmbeddingClient(deploymentName: model)
         .AsIEmbeddingGenerator();
 // </SnippetEmbeddingGenerator>

docs/ai/vector-stores/snippets/chat-with-data/azure-openai/VectorDataAI.csproj

@@ -14,7 +14,7 @@
     <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.7.0" />
     <PackageReference Include="Microsoft.Extensions.Configuration" Version="10.0.9" />
     <PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="10.0.9" />
-    <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" Version="1.55.0-preview" />
+    <PackageReference Include="CommunityToolkit.VectorData.InMemory" Version="1.0.0" />
   </ItemGroup>
 
 </Project>

docs/ai/vector-stores/snippets/chat-with-data/openai/CloudService.cs

@@ -14,7 +14,7 @@ internal class CloudService
     public string Description { get; set; }
 
     [VectorStoreVector(
-        Dimensions: 384,
+        dimensions: 384,
         DistanceFunction = DistanceFunction.CosineSimilarity)]
     public ReadOnlyMemory<float> Vector { get; set; }
 }

docs/ai/vector-stores/snippets/chat-with-data/openai/Program.cs

@@ -1,7 +1,7 @@
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.Configuration;
 using Microsoft.Extensions.VectorData;
-using Microsoft.SemanticKernel.Connectors.InMemory;
+using CommunityToolkit.VectorData.InMemory;
 using OpenAI;
 using System.ClientModel;
 using VectorDataAI;

docs/ai/vector-stores/snippets/chat-with-data/openai/VectorDataAI.csproj

@@ -12,7 +12,7 @@
     <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.7.0" />
     <PackageReference Include="Microsoft.Extensions.Configuration" Version="10.0.9" />
     <PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="10.0.9" />
-    <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" Version="1.55.0-preview" />
+    <PackageReference Include="CommunityToolkit.VectorData.InMemory" Version="1.0.0" />
   </ItemGroup>
 
 </Project>

docs/ai/vector-stores/snippets/conceptual/VectorStoreSnippets.csproj

@@ -11,11 +11,10 @@
     <PackageReference Include="Azure.Search.Documents" Version="12.0.0" />
     <PackageReference Include="Microsoft.Extensions.AI" Version="10.7.0" />
     <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.7.0" />
-    <PackageReference Include="Microsoft.SemanticKernel.Connectors.AzureAISearch" Version="1.*-*" />
-    <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" Version="1.*-*" />
-    <PackageReference Include="Microsoft.SemanticKernel.Connectors.Qdrant" Version="1.*-*" />
+    <PackageReference Include="CommunityToolkit.VectorData.AzureAISearch" Version="1.0.0" />
+    <PackageReference Include="CommunityToolkit.VectorData.InMemory" Version="1.0.0" />
+    <PackageReference Include="CommunityToolkit.VectorData.Qdrant" Version="1.0.0" />
     <PackageReference Include="OpenAI" Version="2.*" />
-    <PackageReference Include="Qdrant.Client" Version="1.*" />
   </ItemGroup>
 
 </Project>

docs/ai/vector-stores/snippets/conceptual/defining-your-data-model.cs

@@ -17,7 +17,7 @@ public class Hotel
     public required string Description { get; set; }
 
     // <VectorStoreVectorAttribute1>
-    [VectorStoreVector(Dimensions: 4, DistanceFunction = DistanceFunction.CosineSimilarity, IndexKind = IndexKind.Hnsw)]
+    [VectorStoreVector(dimensions: 4, DistanceFunction = DistanceFunction.CosineSimilarity, IndexKind = IndexKind.Hnsw)]
     public ReadOnlyMemory<float>? DescriptionEmbedding { get; set; }
     // </VectorStoreVectorAttribute1>
 

docs/ai/vector-stores/snippets/conceptual/dynamic-data-model.cs

@@ -1,5 +1,5 @@
 using Microsoft.Extensions.VectorData;
-using Microsoft.SemanticKernel.Connectors.Qdrant;
+using CommunityToolkit.VectorData.Qdrant;
 using Qdrant.Client;
 
 public class DynamicDataModel

docs/ai/vector-stores/snippets/how-to/Hotel.cs

@@ -12,7 +12,7 @@ public record class Hotel
     [VectorStoreData(IsFullTextIndexed = true)]
     public string? Description { get; set; }
 
-    [VectorStoreVector(Dimensions: 1536, DistanceFunction = DistanceFunction.CosineSimilarity)]
+    [VectorStoreVector(dimensions: 1536, DistanceFunction = DistanceFunction.CosineSimilarity)]
     public ReadOnlyMemory<float>? DescriptionEmbedding { get; set; }
 
     [VectorStoreData(IsIndexed = true)]

docs/ai/vector-stores/snippets/how-to/HowToSnippets.csproj

@@ -14,8 +14,8 @@
     <PackageReference Include="Microsoft.Extensions.AI" Version="10.*" />
     <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.7.0" />
     <PackageReference Include="Microsoft.Extensions.DependencyInjection" Version="10.*" />
-    <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" Version="1.*-*" />
-    <PackageReference Include="Microsoft.SemanticKernel.Connectors.Redis" Version="1.*-*" />
+    <PackageReference Include="CommunityToolkit.VectorData.InMemory" Version="1.0.0" />
+    <PackageReference Include="CommunityToolkit.VectorData.Redis" Version="1.0.0" />
   </ItemGroup>
 
 </Project>

docs/ai/vector-stores/snippets/how-to/Program.cs

@@ -1,5 +1,5 @@
 using Microsoft.Extensions.VectorData;
-using Microsoft.SemanticKernel.Connectors.InMemory;
+using CommunityToolkit.VectorData.InMemory;
 
 // <CreateVectorStore>
 // Create an in-memory vector store (no external service required).

docs/ai/vector-stores/snippets/how-to/PutItTogether.cs

@@ -3,7 +3,7 @@
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.DependencyInjection;
 using Microsoft.Extensions.VectorData;
-using Microsoft.SemanticKernel.Connectors.Redis;
+using CommunityToolkit.VectorData.Redis;
 using VectorIngest;
 
 public class PutItAllTogether