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

semantic-kernel/concepts/vector-store-connectors/embedding-generation.md

@@ -56,7 +56,7 @@ Embedding generators implementing the `Microsoft.Extensions.AI` abstractions are
 
     ```csharp
     using Microsoft.Extensions.AI;
-    using Microsoft.SemanticKernel.Connectors.Qdrant;
+    using CommunityToolkit.VectorData.Qdrant;
     using OpenAI;
     using Qdrant.Client;
 
@@ -78,7 +78,7 @@ Embedding generators implementing the `Microsoft.Extensions.AI` abstractions are
 
     ```csharp
     using Microsoft.Extensions.AI;
-    using Microsoft.SemanticKernel.Connectors.Qdrant;
+    using CommunityToolkit.VectorData.Qdrant;
     using OpenAI;
     using Qdrant.Client;
 
@@ -103,7 +103,7 @@ Embedding generators implementing the `Microsoft.Extensions.AI` abstractions are
     ```csharp
     using Microsoft.Extensions.AI;
     using Microsoft.Extensions.VectorData;
-    using Microsoft.SemanticKernel.Connectors.Qdrant;
+    using CommunityToolkit.VectorData.Qdrant;
     using OpenAI;
     using Qdrant.Client;
 
@@ -155,7 +155,6 @@ Embedding generators implementing the `Microsoft.Extensions.AI` abstractions are
 The following example demonstrates how to use the embedding generator to automatically generate vectors during both upsert and search operations. This approach simplifies workflows by eliminating the need to precompute embeddings manually.
 
 ```csharp
-
 // The data model
 internal class FinanceInfo
 {

semantic-kernel/concepts/vector-store-connectors/how-to/vector-store-data-ingestion.md

@@ -57,14 +57,14 @@ The rest of these instructions will assume that you are using this container usi
 
 ## Create your project
 
-Create a new project and add nuget package references for the Redis connector from Semantic Kernel, the open xml package to read the word
+Create a new project and add NuGet package references for the Redis connector, the open xml package to read the word
 document with and the OpenAI connector from Semantic Kernel for generating embeddings.
 
 ```dotnetcli
 dotnet new console --framework net8.0 --name SKVectorIngest
 cd SKVectorIngest
 dotnet add package Microsoft.SemanticKernel.Connectors.AzureOpenAI
-dotnet add package Microsoft.SemanticKernel.Connectors.Redis --prerelease
+dotnet add package CommunityToolkit.VectorData.Redis
 dotnet add package DocumentFormat.OpenXml
 ```
 

semantic-kernel/concepts/vector-store-connectors/hybrid-search.md

@@ -93,7 +93,7 @@ Only connectors for databases that currently support vector plus keyword hybrid
 Assuming you have a collection that already contains data, you can easily do a hybrid search on it. Here is an example using Qdrant.
 
 ```csharp
-using Microsoft.SemanticKernel.Connectors.Qdrant;
+using CommunityToolkit.VectorData.Qdrant;
 using Microsoft.Extensions.VectorData;
 using Qdrant.Client;
 
@@ -150,7 +150,7 @@ If no `AdditionalProperty` is provided and the data model contains only one full
 If the data model contains no full text search property or multiple full text search properties and `AdditionalProperty` is not provided, the search method will throw.
 
 ```csharp
-using Microsoft.SemanticKernel.Connectors.Qdrant;
+using CommunityToolkit.VectorData.Qdrant;
 using Microsoft.Extensions.VectorData;
 using Qdrant.Client;
 

semantic-kernel/concepts/vector-store-connectors/index.md

@@ -258,7 +258,7 @@ Once you have defined your data model, the next step is to create a VectorStore
 In this example, we'll use Qdrant. You will therefore need to import the Qdrant nuget package.
 
 ```dotnetcli
-dotnet add package Microsoft.SemanticKernel.Connectors.Qdrant --prerelease
+dotnet add package CommunityToolkit.VectorData.Qdrant
 ```
 
 If you want to run Qdrant locally using Docker, use the following command to start the Qdrant container
@@ -275,7 +275,7 @@ Since databases support many different types of keys and records, we allow you t
 In our case, the type of record will be the `Hotel` class we already defined, and the type of key will be `ulong`, since the `HotelId` property is a `ulong` and Qdrant only supports `Guid` or `ulong` keys.
 
 ```csharp
-using Microsoft.SemanticKernel.Connectors.Qdrant;
+using CommunityToolkit.VectorData.Qdrant;
 using Qdrant.Client;
 
 // Create a Qdrant VectorStore object

semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/azure-ai-search-connector.md

@@ -100,10 +100,10 @@ Notable Azure AI Search connector functionality limitations.
 Add the Azure AI Search Vector Store connector NuGet package to your project.
 
 ```dotnetcli
-dotnet add package Microsoft.SemanticKernel.Connectors.AzureAISearch --prerelease
+dotnet add package CommunityToolkit.VectorData.AzureAISearch
 ```
 
-You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by Semantic Kernel.
+You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by the connector package.
 
 ```csharp
 using Azure;
@@ -120,7 +120,6 @@ kernelBuilder.Services
 ```csharp
 using Azure;
 using Microsoft.Extensions.DependencyInjection;
-using Microsoft.SemanticKernel;
 
 // Using IServiceCollection with ASP.NET Core.
 var builder = WebApplication.CreateBuilder(args);
@@ -148,7 +147,6 @@ kernelBuilder.Services.AddAzureAISearchVectorStore();
 using Azure;
 using Azure.Search.Documents.Indexes;
 using Microsoft.Extensions.DependencyInjection;
-using Microsoft.SemanticKernel;
 
 // Using IServiceCollection with ASP.NET Core.
 var builder = WebApplication.CreateBuilder(args);
@@ -164,7 +162,7 @@ You can construct an Azure AI Search Vector Store instance directly.
 ```csharp
 using Azure;
 using Azure.Search.Documents.Indexes;
-using Microsoft.SemanticKernel.Connectors.AzureAISearch;
+using CommunityToolkit.VectorData.AzureAISearch;
 
 var vectorStore = new AzureAISearchVectorStore(
     new SearchIndexClient(
@@ -177,7 +175,7 @@ It is possible to construct a direct reference to a named collection.
 ```csharp
 using Azure;
 using Azure.Search.Documents.Indexes;
-using Microsoft.SemanticKernel.Connectors.AzureAISearch;
+using CommunityToolkit.VectorData.AzureAISearch;
 
 var collection = new AzureAISearchCollection<string, Hotel>(
     new SearchIndexClient(new Uri(azureAISearchUri), new AzureKeyCredential(secret)),

semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/azure-cosmosdb-mongodb-connector.md

@@ -82,10 +82,10 @@ This connector is compatible with Azure Cosmos DB MongoDB (vCore) and is *not* d
 Add the Azure CosmosDB MongoDB Vector Store connector NuGet package to your project.
 
 ```dotnetcli
-dotnet add package Microsoft.SemanticKernel.Connectors.CosmosMongoDB --prerelease
+dotnet add package CommunityToolkit.VectorData.CosmosMongoDB
 ```
 
-You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by Semantic Kernel.
+You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by the connector package.
 
 ```csharp
 using Microsoft.Extensions.DependencyInjection;
@@ -100,7 +100,6 @@ kernelBuilder.Services
 
 ```csharp
 using Microsoft.Extensions.DependencyInjection;
-using Microsoft.SemanticKernel;
 
 // Using IServiceCollection with ASP.NET Core.
 var builder = WebApplication.CreateBuilder(args);
@@ -127,7 +126,6 @@ kernelBuilder.Services.AddCosmosMongoVectorStore();
 
 ```csharp
 using Microsoft.Extensions.DependencyInjection;
-using Microsoft.SemanticKernel;
 using MongoDB.Driver;
 
 // Using IServiceCollection with ASP.NET Core.
@@ -144,7 +142,7 @@ builder.Services.AddCosmosMongoVectorStore();
 You can construct an Azure CosmosDB MongoDB Vector Store instance directly.
 
 ```csharp
-using Microsoft.SemanticKernel.Connectors.CosmosMongoDB;
+using CommunityToolkit.VectorData.CosmosMongoDB;
 using MongoDB.Driver;
 
 var mongoClient = new MongoClient(connectionString);
@@ -155,7 +153,7 @@ var vectorStore = new CosmosMongoVectorStore(database);
 It is possible to construct a direct reference to a named collection.
 
 ```csharp
-using Microsoft.SemanticKernel.Connectors.CosmosMongoDB;
+using CommunityToolkit.VectorData.CosmosMongoDB;
 using MongoDB.Driver;
 
 var mongoClient = new MongoClient(connectionString);

semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/azure-cosmosdb-nosql-connector.md

@@ -66,10 +66,10 @@ var cosmosClient = new CosmosClient(connectionString, new CosmosClientOptions()
 Add the Azure CosmosDB NoSQL Vector Store connector NuGet package to your project.
 
 ```dotnetcli
-dotnet add package Microsoft.SemanticKernel.Connectors.CosmosNoSql --prerelease
+dotnet add package CommunityToolkit.VectorData.CosmosNoSql
 ```
 
-You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by Semantic Kernel.
+You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by the connector package.
 
 ```csharp
 using Microsoft.Extensions.DependencyInjection;
@@ -84,7 +84,6 @@ kernelBuilder.Services
 
 ```csharp
 using Microsoft.Extensions.DependencyInjection;
-using Microsoft.SemanticKernel;
 
 // Using IServiceCollection with ASP.NET Core.
 var builder = WebApplication.CreateBuilder(args);
@@ -120,7 +119,6 @@ kernelBuilder.Services.AddCosmosNoSqlVectorStore();
 using System.Text.Json;
 using Microsoft.Azure.Cosmos;
 using Microsoft.Extensions.DependencyInjection;
-using Microsoft.SemanticKernel;
 
 // Using IServiceCollection with ASP.NET Core.
 var builder = WebApplication.CreateBuilder(args);
@@ -144,7 +142,7 @@ You can construct an Azure CosmosDB NoSQL Vector Store instance directly.
 ```csharp
 using System.Text.Json;
 using Microsoft.Azure.Cosmos;
-using Microsoft.SemanticKernel.Connectors.CosmosNoSql;
+using CommunityToolkit.VectorData.CosmosNoSql;
 
 var cosmosClient = new CosmosClient(connectionString, new CosmosClientOptions()
 {
@@ -162,7 +160,7 @@ It is possible to construct a direct reference to a named collection.
 ```csharp
 using System.Text.Json;
 using Microsoft.Azure.Cosmos;
-using Microsoft.SemanticKernel.Connectors.CosmosNoSql;
+using CommunityToolkit.VectorData.CosmosNoSql;
 
 var cosmosClient = new CosmosClient(connectionString, new CosmosClientOptions()
 {
@@ -192,7 +190,7 @@ must be passed to the `CosmosNoSqlCollection` on construction.
 ```csharp
 using System.Text.Json;
 using Microsoft.Azure.Cosmos;
-using Microsoft.SemanticKernel.Connectors.CosmosNoSql;
+using CommunityToolkit.VectorData.CosmosNoSql;
 
 var jsonSerializerOptions = new JsonSerializerOptions { PropertyNamingPolicy = JsonNamingPolicy.SnakeCaseUpper };
 

semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/couchbase-connector.md

@@ -43,7 +43,7 @@ Add the Couchbase Vector Store connector NuGet package to your project.
 dotnet add package CouchbaseConnector.SemanticKernel --prerelease
 ```
 
-You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by Semantic Kernel.
+You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by the connector package.
 
 ```csharp
 using Microsoft.SemanticKernel;

semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/elasticsearch-connector.md

@@ -48,7 +48,7 @@ Add the Elasticsearch Vector Store connector NuGet package to your project.
 dotnet add package Elastic.SemanticKernel.Connectors.Elasticsearch --prerelease
 ```
 
-You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by Semantic Kernel.
+You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by the connector package.
 
 ```csharp
 using Microsoft.SemanticKernel;

semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/inmemory-connector.md

@@ -58,10 +58,10 @@ The connector has the following characteristics.
 Add the Semantic Kernel Core nuget package to your project.
 
 ```dotnetcli
-dotnet add package Microsoft.SemanticKernel.Connectors.InMemory --prerelease
+dotnet add package CommunityToolkit.VectorData.InMemory
 ```
 
-You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by Semantic Kernel.
+You can add the vector store to the dependency injection container available on the `KernelBuilder` or to the `IServiceCollection` dependency injection container using extension methods provided by the connector package.
 
 ```csharp
 using Microsoft.Extensions.DependencyInjection;
@@ -76,7 +76,6 @@ kernelBuilder.Services
 
 ```csharp
 using Microsoft.Extensions.DependencyInjection;
-using Microsoft.SemanticKernel;
 
 // Using IServiceCollection with ASP.NET Core.
 var builder = WebApplication.CreateBuilder(args);
@@ -86,15 +85,15 @@ builder.Services.AddInMemoryVectorStore();
 You can construct an InMemory Vector Store instance directly.
 
 ```csharp
-using Microsoft.SemanticKernel.Connectors.InMemory;
+using CommunityToolkit.VectorData.InMemory;
 
 var vectorStore = new InMemoryVectorStore();
 ```
 
 It is possible to construct a direct reference to a named collection.
 
 ```csharp
-using Microsoft.SemanticKernel.Connectors.InMemory;
+using CommunityToolkit.VectorData.InMemory;
 
 var collection = new InMemoryCollection<string, Hotel>("skhotels");
 ```

semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/oracle-connector.md

@@ -104,7 +104,7 @@ Add the Oracle Database Vector Store connector NuGet package to your project.
 dotnet add package Oracle.VectorData --prerelease
 ```
 
-You can add the vector store to the `IServiceCollection` dependency injection container using extension methods provided by Semantic Kernel. In this case, an instance of the `Oracle.VectorData.OracleVectorStore` class also gets registered with the container.
+You can add the vector store to the `IServiceCollection` dependency injection container using extension methods provided by the connector package. In this case, an instance of the `Oracle.VectorData.OracleVectorStore` class also gets registered with the container.
 
 ```csharp
 using Microsoft.SemanticKernel;