diff --git a/dotnet/samples/Concepts/Memory/VectorStore_GenericDataModel_Interop.cs b/dotnet/samples/Concepts/Memory/VectorStore_GenericDataModel_Interop.cs
new file mode 100644
index 000000000000..faa5d579d3b8
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+++ b/dotnet/samples/Concepts/Memory/VectorStore_GenericDataModel_Interop.cs
@@ -0,0 +1,201 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text.Json;
+using Azure.Identity;
+using Memory.VectorStoreFixtures;
+using Microsoft.SemanticKernel.Connectors.AzureOpenAI;
+using Microsoft.SemanticKernel.Connectors.Qdrant;
+using Microsoft.SemanticKernel.Data;
+using Microsoft.SemanticKernel.Embeddings;
+using Qdrant.Client;
+
+namespace Memory;
+
+/// <summary>
+/// Semantic Kernel provides a generic data model for vector stores that can be used with any
+/// schema. The schema still has to be provided in the form of a record definition, but no
+/// custom data model is required.
+///
+/// The sample shows how to
+/// 1. Upsert data using the generic data model and retrieve it from the vector store using a custom data model.
+/// 2. Upsert data using a custom data model and retrieve it from the vector store using the generic data model.
+/// </summary>
+public class VectorStore_GenericDataModel_Interop(ITestOutputHelper output, VectorStoreQdrantContainerFixture qdrantFixture) : BaseTest(output), IClassFixture<VectorStoreQdrantContainerFixture>
+{
+    private static readonly JsonSerializerOptions s_indentedSerializerOptions = new() { WriteIndented = true };
+
+    private static readonly VectorStoreRecordDefinition s_vectorStoreRecordDefinition = new()
+    {
+        Properties = new List<VectorStoreRecordProperty>
+        {
+            new VectorStoreRecordKeyProperty("Key", typeof(ulong)),
+            new VectorStoreRecordDataProperty("Term", typeof(string)),
+            new VectorStoreRecordDataProperty("Definition", typeof(string)),
+            new VectorStoreRecordVectorProperty("DefinitionEmbedding", typeof(ReadOnlyMemory<float>)) { Dimensions = 1536 }
+        }
+    };
+
+    [Fact]
+    public async Task UpsertWithGenericRetrieveWithCustomAsync()
+    {
+        // Create an embedding generation service.
+        var textEmbeddingGenerationService = new AzureOpenAITextEmbeddingGenerationService(
+                TestConfiguration.AzureOpenAIEmbeddings.DeploymentName,
+                TestConfiguration.AzureOpenAIEmbeddings.Endpoint,
+                new AzureCliCredential());
+
+        // Initiate the docker container and construct the vector store.
+        await qdrantFixture.ManualInitializeAsync();
+        var vectorStore = new QdrantVectorStore(new QdrantClient("localhost"));
+
+        // Get and create collection if it doesn't exist using the generic data model and record definition that defines the schema.
+        var genericDataModelCollection = vectorStore.GetCollection<ulong, VectorStoreGenericDataModel<ulong>>("skglossary", s_vectorStoreRecordDefinition);
+        await genericDataModelCollection.CreateCollectionIfNotExistsAsync();
+
+        // Create glossary entries and generate embeddings for them.
+        var glossaryEntries = CreateGenericGlossaryEntries().ToList();
+        var tasks = glossaryEntries.Select(entry => Task.Run(async () =>
+        {
+            entry.Vectors["DefinitionEmbedding"] = await textEmbeddingGenerationService.GenerateEmbeddingAsync((string)entry.Data["Definition"]!);
+        }));
+        await Task.WhenAll(tasks);
+
+        // Upsert the glossary entries into the collection and return their keys.
+        var upsertedKeysTasks = glossaryEntries.Select(x => genericDataModelCollection.UpsertAsync(x));
+        var upsertedKeys = await Task.WhenAll(upsertedKeysTasks);
+
+        // Get the collection using the custom data model.
+        var customDataModelCollection = vectorStore.GetCollection<ulong, Glossary>("skglossary");
+
+        // Retrieve one of the upserted records from the collection.
+        var upsertedRecord = await customDataModelCollection.GetAsync(upsertedKeys.First(), new() { IncludeVectors = true });
+
+        // Write upserted keys and one of the upserted records to the console.
+        Console.WriteLine($"Upserted keys: {string.Join(", ", upsertedKeys)}");
+        Console.WriteLine($"Upserted record: {JsonSerializer.Serialize(upsertedRecord, s_indentedSerializerOptions)}");
+    }
+
+    [Fact]
+    public async Task UpsertWithCustomRetrieveWithGenericAsync()
+    {
+        // Create an embedding generation service.
+        var textEmbeddingGenerationService = new AzureOpenAITextEmbeddingGenerationService(
+                TestConfiguration.AzureOpenAIEmbeddings.DeploymentName,
+                TestConfiguration.AzureOpenAIEmbeddings.Endpoint,
+                new AzureCliCredential());
+
+        // Initiate the docker container and construct the vector store.
+        await qdrantFixture.ManualInitializeAsync();
+        var vectorStore = new QdrantVectorStore(new QdrantClient("localhost"));
+
+        // Get and create collection if it doesn't exist using the custom data model.
+        var customDataModelCollection = vectorStore.GetCollection<ulong, Glossary>("skglossary");
+        await customDataModelCollection.CreateCollectionIfNotExistsAsync();
+
+        // Create glossary entries and generate embeddings for them.
+        var glossaryEntries = CreateCustomGlossaryEntries().ToList();
+        var tasks = glossaryEntries.Select(entry => Task.Run(async () =>
+        {
+            entry.DefinitionEmbedding = await textEmbeddingGenerationService.GenerateEmbeddingAsync(entry.Definition);
+        }));
+        await Task.WhenAll(tasks);
+
+        // Upsert the glossary entries into the collection and return their keys.
+        var upsertedKeysTasks = glossaryEntries.Select(x => customDataModelCollection.UpsertAsync(x));
+        var upsertedKeys = await Task.WhenAll(upsertedKeysTasks);
+
+        // Get the collection using the generic data model.
+        var genericDataModelCollection = vectorStore.GetCollection<ulong, VectorStoreGenericDataModel<ulong>>("skglossary", s_vectorStoreRecordDefinition);
+
+        // Retrieve one of the upserted records from the collection.
+        var upsertedRecord = await genericDataModelCollection.GetAsync(upsertedKeys.First(), new() { IncludeVectors = true });
+
+        // Write upserted keys and one of the upserted records to the console.
+        Console.WriteLine($"Upserted keys: {string.Join(", ", upsertedKeys)}");
+        Console.WriteLine($"Upserted record: {JsonSerializer.Serialize(upsertedRecord, s_indentedSerializerOptions)}");
+    }
+
+    /// <summary>
+    /// Sample model class that represents a glossary entry.
+    /// </summary>
+    /// <remarks>
+    /// Note that each property is decorated with an attribute that specifies how the property should be treated by the vector store.
+    /// This allows us to create a collection in the vector store and upsert and retrieve instances of this class without any further configuration.
+    /// </remarks>
+    private sealed class Glossary
+    {
+        [VectorStoreRecordKey]
+        public ulong Key { get; set; }
+
+        [VectorStoreRecordData]
+        public string Term { get; set; }
+
+        [VectorStoreRecordData]
+        public string Definition { get; set; }
+
+        [VectorStoreRecordVector(1536)]
+        public ReadOnlyMemory<float> DefinitionEmbedding { get; set; }
+    }
+
+    /// <summary>
+    /// Create some sample glossary entries using the custom data model.
+    /// </summary>
+    /// <returns>A list of sample glossary entries.</returns>
+    private static IEnumerable<Glossary> CreateCustomGlossaryEntries()
+    {
+        yield return new Glossary
+        {
+            Key = 1,
+            Term = "API",
+            Definition = "Application Programming Interface. A set of rules and specifications that allow software components to communicate and exchange data.",
+        };
+
+        yield return new Glossary
+        {
+            Key = 2,
+            Term = "Connectors",
+            Definition = "Connectors allow you to integrate with various services provide AI capabilities, including LLM, AudioToText, TextToAudio, Embedding generation, etc.",
+        };
+
+        yield return new Glossary
+        {
+            Key = 3,
+            Term = "RAG",
+            Definition = "Retrieval Augmented Generation - a term that refers to the process of retrieving additional data to provide as context to an LLM to use when generating a response (completion) to a user’s question (prompt).",
+        };
+    }
+
+    /// <summary>
+    /// Create some sample glossary entries using the generic data model.
+    /// </summary>
+    /// <returns>A list of sample glossary entries.</returns>
+    private static IEnumerable<VectorStoreGenericDataModel<ulong>> CreateGenericGlossaryEntries()
+    {
+        yield return new VectorStoreGenericDataModel<ulong>(1)
+        {
+            Data = new Dictionary<string, object?>
+            {
+                ["Term"] = "API",
+                ["Definition"] = "Application Programming Interface. A set of rules and specifications that allow software components to communicate and exchange data.",
+            }
+        };
+
+        yield return new VectorStoreGenericDataModel<ulong>(2)
+        {
+            Data = new Dictionary<string, object?>
+            {
+                ["Term"] = "Connectors",
+                ["Definition"] = "Connectors allow you to integrate with various services provide AI capabilities, including LLM, AudioToText, TextToAudio, Embedding generation, etc.",
+            }
+        };
+
+        yield return new VectorStoreGenericDataModel<ulong>(3)
+        {
+            Data = new Dictionary<string, object?>
+            {
+                ["Term"] = "RAG",
+                ["Definition"] = "Retrieval Augmented Generation - a term that refers to the process of retrieving additional data to provide as context to an LLM to use when generating a response (completion) to a user’s question (prompt).",
+            }
+        };
+    }
+}
diff --git a/dotnet/samples/Concepts/README.md b/dotnet/samples/Concepts/README.md
index 08bfd46791c9..61845a53291c 100644
--- a/dotnet/samples/Concepts/README.md
+++ b/dotnet/samples/Concepts/README.md
@@ -133,6 +133,7 @@ dotnet test -l "console;verbosity=detailed" --filter "FullyQualifiedName=ChatCom
 - [VectorStore_DataIngestion_Simple: A simple example of how to do data ingestion into a vector store when getting started.](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/Memory/VectorStore_DataIngestion_Simple.cs)
 - [VectorStore_DataIngestion_MultiStore: An example of data ingestion that uses the same code to ingest into multiple vector stores types.](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/Memory/VectorStore_DataIngestion_MultiStore.cs)
 - [VectorStore_DataIngestion_CustomMapper: An example that shows how to use a custom mapper for when your data model and storage model doesn't match.](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/Memory/VectorStore_DataIngestion_CustomMapper.cs)
+- [VectorStore_GenericDataModel_Interop: An example that shows how you can use the built-in, generic data model from Semantic Kernel to read and write to a Vector Store.](https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/Memory/VectorStore_GenericDataModel_Interop.cs)
 
 ### Optimization - Examples of different cost and performance optimization techniques