chore: import upstream snapshot with attribution

dotnet/samples/GettingStartedWithVectorStores/Step4_Use_DynamicDataModel.cs

@@ -0,0 +1,74 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel.Connectors.Redis;
+using StackExchange.Redis;
+
+namespace GettingStartedWithVectorStores;
+
+/// <summary>
+/// Example that shows that you can use the dynamic data modeling to interact with a vector database.
+/// This makes it possible to use the vector store abstractions without having to create your own strongly-typed data model.
+/// </summary>
+public class Step4_Use_DynamicDataModel(ITestOutputHelper output, VectorStoresFixture fixture) : BaseTest(output), IClassFixture<VectorStoresFixture>
+{
+    /// <summary>
+    /// Example showing how to query a vector store that uses dynamic data modeling.
+    ///
+    /// This example requires a Redis server running on localhost:6379. To run a Redis server in a Docker container, use the following command:
+    /// docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
+    /// </summary>
+    [Fact]
+    public async Task SearchAVectorStoreWithDynamicMappingAsync()
+    {
+        // Construct a redis vector store.
+        var vectorStore = new RedisVectorStore(ConnectionMultiplexer.Connect("localhost:6379").GetDatabase());
+
+        // First, let's use the code from step 1 to ingest data into the vector store
+        // using the custom data model, simulating a scenario where someone else ingested
+        // the data into the database previously.
+        var collection = vectorStore.GetCollection<string, Glossary>("skglossary");
+        var customDataModelCollection = vectorStore.GetCollection<string, Glossary>("skglossary");
+        await Step1_Ingest_Data.IngestDataIntoVectorStoreAsync(customDataModelCollection, fixture.EmbeddingGenerator);
+
+        // To use dynamic data modeling, we still have to describe the storage schema to the vector store
+        // using a record definition. The benefit over a custom data model is that this definition
+        // does not have to be known at compile time.
+        // E.g. it can be read from a configuration or retrieved from a service.
+        var recordDefinition = new VectorStoreCollectionDefinition
+        {
+            Properties =
+            [
+                new VectorStoreKeyProperty("Key", typeof(string)),
+                new VectorStoreDataProperty("Category", typeof(string)),
+                new VectorStoreDataProperty("Term", typeof(string)),
+                new VectorStoreDataProperty("Definition", typeof(string)),
+                new VectorStoreVectorProperty("DefinitionEmbedding", typeof(ReadOnlyMemory<float>), 1536),
+            ]
+        };
+
+        // Now, let's create a collection that uses a dynamic data model.
+        var dynamicDataModelCollection = vectorStore.GetDynamicCollection("skglossary", recordDefinition);
+
+        // Generate an embedding from the search string.
+        var searchString = "How do I provide additional context to an LLM?";
+        var searchVector = (await fixture.EmbeddingGenerator.GenerateAsync(searchString)).Vector;
+
+        // Search the generic data model collection and get the single most relevant result.
+        var searchResultItems = await dynamicDataModelCollection.SearchAsync(
+            searchVector,
+            top: 1).ToListAsync();
+
+        // Write the search result with its score to the console.
+        // Note that here we can loop through all the properties
+        // without knowing the schema, since the properties are
+        // stored as a dictionary of string keys and object values
+        // when using the dynamic data model.
+        foreach (var property in searchResultItems.First().Record)
+        {
+            Console.WriteLine($"{property.Key}: {property.Value}");
+        }
+        Console.WriteLine(searchResultItems.First().Score);
+    }
+}